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Data Collection Plan: What it is + Steps to Do It

Several plans work together to create a robust, comprehensive, and overall successful market research initiative, and one of the most important pieces is a data collection plan.

A data collection plan describes how your organization’s data will flow from its source to actionable insights. The process of creating this plan will reveal where your data comes from, who has access to it, and how it is collected and stored.

Below, we explain a data collection plan, why you need one, and how you will use it. We also go over the key steps to creating a data-collecting plan to ensure your data is on track to produce actionable insights that drive your business forward.

What is a data collection plan?

A data collection plan is a detailed document outlining the exact steps and sequences to collect data for a project. It is a statistical analysis plan to achieve breakthrough improvement by reducing variation and defects.

A data-collecting plan ensures that data is accurately sent to the organization’s key stakeholders, who will assist them in meeting their data needs. The plan aims to ensure that the data gathered are valid and meaningful.

We need a data-collecting plan to avoid wasting resources on irrelevant or unusable data. By making a plan for collecting data, we can focus on answering specific business-related questions.

Why Do You Need a Data Collection Plan?

Gathering a lot of different kinds of data and looking at them won’t help much if you can’t figure out what they mean. A good plan for collecting data helps save money and time since gathering data without a plan can take a long time. Also, it may not be possible to get all the data when it is needed. 

And these are the most important reasons your business needs a plan for collecting data. By making a plan for collecting data, a business can focus on answering specific questions important to the business.

When and how to utilize a data collection plan?

A comprehensive data-collecting plan ensures that the gathered data is useful and well-organized. The plan is utilized to evaluate the existing state of a process or to enhance a project or process. In addition, it is useful during the last phase of a project when generating new metrics and the necessary evaluation procedures.

A proper data collecting plan means taking a systematic approach, including:

• Identify the data to collect.
• How the data will be collected
• Collecting the data

Steps to create a data collection plan

Now, we will explore the steps of a data collection plan to explain how to make one. The plan for gathering data involves 8 steps:

• Identify the questions

The first step in making a data collection plan is to decide what questions we want to answer. Our information has to be useful for the project. These questions should be based on what our process is really like in its current state.

The best way to gather data is to use the SIPOC diagram as a guide. We also have to decide what measurements or metrics we want to use.

• Identify the accessible data

The second step in developing a data collecting plan is to determine what types of data can be collected. A specific piece of data may occasionally provide us with many solutions. Make sure you list all the data points you need to answer the questions on which the project is based.

• Determine how much data is required

A data collection plan’s third step is determining the required data. Write down how much data is required for each bit on the list. Our goal is to gather enough information to do appropriate data analysis and identify patterns and trends.

• Decide how to measure the data

The fourth step in developing a data collection plan is determining how we measure the data. Data can be measured in various ways, such as check sheets, survey responses, etc. The type of data we seek will determine how we measure it. 

Contact QuestionPro if you want to measure your data by conducting surveys. QuestionPro is survey software with a wide variety of survey features.

• Determine who will collect the data

The fifth step in developing a data collecting plan is determining who will collect the data. Data can now be collected using automated software. We may need to communicate with the person in charge of the software to ensure that the data is in the proper format.

• Choose the data source

The sixth step is to determine the data sources. Choosing a location or data source is what it signifies. Location does not always mean a physical place. It is the place where the process is at. The data collection plan must specifically state where data must be collected throughout the process.

• Choose whether to measure a sample or the entire population

The seventh step is deciding whether or not to sample the data. It is often impractical to measure a whole data Pre-population . In this situation, we then collect a sample of data. The project team may need to investigate the following question: What should our sampling method and sample size be to produce statistically accurate judgments?

• Determine data display format

The eighth step is to decide how to display the data. We can show data in many ways, such as Pareto Diagrams, Scatter Diagrams, etc.

We gained a strong understanding of the data collection plan, why you need it for your business, and how you use it from the talk above. Additionally, we discussed steps that will assist you in creating your own.

The data collecting plan states what kinds of data need to be collected, how, where, how often, and who is responsible for collecting the data. This document ensures that all relevant employees understand the requirements for data collection methods .

Now you know how to develop a data collection plan for your business. If you need help making your plan, you can contact QuestionPro. QuestionPro is a survey software and data management software that assists in data management and provides real-time data and business insight. To learn more, ask for a free trial now!

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How to Write a Data Collection Plan (Templates and Examples Included)

In a world where data drives decisions, how do you make sure you're gathering the right information? With a clear data collection plan in place, you ensure that the collected data leads to actionable insights.

Effective data collection is key to smart decision-making, grounding strategies in solid evidence rather than guesses. A well-designed data collection plan guarantees that you're collecting not just any data, but the right data, crucial for spotting trends, refining processes, and deeply understanding customer needs in any sector.

By the end of this article, you'll understand the importance of planning your data collection and how to do it effectively.

What is a data collection plan?

A data collection plan is a roadmap for identifying what data you need, the ways in which you'll collect it, and how you'll analyze it. The core purpose is to ensure that your data collection is targeted, efficient, and reliable, providing meaningful insights for your project or study.

Data collection plans should be developed at the start of a project or study, before any data is collected. Typically, this responsibility falls to project leaders, researchers, data analysts, or a designated team member with expertise in data management.

What does a typical data collection plan document cover

From setting clear objectives to establishing robust communication channels, each section of the plan is a stepping stone towards having a thorough data collection strategy:

• Objectives: Start with a specific goal for your data collection. Clearly state why this data is crucial and how it will impact your project or decision-making. This step ensures that every part of your plan aligns with your end goal.
• Data typology: Decide whether you need quantitative (numerical) or qualitative (descriptive) data. Clarify the importance of each data type in the context of your objectives. This clarity helps in selecting the right tools and methods for data collection.
• Collection methodology: Select appropriate methods like surveys, interviews, or analysis of existing data. Prioritize data quality; for surveys, this means clear, unbiased questions; for interviews, standardized interviewing techniques; etc.
• Data management protocols: Plan for the storage, organization, and protection of your data. Address ethical considerations, especially for sensitive information. Include a system for updating and correcting data to maintain its accuracy over time.
• Project timeline : Outline a realistic timeline with start and end dates, including key milestones. Incorporate flexibility for unforeseen delays or challenges.
• Needed resources: Identify the team, tools, and budget required. Clearly define roles and responsibilities to ensure a smooth data collection process.
• Data analysis strategy: Determine how you'll analyze the collected data. Include methods for dealing with unexpected findings, like ambiguous, conflicting, corrupted, or incomplete data.
• Feedback mechanisms:  Establish a mechanism for ongoing assessment and adjustment of your data collection methods. This allows you to adapt and refine your approach as needed.
• Communication framework: Decide how and when you'll communicate your findings. Depending on the project, you might need to keep stakeholders updated throughout the process, not just at the end, to maintain engagement and transparency.

Try to meticulously address each of these elements to set the stage for successful data gathering.

Ways to collect data

Collecting data is akin to gathering and sorting the pieces for a puzzle. Each piece, or data point, is critical to form a complete and accurate picture of the subject under study. 

To ensure that this picture is as clear and precise as possible, researchers and analysts employ a variety of data collection methods outlined in the image below.

• Surveys and questionnaires: These involve asking structured questions to a large group of people. Consider the timing of your survey distribution — sending out surveys at a time when your target audience is likely to be available and attentive can significantly improve the response quality.
• Interviews: One-on-one conversations that allow for deep dives into subjects' thoughts and experiences. Record interviews (with permission) and note non-verbal cues. These can provide context often lost in written notes, like the respondent's tone or hesitation.
• Focus groups: Small groups of people discuss specific topics, providing qualitative data on opinions and behaviors. Use a skilled moderator who can encourage quieter members to speak up and keep dominant personalities from overtaking the conversation.  
• Observations: Watching and recording behavior or events as they naturally occur. If possible, conduct observations at different times or in varied settings. This helps in understanding if the observed behavior is consistent or situation-dependent.
• Inspections and assessments: Examining objects, processes, or places in detail, often using a structured approach supported by pre-made checklists. 
• Document review and analysis: Systematically reviewing and interpreting existing documents to extract data. Cross-reference information from different documents for a more comprehensive understanding. This triangulation can validate findings and reveal deeper insights.

Each of these methods offers a unique way to gather data and comes with its own set of pros and cons. Take your time to decide which data collection methods are the best fit for your use case.

Steps for writing an effective data collection plan

With the theory out of the way, let’s see how to write a proper data collection plan, step by step.

1. Define objectives and research questions

Write down a statement of purpose that explains what you intend to discover, decide, or achieve. This statement will act as the compass for your data collection journey.

Your research questions must be clear, focused, and aligned with your stated objectives. For every objective, draft at least one research question that, when answered, will bring you closer to your goal. 

When finalizing your list of research questions, don't overlook the "so what?" factor. For each one, ask yourself what the implications are if the question is answered or the objective is met. How will it change your understanding, decision-making, or actions? This ensures that your plan has practical value and isn't just an academic exercise.

2. Identify data requirements and availability

Identifying your data requirements is a two-part process: you need to understand the type of data you need and assess the data that is already available to you. 

Here's how to understand the type of data you need:

• Consider the nature of your research questions: What data will provide the answers? Is it demographic information, behavioral metrics, financial statistics, etc.?
• Determine the data quantity: How much data is enough to make your results reliable? This can depend on the statistical methods you plan to use and the scale of your project.
• Think about the data quality: What level of accuracy is required? Does the data need to be current, historical, or predictive?

Create a data inventory list. For each research question, list the types of data that could potentially answer it. Next to each type, note down the attributes of the data you need (timeframe, demographic details, granularity, etc.).

To assess the data that is already available to you, follow these:

• Look internally first: Does your organization already have some of the data you need? This could be sales records, customer feedback, or past survey results.
• Consider external sources: Is there public data available that fits your needs, such as government databases, research papers, or industry reports?
• Evaluate accessibility: Can you easily access this data, or are there barriers (e.g., paywalls, privacy laws, data sharing agreements) that you need to consider?

For each piece of required data, try to record its source, format, any costs associated with obtaining it, and any potential challenges in accessing it. If data is not available, note down what proxies could be used or whether secondary data collection is necessary.

Completing this step will form the backbone of your data collection strategy, guiding you on where to focus your resources.

3. Choose how you will collect data

Based on your data requirements, select the most suitable collection methods. Will you use surveys, interviews, observations, experiments, or a combination of multiple methods? 

Match data collection methods to the type of data you need. For quantitative data, you might use surveys or sensor data. For qualitative data, consider interviews or focus groups. Think about the context of your research — does it call for controlled experiments, or would field studies yield better results?

Once you've selected a method, it's time to think about who will shoulder the task. The 'who' could range from your own team members to external professionals, depending on the expertise required.

Incorporate quality control measures right from the start. This should include when and where data will be collected, the tools or technologies used, and the step-by-step process for gathering the data.

Finally, address ethical considerations, especially if you’re dealing with human subjects or sensitive data. Obtain necessary permissions and ensure you’re compliant with relevant laws and regulations.

4. Outline how you will measure data and ensure its integrity

Clearly specify what you are measuring and how it will be quantified. Are you looking at frequencies, averages, percentages, or growth rates? Ensure that the chosen metrics align directly with your research questions and objectives.

Develop and document standardized procedures for data measurement: define operational terms, detail measurement techniques, and specify the equipment or software used.

For each variable, write down a clear operational definition, which is a detailed description of the procedures used to measure it . For example, if you're measuring customer satisfaction, define what constitutes satisfaction and the scale you're using (e.g., 1-5 likert scale ).

To ensure data integrity, team members tasked with collecting and analyzing data really need to know what they’re doing. If you’re using instruments or software, ensure they are calibrated and tested before data collection begins. Consider running a pilot study or trial to test your measurement processes and make adjustments where necessary. This helps you catch potential issues before you roll out large-scale data collection.

Create a data log that records when and by whom data was collected, entered, and verified. Make sure to regularly check a sample of data entries against the original data to ensure accuracy. If you’re using mobile forms or other digital tools to collect data, most of this can be automated. 

Lastly, decide in advance how you will deal with missing data or outliers. Will you use imputation methods , or will you exclude it? Make sure your approach is consistent and documented.

5. Decide how will data be analyzed and presented

Outlines each step of your analysis process: the methods you'll use, the required tools, and the sequence of analysis. 

Choose analysis methods that align with your data types and objectives. For analyzing quantitative data , statistical methods like regression analysis, ANOVA, or cluster analysis might be appropriate. For analyzing qualitative data , try content analysis, thematic analysis, or discourse analysis.

If you have a complex project and plan to use specific software to analyze data, decide which one that is going to be. Options could range from statistical software like SPSS or R for quantitative analysis to software like NVivo for qualitative data analysis.

Think about how you will present your data. This could be in the form of reports, infographics, dashboards, or presentations. Choose the format with your audience in mind — what format will be most clear and persuasive to them? 

Try sketching out a draft of your final report or presentation early in the planning process. This helps you visualize the end product and ensure that your data collection and analysis will support this outcome.

Data collection plan examples and templates

Below are four different examples and templates you can use to build your own data collection plans.

Streamline data collection process with Forms On Fire

Forms on Fire offers a flexible, accessible, and efficient platform for automating and simplifying data collection and management:

• Customizable forms and mobile accessibility: Design forms specific to your needs and capture data on the go, even offline, with mobile device support. 
• Automated workflows and integration capabilities: Automate processes like notifications and data analysis upon form submission, while seamlessly integrating with over 1,500 systems. 
• Real-time data access and reporting: Access and analyze data instantly for timely decision-making, trend spotting, and addressing issues quickly.
• Cloud-based data storage: Securely store all data in the cloud, ensuring safety and availability from any location at any time.
• User-friendly interface: Enjoy an intuitive platform that requires no coding skills, making it easy for anyone in your organization to create and use forms.

Ready to transform your data collection process? Start a free trial below or schedule a product demo and see how our solution can answer your data management needs!

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Planning Your Data Collection: Designing methods for effective research

Planning your research is very important to obtain desirable results. In research, the relevance of data cannot be overlooked. It plays a pivotal role in laying a foundation to your study. Improper data has the potential to introduce bias and question the validity of your findings. Therefore, data collection is a critical step of any research project. It involves strategizing the process of gathering data to ensure its accuracy, and reliability. Without a thoughtful and systematic approach to data collection, researchers compromise the integrity and validity of their findings. By understanding the principles and strategies behind planning data collection, researchers and academicians can enhance the quality and impact of their research endeavors.

Table of Contents

What Is Data Collection?

Data collection is the systematic gathering and measurement of information from relevant sources to address a research problem. It forms the backbone of any research, as it helps in decision-making and builds the foundation to establish solid conclusions. A data collection plan is an outline of the steps to gather data for research.

Purpose of Data Collection

The data collection element of research is common to all fields of study. Collecting data without a proper strategy can result in inconclusive or unreliable findings. To ensure the success of your research, it is essential to develop a comprehensive data collection plan.

Types of Data Collection Methods

The data collection process is approached by various methods that can be categorized into quantitative, qualitative, and mixed approaches. However, it is important to understand each method and select the most appropriate one to effectively achieve your research objectives.

9 Steps for Planning Your Data Collection

Planning your data collection is important to achieve directionality in your research. Following a proper plan can help you to simplify your data collection procedure as it organizes the entire process. The steps for planning your data collection are as follows:

1. Define Your Research Objectives

Before beginning data collection, it is essential to clearly formulate your research objectives . Defining these objectives will facilitate the identification of the types of data that need to be collected. Formulate research questions and try to define them. This will establish a clear direction for the study.

2. Identify the Data Requirements

After defining the research objectives, you must identify the specific data elements required to address the research questions. Furthermore, identify the available and accessible data and assess its efficacy. Consider both qualitative and quantitative data sources, such as surveys, interviews, observations, existing datasets, or experiments.  Also, determine the level of detail for each data point and the suitable methods for data collection.

3. Select Appropriate Data Collection Methods

Choose data collection methods that align with your research objectives and data requirements. Evaluate the strengths and limitations of each methods and select the most appropriate approach accordingly. There are various data collection tools to choose from, such as interviews, role-playing, focus groups, in-person surveys, online surveys, telephonic surveys and observation. Assess the feasibility of the selected method and understand the pros and cons of each technique to make an informed decision.

4. Set a Realistic Timeline

Set a realistic timeline for your data collection process. This will not only help to organize your study but also ensures that you arrive at a conclusion within a specified timeframe. However, consider the time required for method design, pilot study, data interpretation, and analysis. Additionally, consider the available resources and constraints to create a feasible timeline.

5. Design the Method

After selecting your data collection process, design the method using the necessary instruments or resources. For surveys, create clear, concise, and unbiased questions that effectively capture the desired information. Develop interview protocols that cover the key topics you wish to explore. Carefully design observation protocols to ensure consistency and accuracy in data recording. Identify ways to collect maximum useful data and establish methods to interpret it. Also, determine ways to accurately measure the collected data.

6. Pilot Testing

Before launching your data collection, conduct a pilot test to evaluate the effectiveness of your instruments and procedures. A small-scale trial run allows you to identify any ambiguities in the data collection process. Also, make the necessary changes based on the pilot test feedback to enhance the reliability of your data.

7. Standardization

Establish a detailed standardized protocol based on the type of data and the results of the pilot testing. Also, record the specific instruments and standard conditions required for the study. Standardization of the protocol can facilitate the repetition of the study to check its reproducibility.

8. Establish Data Collection Procedures

Outline step-by-step procedures for data collection. Clearly document the process, including instructions for administering surveys , conducting interviews or observations, and handling any ethical considerations. Moreover, the researcher must be well-trained with the data collection method and must ensure its clear documentation.

9. Data Analysis Plan

Parallel to developing your data collection strategy, it is essential to plan your data analysis process. Determine and design the statistical methods or qualitative analysis techniques to derive meaningful insights from the collected data. Operationalize the data for variables that cannot be measured. Also, determine how to effectively represent your data.

Advantages and Disadvantages of Data Collection Methods

Understanding the advantages and disadvantages of the data collection method aids in planning your data collection method efficiently.

Following a structured plan for data collection can ease the process of research. Regardless of the field of the study, accurate and honest data collection plays a pivotal role in maintaining research integrity. Therefore, ethics should be at the forefront of any data collection strategy.

Ethical Reporting of the Collected Data ­— Especially with the dawn of AI

In the era of rapid technological advancements, the role of artificial intelligence (AI) in data collection and analysis has become increasingly prominent. Good research data management practices and principles of data sharing have become all the more crucial with the advent of AI in research data management. AI offers immense potential for processing and analyzing large datasets, enabling researchers to uncover valuable insights. However, using AI in data management accompanies several concerns.

While AI simplifies the hassles of data management, it must be handled following ethical principles. Therefore, it is crucial to address ethical considerations when reporting the collected data using AI to ensure responsible and transparent research practices. Are you a researcher clueless of managing your research data and understanding the proper use of AI? Watch this webinar “Important tips for managing your research data” for FREE and get some clarity.

Six ways to ensure ethical reporting of collected data are:

1. Informed Consent

Obtaining informed consent from participants is an integral part of ethical principles in research. Therefore, participants must be adequately informed about the procedures, potential risks, and the expected duration of their commitment.

2. Data Privacy

Respecting participants’ rights to privacy is an important ethical guideline that researchers must adhere to. When using AI for data collection, participants should be informed about the involvement of AI algorithms and how their data will be used. Also, researchers should employ adequate measures to protect participants’ data, such as anonymization and de-identification techniques. Prior to reporting data, personal identifiers should be removed or masked to prevent the identification of individuals. Furthermore, employing different privacy techniques can protect participants’ identities.

3. Transparency

Transparency becomes crucial when utilizing AI algorithms for data analysis. Therefore, researchers should strive to provide clear explanations of the algorithms used and how they influence the analysis and reporting of data. This includes disclosing any biases, limitations, or potential errors associated with the AI algorithms used. Consequently, transparent reporting enhances the accountability of research findings.

4. Mitigating Bias

AI algorithms are prone to inheriting biases present in the training data. Researchers must be aware of these biases and evaluate them. Additionally, AI systems should be monitored for potential biases related as race, gender, or other sensitive attributes. Therefore, actions should be taken to introduce fairness and mitigate bias in the reporting of results.

5. Ensuring Data Security

Safeguarding collected data against unauthorized access or breaches is vital to ensure data security. Implement robust security measures to protect data integrity and confidentiality. Also, utilize encryption techniques, access controls, and secure storage protocols to prevent unauthorized access or data leaks. Clearly communicate these security measures in your reporting to assure participants that their data is handled responsibly.

6. Ethical Reporting of Results

When reporting findings, researchers must present data accurately and objectively. Avoid misrepresentation or manipulation of data to support predetermined conclusions. Also, acknowledge uncertainties or limitations associated with the data collection process and the involvement of AI. Clearly document and provide detailed descriptions of the data collection methods, AI algorithms used, and data analysis techniques employed.

By prioritizing ethical considerations when reporting data collected using AI, researchers can uphold the integrity of their research and ensure the protection of participants’ rights. Transparently documenting the data collection process, ensuring data privacy, addressing bias in AI algorithms, and maintaining accountability and reproducibility contribute to responsible and trustworthy reporting practices in the AI-driven research landscape.

Have you faced any challenges during data collection for your research? Share your experience in the form of a thought piece or an article on Enago Academy Open Platform .

Frequently Asked Questions

Data collection is important because it helps in making informed decisions and provides the answers to your research questions.

Data collection in research is a step where information is gathered based on variables of interest, in a systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes.

Data collection methods can be broadly classified as: • Qualitative method: Qualitative methods deals with descriptive and conceptual data. • Quantitative method: Quantitative method deals with numerical data which can be ranked or measured. • Mixed method: Mixed method deals with both the numerical as well as the conceptual data.

Data can be collected for research using different means like interviews, surveys, observation, focus group discussion, forms, online monitoring, experiments, etc.

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A Guide to Data Collection: Methods, Process, and Tools

Whether your field is development economics, international development, the nonprofit sector, or myriad other industries, effective data collection is essential. It informs decision-making and increases your organization’s impact. However, the process of data collection can be complex and challenging. If you’re in the beginning stages of creating a data collection process, this guide is for you. It outlines tested methods, efficient procedures, and effective tools to help you improve your data collection activities and outcomes. At SurveyCTO, we’ve used our years of experience and expertise to build a robust, secure, and scalable mobile data collection platform. It’s trusted by respected institutions like The World Bank, J-PAL, Oxfam, and the Gates Foundation, and it’s changed the way many organizations collect and use data. With this guide, we want to share what we know and help you get ready to take the first step in your data collection journey.

Main takeaways from this guide

• Before starting the data collection process, define your goals and identify data sources, which can be primary (first-hand research) or secondary (existing resources).
• Your data collection method should align with your goals, resources, and the nature of the data needed. Surveys, interviews, observations, focus groups, and forms are common data collection methods. 
• Sampling involves selecting a representative group from a larger population. Choosing the right sampling method to gather representative and relevant data is crucial.
• Crafting effective data collection instruments like surveys and questionnaires is key. Instruments should undergo rigorous testing for reliability and accuracy.
• Data collection is an ongoing, iterative process that demands real-time monitoring and adjustments to ensure high-quality, reliable results.
• After data collection, data should be cleaned to eliminate errors and organized for efficient analysis. The data collection journey further extends into data analysis, where patterns and useful information that can inform decision-making are discovered.
• Common challenges in data collection include data quality and consistency issues, data security concerns, and limitations with offline surveys . Employing robust data validation processes, implementing strong security protocols, and using offline-enabled data collection tools can help overcome these challenges.
• Data collection, entry, and management tools and data analysis, visualization, reporting, and workflow tools can streamline the data collection process, improve data quality, and facilitate data analysis.

What is data collection?

The traditional definition of data collection might lead us to think of gathering information through surveys, observations, or interviews. However, the modern-age definition of data collection extends beyond conducting surveys and observations. It encompasses the systematic gathering and recording of any kind of information through digital or manual methods. Data collection can be as routine as a doctor logging a patient’s information into an electronic medical record system during each clinic visit, or as specific as keeping a record of mosquito nets delivered to a rural household.

Getting started with data collection

Before starting your data collection process, you must clearly understand what you aim to achieve and how you’ll get there. Below are some actionable steps to help you get started.

1. Define your goals

Defining your goals is a crucial first step. Engage relevant stakeholders and team members in an iterative and collaborative process to establish clear goals. It’s important that projects start with the identification of key questions and desired outcomes to ensure you focus your efforts on gathering the right information. 

Start by understanding the purpose of your project– what problem are you trying to solve, or what change do you want to bring about? Think about your project’s potential outcomes and obstacles and try to anticipate what kind of data would be useful in these scenarios. Consider who will be using the data you collect and what data would be the most valuable to them. Think about the long-term effects of your project and how you will measure these over time. Lastly, leverage any historical data from previous projects to help you refine key questions that may have been overlooked previously. 

Once questions and outcomes are established, your data collection goals may still vary based on the context of your work. To demonstrate, let’s use the example of an international organization working on a healthcare project in a remote area.

• If you’re a researcher , your goal will revolve around collecting primary data to answer specific questions. This could involve designing a survey or conducting interviews to collect first-hand data on patient improvement, disease or illness prevalence, and behavior changes (such as an increase in patients seeking healthcare).
• If you’re part of the monitoring and evaluation ( M&E) team , your goal will revolve around measuring the success of your healthcare project. This could involve collecting primary data through surveys or observations and developing a dashboard to display real-time metrics like the number of patients treated, percentage of reduction in incidences of disease,, and average patient wait times. Your focus would be using this data to implement any needed program changes and ensure your project meets its objectives.
• If you’re part of a field team , your goal will center around the efficient and accurate execution of project plans. You might be responsible for using data collection tools to capture pertinent information in different settings, such as in interviews takendirectly from the sample community or over the phone. The data you collect and manage will directly influence the operational efficiency of the project and assist in achieving the project’s overarching objectives.

2. Identify your data sources

The crucial next step in your research process is determining your data source. Essentially, there are two main data types to choose from: primary and secondary.

• Primary data is the information you collect directly from first-hand engagements. It’s gathered specifically for your research and tailored to your research question. Primary data collection methods can range from surveys and interviews to focus groups and observations. Because you design the data collection process, primary data can offer precise, context-specific information directly related to your research objectives. For example, suppose you are investigating the impact of a new education policy. In that case, primary data might be collected through surveys distributed to teachers or interviews with school administrators dealing directly with the policy’s implementation.
• Secondary data, on the other hand, is derived from resources that already exist. This can include information gathered for other research projects, administrative records, historical documents, statistical databases, and more. While not originally collected for your specific study, secondary data can offer valuable insights and background information that complement your primary data. For instance, continuing with the education policy example, secondary data might involve academic articles about similar policies, government reports on education or previous survey data about teachers’ opinions on educational reforms.

While both types of data have their strengths, this guide will predominantly focus on primary data and the methods to collect it. Primary data is often emphasized in research because it provides fresh, first-hand insights that directly address your research questions. Primary data also allows for more control over the data collection process, ensuring data is relevant, accurate, and up-to-date.

However, secondary data can offer critical context, allow for longitudinal analysis, save time and resources, and provide a comparative framework for interpreting your primary data. It can be a crucial backdrop against which your primary data can be understood and analyzed. While we focus on primary data collection methods in this guide, we encourage you not to overlook the value of incorporating secondary data into your research design where appropriate.

3. Choose your data collection method

When choosing your data collection method, there are many options at your disposal. Data collection is not limited to methods like surveys and interviews. In fact, many of the processes in our daily lives serve the goal of collecting data, from intake forms to automated endpoints, such as payment terminals and mass transit card readers. Let us dive into some common types of data collection methods: 

Surveys and Questionnaires

Surveys and questionnaires are tools for gathering information about a group of individuals, typically by asking them predefined questions. They can be used to collect quantitative and qualitative data and be administered in various ways, including online, over the phone, in person (offline), or by mail.

• Advantages : They allow researchers to reach many participants quickly and cost-effectively, making them ideal for large-scale studies. The structured format of questions makes analysis easier.
• Disadvantages : They may not capture complex or nuanced information as participants are limited to predefined response choices. Also, there can be issues with response bias, where participants might provide socially desirable answers rather than honest ones.

Interviews involve a one-on-one conversation between the researcher and the participant. The interviewer asks open-ended questions to gain detailed information about the participant’s thoughts, feelings, experiences, and behaviors.

• Advantages : They allow for an in-depth understanding of the topic at hand. The researcher can adapt the questioning in real time based on the participant’s responses, allowing for more flexibility.
• Disadvantages : They can be time-consuming and resource-intensive, as they require trained interviewers and a significant amount of time for both conducting and analyzing responses. They may also introduce interviewer bias if not conducted carefully, due to how an interviewer presents questions and perceives the respondent, and how the respondent perceives the interviewer. 

Observations

Observations involve directly observing and recording behavior or other phenomena as they occur in their natural settings.

• Advantages : Observations can provide valuable contextual information, as researchers can study behavior in the environment where it naturally occurs, reducing the risk of artificiality associated with laboratory settings or self-reported measures.
• Disadvantages : Observational studies may suffer from observer bias, where the observer’s expectations or biases could influence their interpretation of the data. Also, some behaviors might be altered if subjects are aware they are being observed.

Focus Groups

Focus groups are guided discussions among selected individuals to gain information about their views and experiences.

• Advantages : Focus groups allow for interaction among participants, which can generate a diverse range of opinions and ideas. They are good for exploring new topics where there is little pre-existing knowledge.
• Disadvantages : Dominant voices in the group can sway the discussion, potentially silencing less assertive participants. They also require skilled facilitators to moderate the discussion effectively.

Forms are standardized documents with blank fields for collecting data in a systematic manner. They are often used in fields like Customer Relationship Management (CRM) or Electronic Medical Records (EMR) data entry. Surveys may also be referred to as forms.

• Advantages : Forms are versatile, easy to use, and efficient for data collection. They can streamline workflows by standardizing the data entry process.
• Disadvantages : They may not provide in-depth insights as the responses are typically structured and limited. There is also potential for errors in data entry, especially when done manually.

Selecting the right data collection method should be an intentional process, taking into consideration the unique requirements of your project. The method selected should align with your goals, available resources, and the nature of the data you need to collect.

If you aim to collect quantitative data, surveys, questionnaires, and forms can be excellent tools, particularly for large-scale studies. These methods are suited to providing structured responses that can be analyzed statistically, delivering solid numerical data.

However, if you’re looking to uncover a deeper understanding of a subject, qualitative data might be more suitable. In such cases, interviews, observations, and focus groups can provide richer, more nuanced insights. These methods allow you to explore experiences, opinions, and behaviors deeply. Some surveys can also include open-ended questions that provide qualitative data.

The cost of data collection is also an important consideration. If you have budget constraints, in-depth, in-person conversations with every member of your target population may not be practical. In such cases, distributing questionnaires or forms can be a cost-saving approach.

Additional considerations include language barriers and connectivity issues. If your respondents speak different languages, consider translation services or multilingual data collection tools . If your target population resides in areas with limited connectivity and your method will be to collect data using mobile devices, ensure your tool provides offline data collection , which will allow you to carry out your data collection plan without internet connectivity.

4. Determine your sampling method

Now that you’ve established your data collection goals and how you’ll collect your data, the next step is deciding whom to collect your data from. Sampling involves carefully selecting a representative group from a larger population. Choosing the right sampling method is crucial for gathering representative and relevant data that aligns with your data collection goal.

Consider the following guidelines to choose the appropriate sampling method for your research goal and data collection method:

• Understand Your Target Population: Start by conducting thorough research of your target population. Understand who they are, their characteristics, and subgroups within the population.
• Anticipate and Minimize Biases: Anticipate and address potential biases within the target population to help minimize their impact on the data. For example, will your sampling method accurately reflect all ages, gender, cultures, etc., of your target population? Are there barriers to participation for any subgroups? Your sampling method should allow you to capture the most accurate representation of your target population.
• Maintain Cost-Effective Practices: Consider the cost implications of your chosen sampling methods. Some sampling methods will require more resources, time, and effort. Your chosen sampling method should balance the cost factors with the ability to collect your data effectively and accurately. 
• Consider Your Project’s Objectives: Tailor the sampling method to meet your specific objectives and constraints, such as M&E teams requiring real-time impact data and researchers needing representative samples for statistical analysis.

By adhering to these guidelines, you can make informed choices when selecting a sampling method, maximizing the quality and relevance of your data collection efforts.

5. Identify and train collectors

Not every data collection use case requires data collectors, but training individuals responsible for data collection becomes crucial in scenarios involving field presence.

The SurveyCTO platform supports both self-response survey modes and surveys that require a human field worker to do in-person interviews. Whether you’re hiring and training data collectors, utilizing an existing team, or training existing field staff, we offer comprehensive guidance and the right tools to ensure effective data collection practices.  

Here are some common training approaches for data collectors:

• In-Class Training: Comprehensive sessions covering protocols, survey instruments, and best practices empower data collectors with skills and knowledge.
• Tests and Assessments: Assessments evaluate collectors’ understanding and competence, highlighting areas where additional support is needed.
• Mock Interviews: Simulated interviews refine collectors’ techniques and communication skills.
• Pre-Recorded Training Sessions: Accessible reinforcement and self-paced learning to refresh and stay updated.

Training data collectors is vital for successful data collection techniques. Your training should focus on proper instrument usage and effective interaction with respondents, including communication skills, cultural literacy, and ethical considerations.

Remember, training is an ongoing process. Knowledge gaps and issues may arise in the field, necessitating further training.

Moving Ahead: Iterative Steps in Data Collection

Once you’ve established the preliminary elements of your data collection process, you’re ready to start your data collection journey. In this section, we’ll delve into the specifics of designing and testing your instruments, collecting data, and organizing data while embracing the iterative nature of the data collection process, which requires diligent monitoring and making adjustments when needed.

6. Design and test your instruments

Designing effective data collection instruments like surveys and questionnaires is key. It’s crucial to prioritize respondent consent and privacy to ensure the integrity of your research. Thoughtful design and careful testing of survey questions are essential for optimizing research insights. Other critical considerations are: 

• Clear and Unbiased Question Wording: Craft unambiguous, neutral questions free from bias to gather accurate and meaningful data. For example, instead of asking, “Shouldn’t we invest more into renewable energy that will combat the effects of climate change?” ask your question in a neutral way that allows the respondent to voice their thoughts. For example: “What are your thoughts on investing more in renewable energy?”
• Logical Ordering and Appropriate Response Format: Arrange questions logically and choose response formats (such as multiple-choice, Likert scale, or open-ended) that suit the nature of the data you aim to collect.
• Coverage of Relevant Topics: Ensure that your instrument covers all topics pertinent to your data collection goals while respecting cultural and social sensitivities. Make sure your instrument avoids assumptions, stereotypes, and languages or topics that could be considered offensive or taboo in certain contexts. The goal is to avoid marginalizing or offending respondents based on their social or cultural background.
• Collect Only Necessary Data: Design survey instruments that focus solely on gathering the data required for your research objectives, avoiding unnecessary information.
• Language(s) of the Respondent Population: Tailor your instruments to accommodate the languages your target respondents speak, offering translated versions if needed. Similarly, take into account accessibility for respondents who can’t read by offering alternative formats like images in place of text.
• Desired Length of Time for Completion: Respect respondents’ time by designing instruments that can be completed within a reasonable timeframe, balancing thoroughness with engagement. Having a general timeframe for the amount of time needed to complete a response will also help you weed out bad responses. For example, a response that was rushed and completed outside of your response timeframe could indicate a response that needs to be excluded.
• Collecting and Documenting Respondents’ Consent and Privacy: Ensure a robust consent process, transparent data usage communication, and privacy protection throughout data collection.

Perform Cognitive Interviewing

Cognitive interviewing is a method used to refine survey instruments and improve the accuracy of survey responses by evaluating how respondents understand, process, and respond to the instrument’s questions. In practice, cognitive interviewing involves an interview with the respondent, asking them to verbalize their thoughts as they interact with the instrument. By actively probing and observing their responses, you can identify and address ambiguities, ensuring accurate data collection.  

Thoughtful question wording, well-organized response options, and logical sequencing enhance comprehension, minimize biases, and ensure accurate data collection. Iterative testing and refinement based on respondent feedback improve the validity, reliability, and actionability of insights obtained.

Put Your Instrument to the Test

Through rigorous testing, you can uncover flaws, ensure reliability, maximize accuracy, and validate your instrument’s performance. This can be achieved by:

• Conducting pilot testing to enhance the reliability and effectiveness of data collection. Administer the instrument, identify difficulties, gather feedback, and assess performance in real-world conditions.
• Making revisions based on pilot testing to enhance clarity, accuracy, usability, and participant satisfaction. Refine questions, instructions, and format for effective data collection.
• Continuously iterating and refining your instrument based on feedback and real-world testing. This ensures reliable, accurate, and audience-aligned methods of data collection. Additionally, this ensures your instrument adapts to changes, incorporates insights, and maintains ongoing effectiveness.

7. Collect your data

Now that you have your well-designed survey, interview questions, observation plan, or form, it’s time to implement it and gather the needed data. Data collection is not a one-and-done deal; it’s an ongoing process that demands attention to detail. Imagine spending weeks collecting data, only to discover later that a significant portion is unusable due to incomplete responses, improper collection methods, or falsified responses. To avoid such setbacks, adopt an iterative approach.

Leverage data collection tools with real-time monitoring to proactively identify outliers and issues. Take immediate action by fine-tuning your instruments, optimizing the data collection process, addressing concerns like additional training, or reevaluating personnel responsible for inaccurate data (for example, a field worker who sits in a coffee shop entering fake responses rather than doing the work of knocking on doors).

SurveyCTO’s Data Explorer was specifically designed to fulfill this requirement, empowering you to monitor incoming data, gain valuable insights, and know where changes may be needed. Embracing this iterative approach ensures ongoing improvement in data collection, resulting in more reliable and precise results.

8. Clean and organize your data

After data collection, the next step is to clean and organize the data to ensure its integrity and usability.

• Data Cleaning: This stage involves sifting through your data to identify and rectify any errors, inconsistencies, or missing values. It’s essential to maintain the accuracy of your data and ensure that it’s reliable for further analysis. Data cleaning can uncover duplicates, outliers, and gaps that could skew your results if left unchecked. With real-time data monitoring , this continuous cleaning process keeps your data precise and current throughout the data collection period. Similarly, review and corrections workflows allow you to monitor the quality of your incoming data.
• Organizing Your Data: Post-cleaning, it’s time to organize your data for efficient analysis and interpretation. Labeling your data using appropriate codes or categorizations can simplify navigation and streamline the extraction of insights. When you use a survey or form, labeling your data is often not necessary because you can design the instrument to collect in the right categories or return the right codes. An organized dataset is easier to manage, analyze, and interpret, ensuring that your collection efforts are not wasted but lead to valuable, actionable insights.

Remember, each stage of the data collection process, from design to cleaning, is iterative and interconnected. By diligently cleaning and organizing your data, you are setting the stage for robust, meaningful analysis that can inform your data-driven decisions and actions.

What happens after data collection?

The data collection journey takes us next into data analysis, where you’ll uncover patterns, empowering informed decision-making for researchers, evaluation teams, and field personnel.

Process and Analyze Your Data

Explore data through statistical and qualitative techniques to discover patterns, correlations, and insights during this pivotal stage. It’s about extracting the essence of your data and translating numbers into knowledge. Whether applying descriptive statistics, conducting regression analysis, or using thematic coding for qualitative data, this process drives decision-making and charts the path toward actionable outcomes.

Interpret and Report Your Results

Interpreting and reporting your data brings meaning and context to the numbers. Translating raw data into digestible insights for informed decision-making and effective stakeholder communication is critical.

The approach to interpretation and reporting varies depending on the perspective and role:

• Researchers often lean heavily on statistical methods to identify trends, extract meaningful conclusions, and share their findings in academic circles, contributing to their knowledge pool.
• M&E teams typically produce comprehensive reports, shedding light on the effectiveness and impact of programs. These reports guide internal and sometimes external stakeholders, supporting informed decisions and driving program improvements.

Field teams provide a first-hand perspective. Since they are often the first to see the results of the practical implementation of data, field teams are instrumental in providing immediate feedback loops on project initiatives. Field teams do the work that provides context to help research and M&E teams understand external factors like the local environment, cultural nuances, and logistical challenges that impact data results.

Safely store and handle data

Throughout the data collection process, and after it has been collected, it is vital to follow best practices for storing and handling data to ensure the integrity of your research. While the specifics of how to best store and handle data will depend on your project, here are some important guidelines to keep in mind:

• Use cloud storage to hold your data if possible, since this is safer than storing data on hard drives and keeps it more accessible,
• Periodically back up and purge old data from your system, since it’s safer to not retain data longer than necessary,
• If you use mobile devices to collect and store data, use options for private, internal apps-specific storage if and when possible,
• Restrict access to stored data to only those who need to work with that data.

Further considerations for data safety are discussed below in the section on data security .

Remember to uphold ethical standards in interpreting and reporting your data, regardless of your role. Clear communication, respectful handling of sensitive information, and adhering to confidentiality and privacy rights are all essential to fostering trust, promoting transparency, and bolstering your work’s credibility.

Common Data Collection Challenges

Data collection is vital to data-driven initiatives, but it comes with challenges. Addressing common challenges such as poor data quality, privacy concerns, inadequate sample sizes, and bias is essential to ensure the collected data is reliable, trustworthy, and secure. 

In this section, we’ll explore three major challenges: data quality and consistency issues, data security concerns, and limitations with offline data collection , along with strategies to overcome them.

Data Quality and Consistency

Data quality and consistency refer to data accuracy and reliability throughout the collection and analysis process. 

Challenges such as incomplete or missing data, data entry errors, measurement errors, and data coding/categorization errors can impact the integrity and usefulness of the data. 

To navigate these complexities and maintain high standards, consistency, and integrity in the dataset:

• Implement robust data validation processes, 
• Ensure proper training for data entry personnel, 
• Employ automated data validation techniques, and 
• Conduct regular data quality audits.

Data security

Data security encompasses safeguarding data through ensuring data privacy and confidentiality, securing storage and backup, and controlling data sharing and access.

Challenges include the risk of potential breaches, unauthorized access, and the need to comply with data protection regulations.

To address these setbacks and maintain privacy, trust, and confidence during the data collection process: 

• Use encryption and authentication methods, 
• Implement robust security protocols, 
• Update security measures regularly, 
• Provide employee training on data security, and 
• Adopt secure cloud storage solutions.

Offline Data Collection

Offline data collection refers to the process of gathering data using modes like mobile device-based computer-assisted personal interviewing (CAPI) when t here is an inconsistent or unreliable internet connection, and the data collection tool being used for CAPI has the functionality to work offline. 

Challenges associated with offline data collection include synchronization issues, difficulty transferring data, and compatibility problems between devices, and data collection tools. 

To overcome these challenges and enable efficient and reliable offline data collection processes, employ the following strategies: 

• Leverage offline-enabled data collection apps or tools  that enable you to survey respondents even when there’s no internet connection, and upload data to a central repository at a later time. 
• Your data collection plan should include times for periodic data synchronization when connectivity is available, 
• Use offline, device-based storage for seamless data transfer and compatibility, and 
• Provide clear instructions to field personnel on handling offline data collection scenarios.

Utilizing Technology in Data Collection

Embracing technology throughout your data collection process can help you overcome many challenges described in the previous section. Data collection tools can streamline your data collection, improve the quality and security of your data, and facilitate the analysis of your data. Let’s look at two broad categories of tools that are essential for data collection:

Data Collection, Entry, & Management Tools

These tools help with data collection, input, and organization. They can range from digital survey platforms to comprehensive database systems, allowing you to gather, enter, and manage your data effectively. They can significantly simplify the data collection process, minimize human error, and offer practical ways to organize and manage large volumes of data. Some of these tools are:

• Microsoft Office
• Google Docs
• SurveyMonkey
• Google Forms

Data Analysis, Visualization, Reporting, & Workflow Tools

These tools assist in processing and interpreting the collected data. They provide a way to visualize data in a user-friendly format, making it easier to identify trends and patterns. These tools can also generate comprehensive reports to share your findings with stakeholders and help manage your workflow efficiently. By automating complex tasks, they can help ensure accuracy and save time. Tools for these purposes include:

• Google sheets

Data collection tools like SurveyCTO often have integrations to help users seamlessly transition from data collection to data analysis, visualization, reporting, and managing workflows.

Master Your Data Collection Process With SurveyCTO

As we bring this guide to a close, you now possess a wealth of knowledge to develop your data collection process. From understanding the significance of setting clear goals to the crucial process of selecting your data collection methods and addressing common challenges, you are equipped to handle the intricate details of this dynamic process.

Remember, you’re not venturing into this complex process alone. At SurveyCTO, we offer not just a tool but an entire support system committed to your success. Beyond troubleshooting support, our success team serves as research advisors and expert partners, ready to provide guidance at every stage of your data collection journey.

With SurveyCTO , you can design flexible surveys in Microsoft Excel or Google Sheets, collect data online and offline with above-industry-standard security, monitor your data in real time, and effortlessly export it for further analysis in any tool of your choice. You also get access to our Data Explorer, which allows you to visualize incoming data at both individual survey and aggregate levels instantly.

In the iterative data collection process, our users tell us that SurveyCTO stands out with its capacity to establish review and correction workflows. It enables you to monitor incoming data and configure automated quality checks to flag error-prone submissions.

Finally, data security is of paramount importance to us. We ensure best-in-class security measures like SOC 2 compliance, end-to-end encryption, single sign-on (SSO), GDPR-compliant setups, customizable user roles, and self-hosting options to keep your data safe.

As you embark on your data collection journey, you can count on SurveyCTO’s experience and expertise to be by your side every step of the way. Our team would be excited and honored to be a part of your research project, offering you the tools and processes to gain informative insights and make effective decisions. Partner with us today and revolutionize the way you collect data.

Better data, better decision making, better world.

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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• Research Data Lifecycle Guide

Data Collection

Data collection is the process of gathering and measuring information used for research. Collecting data is one of the most important steps in the research process, and is part of all disciplines including physical and social sciences, humanities, business, etc. Data comes in many forms with different ways to store and record data, either written in a lab notebook and or recorded digitally on a computer system. 

While methods may differ across disciplines,  good data management processes begin with accurately and clearly describing the information recorded, the process used to collect the data, practices that ensure the quality of the data, and sharing data to enable reproducibility. This section breaks down different topics that need to be addressed while collecting and managing data for research.

Learn more about what’s required for data collection as a researcher at Case Western Reserve University. 

Ensuring Accurate and Appropriate Data Collection

Accurate data collection is vital to ensure the integrity of research . It is important when planning and executing a research project to consider methods collection and the storage of data to ensure that results can be used for publications and reporting.   The consequences from improper data collection include:

• inability to answer research questions accurately
• inability to repeat and validate the study
• distorted findings resulting in wasted resources
• misleading other researchers to pursue fruitless avenues of investigation
• compromising decisions for public policy
• causing harm to human participants and animal subjects

While the degree of impact from inaccurate data may vary by discipline, there is a potential to cause disproportionate harm when data is misrepresented and misused. This includes fraud or scientific misconduct.

Any data collected in the course of your research should follow RDM best practices to ensure accurate and appropriate data collection. This includes as appropriate, developing data collection protocols and processes to ensure inconsistencies and other errors are caught and corrected in a timely manner.

Examples of Research Data

Research data is any information that has been collected, observed, generated or created in association with research processes and findings.

Much research data is digital in format, but research data can also be extended to include non-digital formats such as laboratory notebook, diaries, or written responses to surveys. Examples may include (but are not limited to):

• Excel spreadsheets that contains instrument data
• Documents (text, Word), containing study results
• Laboratory notebooks, field notebooks, diaries
• Questionnaires, transcripts, codebooks
• Audiotapes, videotapes
• Photographs, films
• Protein or genetic sequences
• Test responses
• Slides, artifacts, specimens, samples
• Collection of digital objects acquired and generated during the process of research
• Database contents (video, audio, text, images)
• Models, algorithms, scripts
• Contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
• Source code used in application development

To ensure reproducibility of experiments and results, be sure to include and document information such as: 

• Methodologies and workflows
• Standard operating procedures and protocols

Data Use Agreements 

When working with data it is important to understand any restrictions that need to be addressed due to the sensitivity of the data. This includes how you download and share with other collaborators, and how it needs to be properly secured. 

Datasets can include potentially sensitive data that needs to be protected, and not openly shared. In this case, the dataset cannot be shared and or downloaded without permission from CWRU Research Administration and may require an agreement between collaborators and their institutions. All parties will need to abide by the agreement terms including the destruction of data once the collaboration is complete.

Storage Options 

UTech provides cloud and on-premise storage to support the university research mission. This includes Google Drive , Box , Microsoft 365 , and various on-premise solutions for high speed access and mass storage. A listing of supported options can be found on UTech’s website .

In addition to UTech-supported storage solutions, CWRU also maintains an institutional subscription to OSF (Open Science Framework) . OSF is a cloud-based data storage, sharing, and project collaboration platform that connects to many other cloud services like Drive, Box, and Github to amplify your research and data visibility and discoverability. OSF storage is functionally unlimited.

When selecting a storage platform it is important to understand how you plan to analyze and store your data. Cloud storage provides the ability to store and share data effortlessly and provides capabilities such as revisioning and other means to protect your data. On-premise storage is useful when you have large storage demands and require a high speed connection to instruments that generate data and systems that process data. Both types of storage have their advantages and disadvantages that you should consider when planning your research project.

Data Security

Data security is a set of processes and ongoing practices designed to protect information and the systems used to store and process data. This includes computer systems, files, databases, applications, user accounts, networks, and services on institutional premises, in the cloud, and remotely at the location of individual researchers. 

Effective data security takes into account the confidentiality, integrity, and availability of the information and its use. This is especially important when data contains personally identifiable information, intellectual property, trade secrets, and or technical data supporting technology transfer agreements (before public disclosure decisions have been made).

Data Categorization 

CWRU uses a 3-tier system to categorize research data based on information types and sensitivity . Determination is based upon risk to the University in the areas of confidentiality, integrity, and availability of data in support of the University's research mission. In this context, confidentiality measures to what extent information can be disclosed to others, integrity is the assurance that the information is trustworthy and accurate, and availability is a guarantee of reliable access to the information by authorized users.

Information (or data) owners are responsible for determining the impact levels of their information, i.e. what happens if the data is improperly accessed or lost accidentally, implementing the necessary security controls, and managing the risk of negative events including data loss and unauthorized access.

Loss, corruption, or inappropriate access to information can interfere with CWRU's mission, interrupt business and damage reputations or finances. 

Securing Data

The classification of data requires certain safeguards or countermeasures, known as controls, to be applied to systems that store data. This can include restricting access to the data, detecting unauthorized access, preventative measures to avoid loss of data, encrypting the transfer and storage of data, keeping the system and data in a secure location, and receiving training on best practices for handling data. Controls are classified according to their characteristics, for example:

• Physical controls e.g. doors, locks, climate control, and fire extinguishers;
• Procedural or administrative controls e.g. policies, incident response processes, management oversight, security awareness and training;
• Technical or logical controls e.g. user authentication (login) and logical access controls, antivirus software, firewalls;
• Legal and regulatory or compliance controls e.g. privacy laws, policies and clauses.

Principal Investigator (PI) Responsibilities

The CWRU Faculty Handbook provides guidelines for PIs regarding the custody of research data. This includes, where applicable, appropriate measures to protect confidential information. It is everyone’s responsibility to ensure that our research data is kept securely and available for reproducibility and future research opportunities.

University Technology provides many services and resources related to data security including assistance with planning and securing data. This includes processing and storing restricted information used in research. 

Data Collected as Part of Human Subject Research 

To ensure the privacy and safety of the individual participating in a human subject research study, additional rules and processes are in place that describe how one can use and disclose data collected,  The Office of Research Administration provides information relevant to conducting this type of research. This includes:

• Guidance on data use agreements and processes for agreements that involve human-related data or human-derived samples coming in or going out of CWRU.
• Compliance with human subject research rules and regulations.

According to 45 CFR 46 , a human subject is "a living individual about whom an investigator (whether professional or student) conducting research:

• Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or
• Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens."

The CWRU Institutional Review Board reviews social science/behavioral studies, and low-risk biomedical research not conducted in a hospital setting for all faculty, staff, and students of the University. This includes data collected and used for human subjects research. 

Research conducted in a hospital setting including University Hospitals requires IRB protocol approval.

Questions regarding the management of human subject research data should be addressed to the CWRU Institutional Review Board .

Getting Help With Data Collection

If you are looking for datasets and other resources for your research you can contact your subject area librarian for assistance.

• Kelvin Smith Library

If you need assistance with administrative items such as data use agreements or finding the appropriate storage solution please contact the following offices.

• Research Administration
• UTech Research Computing
• Information Security Office

Guidance and Resources

• Information Security Policy
• Research Data Protection
• CWRU Faculty Handbook
• CWRU IRB Guidance

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The 5-Step Marketing Research Process

March 14, 2023

By Sandy Moore

With constant change being the norm in marketing and business, one thing remains the same: the need for marketing research. Marketing research is a helpful tool for organizations to better identify marketing strategies and evaluate business decisions using data. Just as you wouldn’t go on vacation without making any plans, you shouldn’t design marketing strategies without backing them with research and data. In short, the marketing research process is the backbone of informed business and marketing decisions. 

You might be surprised to hear that marketing research is one of the first things that organizations cut from their marketing budgets because of the high time (and sometimes monetary) investment. This is not the best decision, especially when your company is planning to launch a new product or venturing into a new market. As some savvy startups have learned, marketing research doesn't have to be expensive if you do it right and follow the right process. 

Let’s review best practices when going through the five-step marketing research process:

1. Define the Problem or Opportunity

The most important part of the marketing research process is defining the problem. In order to do any research and collect data, you have to know what you are trying to learn from the research. In marketing research, defining the problem you need to solve will determine what information you need and how you can get that information. This will help your organization clarify the overarching problem or opportunity, such as how to best address the loss of market share or how to launch a new product to a specific demographic. 

Develop questions that will allow you to define your problem (or opportunity), and examine all potential causes so that the research can be whittled down to the information you actually need to solve that problem or determine what action to take regarding an opportunity. Oftentimes, these are questions about who your target market or ideal buyer persona is (for example: “What does our ideal customer look like?”). These might include questions about demographics, what their occupation is, what they like to do in their spare time—anything to help you get a clearer picture of who your ideal buyer persona is. Consider as many variables and potential causes as possible.

2. Develop Your Marketing Research Plan

After you’ve examined all potential causes of the problem and have used those questions to boil down exactly what you’re trying to solve, it’s time to build the research plan. Your research plan can be overwhelming to create because it can include any method that will help you answer the research problem or explore an opportunity identified in step one. 

To help you develop the research plan, let’s review a few techniques for conducting research:

• I nterview prospects and customers . Oftentimes, you get the best feedback by using this tactic because you’re going straight to the source. This might take the form of a focus group or one-on-one interviews. Use your defined research problem to help select the right people to interview.
• Conduct a survey using SurveyMonkey or another tool.
• Run user tests on your website or landing page(s). This is a cost-effective approach that can provide a lot of insight and data on how your customers or potential customers behave or respond to something, whether it’s new messaging or branding or a modified product or service you are thinking about offering. Simple A/B tests can go a long way in discovering user behavior. Use heatmapping tools, such as Hotjar or Lucky Orange, and website analytics tools , such as Google Analytics or HubSpot analytics , to track results depending on what data you need to collect.

Oftentimes, we do all of this work and gather all of the data—only to realize that we didn’t have to reinvent the wheel because someone had already run a similar, credible study or solved the same problem. That doesn’t mean you don’t need to do any research, but learning about what other organizations have done to solve a problem or seize an opportunity can help you tweak your research study and save you time when considering all of the research options. In marketing research, this is called secondary data because it has been collected by someone else, versus the primary data that you would collect through your own research study.

3. Collect Relevant Data and Information

In marketing research, most of the data you collect will be quantitative (numbers or data) versus qualitative, which is descriptive and observational. Ideally, you will gather a mix of the two types of data. For example, you might run an A/B test on your website to see if a new pricing tier would bring in more business. In that research study, you might also interview several customers about whether or not the new pricing tier would appeal to them. This way, you’re receiving hard data and qualitative data that provide more color and insight.

When collecting data, make sure it's valid and unbiased. You should never ask a research interviewee, “You think that we should offer a higher pricing tier with additional services, correct?” This type of question is clearly designed to influence the way the person responds. Try asking both open-ended and closed-ended questions (for instance, a multiple-choice question asking what income range best describes you).

4. Analyze Data and Report Findings

Now that you’ve gathered all of the information you need, it’s time for the fun part: analyzing the data. Although one piece of information or data might jump out at you, it’s important to look for trends as opposed to specific pieces of information. As you're analyzing your data, don't try to find patterns based on your assumptions prior to collecting the data. 

Sometimes, it’s important to write up a summary of the study, including the process that you followed, the results, conclusions, and what steps you recommend taking based on those results. Even if you don’t need a formal marketing research report, be sure that you review the study and results so that you can articulate the recommended course of action. Sharing the charts and data you collected is pointless if it doesn’t lead to action. Tools like HubSpot's free blog maker can help you publish the results in an easy way.

Was your hypothesis proven wrong? Great—that's why you do testing and don't run with assumptions when making decisions that could have a major impact on your organization. It’s always better to take the results as they are than to twist the data to prove yourself right.

5. Put Your Research into Action

Your research is complete. It's time to present your findings and take action. Start developing your inbound marketing strategies and campaigns. Put your findings to the test and get going! The biggest takeaway here is that, although this round of research is complete, it's not over.

The problems, business environment, and trends are constantly changing, which means that your research is never over. The trends you discovered through your research are evolving. You should be analyzing your data on a regular basis to see where you can improve. The more you know about your buyer personas, industry, and company, the more successful your marketing efforts and company will be. When you look at it that way, you should start to wonder why so many organizations don’t budget time and resources for marketing research. 

Of course, there is a lot more to the marketing research process than these five core steps, but these are enough to get you started. Good luck, and be sure to share any tips you have discovered for conducting marketing research!

Frequently Asked Questions 

Faq #1: how many steps are in the marketing research process, faq #2: what is the purpose of the five-step marketing research process, faq #3: what are the steps in the marketing research process, faq #4: what is the first step in the marketing research process, faq #5: what is the final step in the marketing research process, faq #6: why is the marketing research process important.

This post was originally published in June 2016 and has been updated since.

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About the author

Sandy Moore Sandy Moore is a Senior Director, Marketing Strategy at SmartBug Media. She has more than 20 years of experience in marketing with extensive knowledge in outbound and inbound marketing, advertising sales, promotions, public relations, and sales enablement. Read more articles by Sandy Moore .

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Module 6: Marketing Information and Research

The marketing research process, learning objectives.

• Identify the steps of conducting a marketing research project

A Standard Approach to Research Inquiries

Marketing research is a useful and necessary tool for helping marketers and an organization’s executive leadership make wise decisions. Carrying out marketing research can involve highly specialized skills that go deeper than the information outlined in this module. However, it is important for any marketer to be familiar with the basic procedures and techniques of marketing research.

It is very likely that at some point a marketing professional will need to supervise an internal marketing research activity or to work with an outside marketing research firm to conduct a research project. Managers who understand the research function can do a better job of framing the problem and critically appraising the proposals made by research specialists. They are also in a better position to evaluate their findings and recommendations.

Periodically marketers themselves need to find solutions to marketing problems without the assistance of marketing research specialists inside or outside the company. If you are familiar with the basic procedures of marketing research, you can supervise and even conduct a reasonably satisfactory search for the information needed.

Step 1: Identify the Problem

The first step for any marketing research activity is to clearly identify and define the problem you are trying to solve. You start by stating the marketing or business problem you need to address and for which you need additional information to figure out a solution. Next, articulate the objectives for the research: What do you want to understand by the time the research project is completed? What specific information, guidance, or recommendations need to come out of the research in order to make it a worthwhile investment of the organization’s time and money?

It’s important to share the problem definition and research objectives with other team members to get their input and further refine your understanding of the problem and what is needed to solve it. At times, the problem you really need to solve is not the same problem that appears on the surface. Collaborating with other stakeholders helps refine your understanding of the problem, focus your thinking, and prioritize what you hope to learn from the research. Prioritizing your objectives is particularly helpful if you don’t have the time or resources to investigate everything you want.

To flesh out your understanding of the problem, it’s useful to begin brainstorming actual research questions you want to explore. What are the questions you need to answer in order to get to the research outcomes? What is the missing information that marketing research will help you find? The goal at this stage is to generate a set of preliminary, big-picture questions that will frame your research inquiry. You will revisit these research questions later in the process, but when you’re getting started, this exercise helps clarify the scope of the project, whom you need to talk to, what information may already be available, and where to look for the information you don’t yet have.

Applied Example: Marketing Research for Bookends

To illustrate the marketing research process, let’s return to Uncle Dan and his ailing bookstore, Bookends. You need a lot of information if you’re going to help Dan turn things around, so marketing research is a good idea. You begin by identifying the problem and then work to set down your research objectives and initial research questions:

Step 2: Develop a Research Plan

Once you have a problem definition, research objectives, and a preliminary set of research questions, the next step is to develop a research plan. Essential to this plan is identifying precisely what information you need to answer your questions and achieve your objectives. Do you need to understand customer opinions about something? Are you looking for a clearer picture of customer needs and related behaviors? Do you need sales, spending, or revenue data? Do you need information about competitors’ products, or insight about what will make prospective customers notice you? When do need the information, and what’s the time frame for getting it? What budget and resources are available?

Once you have clarified what kind of information you need and the timing and budget for your project, you can develop the research design. This details how you plan to collect and analyze the information you’re after. Some types of information are readily available through  secondary research and secondary data sources. Secondary research analyzes information that has already been collected for another purpose by a third party, such as a government agency, an industry association, or another company. Other types of information need to from talking directly to customers about your research questions. This is known as primary research , which collects primary data captured expressly for your research inquiry.   Marketing research projects may include secondary research, primary research, or both.

Depending on your objectives and budget, sometimes a small-scale project will be enough to get the insight and direction you need. At other times, in order to reach the level of certainty or detail required, you may need larger-scale research involving participation from hundreds or even thousands of individual consumers. The research plan lays out the information your project will capture—both primary and secondary data—and describes what you will do with it to get the answers you need. (Note: You’ll learn more about data collection methods and when to use them later in this module.)

Your data collection plan goes hand in hand with your analysis plan. Different types of analysis yield different types of results. The analysis plan should match the type of data you are collecting, as well as the outcomes your project is seeking and the resources at your disposal. Simpler research designs tend to require simpler analysis techniques. More complex research designs can yield powerful results, such as understanding causality and trade-offs in customer perceptions. However, these more sophisticated designs can require more time and money to execute effectively, both in terms of data collection and analytical expertise.

The research plan also specifies who will conduct the research activities, including data collection, analysis, interpretation, and reporting on results. At times a singlehanded marketing manager or research specialist runs the entire research project. At other times, a company may contract with a marketing research analyst or consulting firm to conduct the research. In this situation, the marketing manager provides supervisory oversight to ensure the research delivers on expectations.

Finally, the research plan indicates who will interpret the research findings and how the findings will be reported. This part of the research plan should consider the internal audience(s) for the research and what reporting format will be most helpful. Often, senior executives are primary stakeholders, and they’re anxious for marketing research to inform and validate their choices. When this is the case, getting their buy-in on the research plan is recommended to make sure that they are comfortable with the approach and receptive to the potential findings.

Applied Example: A Bookends Research Plan

You talk over the results of your problem identification work with Dan. He thinks you’re on the right track and wants to know what’s next. You explain that the next step is to put together a detailed plan for getting answers to the research questions.

Dan is enthusiastic, but he’s also short on money. You realize that such a financial constraint will limit what’s possible, but with Dan’s help you can do something worthwhile. Below is the research plan you sketch out:

Step 3: Conduct the Research

Conducting research can be a fun and exciting part of the marketing research process. After struggling with the gaps in your knowledge of market dynamics—which led you to embark on a marketing research project in the first place—now things are about to change. Conducting research begins to generate information that helps answer your urgent marketing questions.

Typically data collection begins by reviewing any existing research and data that provide some information or insight about the problem. As a rule, this is secondary research. Prior research projects, internal data analyses, industry reports, customer-satisfaction survey results, and other information sources may be worthwhile to review. Even though these resources may not answer your research questions fully, they may further illuminate the problem you are trying to solve. Secondary research and data sources are nearly always cheaper than capturing new information on your own. Your marketing research project should benefit from prior work wherever possible.

After getting everything you can from secondary research, it’s time to shift attention to primary research, if this is part of your research plan. Primary research involves asking questions and then listening to and/or observing the behavior of the target audience you are studying. In order to generate reliable, accurate results, it is important to use proper scientific methods for primary research data collection and analysis. This includes identifying the right individuals and number of people to talk to, using carefully worded surveys or interview scripts, and capturing data accurately.

Without proper techniques, you may inadvertently get bad data or discover bias in the responses that distorts the results and points you in the wrong direction. The module on Marketing Research Techniques discusses these issues in further detail, since the procedures for getting reliable data vary by research method.

Applied Example: Getting the Data on Bookends

Dan is on board with the research plan, and he’s excited to dig into the project. You start with secondary data, getting a dump of Dan’s sales data from the past two years, along with related information: customer name, zip code, frequency of purchase, gender, date of purchase, and discounts/promotions (if any).

You visit the U.S. Census Bureau Web site to download demographic data about your metro area. The data show all zip codes in the area, along with population size, gender breakdown, age ranges, income, and education levels.

The next part of the project is customer-survey data. You work with Dan to put together a short survey about customer attitudes toward Bookends, how often and why they come, where else they spend money on books and entertainment, and why they go other places besides Bookends. Dan comes up with the great idea of offering a 5 percent discount coupon to anyone who completes the survey. Although it eats into his profits, this scheme gets more people to complete the survey and buy books, so it’s worth it.

For a couple of days, you and Dan take turns doing “man on the street” interviews (you interview the guy in the red hat, for instance). You find people who say they’ve never been to Bookends and ask them a few questions about why they haven’t visited the store, where else they buy books and other entertainment, and what might get them interested in visiting Bookends sometime. This is all a lot of work, but for a zero-budget project, it’s coming together pretty well.

Step 4: Analyze and Report Findings

Analyzing the data obtained in a market survey involves transforming the primary and/or secondary data into useful information and insights that answer the research questions. This information is condensed into a format to be used by managers—usually a presentation or detailed report.

Analysis starts with formatting, cleaning, and editing the data to make sure that it’s suitable for whatever analytical techniques are being used. Next, data are tabulated to show what’s happening: What do customers actually think? What’s happening with purchasing or other behaviors? How do revenue figures actually add up? Whatever the research questions, the analysis takes source data and applies analytical techniques to provide a clearer picture of what’s going on. This process may involve simple or sophisticated techniques, depending on the research outcomes required. Common analytical techniques include regression analysis to determine correlations between factors; conjoint analysis to determine trade-offs and priorities; predictive modeling to anticipate patterns and causality; and analysis of unstructured data such as Internet search terms or social media posts to provide context and meaning around what people say and do.

Good analysis is important because the interpretation of research data—the “so what?” factor—depends on it. The analysis combs through data to paint a picture of what’s going on. The interpretation goes further to explain what the research data mean and make recommendations about what managers need to know and do based on the research results. For example, what is the short list of key findings and takeaways that managers should remember from the research? What are the market segments you’ve identified, and which ones should you target?  What are the primary reasons your customers choose your competitor’s product over yours, and what does this mean for future improvements to your product?

Individuals with a good working knowledge of the business should be involved in interpreting the data because they are in the best position to identify significant insights and make recommendations from the research findings. Marketing research reports incorporate both analysis and interpretation of data to address the project objectives.

The final report for a marketing research project may be in written form or slide-presentation format, depending on organizational culture and management preferences. Often a slide presentation is the preferred format for initially sharing research results with internal stakeholders. Particularly for large, complex projects, a written report may be a better format for discussing detailed findings and nuances in the data, which managers can study and reference in the future.

Applied Example: Analysis and Insights for Bookends

Getting the data was a bit of a hassle, but now you’ve got it, and you’re excited to see what it reveals. Your statistician cousin, Marina, turns out to be a whiz with both the sales data and the census data. She identified several demographic profiles in the metro area that looked a lot like lifestyle segments. Then she mapped Bookends’ sales data into those segments to show who is and isn’t visiting Bookends. After matching customer-survey data to the sales data, she broke down the segments further based on their spending levels and reasons they visit Bookends.

Gradually a clearer picture of Bookends’ customers is beginning to emerge: who they are, why they come, why they don’t come, and what role Bookends plays in their lives. Right away, a couple of higher-priority segments—based on their spending levels, proximity, and loyalty to Bookends—stand out. You and your uncle are definitely seeing some possibilities for making the bookstore a more prominent part of their lives. You capture these insights as “recommendations to be considered” while you evaluate the right marketing mix for each of the new segments you’d like to focus on.

Step 5: Take Action

Once the report is complete, the presentation is delivered, and the recommendations are made, the marketing research project is over, right? Wrong.

What comes next is arguably the most important step of all: taking action based on your research results.

If your project has done a good job interpreting the findings and translating them into recommendations for the marketing team and other areas of the business, this step may seem relatively straightforward. When the research results validate a path the organization is already on, the “take action” step can galvanize the team to move further and faster in that same direction.

Things are not so simple when the research results indicate a new direction or a significant shift is advisable. In these cases, it’s worthwhile to spend time helping managers understand the research, explain why it is wise to shift course, and explain how the business will benefit from the new path. As with any important business decision, managers must think deeply about the new approach and carefully map strategies, tactics, and available resources to plan effectively. By making the results available and accessible to managers and their execution teams, the marketing research project can serve as an ongoing guide and touchstone to help the organization plan, execute, and adjust course as it works toward desired goals and outcomes.

It is worth mentioning that many marketing research projects are never translated into management action. Sometimes this is because the report is too technical and difficult to understand. In other cases, the research conclusions fail to provide useful insights or solutions to the problem, or the report writer fails to offer specific suggestions for translating the research findings into management strategy. These pitfalls can be avoided by paying due attention to the research objectives throughout the project and allocating sufficient time and resources to do a good job interpreting research results for those who will need to act on them.

Applied Example: Bookends’ New Customer Campaign

Your research findings and recommendations identified three segments for Bookends to focus on. Based on the demographics, lifestyle, and spending patterns found during your marketing research, you’re able to name them: 1) Bored Empty-Nesters, 2) Busy Families, and 3) Hipster Wannabes. Dan has a decent-sized clientele across all three groups, and they are pretty good spenders when they come in. But until now he hasn’t done much to purposely attract any of them.

With newly identified segments in focus, you and Dan begin brainstorming about a marketing mix to target each group. What types of books and other products would appeal to each one? What activities or events would bring them into the store? Are there promotions or particular messages that would induce them to buy at Bookends instead of Amazon or another bookseller? How will Dan reach and communicate with each group? And what can you do to bring more new customers into the store within these target groups?

Even though Bookends is a real-life project with serious consequences for your uncle Dan, it’s also a fun laboratory where you can test out some of the principles you’re learning in your marketing class. You’re figuring out quickly what it’s like to be a marketer.

Well done, rookie!

Check Your Understanding

Answer the question(s) below to see how well you understand the topics covered in this outcome. This short quiz does  not  count toward your grade in the class, and you can retake it an unlimited number of times.

Use this quiz to check your understanding and decide whether to (1) study the previous section further or (2) move on to the next section.

• Revision and Adaptation. Authored by : Lumen Learning. License : CC BY: Attribution
• Chapter 3: Marketing Research: An Aid to Decision Making, from Introducing Marketing. Authored by : John Burnett. Provided by : Global Text. Located at : http://solr.bccampus.ca:8001/bcc/file/ddbe3343-9796-4801-a0cb-7af7b02e3191/1/Core%20Concepts%20of%20Marketing.pdf . License : CC BY: Attribution
• Urban life (Version 2.0). Authored by : Ian D. Keating. Located at : https://www.flickr.com/photos/ian-arlett/19313315520/ . License : CC BY: Attribution

Developing the Overall Research Plan

• Market Research
• Sustainable Businesses
• Supply Chain Management
• Operations & Technology
• Business Law & Taxes
• Business Insurance
• Business Finance
• Becoming an Owner
• University of Washington
• San Jose State University
• University of California, San Diego

The market research process consists of six discrete stages or steps:

• Step 1 - Articulate the research problem and objectives
• Step 2 - Develop the overall research plan
• Step 3 – Collect the data or information
• Step 4 – Analyze the data or information
• Step 5 – Present or disseminate the findings
• Step 6 – Use the findings to make the decision

This article focuses on Step 2. Developing a research plan is a complex undertaking because it involves so many different decisions.

Designing a market research plan begins with determining the most efficacious way to  collect the information . Innovative technology has enabled research tools to move to digital environments. Still, the fundamentals haven't changed: A market researcher must determine what data sources to use, the type of research approaches to take, how to  limit distortions of the data , what research instruments to employ, how a  sampling plan should be configured, how to protect participants' privacy, and what methods will be used to  contact research participants . Another critical concern is how much it will cost to implement the research plan.

Primary vs. Secondary Data

Before the market researcher can determine what research instruments to use, a decision must be made about the data sources. A market researcher can choose to collect primary data, secondary data, or both types of information. Primary data is gathered for the first time for a particular project or a specific purpose. Secondary data exists prior to the initialization of a new research project, having been collected for another purpose.

The fresh gathering of data, using primary research constitutes the main distinction between primary data and secondary data is that freshly gathered data is associated with primary research. A common form of primary research data is syndicated research, in which a group of researchers who are interested in the same research problem commission an independent market research provider to conduct a study and share the results with the purchasing participants.

While primary data seems the preferable course, a common and prudent practice is for a market researcher to explore possible secondary sources of data to determine if they will suffice to answer research questions.  Primary data collection can be expensive. Secondary data is generally of low cost or even available for free, and it is immediately available without having to wait for a research study to be completed.

Of course, a fundamental disadvantage of secondary data is that it will generally not have been configured precisely to fulfill a research agenda. As such, secondary data may be incomplete, inaccurate, dated, or unreliable. In such cases, a market researcher will necessarily need to commit to some type of primary data collection process.

Pilot Testing

Typically, primary data collection begins with some type of pilot testing, even if it is as simple as interviewing people in groups or individually to get the feel for  how people perceive some topic or question . Then a formal research instrument is developed, pilot-tested again for problems, and then used in the field to conduct the desired research, all according to the research plan. There are four main types of research data instruments available to market researchers:

Questionnaires or Surveys

For gathering primary research data , surveys are the most commonly used of the instruments. Although  the survey instrument is flexible and relatively inexpensive, it requires careful attention during development. All surveys should be pilot tested, at least to some degree, before they are released and administered to a target sample. The forms that the questions take should be carefully considered to ensure they perform as expected and that they fit well into the survey document as a whole. Developing survey questions is both an art and a science. Fortunately, many guidelines to survey construction, administration, and scoring are available.

Psychological Tools

Three commonly used psychological tools used to collect primary data are laddering questions techniques , in-depth interviews , and Rorschach-like tests.

• Laddering questions continue to probe deeper into the perspectives and opinions of respondents. The technique is iterative so that each subsequent question is generated according to the response to the previous question. Laddering is a technique that has been widely used in creative problem-solving methods and workshops. In-depth interviewing consists of probing ever deeper into the customer experience.
• The technique of in-depth interviewing was developed by Ernest Dichter. He differentiated between  qualitative research and quantitative research, referring to the former as head shrinking and the latter as nose counting . (Needless to say, Dichter was a proponent of qualitative research .)
• An interview technique similar to that used in Rorschach testing has been developed for market research by Gerald Zaltman of Olson Zaltman Associates. The instrument is known as the Zaltman Metaphoric Elicitation Technique (ZMET) and uses metaphoric images to access the associations that consumers have with certain product types. Typically, a participant in a ZMET-based study will collect images from a wide array of pictures that have no verbal content in order to  express the associated feelings and thoughts they have with regard to a product type.

Mechanical Devices

Mechanical devices are sometimes used to measure the physiological responses of research participants to product attributes or advertisements. Generally, what is measured is interest or emotions in response to what is seen, heard, felt, or smelled. Mechanical devices used in primary research data collection include Galvanometers, eye cameras, eye gaze recorders, audiometers, and tachistoscopes that show an image or ad for a brief flash.

Qualitative Measures

Qualitative measures are becoming more common in primary research as advanced in technology support different approaches, such as online surveys enabled by SurveyMonkey. Consumers are being turned loose with sophisticated technology on which they can record their impressions of product or aspects of their consumer experience.

Some market research provider agencies even go into the homes of consumers to film their interactions with products. These videos are trimmed down to a highlight reel that is used to analyze consumer behavior. One of the primary reasons for preferring qualitative measures to surveys or interviewing is that  the expressed beliefs and intentions of consumers often fail to match their actual behavior in the realm of brand engagement or purchase decisions.

Kotler, P. (2003). Marketing Management (11th ed.). Upper Saddle River, NJ: Pearson Education, Inc., Prentice Hall.

Lehmann, D. R. Gupta, S., and Seckel, J. (1997). Market Research. Reading, MA: Addison-Wesley.

• Introduction to Data Collection in Market Research
• Market Research 101: Data Analysis
• Market Research 101: Develop the Research Plan
• What Is Market Research?
• Qualitative or Quantitative? How to Choose a Method for Your Survey
• How Secondary Market Research Works
• Motivation Research and How It Is Used
• How to Conduct Market Research Surveys
• How to Analyze Interview Data and Survey Responses
• Advantages and Disadvantages of Quantitative Research
• 8 Ways Even Small Nonprofits Can Do Market Research
• Deductive and Inductive Market Research
• The Difference Between Primary and Secondary Research
• Learn About Syndicated Market Research
• What Is a Market Research Focus Group?
• Quantitative and Qualitative Segmentation Research

6.3 Steps in a Successful Marketing Research Plan

Learning outcomes.

By the end of this section, you will be able to:

• 1 Identify and describe the steps in a marketing research plan.
• 2 Discuss the different types of data research.
• 3 Explain how data is analyzed.
• 4 Discuss the importance of effective research reports.

Define the Problem

There are seven steps to a successful marketing research project (see Figure 6.3 ). Each step will be explained as we investigate how a marketing research project is conducted.

The first step, defining the problem, is often a realization that more information is needed in order to make a data-driven decision. Problem definition is the realization that there is an issue that needs to be addressed. An entrepreneur may be interested in opening a small business but must first define the problem that is to be investigated. A marketing research problem in this example is to discover the needs of the community and also to identify a potentially successful business venture.

Many times, researchers define a research question or objectives in this first step. Objectives of this research study could include: identify a new business that would be successful in the community in question, determine the size and composition of a target market for the business venture, and collect any relevant primary and secondary data that would support such a venture. At this point, the definition of the problem may be “Why are cat owners not buying our new cat toy subscription service?”

Additionally, during this first step we would want to investigate our target population for research. This is similar to a target market, as it is the group that comprises the population of interest for the study. In order to have a successful research outcome, the researcher should start with an understanding of the problem in the current situational environment.

Develop the Research Plan

Step two is to develop the research plan. What type of research is necessary to meet the established objectives of the first step? How will this data be collected? Additionally, what is the time frame of the research and budget to consider? If you must have information in the next week, a different plan would be implemented than in a situation where several months were allowed. These are issues that a researcher should address in order to meet the needs identified.

Research is often classified as coming from one of two types of data: primary and secondary. Primary data is unique information that is collected by the specific researcher with the current project in mind. This type of research doesn’t currently exist until it is pulled together for the project. Examples of primary data collection include survey, observation, experiment, or focus group data that is gathered for the current project.

Secondary data is any research that was completed for another purpose but can be used to help inform the research process. Secondary data comes in many forms and includes census data, journal articles, previously collected survey or focus group data of related topics, and compiled company data. Secondary data may be internal, such as the company’s sales records for a previous quarter, or external, such as an industry report of all related product sales. Syndicated data , a type of external secondary data, is available through subscription services and is utilized by many marketers. As you can see in Table 6.1 , primary and secondary data features are often opposite—the positive aspects of primary data are the negative side of secondary data.

There are four research types that can be used: exploratory, descriptive, experimental, and ethnographic research designs (see Figure 6.4 ). Each type has specific formats of data that can be collected. Qualitative research can be shared through words, descriptions, and open-ended comments. Qualitative data gives context but cannot be reduced to a statistic. Qualitative data examples are categorical and include case studies, diary accounts, interviews, focus groups, and open-ended surveys. By comparison, quantitative data is data that can be reduced to number of responses. The number of responses to each answer on a multiple-choice question is quantitative data. Quantitative data is numerical and includes things like age, income, group size, and height.

Exploratory research is usually used when additional general information in desired about a topic. When in the initial steps of a new project, understanding the landscape is essential, so exploratory research helps the researcher to learn more about the general nature of the industry. Exploratory research can be collected through focus groups, interviews, and review of secondary data. When examining an exploratory research design, the best use is when your company hopes to collect data that is generally qualitative in nature. 7

For instance, if a company is considering a new service for registered users but is not quite sure how well the new service will be received or wants to gain clarity of exactly how customers may use a future service, the company can host a focus group. Focus groups and interviews will be examined later in the chapter. The insights collected during the focus group can assist the company when designing the service, help to inform promotional campaign options, and verify that the service is going to be a viable option for the company.

Descriptive research design takes a bigger step into collection of data through primary research complemented by secondary data. Descriptive research helps explain the market situation and define an “opinion, attitude, or behavior” of a group of consumers, employees, or other interested groups. 8 The most common method of deploying a descriptive research design is through the use of a survey. Several types of surveys will be defined later in this chapter. Descriptive data is quantitative in nature, meaning the data can be distilled into a statistic, such as in a table or chart.

Again, descriptive data is helpful in explaining the current situation. In the opening example of LEGO , the company wanted to describe the situation regarding children’s use of its product. In order to gather a large group of opinions, a survey was created. The data that was collected through this survey allowed the company to measure the existing perceptions of parents so that alterations could be made to future plans for the company.

Experimental research , also known as causal research , helps to define a cause-and-effect relationship between two or more factors. This type of research goes beyond a correlation to determine which feature caused the reaction. Researchers generally use some type of experimental design to determine a causal relationship. An example is A/B testing, a situation where one group of research participants, group A, is exposed to one treatment and then compared to the group B participants, who experience a different situation. An example might be showing two different television commercials to a panel of consumers and then measuring the difference in perception of the product. Another example would be to have two separate packaging options available in different markets. This research would answer the question “Does one design sell better than the other?” Comparing that to the sales in each market would be part of a causal research study. 9

The final method of collecting data is through an ethnographic design. Ethnographic research is conducted in the field by watching people interact in their natural environment. For marketing research, ethnographic designs help to identify how a product is used, what actions are included in a selection, or how the consumer interacts with the product. 10

Examples of ethnographic research would be to observe how a consumer uses a particular product, such as baking soda. Although many people buy baking soda, its uses are vast. So are they using it as a refrigerator deodorizer, a toothpaste, to polish a belt buckle, or to use in baking a cake?

Select the Data Collection Method

Data collection is the systematic gathering of information that addresses the identified problem. What is the best method to do that? Picking the right method of collecting data requires that the researcher understand the target population and the design picked in the previous step. There is no perfect method; each method has both advantages and disadvantages, so it’s essential that the researcher understand the target population of the research and the research objectives in order to pick the best option.

Sometimes the data desired is best collected by watching the actions of consumers. For instance, how many cars pass a specific billboard in a day? What website led a potential customer to the company’s website? When are consumers most likely to use the snack vending machines at work? What time of day has the highest traffic on a social media post? What is the most streamed television program this week? Observational research is the collecting of data based on actions taken by those observed. Many data observations do not require the researched individuals to participate in the data collection effort to be highly valuable. Some observation requires an individual to watch and record the activities of the target population through personal observations .

Unobtrusive observation happens when those being observed aren’t aware that they are being watched. An example of an unobtrusive observation would be to watch how shoppers interact with a new stuffed animal display by using a one-way mirror. Marketers can identify which products were handled more often while also determining which were ignored.

Other methods can use technology to collect the data instead. Instances of mechanical observation include the use of vehicle recorders, which count the number of vehicles that pass a specific location. Computers can also assess the number of shoppers who enter a store, the most popular entry point for train station commuters, or the peak time for cars to park in a parking garage.

When you want to get a more in-depth response from research participants, one method is to complete a one-on-one interview . One-on-one interviews allow the researcher to ask specific questions that match the respondent’s unique perspective as well as follow-up questions that piggyback on responses already completed. An interview allows the researcher to have a deeper understanding of the needs of the respondent, which is another strength of this type of data collection. The downside of personal interviews it that a discussion can be very time-consuming and results in only one respondent’s answers. Therefore, in order to get a large sample of respondents, the interview method may not be the most efficient method.

Taking the benefits of an interview and applying them to a small group of people is the design of a focus group . A focus group is a small number of people, usually 8 to 12, who meet the sample requirements. These individuals together are asked a series of questions where they are encouraged to build upon each other’s responses, either by agreeing or disagreeing with the other group members. Focus groups are similar to interviews in that they allow the researcher, through a moderator, to get more detailed information from a small group of potential customers (see Figure 6.5 ).

Link to Learning

Focus groups.

Focus groups are a common method for gathering insights into consumer thinking and habits. Companies will use this information to develop or shift their initiatives. The best way to understand a focus group is to watch a few examples or explanations. TED-Ed has this video that explains how focus groups work.

You might be asking when it is best to use a focus group or a survey. Learn the differences, the pros and cons of each, and the specific types of questions you ask in both situations in this article .

Preparing for a focus group is critical to success. It requires knowing the material and questions while also managing the group of people. Watch this video to learn more about how to prepare for a focus group and the types of things to be aware of.

One of the benefits of a focus group over individual interviews is that synergy can be generated when a participant builds on another’s ideas. Additionally, for the same amount of time, a researcher can hear from multiple respondents instead of just one. 11 Of course, as with every method of data collection, there are downsides to a focus group as well. Focus groups have the potential to be overwhelmed by one or two aggressive personalities, and the format can discourage more reserved individuals from speaking up. Finally, like interviews, the responses in a focus group are qualitative in nature and are difficult to distill into an easy statistic or two.

Combining a variety of questions on one instrument is called a survey or questionnaire . Collecting primary data is commonly done through surveys due to their versatility. A survey allows the researcher to ask the same set of questions of a large group of respondents. Response rates of surveys are calculated by dividing the number of surveys completed by the total number attempted. Surveys are flexible and can collect a variety of quantitative and qualitative data. Questions can include simplified yes or no questions, select all that apply, questions that are on a scale, or a variety of open-ended types of questions. There are four types of surveys (see Table 6.2 ) we will cover, each with strengths and weaknesses defined.

Let’s start off with mailed surveys —surveys that are sent to potential respondents through a mail service. Mailed surveys used to be more commonly used due to the ability to reach every household. In some instances, a mailed survey is still the best way to collect data. For example, every 10 years the United States conducts a census of its population (see Figure 6.6 ). The first step in that data collection is to send every household a survey through the US Postal Service (USPS). The benefit is that respondents can complete and return the survey at their convenience. The downside of mailed surveys are expense and timeliness of responses. A mailed survey requires postage, both when it is sent to the recipient and when it is returned. That, along with the cost of printing, paper, and both sending and return envelopes, adds up quickly. Additionally, physically mailing surveys takes time. One method of reducing cost is to send with bulk-rate postage, but that slows down the delivery of the survey. Also, because of the convenience to the respondent, completed surveys may be returned several weeks after being sent. Finally, some mailed survey data must be manually entered into the analysis software, which can cause delays or issues due to entry errors.

Phone surveys are completed during a phone conversation with the respondent. Although the traditional phone survey requires a data collector to talk with the participant, current technology allows for computer-assisted voice surveys or surveys to be completed by asking the respondent to push a specific button for each potential answer. Phone surveys are time intensive but allow the respondent to ask questions and the surveyor to request additional information or clarification on a question if warranted. Phone surveys require the respondent to complete the survey simultaneously with the collector, which is a limitation as there are restrictions for when phone calls are allowed. According to Telephone Consumer Protection Act , approved by Congress in 1991, no calls can be made prior to 8:00 a.m. or after 9:00 p.m. in the recipient’s time zone. 12 Many restrictions are outlined in this original legislation and have been added to since due to ever-changing technology.

In-person surveys are when the respondent and data collector are physically in the same location. In-person surveys allow the respondent to share specific information, ask questions of the surveyor, and follow up on previous answers. Surveys collected through this method can take place in a variety of ways: through door-to-door collection, in a public location, or at a person’s workplace. Although in-person surveys are time intensive and require more labor to collect data than some other methods, in some cases it’s the best way to collect the required data. In-person surveys conducted through a door-to-door method is the follow-up used for the census if respondents do not complete the mailed survey. One of the downsides of in-person surveys is the reluctance of potential respondents to stop their current activity and answer questions. Furthermore, people may not feel comfortable sharing private or personal information during a face-to-face conversation.

Electronic surveys are sent or collected through digital means and is an opportunity that can be added to any of the above methods as well as some new delivery options. Surveys can be sent through email, and respondents can either reply to the email or open a hyperlink to an online survey (see Figure 6.7 ). Additionally, a letter can be mailed that asks members of the survey sample to log in to a website rather than to return a mailed response. Many marketers now use links, QR codes, or electronic devices to easily connect to a survey. Digitally collected data has the benefit of being less time intensive and is often a more economical way to gather and input responses than more manual methods. A survey that could take months to collect through the mail can be completed within a week through digital means.

Design the Sample

Although you might want to include every possible person who matches your target market in your research, it’s often not a feasible option, nor is it of value. If you did decide to include everyone, you would be completing a census of the population. Getting everyone to participate would be time-consuming and highly expensive, so instead marketers use a sample , whereby a portion of the whole is included in the research. It’s similar to the samples you might receive at the grocery store or ice cream shop; it isn’t a full serving, but it does give you a good taste of what the whole would be like.

So how do you know who should be included in the sample? Researchers identify parameters for their studies, called sample frames . A sample frame for one study may be college students who live on campus; for another study, it may be retired people in Dallas, Texas, or small-business owners who have fewer than 10 employees. The individual entities within the sampling frame would be considered a sampling unit . A sampling unit is each individual respondent that would be considered as matching the sample frame established by the research. If a researcher wants businesses to participate in a study, then businesses would be the sampling unit in that case.

The number of sampling units included in the research is the sample size . Many calculations can be conducted to indicate what the correct size of the sample should be. Issues to consider are the size of the population, the confidence level that the data represents the entire population, the ease of accessing the units in the frame, and the budget allocated for the research.

There are two main categories of samples: probability and nonprobability (see Figure 6.8 ). Probability samples are those in which every member of the sample has an identified likelihood of being selected. Several probability sample methods can be utilized. One probability sampling technique is called a simple random sample , where not only does every person have an identified likelihood of being selected to be in the sample, but every person also has an equal chance of exclusion. An example of a simple random sample would be to put the names of all members of a group into a hat and simply draw out a specific number to be included. You could say a raffle would be a good example of a simple random sample.

Another probability sample type is a stratified random sample , where the population is divided into groups by category and then a random sample of each category is selected to participate. For instance, if you were conducting a study of college students from your school and wanted to make sure you had all grade levels included, you might take the names of all students and split them into different groups by grade level—freshman, sophomore, junior, and senior. Then, from those categories, you would draw names out of each of the pools, or strata.

A nonprobability sample is a situation in which each potential member of the sample has an unknown likelihood of being selected in the sample. Research findings that are from a nonprobability sample cannot be applied beyond the sample. Several examples of nonprobability sampling are available to researchers and include two that we will look at more closely: convenience sampling and judgment sampling.

The first nonprobability sampling technique is a convenience sample . Just like it sounds, a convenience sample is when the researcher finds a group through a nonscientific method by picking potential research participants in a convenient manner. An example might be to ask other students in a class you are taking to complete a survey that you are doing for a class assignment or passing out surveys at a basketball game or theater performance.

A judgment sample is a type of nonprobability sample that allows the researcher to determine if they believe the individual meets the criteria set for the sample frame to complete the research. For instance, you may be interested in researching mothers, so you sit outside a toy store and ask an individual who is carrying a baby to participate.

Collect the Data

Now that all the plans have been established, the instrument has been created, and the group of participants has been identified, it is time to start collecting data. As explained earlier in this chapter, data collection is the process of gathering information from a variety of sources that will satisfy the research objectives defined in step one. Data collection can be as simple as sending out an email with a survey link enclosed or as complex as an experiment with hundreds of consumers. The method of collection directly influences the length of this process. Conducting personal interviews or completing an experiment, as previously mentioned, can add weeks or months to the research process, whereas sending out an electronic survey may allow a researcher to collect the necessary data in a few days. 13

Analyze and Interpret the Data

Once the data has been collected, the process of analyzing it may begin. Data analysis is the distillation of the information into a more understandable and actionable format. The analysis itself can take many forms, from the use of basic statistics to a more comprehensive data visualization process. First, let’s discuss some basic statistics that can be used to represent data.

The first is the mean of quantitative data. A mean is often defined as the arithmetic average of values. The formula is:

A common use of the mean calculation is with exam scores. Say, for example, you have earned the following scores on your marketing exams: 72, 85, 68, and 77. To find the mean, you would add up the four scores for a total of 302. Then, in order to generate a mean, that number needs to be divided by the number of exam scores included, which is 4. The mean would be 302 divided by 4, for a mean test score of 75.5. Understanding the mean can help to determine, with one number, the weight of a particular value.

Another commonly used statistic is median. The median is often referred to as the middle number. To generate a median, all the numeric answers are placed in order, and the middle number is the median. Median is a common statistic when identifying the income level of a specific geographic region. 14 For instance, the median household income for Albuquerque, New Mexico, between 2015 and 2019 was $52,911. 15 In this case, there are just as many people with an income above the amount as there are below.

Mode is another statistic that is used to represent data of all types, as it can be used with quantitative or qualitative data and represents the most frequent answer. Eye color, hair color, and vehicle color can all be presented with a mode statistic. Additionally, some researchers expand on the concept of mode and present the frequency of all responses, not just identifying the most common response. Data such as this can easily be presented in a frequency graph, 16 such as the one in Figure 6.9 .

Additionally, researchers use other analyses to represent the data rather than to present the entirety of each response. For example, maybe the relationship between two values is important to understand. In this case, the researcher may share the data as a cross tabulation (see Figure 6.10 ). Below is the same data as above regarding social media use cross tabulated with gender—as you can see, the data is more descriptive when you can distinguish between the gender identifiers and how much time is spent per day on social media.

Not all data can be presented in a graphical format due to the nature of the information. Sometimes with qualitative methods of data collection, the responses cannot be distilled into a simple statistic or graph. In that case, the use of quotations, otherwise known as verbatims , can be used. These are direct statements presented by the respondents. Often you will see a verbatim statement when reading a movie or book review. The critic’s statements are used in part or in whole to represent their feelings about the newly released item.

Infographics

As they say, a picture is worth a thousand words. For this reason, research results are often shown in a graphical format in which data can be taken in quickly, called an infographic .

Check out this infographic on what components make for a good infographic. As you can see, a good infographic needs four components: data, design, a story, and the ability to share it with others. Without all four pieces, it is not as valuable a resource as it could be. The ultimate infographic is represented as the intersection of all four.

Infographics are particularly advantageous online. Refer to this infographic on why they are beneficial to use online .

Prepare the Research Report

The marketing research process concludes by sharing the generated data and makes recommendations for future actions. What starts as simple data must be interpreted into an analysis. All information gathered should be conveyed in order to make decisions for future marketing actions. One item that is often part of the final step is to discuss areas that may have been missed with the current project or any area of further study identified while completing it. Without the final step of the marketing research project, the first six steps are without value. It is only after the information is shared, through a formal presentation or report, that those recommendations can be implemented and improvements made. The first six steps are used to generate information, while the last is to initiate action. During this last step is also when an evaluation of the process is conducted. If this research were to be completed again, how would we do it differently? Did the right questions get answered with the survey questions posed to the respondents? Follow-up on some of these key questions can lead to additional research, a different study, or further analysis of data collected.

Methods of Quantifying Marketing Research

One of the ways of sharing information gained through marketing research is to quantify the research . Quantifying the research means to take a variety of data and compile into a quantity that is more easily understood. This is a simple process if you want to know how many people attended a basketball game, but if you want to quantify the number of students who made a positive comment on a questionnaire, it can be a little more complicated. Researchers have a variety of methods to collect and then share these different scores. Below are some of the most common types used in business.

Is a customer aware of a product, brand, or company? What is meant by awareness? Awareness in the context of marketing research is when a consumer is familiar with the product, brand, or company. It does not assume that the consumer has tried the product or has purchased it. Consumers are just aware. That is a measure that many businesses find valuable. There are several ways to measure awareness. For instance, the first type of awareness is unaided awareness . This type of awareness is when no prompts for a product, brand, or company are given. If you were collecting information on fast-food restaurants, you might ask a respondent to list all the fast-food restaurants that serve a chicken sandwich. Aided awareness would be providing a list of products, brands, or companies and the respondent selects from the list. For instance, if you give a respondent a list of fast-food restaurants and ask them to mark all the locations with a chicken sandwich, you are collecting data through an aided method. Collecting these answers helps a company determine how the business location compares to those of its competitors. 17

Customer Satisfaction (CSAT)

Have you ever been asked to complete a survey at the end of a purchase? Many businesses complete research on buying, returning, or other customer service processes. A customer satisfaction score , also known as CSAT, is a measure of how satisfied customers are with the product, brand, or service. A CSAT score is usually on a scale of 0 to 100 percent. 18 But what constitutes a “good” CSAT score? Although what is identified as good can vary by industry, normally anything in the range from 75 to 85 would be considered good. Of course, a number higher than 85 would be considered exceptional. 19

Customer Acquisition Cost (CAC) and Customer Effort Score (CES)

Other metrics often used are a customer acquisition cost (CAC) and customer effort score (CES). How much does it cost a company to gain customers? That’s the purpose of calculating the customer acquisition cost. To calculate the customer acquisition cost , a company would need to total all expenses that were accrued to gain new customers. This would include any advertising, public relations, social media postings, etc. When a total cost is determined, it is divided by the number of new customers gained through this campaign.

The final score to discuss is the customer effort score , also known as a CES. The CES is a “survey used to measure the ease of service experience with an organization.” 20 Companies that are easy to work with have a better CES than a company that is notorious for being difficult. An example would be to ask a consumer about the ease of making a purchase online by incorporating a one-question survey after a purchase is confirmed. If a number of responses come back negative or slightly negative, the company will realize that it needs to investigate and develop a more user-friendly process.

Knowledge Check

It’s time to check your knowledge on the concepts presented in this section. Refer to the Answer Key at the end of the book for feedback.

• Defining the problem
• Developing the research plan
• Selecting a data collection method
• Designing the sample
• you are able to send it to all households in an area
• it is inexpensive
• responses are automatically loaded into the software
• the data comes in quickly
• Primary data
• Secondary data
• Secondary and primary data
• Professional data
• It shows how respondents answered two variables in relation to each other and can help determine patterns by different groups of respondents.
• By presenting the data in the form of a picture, the information is easier for the reader to understand.
• It is an easy way to see how often one answer is selected by the respondents.
• This analysis can used to present interview or focus group data.

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• Book title: Principles of Marketing
• Publication date: Jan 25, 2023
• Location: Houston, Texas
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Methodology

• Data Collection | Definition, Methods & Examples

Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, other interesting articles, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analyzed through statistical methods .
• Qualitative data is expressed in words and analyzed through interpretations and categorizations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ).

Operationalization

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and timeframe of the data collection.

Standardizing procedures

If multiple researchers are involved, write a detailed manual to standardize data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias .

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organize and store your data.

• If you are collecting data from people, you will likely need to anonymize and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimize distortion.
• You can prevent loss of data by having an organization system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns.

To ensure that high quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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Factors to consider when developing a data collection plan

• 3 mins to read
• October 14, 2022
• By Reagan Pannell

An Introduction to Data Collection Plans

A Data Collection Plan is a tool project managers use to track and collect project data. The data collected can be used to assess the project’s progress, identify improvement areas, and track the project’s overall success. Many different types of data can be collected, but some common data points include project milestones, budget tracking, and risk management.

Why Use a Data Collection Plan?

There are many benefits to using a Data Collection Plan. One of the most important benefits is that it allows you to collect accurate and timely data. This data can then be used to make informed decisions about the project. Additionally, a Data Collection Plan can help you identify areas of improvement and track the project’s overall success.

There are several factors to consider when developing a data collection plan, including the following:

• -The purpose of the data collection
• -The target population
• -The sampling method
• -The data collection instruments
• -The data analysis procedures 
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There are a few steps that you need to follow to create an effective Data Collection Plan. First, you need to determine the data type you want to collect. This will vary depending on the type of project you are working on. Next, you need to decide how you will collect this data. Many different options are available, such as surveys, interviews, focus groups, or observation. Once you have determined how you will collect the data, you need to develop a plan. This plan should include who will be responsible for collecting the data, when they will do so, and how often they will collect it. Finally, once you have collected the data, you need to analyze it and use it to make informed decisions about the project.

Tips for effective data collection

1. define the purpose of your data collection..

Before you can start collecting data, you need to establish the purpose of your data collection. What question are you trying to answer with your data? Once you have defined the purpose of your data collection, you can start thinking about what types of data you need to collect to answer that question.

2. Identify the types of data you need.

There are two main types of data: primary and secondary. Primary data is collected directly from people through surveys, interviews, focus groups, or observation. Secondary data is sourced from existing reports or research studies (i.e., it has already been collected by someone else). Depending on your research question, you may need to collect both primary and secondary data.

3. Develop a plan for how you will collect the data.

Now that you know what types of data you need to collect, it’s time to develop a plan for how you will collect that data. Will you conduct surveys? interviews? focus groups? observations? If so, who will be responsible for conducting those surveys/interviews/focus groups/observations? When and where will they take place? How many people do you need to surveyed/interviewed/observed? By answering these questions, you can avoid any last-minute scrambling when it comes time to collect your data.

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4. decide who will be responsible for collecting the data..

It’s crucial to decide who on your team will collect each data type. This ensures that nothing falls through the cracks and that everyone knows their role in the data collection process. Be sure to assign specific tasks and deadlines to individuals or teams so that everyone knows exactly what they need to do and when they need to do it by.

Conclusion:

A Data Collection Plan is essential for ensuring that your data collection is systematic and efficient. By developing a Data Collection Plan at the beginning of your project, you can avoid any last-minute scrambling later on down the line. In short, a Data Collection Plan should describe what types of data you need, how you will collect it, who will be responsible for collecting it, and when/where it will be collected. Defining these elements ahead of time ensures that everyone on your team knows their role in the data collection process and that nothing falls through the cracks!

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Reagan Pannell is a highly accomplished professional with 15 years of experience in building lean management programs for corporate companies. With his expertise in strategy execution, he has established himself as a trusted advisor for numerous organisations seeking to improve their operational efficiency.

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Home / Six Sigma / Data Collection Plan: Learn to Create It In 8 Steps

Data Collection Plan: Learn to Create It In 8 Steps

In the DMAIC framework of the Six Sigma Method , a Data Collection Plan is created during the Measure phase . A project manager who already has a Six Sigma Green Belt training will know that it is a useful tool to focus your efforts on. It is a detailed document that describes the exact steps as well as the sequence that needs to be followed in gathering the data for the given Six Sigma project.

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Green Belt training is a good way to learn more about how a Data Collection Plan fits into the DMAIC process outside of what we will be discussing in this article.

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A Data Collection Plan ensures that everyone, working on a Lean Six Sigma project, is on the same page with regard to the data plan. It also ensures that this information is correctly channeled to the right stakeholders in the organization who are going to help us with our data needs. The purpose of the plan is to make sure that the data collected are meaningful and valid and that all relevant data are collected concurrently, the Xs and the Ys.

We need a Data Collection Plan here because we want to be efficient by not wasting resources on collecting data that are irrelevant to the project or not usable. By creating a Data Collection Plan, we can focus our efforts on answering specific questions that have business value. This directed approach with a Data Collection Plan helps us avoid locating and measuring data just for the sake of doing so.

How to Create a Data Collection Plan Six Sigma?

1) Identify the questions that you want to answer

The first step in creating a Data Collection Plan is to identify the questions we want to answer. Our data must be relevant to the project . The entire reason to have a DMAIC project is to improve a process. Hence, these questions should be centered on what the reality of our process is under the current state of affairs or status quo. The best practice is to use the SIPOC diagram as a guide for data collection. We also need to figure out the type of measurements or metrics we want to include.

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2) Determine the kind of data that is available

The second step in creating a Data Collection Plan is to find out what kind of data is available to collect.  What data exists that can give us all the required answers? Sometimes, a particular piece of data can give us multiple answers. Make sure that you make a list of all of the data points that are needed to answer the questions the project is centered on.

3) Determine how much data is needed

The third step in creating a Data Collection Plan is to decide how much data we need. We want to get enough data so that we can see patterns and trends . For each data element on the list, write down how much data is actually needed.

4) Determine how to measure the data

The fourth step in creating a data collection plan is to see how we are going to measure the data. As we all know, data can be measured in different ways : check sheets, survey answers, etc. The way we measure will be dependent upon the type of data we seek.

5) Decide who is going to gather data

The fifth step in creating a data collection plan is to decide who is going to collect the data. Nowadays, the data can also be collected through automated software . We may be required to liaise with the person in charge of the software to ensure the data is available and in the correct format.

6) Determine where the data will be collected from

The sixth step is to check where to collect the data from. It means deciding on the location and/or source of data. The location does not mean any physical location. It is the location within the process. The data collection plan must explicitly specify where in the process data must be collected from.

7) Decide whether to measure a sample or the whole population

The seventh step is to decide whether to sample the data or not . Sometimes it is impractical to measure an entire population of data. In such a case, we then take a sample of data. The question that the project team needs to look into could be: What should be our sampling method and sample size be to make statistically-sound judgments?

8) Determine in what format the data will be displayed

The eighth step is to decide the format of displaying the data. We can display data in many ways such as Pareto Diagrams, Scatter Diagram, etc.

A Sample Plan Template

The typical components of a Data Collection Plan are as follows:

The first thing that needs to be clarified before any effort is done is the purpose. The most common purposes include finding whether a process is stable? Whether a process is capable?

Data Collection Plan/Matrix Introduction

Have a look at the example of a Data Collection Plan or Matrix in the figure below. Let’s try to understand the different elements of the Data Collection Plan here. First, we start with capturing the process name.  The name of the process owner is filled in accompanied by their contact information, location and area. The stakeholder who prepares the Data Collection Plan has to sign it off and then the first level, second level, and third level of authorized stakeholders are supposed to sign the off the Data Collection Plan. If the Data Collection Plan runs into multiple pages, you can add the page number. The document number and revision data would need to be mentioned for audit purposes.

• Process Steps:  We have now reached the main section of the Data Collection Plan. The first column of the data collection plan shall capture all process steps which we plan to collect the data for. Remember that we are currently progressing in the  Six Sigma MEASURE phase. The objective of the Measure phase is to assess the status quo of a business problem and/or process improvement opportunity with the help of data collection and analysis. In Lean Six Sigma projects, the business problem and/or process improvement opportunity typically relates to the process under consideration. That is why the Data Collection Plan requires us to make a note of relevant process steps in the beginning.
• Critical to Qualities:  The second column of the Data Collection Plan talks about two types of CTQ s (Critical to Quality) . The first one is Key Process Input Variables (KPIV) and the second one is Key Process Output Variables (KPOV).  A KPIV is a process input that provides a significant impact on the output variation of a process or a system or on the Key Process Output Variable (KPOV) of a product. This means that the KPOV is determined by the KPIV. If the KPIV is held constant, then it would yield a predictable and consistent output. Let’s consider the simple process of preparing a cup of tea. For preparing a cup of tea, hot water, sugar, milk, etc. are the ingredients. The quality and quantity of each of these ingredients for a cup of tea will be the KPIV. The KPOV here would be the taste of tea and/or customer satisfaction after drinking that cup of tea. The CTQ column shall note KPIVs and KPOVs applicable to the respective process step.
• Metrics and data types:  The third column is all for capturing all metrics ; applicable to the respective process step. The fourth column shall indicate the data type for the data points collected or to be collected in the context of the relevant process step only.
• Operational Definition:  The fifth column records an Operational Definition for each measurement or metric. An Operational Definition is a document that details exactly how a specific metric will be measured.
• Specification limits:  The sixth column helps us make a note of specification limits for each process step. The quantified specification limits, in terms of upper and lower specification limits (USL and LSL), are noted in the Data Collection Plan for all relevant process steps.
• The method of data collection:  The seventh column records the method of collecting and measuring different pieces of data. We may also need to mention the Unit of Measurement (UOM) for measuring different data types only for relevant process steps.

• Sample Size: The eighth column refers to the sample size for collecting different data points. As we all know, sample data represent population data. In order to make it happen in reality, we have to choose the right sampling method followed by the right sample size. If it is practically possible to go ahead with population data, then we must do so. If we plan to make use of only sample data, then we must determine both the sampling method and the sample size for collecting a variety of data as needed. In such a case, we might be required to sort and even prioritize our data collection requirements. The ninth column of the Data Collection Plan refers to the frequency of data collection for instance, daily, weekly, bi-monthly or monthly, etc.
• Process step deliverables:  The tenth column has to mention the deliverables for each process step under consideration. It is also important to know who owns those deliverables.
• Source and location of data:  The eleventh column of the Plan records the source for the availability of data accompanied by the location of data . The location of data does not refer to the physical location. It mainly refers to the location within the process . The Data Collection Plan must explicitly specify where in the process data must be collected from.
• Reporting format:  The twelfth column of the Plan talks about the method or format of reporting to the internal and/or external stakeholders. The data collection plan shall explain the format in which the collected data needs to be displayed to internal and/or external stakeholders. Most probably a graphical method is used because it is intuitively easier to use.
• Standard Operating Procedure:  The final column of the Plan shall incorporate Standard Operating Procedure (SOP) document for everybody’s reference

A solid Data Collection Plan will help Lean Six Sigma teams to collect data in the Measure phase of the DMAIC cycle with accuracy, precision, and transparency. With a Data Collection Plan, all the stakeholders will be informed and there will be an opportunity to question some of the Data Collection Plan ideas before the actual data collection begins. Creating a Data Collection Plan avoids teams just jumping in and collecting data at random. A Data Collection Plan is a structured way of stipulating exactly how the project’s data will be collected.

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What Is Data Analysis? (With Examples)

Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.

"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.

This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.

Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.

The World Economic Forum Future of Jobs Report 2023 listed data analysts and scientists as one of the most in-demand jobs, alongside AI and machine learning specialists and big data specialists [ 1 ]. In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field.

Read more: How to Become a Data Analyst (with or Without a Degree)

Beginner-friendly data analysis courses

Interested in building your knowledge of data analysis today? Consider enrolling in one of these popular courses on Coursera:

In Google's Foundations: Data, Data, Everywhere course, you'll explore key data analysis concepts, tools, and jobs.

In Duke University's Data Analysis and Visualization course, you'll learn how to identify key components for data analytics projects, explore data visualization, and find out how to create a compelling data story.

Data analysis process

As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. The data analysis process typically moves through several iterative phases. Let’s take a closer look at each.

Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it? 

Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs). 

Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.

Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.

Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions? 

You can complete hands-on projects for your portfolio while practicing statistical analysis, data management, and programming with Meta's beginner-friendly Data Analyst Professional Certificate . Designed to prepare you for an entry-level role, this self-paced program can be completed in just 5 months.

Or, L earn more about data analysis in this lecture by Kevin, Director of Data Analytics at Google, from Google's Data Analytics Professional Certificate :

Read more: What Does a Data Analyst Do? A Career Guide

Types of data analysis (with examples)

Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your date, it can help to familiarize yourself with the four types of data analysis commonly used in the field.

In this section, we’ll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.

Descriptive analysis

Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee. 

Descriptive analysis answers the question, “what happened?”

Diagnostic analysis

If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.

Diagnostic analysis answers the question, “why did it happen?”

Predictive analysis

So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.

Predictive analysis answers the question, “what might happen in the future?”

Prescriptive analysis

Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months. 

Prescriptive analysis answers the question, “what should we do about it?”

This last type is where the concept of data-driven decision-making comes into play.

Read more : Advanced Analytics: Definition, Benefits, and Use Cases

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [ 2 ].

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If you’re interested in a career in the high-growth field of data analytics, consider these top-rated courses on Coursera:

Begin building job-ready skills with the Google Data Analytics Professional Certificate . Prepare for an entry-level job as you learn from Google employees—no experience or degree required.

Practice working with data with Macquarie University's Excel Skills for Business Specialization . Learn how to use Microsoft Excel to analyze data and make data-informed business decisions.

Deepen your skill set with Google's Advanced Data Analytics Professional Certificate . In this advanced program, you'll continue exploring the concepts introduced in the beginner-level courses, plus learn Python, statistics, and Machine Learning concepts.

Frequently asked questions (FAQ)

Where is data analytics used ‎.

Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans to improve their overall business performance. ‎

What are the top skills for a data analyst? ‎

Data analysis makes use of a range of analysis tools and technologies. Some of the top skills for data analysts include SQL, data visualization, statistical programming languages (like R and Python),  machine learning, and spreadsheets.

Read : 7 In-Demand Data Analyst Skills to Get Hired in 2022 ‎

What is a data analyst job salary? ‎

Data from Glassdoor indicates that the average base salary for a data analyst in the United States is $75,349 as of March 2024 [ 3 ]. How much you make will depend on factors like your qualifications, experience, and location. ‎

Do data analysts need to be good at math? ‎

Data analytics tends to be less math-intensive than data science. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistical analysis can help set you up for success.

Learn more: Data Analyst vs. Data Scientist: What’s the Difference? ‎

Article sources

World Economic Forum. " The Future of Jobs Report 2023 , https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf." Accessed March 19, 2024.

McKinsey & Company. " Five facts: How customer analytics boosts corporate performance , https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Accessed March 19, 2024.

Glassdoor. " Data Analyst Salaries , https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm" Accessed March 19, 2024.

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This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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The state of AI in 2022—and a half decade in review

You have reached a page with older survey data. please see our 2024 survey results here ..

Adoption has more than doubled since 2017, though the proportion of organizations using AI 1 In the survey, we defined AI as the ability of a machine to perform cognitive functions that we associate with human minds (for example, natural-language understanding and generation) and to perform physical tasks using cognitive functions (for example, physical robotics, autonomous driving, and manufacturing work). has plateaued between 50 and 60 percent for the past few years. A set of companies seeing the highest financial returns from AI continue to pull ahead of competitors. The results show these leaders making larger investments in AI, engaging in increasingly advanced practices known to enable scale and faster AI development , and showing signs of faring better in the tight market for AI talent. On talent, for the first time, we looked closely at AI hiring and upskilling. The data show that there is significant room to improve diversity on AI teams, and, consistent with other studies, diverse teams correlate with outstanding performance.

Table of Contents

• Five years in review: AI adoption, impact, and spend
• Mind the gap: AI leaders pulling ahead
• AI talent tales: New hot roles, continued diversity woes

About the research

1. five years in review: ai adoption, impact, and spend.

This marks the fifth consecutive year we’ve conducted research globally on AI’s role in business, and we have seen shifts over this period.

2. Mind the gap: AI leaders pulling ahead

Over the past five years we have tracked the leaders in AI—we refer to them as AI high performers—and examined what they do differently. We see more indications that these leaders are expanding their competitive advantage than we find evidence that others are catching up.

First, we haven’t seen an expansion in the size of the leader group. For the past three years, we have defined AI high performers as those organizations that respondents say are seeing the biggest bottom-line impact from AI adoption—that is, 20 percent or more of EBIT from AI use. The proportion of respondents falling into that group has remained steady at about 8 percent. The findings indicate that this group is achieving its superior results mainly from AI boosting top-line gains, as they’re more likely to report that AI is driving revenues rather than reducing costs, though they do report AI decreasing costs as well.

Next, high performers are more likely than others to follow core practices that unlock value, such as linking their AI strategy to business outcomes  (Exhibit 1). 2 All questions about AI-related strengths and practices were asked only of the 744 respondents who said their organizations had adopted AI in at least one function, n = 744. Also important, they are engaging more often in “frontier” practices that enable AI development and deployment at scale , or what some call the “ industrialization of AI .” For example, leaders are more likely to have a data architecture that is modular enough to accommodate new AI applications rapidly. They also often automate most data-related processes, which can both improve efficiency in AI development and expand the number of applications they can develop by providing more high-quality data to feed into AI algorithms. And AI high performers are 1.6 times more likely than other organizations to engage nontechnical employees in creating AI applications by using emerging low-code or no-code programs , which allow companies to speed up the creation of AI applications. In the past year, high performers have become even more likely than other organizations to follow certain advanced scaling practices, such as using standardized tool sets to create production-ready data pipelines and using an end-to-end platform for AI-related data science, data engineering, and application development that they’ve developed in-house.

High performers might also have a head start on managing potential AI-related risks, such as personal privacy and equity and fairness, that other organizations have not addressed yet. While overall, we have seen little change in organizations reporting recognition and mitigation of AI-related risks since we began asking about them four years ago, respondents from AI high performers are more likely than others to report that they engage in practices that are known to help mitigate risk . These include ensuring AI and data governance , standardizing processes and protocols , automating processes such as data quality control to remove errors introduced through manual work, and testing the validity of models and monitoring them over time for potential issues.

AI use and sustainability efforts

The survey findings suggest that many organizations that have adopted AI are integrating AI capabilities into their sustainability efforts and are also actively seeking ways to reduce the environmental impact of their AI use (exhibit). Of respondents from organizations that have adopted AI, 43 percent say their organizations are using AI to assist in sustainability efforts, and 40 percent say their organizations are working to reduce the environmental impact of their AI use by minimizing the energy used to train and run AI models. As companies that have invested more in AI and have more mature AI efforts than others, high performers are 1.4 times more likely than others to report AI-enabled sustainability efforts as well as to say their organizations are working to decrease AI-related emissions. Both efforts are more commonly seen at organizations based in Greater China, Asia–Pacific, and developing markets, while respondents in North America are least likely to report them.

When asked about the types of sustainability efforts using AI, respondents most often mention initiatives to improve environmental impact, such as optimization of energy efficiency or waste reduction. AI use is least common in efforts to improve organizations’ social impact (for example, sourcing of ethically made products), though respondents working for North American organizations are more likely than their peers to report that use.

Investment is yet another area that could contribute to the widening of the gap: AI high performers are poised to continue outspending other organizations on AI efforts. Even though respondents at those leading organizations are just as likely as others to say they’ll increase investments in the future, they’re spending more than others now, meaning they’ll be increasing from a base that is a higher percentage of revenues. Respondents at AI high performers are nearly eight times more likely than their peers to say their organizations spend at least 20 percent of their digital-technology budgets on AI-related technologies. And these digital budgets make up a much larger proportion of their enterprise spend: respondents at AI high performers are over five times more likely than other respondents to report that their organizations spend more than 20 percent of their enterprise-wide revenue on digital technologies.

Finally, all of this may be giving AI high performers a leg up in attracting AI talent. There are indications that these organizations have less difficulty hiring for roles such as AI data scientist and data engineer. Respondents from organizations that are not AI high performers say filling those roles has been “very difficult” much more often than respondents from AI high performers do.

The bottom line: high performers are already well positioned for sustained AI success, improved efficiency in new AI development, and a resultingly more attractive environment for talent. The good news for organizations outside the leader group is that there’s a clear blueprint of best practices for success.

3. AI talent tales: New hot roles, continued diversity woes

Our first detailed look at the AI talent picture signals the maturation of AI, surfaces the most common strategies organizations employ for talent sourcing and upskilling, and shines a light on AI’s diversity problem—while showing yet again a link between diversity and success.

Hiring is a challenge, but less so for high performers

All organizations report that hiring AI talent, particularly data scientists, remains difficult. AI high performers report slightly less difficulty and hired some roles, like machine learning engineers, more often than other organizations.

Reskilling and upskilling are common alternatives to hiring

When it comes to sourcing AI talent, the most popular strategy among all respondents is reskilling existing employees. Nearly half are doing so. Recruiting from top-tier universities as well as from technology companies that aren’t in the top tier, such as regional leaders, are also common strategies. But a look at the strategies of high performers suggests organizations might be best served by tapping as many recruiting channels as possible (Exhibit 2). These companies are doing more than others to recruit AI-related talent from various sources. The findings show that while they’re more likely to recruit from top-tier technical universities and tech companies, they’re also more likely to source talent from other universities, training academies, and diversity-focused programs or professional organizations.

Responses suggest that both AI high performers and other organizations are upskilling technical and nontechnical employees on AI, with nearly half of respondents at both AI high performers and other organizations saying they are reskilling as a way of gaining more AI talent. However, high performers are taking more steps than other organizations to build employees’ AI-related skills.

Respondents at high performers are nearly three times more likely than other respondents to say their organizations have capability-building programs to develop technology personnel’s AI skills. The most common approaches they use are experiential learning , self-directed online courses, and certification programs, whereas other organizations most often lean on self-directed online courses.

High performers are also much more likely than other organizations to go beyond providing access to self-directed online course work to upskill nontechnical employees on AI. Respondents at high performers are nearly twice as likely as others to report offering peer-to-peer learning and certification programs to nontechnical personnel.

Increasing diversity on AI teams is a work in progress

We also explored the level of diversity within organizations’ AI-focused teams, and we see that there is significant room for improvement at most organizations. The average share of employees on these teams at respondents’ organizations who identify as women is just 27 percent (Exhibit 3). The share is similar when looking at the average proportion of racial or ethnic minorities developing AI solutions: just 25 percent. What’s more, 29 percent of respondents say their organizations have no minority employees working on their AI solutions.

Some companies are working to improve the diversity of their AI talent, though there’s more being done to improve gender diversity than ethnic diversity. Forty-six percent of respondents say their organizations have active programs to increase gender diversity within the teams that are developing AI solutions, through steps such as partnering with diversity-focused professional associations to recruit candidates. One-third say their organizations have programs to increase racial and ethnic diversity. We also see that organizations with women or minorities working on AI solutions often have programs in place to address these employees’ experiences.

In line with previous McKinsey studies , the research shows a correlation between diversity and outperformance. Organizations at which respondents say at least 25 percent of AI development employees identify as women are 3.2 times more likely than others to be AI high performers. Those at which at least one-quarter of AI development employees are racial or ethnic minorities are more than twice as likely to be AI high performers.

The online survey was in the field from May 3 to May 27, 2022, and from August 15 to August 17, 2022, and garnered responses from 1,492 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 744 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

The survey content and analysis were developed by Michael Chui , a partner at the McKinsey Global Institute and a partner in McKinsey’s Bay Area office; Bryce Hall , an associate partner in the Washington, DC, office; Helen Mayhew , a partner in the Sydney office; and Alex Singla , a senior partner in the Chicago office, and Alex Sukharevsky , a senior partner in the London office, global leaders of QuantumBlack, AI by McKinsey.

The authors wish to thank Sanath Angalakudati, Medha Bankhwal, David DeLallo, Heather Hanselman, Vishan Patel, and Wilbur Wang for their contributions to this work.

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Funding News Edition: June 5, 2024 See more articles in this edition

NIH’s  Sexual & Gender Minority Research Office (SGMRO) is developing the NIH-Wide Strategic Plan for Sexual and Gender Minority Health Research for Fiscal Years 2026–2030.  

In accordance with the 21 st Century Cures Act, NIH is required to update its strategic plans regularly. NIH published a  Request for Information (RFI) on NIH-Wide Strategic Plan for Sexual and Gender Minority (SGM) Health Research to collect input from the scientific research community, the public, and other interested constituents on topics to include for the next Strategic Plan to optimize NIH’s research investments. 

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Provide feedback on any other relevant topics that NIH should consider when developing the next NIH-wide Strategic Plan for SGM Health Research.  

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About CHIPS for America

Semiconductors, or chips, are tiny electronic devices that are integral to America’s economic and national security. These devices power tools as simple as a light switch and as complex as a fighter jet or a smartphone. Semiconductors power our consumer electronics, automobiles, data centers, critical infrastructure, and virtually all military systems. They are also essential building blocks of the technologies that will shape our future, including artificial intelligence, biotechnology, and clean energy.

While the United States remains a global leader in semiconductor design and research and development, it has fallen behind in manufacturing and now accounts for only about 10 percent of global commercial production. Today, none of the most advanced logic and memory chips—the chips that power PCs, smartphones, and supercomputers—are manufactured at commercial scale in the United States. In addition, many elements of the semiconductor supply chain are geographically concentrated, leaving them vulnerable to disruption and endangering the global economy and U.S. national security.

That’s why President Biden signed the bipartisan CHIPS and Science Act of 2022 into law. The law provides the Department of Commerce with $50 billion for a suite of programs to strengthen and revitalize the U.S. position in semiconductor research, development, and manufacturing—while also investing in American workers. CHIPS for America encompasses two offices responsible for implementing the law: The CHIPS Research and Development Office is investing $11 billion into developing a robust domestic R&D ecosystem, while the CHIPS Program Office is dedicating $39 billion to provide incentives for investment in facilities and equipment in the United States. Learn more about CHIPS for America from this video message from the Secretary of Commerce . 

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• Data center hardware and strategy

The worlds of IT and OT are converging. Advances in technologies, such as the internet of things and big data analytics, are systematically enabling the digital information world to see, understand and influence the physical operational world. When implemented properly, IT/OT convergence can merge business processes, insights and controls into a single uniform environment. Use this guide to learn more about IT/OT convergence, its benefits and challenges, plus industry examples, use cases and more.

Information technology (it).

• Kinza Yasar, Technical Writer

What is information technology?

Information technology (IT) is the use of computers, storage, networking and other physical devices, infrastructure and processes to create, process, store, secure and exchange all forms of electronic data. Typically, IT is used in the context of business operations, as opposed to the technology used for personal or entertainment purposes. The commercial use of IT encompasses both computer technology and telecommunications .

Harvard Business Review coined the term information technology in 1958 to distinguish between purpose-built machines designed to perform a limited scope of functions and general-purpose computing machines that could be programmed for various tasks. As the IT industry evolved from the mid-20th century, computing capability increased, while device cost and energy consumption decreased, a cycle that continues today when new technologies emerge.

Types of information technology

Information technology encompasses a wide range of technologies and systems that are used to store, retrieve, process and transmit data for specific use cases.

Common information technology types include the following:

• Internet and web technologies. This includes the tools and protocols used to access, navigate and interact with information on the internet. Examples include web browsers, websites, web servers, Hypertext Markup Language , cascading style sheets , JavaScript , HTTP and other internet-related technologies.
• Cloud computing. This involves the delivery of computing resources and services over the internet on a pay-per-use basis. This can include infrastructure as a service , platform as a service, software as a service and cloud storage options.
• Databases. This includes IT systems and software used to store, organize and retrieve data. Examples include MySQL , NoSQL , relational database management systems and MongoDB .
• Artificial intelligence and machine learning. AI and ML-based IT technologies use algorithms and statistical models to enable computers to perform tasks that typically require human intelligence. Examples include speech recognition, image recognition and natural language processing .
• Cybersecurity. This type of IT includes technologies and best practices designed to protect IT systems , networks and data from unauthorized access, cyber attacks and other security threats. Cybersecurity can be enforced through firewalls, antivirus software, encryption , intrusion detection systems and security policies.
• Internet of things. This includes the network of interconnected devices and sensors that collect, exchange and analyze data. IoT technologies enable the integration of physical objects into computer systems, providing automation, monitoring and control in various domains.
• IT governance. This involves making policies and rules for the organization to ensure effective operation.
• Data analytics and business intelligence. BI focuses on tools and techniques for extracting insights from large data sets to support decision-making and business operations. This can include data mining , statistical analysis, data visualization and predictive modeling .

What does information technology encompass?

The IT department ensures that the organization's systems, networks, applications, data and information all connect and function properly. The IT team handles the following three major areas:

This article is part of

What is IT/OT convergence? Everything you need to know

• Which also includes:
• Benefits and challenges of IT/OT convergence
• Understand the best IT/OT convergence strategies
• 5 ways to facilitate the convergence of IT and OT in IoT
• Deploying and maintaining business applications, services and infrastructure -- including servers, networks and storage.
• Monitoring, optimizing and troubleshooting the performance of applications , services and infrastructure.
• Overseeing the security and governance of applications, services and infrastructure.

Most IT staff have different responsibilities within the team that can be broken into the following key areas:

• Administration. Administrators handle the day-to-day deployment, operation and monitoring of an IT environment, including systems, networks and applications. Admins often perform a range of other duties such as software upgrades, user training, software license management, procurement, security, data management and observing adherence to business process and compliance requirements. Effective delegation is also part of IT administration and is crucial for a team's productivity.
• Support. Help desk staff specialize in answering questions, gathering information and directing troubleshooting efforts for hardware and software. IT support often includes IT asset and change management, helping admins with procurement, handling backup and recovery of data and applications, monitoring and analyzing logs and other performance monitoring tools and following established support workflows and processes.
• Applications. Businesses rely on software to perform work. Some applications, such as email server applications, are procured and deployed by third parties. But many organizations retain a staff of skilled developers that create the applications and interfaces -- such as application programming interfaces -- needed to deliver critical business capabilities and services. Applications might be coded in a wide array of popular programming languages and integrated with other applications to create smooth and seamless interactions between different applications. Developers might also be tasked with creating interactive business websites and building mobile applications. The trend toward agile or continuous development paradigms requires developers to be increasingly involved with IT operations, such as deploying and monitoring applications.
• Compliance. Businesses are obligated to observe varied government and industry-driven regulatory requirements. IT staff play a major role in securing and monitoring access to business data and applications to ensure that such resources are used according to established business governance policy that meets regulatory requirements. Such staff are deeply involved with security tasks and routinely interact with legal and business teams to prevent, detect, investigate and report possible breaches.

Why is information technology important?

It's been said that data is what powers industries worldwide. That might be hyperbole, but few businesses -- large or small -- can remain competitive without the ability to collect data and turn it into useful information. IT provides the means to develop, process, analyze, exchange, store and secure information.

Data processing plays a significant role in the following core business practices:

• Product development and design.
• Marketing and market research.
• Sales and invoicing.
• Data analysis and decision-making.
• Customer development and retention.
• Accounting and taxes.
• Human resources and payroll.
• Regulatory compliance.

Information technology plays a vital role for businesses in the following ways:

• Facilitates communication and collaboration. IT enables seamless communication and collaboration across different locations and time zones. For example, global corporations that are spread across continents can use video conferencing, instant messaging and content collaboration tools to bridge geographical distances and communicate effectively.
• Advances pervasive computing. Computing has penetrated practically every part of business and much of our personal lives. The ubiquity of computing -- also referred to as pervasive computing -- is another reason why IT is critical. Computing devices have evolved well beyond PCs and servers. Today, all businesses and most people have and use multiple computing devices, including phones, tablets, laptops, game consoles and even doorbells, thermostats, vacuums and many kitchen appliances.
• Enhances efficiency and productivity. IT systems and tools streamline processes, automate repetitive tasks and provide access to real-time data, thereby improving the overall efficiency and productivity of businesses. For example, a retail company with an integrated IT system can use an automated inventory management tool to track stock levels in real time and replenish them through automatic reordering.
• Enables access to information. IT provides access to vast amounts of information and knowledge in databases and online libraries, empowering individuals and organizations to make informed decisions and stay updated with the latest developments.
• Supports innovation and creativity. IT fosters innovation by providing platforms for creative expression, experimentation and problem-solving. For example, consider a software development company utilizing IT platforms for innovation. Its developers can collaborate in virtual environments, experiment with new coding techniques and technologies and create groundbreaking software through iterative testing, feedback loops and problem-solving.
• Supports critical business operations. IT is essential for the smooth functioning of modern businesses, from managing operations and finances to marketing and customer service. For example, most customer service teams use IT systems for efficient communication, issue resolution and feedback collection to ensure a positive user experience.
• Helps with education and research. IT plays a vital role in education, providing access to educational resources, facilitating distance learning and supporting research endeavors. Remote learning technologies enable access to education from anywhere, bridging geographical barriers and providing opportunities for lifelong learning.
• Provides cost savings. IT reduces costs associated with paper-based processes, manual labor and physical infrastructure, leading to significant savings for both businesses and individuals.
• Provides connectivity to the internet. Virtually all IT devices, many of which are part of the IoT, tap into the internet, which interconnects billions of devices worldwide.

Examples of information technology

So how is IT involved in day-to-day business? Consider the following six common examples of IT and teams at work:

• Server upgrade. One or more data center servers are near the end of their operational and maintenance lifecycle. IT staff will perform the following:
• Select and procure replacement servers.
• Configure and deploy the new servers.
• Back up applications and data on existing servers.
• Transfer that data and applications to the new servers.
• Validate that the new servers are working properly.
• Repurpose or decommission and dispose of the old servers.
• Security monitoring. Businesses routinely use tools to monitor and log activity in applications, networks and systems. IT staff receive alerts of potential threats or noncompliant behavior, such as a user attempting to access a restricted file; check logs and other reporting tools to investigate and determine the root cause of the alert; take prompt action to address and remediate the threat, often driving changes and improvements to security posture that can prevent similar events in the future.
• New software. The business determines a need for a new mobile application that can enable customers to log in and access account information or conduct other transactions from smartphones and tablets. Developers work to create and refine a suitable application according to a planned roadmap. Operations staff post each iteration of the new mobile application for download and deploy the back-end components of the app to the organization's infrastructure.
• Business improvement. A business requires more availability from a critical application to help with revenue or business continuance strategies. The IT staff might be called upon to architect a high-availability cluster to provide greater performance and resilience for the application to ensure that it can continue to function in the face of single outages. This can be paired with enhancements to data storage protection and recovery.
• User support. Developers are building a major upgrade for a vital business application. Developers and admins collaborate to create new documentation for the upgrade. IT staff might deploy the upgrade for limited beta testing -- enabling a select group of users to try the new version -- while also developing and delivering comprehensive training that prepares all users for the new version's eventual release.
• Digital workplace organization. Employees in a bustling office are wasting too much time trying to locate paper documents, files and office supplies that are scattered throughout the workspace. The office has decided to incorporate a digital filing and inventory management system. Each document in the office is scanned and stored electronically and tagged with relevant keywords. Additionally, office supplies are also tracked in a digital inventory database. Now, whenever an employee needs to access a document or find a tool, they promptly open the digital inventory system. With just a quick search, they pinpoint the precise file or item along with its current physical location in the workspace. This enhances the efficiency and productivity of the employees.

Software vs. hardware

When it comes to IT systems, both software and hardware are integral and interdependent components of computer systems. The following are some main differences between the two:

Software refers to a set of instructions that enable the hardware to perform specific tasks. It includes system software, application software and other programs that run on the computer.

There are two categories of software: system software and applications. System software encompasses the computer programs that manage the basic computing functions. They include the following:

• Operating systems (OSes).
• Boot programs.
• Assemblers.
• Device drivers.

Examples of business applications include the following:

• Databases, such as Microsoft SQL Server.
• Transactional systems, such as real-time order entry.
• Email servers, such as Microsoft Exchange.
• Web servers, such as Apache and Microsoft's Internet Information Services.
• Customer relationship management, such as Oracle NetSuite and HubSpot.
• Enterprise resource planning systems, including SAP S/4HANA.

These applications use programmed instructions to manipulate, consolidate, disperse and otherwise work with data for a business purpose.

Mobile applications that run on smartphones, tablets and other portable devices typically connect with cloud or data center applications over the internet. These applications have expanded the scope of computing and created a new category of software and telecommunications that requires special expertise to maintain.

Hardware refers to the physical components of a computer that come in many different forms, including the monitor, servers, central processing unit, keyboard and mouse. Computer servers run business applications. Servers interact with client devices in the client-server model. They also communicate with other servers across computer networks, which typically link to the internet.

Storage is another type of hardware. It's any technology that holds information as data. Storage can be local on a specific server or shared among many servers, and it could be installed on-premises or accessed via a cloud service. Information that is stored can take many forms, including file, multimedia, telephony, and web and sensor data. Storage hardware includes volatile RAM (random-access memory) as well as non-volatile tape, hard disk drives and solid-state drives.

Telecom equipment -- comprising network interface cards , cabling, wireless communications and switching devices -- connects the hardware elements together and to external networks.

Abstracting hardware and software

Abstraction simplifies resource provisioning, management and scalability. By hiding the complexities of hardware, abstraction streamlines resource allocation, ensuring optimal utilization of available resources.

IT architectures have evolved to include virtualization and cloud computing, where physical resources are abstracted and pooled in different configurations to meet application requirements. Clouds can be distributed across locations and shared with other IT users, or they can be contained within a corporate data center or some combination of both deployments.

Volatility is a characteristic of virtualized resources, enabling them to expand and contract as needed. Subscription-based cloud or locally installed resources, such as storage or composable architectures, can spin up resources, such as servers, OSes and application software, as needed and then release them when processing is complete. Top of Form

Information technology vs. computer science

When researching careers in IT, one is likely to come across the term computer science . While there's an overlap between IT and computer science, the two disciplines are distinct and require different courses of study to prepare for careers.

IT is generally associated with the application of technology to deal with business issues. As such, the IT workforce is oriented toward developed technologies such as hardware systems, OSes and application software. Proficiency in IT is required to identify the hardware and software components that should be used to enhance a specific business process. IT pros work with a variety of technologies, such as server OSes, communications devices and software and applications. Career examples typically include roles such as database administrator , cybersecurity specialist and network administrator.

Preparation for an IT career requires basic courses in hardware and software systems. A bachelor's degree in IT and other programs might include the following subjects:

• Business analysis.
• Project management.
• Telecommunications.
• Network administration.
• Database design.
• Database management.

Computer science focuses on the logic and design of the underpinnings of the components that IT experts use to assemble business systems. A strong mathematics background is required to pursue a computer science career. Much of the work in computer science involves developing the algorithms and logic and writing low-level code that enables computer systems to address business problems.

Computer scientists might participate in the hardware and software engineering work required to develop products. They're also likely to delve into more abstract technologies, such as AI and ML. Roles in computer science include software developer, computer systems analyst, computer programmer and computer information research scientist.

A course of study in computer science requires a foundation in computer concepts and advanced mathematics. It could be complemented with the following subjects:

• Neural networks.
• Security systems.
• Data analytics.
• User experience.

Careers in information technology

A team of administrators and other technical staffers deploy and manage a company's IT infrastructure and assets. IT teams depend on a range of specialized information and technology skills and knowledge to support equipment, applications and activities. Third-party contractors and IT vendor support personnel augment the IT team.

The information technology profession is extremely diverse. IT workers can specialize in fields, including software development; application management; hardware components; server, storage or network administration and network architecture. Many businesses seek IT professionals with mixed or overlapping skill sets.

There's a wide array of IT careers, each with varying technological and managerial requisites. Among the most common IT job titles are the following:

• Chief information officer. A CIO is responsible for IT and computer systems that support the goals of the business.
• Chief technology officer. A CTO sets the technology goals and policies within an organization.
• IT director. An IT director is responsible for the functioning of the business's technology tools and processes. This role might also be called IT manager or IT leader.
• System administrator. A sys admin configures, manages, supports and troubleshoots a multiuser computing environment. Within a business, this role can be divided up by technology, requiring an administrator or team dedicated to server, desktop, network administration, virtualization or other components and technologies.
• Application manager. An application manager's role centers on the provisioning and management of a high-demand business application, such as Microsoft Exchange.
• Developer or software engineer. A software engineer or team writes, updates and tests code for computer programs to meet internal or customer-facing business objectives.
• Chief IT architect or IT architect. An IT architect examines and changes IT functions to best support the business.
• Information security analyst. An information security analyst protects organizations from threats and data breaches.
• Cloud engineer. A cloud engineer is responsible for managing and designing cloud-based systems for organizations.

IT skills and certifications

The U.S. Bureau of Labor Statistics projects a 15% growth in employment within the computer and information technology sector between now and 2032. A successful IT career will involve developing several technical skills. For the current IT job market, the following 10 skills are among those most in demand:

• Cybersecurity.
• Cloud computing.
• Edge computing and IoT.
• IT automation.
• Software development.
• Big data management and data analytics.
• Mobile application development.

In the pursuit of these fundamental IT disciplines, it's advantageous to earn certifications to demonstrate proficiency in specific technologies and areas of expertise. Some of the most highly regarded certifications offered by various technology vendors include the following:

• AWS Certified Solutions Architect -- Professional.
• CompTIA A+.
• Certified Ethical Hacker.
• Certified in Risk and Information Systems Control.
• Certified Information Security Manager.
• Certified Information Systems Security Professional.
• Cisco Certified Network Associate.
• Google Certified Professional Cloud Architect.
• Microsoft role-based certifications.
• Project Management Professional.
• VMware Certified Professional.

As reliance on cloud computing continues to grow, there's a significant demand for skilled cloud professionals. Delve into the top 10 cloud computing career paths and discover how to get started in this thriving field of information technology.

Continue Reading About information technology (IT)

• AI engineers: What they do and how to become one
• Best free DevOps certifications and training courses
• Cybersecurity market trends, from AI to post-quantum crypto
• How to conduct a cloud security assessment
• The best cloud security certifications for IT pros

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