Data Collection:
How to Get Started Build a data collection plan that allows you to
collect clean data and maximize your impact

Data collection is the process of gathering quantitative and qualitative information on specific variables with the aim of evaluating outcomes or gleaning actionable insights. Good data collection requires a clear process to ensure the data you collect is clean, consistent, and reliable.

Establishing that process, however, can be tricky. It involves taking stock of your objectives, identifying your data requirements, deciding on a method of data collection, and finally organizing a data collection plan that synthesizes the most important aspects of your program.

This guide is made up of advice compiled from our helpful partners and experienced Global Services team. If you have any questions, please don’t hesistate to reach out to

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1 How to Define
Your Project Objectives

One crucial mistake we see organizations make is launching into the development of a data collection program without a clearly defined set of project objectives. Well-articulated project objectives are a lens through which you can examine each step of your data collection program.

But starting with clearly-defined project objectives can be easier said than done. A challenge for all monitoring and evaluation (M&E) professionals is cutting through the competing priorities, obstacles, and stakeholders involved in a given project to pinpoint the core objectives their program is looking to address.

Identify your project objectives

In order to define your project objectives, you must start by focusing on the right problem.

Prior to joining Dimagi, our Chief Services Officer, Rowena Luk, worked on building a consultation platform for specialist doctors in hospitals in Ghana. After reflecting on her work, she realized the project had been doomed from the start, because she was focused on an objective that was not solving the most pressing issue for the hospitals at the time: Rather than supporting better care for all patients through digital medical records, she was focusing on a small percentage that needed specialized care.

Problems that matter will draw the attention of the Minister of Health or the Country Director. They will not just look at the results. They will demand them. They do not need to be convinced that this data is interesting. They will see it and know.

Dimagi Chief Services Officer Rowena Luk

The right project objectives target issues that are urgent and clear to the community, whether that’s delivering healthcare to underserved regions, tracking crop yields, or measuring performance and attendance in schools.

Review and confirm your project objectives

Once you determine an issue you think is important to your audience, the next step is to critically examine the problems you identified.

Take note of which of your stakeholders have expressed interest in these objectives. If they don’t, find out why not, and adapt your objectives accordingly. You might find that other objectives are competing for time and resources, so determine whether that means your project should refocus on those objectives or if yours are urgent and vital enough to earn some of those resources.

Organizing your objectives around your partners’ needs and accounting for their other priorities will provide you with reliable guardrails (and investment) as you build out your data collection plan.

A team of community health workers reviews their results framework during a training session.

Organize your project objectives

Once you establish your key project objectives, the next step is to outline how you will achieve them. What results do you need to prove in order to call your project a success? When working with governmental organizations, such as USAID, the map of this journey is called a “logical framework” or “results framework.”

A results framework places your project objectives at the top of the diagram and maps out each of the intermediate results that will add up to their success. In essence, they are project to-do lists that make you ask the question of each aspect of your solution: Does this get me closer to achieving my project objectives?

Your organization might not need a results framework to justify your program for the purposes of funding or additional support. However, a results framework still establishes your project’s objectives as the core focus of your program and helps you think through all the ways you can review its progress and ultimately achieve success.

Focus on your objective

The process of turning a clear and urgent problem into precise project objectives is a crucial step before developing your data collection program. Your project objectives should be at the core of your entire program and should be clearly defined and written down in a document such as a results framework. Anyone on your team should be able to read and understand your approach from your results framework, and they should use it throughout the project’s lifespan to evaluate their decisions. Including considerations from partners will also help to ensure your project is given the proper attention and resources required for success.

It can be hard to get a project moving in the right direction. But once you have a clear destination, it is easier to determine whether the next tool or initiative will take you closer to or further away from where you want to be.

Learn How To Validate Your Objectives

2 How to Identify
Your Data Requirements

So how can you filter out the noise and focus on what you need? A clear understanding of your data requirements will help narrow your focus and identify a method of data collection that serves your needs precisely.

The first step in this process is to make a list of what data you know you need. Whether that’s from results frameworks, requests from supervisors or funders, or scratch, outline an initial list of data requirements for your program. Then, based on what you have, work to expand the list and describe each variable in more depth.

Categorize your data needs

Most often in data collection and service delivery programs, your data can be broken down into two categories: (1) program performance metrics and (2) worker performance metrics. These categories help explain which aspect of your program the data affects and are the best place to start when describing your data needs.

Program performance metrics focus on how well you are meeting the project objectives. On the other hand, worker performance metrics are the best indicators for how well your workers are performing their duties and how much they are contributing to the success of the project.

These two categories with help with getting at the specific insights each variable will offer. Once you have categorized your data at this high level, you can ask more specific questions about each group of variables.

Some variables require checking in with a single source over a period of time

Describe your data needs

Once you have organized a list of your data requirements by category, flesh out their attributes and characteristics. This step of the process should include your whole team for a holistic view of what each variable will mean for your program.

Marcos Lavandera, health analyst at Pro Mujer, a woman’s development organization in Latin America, explained that for his project that focused on women’s healthcare in Mexico, his entire team took part in the process of defining their data’s characteristics.

We had our program director, health analyst, and medical director all working together to make sure we looked at the data from all the possible perspectives.

Is your data quantitative, qualitative, or a combination? Are you collecting data from the same source over time (i.e. longitudinally)? Do certain variables depend on others? Each of the answers to these questions (and more) means something different about the method of data collection your program will need to follow.

Summarize your data requirements

With the myriad methods of data collection, developing a clear, written summary of all the variables you need will help keep you focused. Differentiating between program performance metrics and worker performance metrics will help keep your data organized. The characteristics of your variables will help determine the method and features of data collection that will work best for your program. It won’t surprise you to know that data is the most important piece of any data collection program, so a comprehensive understanding of the data you need to collect is vital.

Read More About Understanding Your Data Needs

3 How to Determine Your
Method of Data Collection

The entire purpose of a data collection program is the data, but it doesn’t collect itself. There are many options to choose from for how to approach this problem, and each one has strengths in a different area. The type of data you are looking to collect, as well as the characteristics of its source and environment, should all inform what method of data collection makes the most sense for your program.

How data requirements inform method selection

The data you mean to collect should inform everything about your program – especially the method of data collection you intend to use. The characteristics of those data will mean different things for that decision, and each method has different strengths and weaknesses: How often you plan to collect data will tell you whether you need one-off surveys or a case management system. The scale of your data collection program can help you figure out whether you can get away with a simple paper-based program or if you would benefit from a mobile data collection or IVR program. Any technical inputs will probably require outside tools (i.e. heart rate monitors, GPS locators, etc.).

Your data will tell you a lot – almost everything – about the tools you’ll need to collect them. Review the characteristics of each variable to make sure the method you choose will actually help you collect it.

Align with your team on your approach to app development – we recommend Agile.

Account for environmental factors

Once you have listed, organized, and described all the variables you need to collect, you still need to account for where you are collecting data and who you are collecting it from. What are the languages spoken by the people involved (both data collectors and beneficiaries)? What is the reading level or digital literacy level of your typical field worker? How is mobile connectivity in the region?

Each of the answers to these questions means something different for how you will need to set up your program – different languages mean translating forms, low or no internet connection means using a tool that works offline. We recommend collaborating with the local workers to overcome any challenges you face. It’s unlikely that this is the first time they are dealing with them.

Storage & security

Certain sectors lean more heavily on this consideration than others. For some beneficiary populations and projects such as those working with HIV patient data, privacy concerns may be much more important than others. Thus, understanding where the data you collect goes is vitally important.

The actual considerations will depend on the sector you are working in. For instance, projects in the public health domain might require you to consider patient confidentiality and HIPAA compliance. The FDA has shared guidelines for the use of electronic health record data that may be helpful for your project, including whether you need to de-identify data or restrict access to it after collection. Make sure you are familiar with the requirements of all parties involved before you decide how to collect your data.

Summary of techniques

There are so many different methods of collecting data available today. The following are just a selection:


Surveys are likely the most famous form of quantitative research. They offer standardized forms for a consistent set of variables to be collected across a wide audience. The three most common approaches to surveys are:

Mobile Data Collection

Mobile data collection is the use of mobile devices (e.g. smartphones, tablets, etc.) to administer surveys directly (via SMS) or through frontline workers (via mobile apps). With proper investment, this approach to data collection offers impressive speed, accuracy, and scalability.

Paper-Based Collection

Paper-based data collection is a classic approach. Printed forms are administered by frontline workers or filled out directly by beneficiaries. While they are prone to slow collection timelines and data errors, paper-based forms are the easiest to quickly spin up small-scale programs (especially for one-off surveys).

Interactive Voice Response (IVR)

IVR is a method of data collection administered over the phone, where a series of questions are asked and respondents’ information is collected virtually. While it can be restrictive in terms of user responses, IVR does offer a kind of plug-and-play approach to data collection for target audiences with phone service.


Interviews can be based on a common set of questions, like a survey, but they allow for more flexibility in the responses. The organic insights gleaned from interviews can give you answers to questions you didn’t even think to ask.

Individual Interviews

Individual interviews are relatively self-explanatory. An individual with experience in the topic you are curious about answers questions from the data collection team. Individual interviews can be a good way to start other types of data collection programs, especially when you are trying to figure out the right question to ask on something like a survey.

Focus Groups

Focus groups are like group interviews, often including a stimulus for discussion. Focus group leaders will prompt the group with a set of questions or statements and gather the reflections of the group. It’s a good way to quickly compare reactions or information from representative sources and an opportunity to see how group dynamics might affect an individual’s reaction.


Observational data collection is much more low-touch. The idea is that in the absence of an opportunity to interact with your subject (either due to distance, scale, or other reasons for their inaccessibility), you can observe them to collect certain types of information.

Firsthand Data Collection

Firsthand observation allows for the data collector to directly observe and gather notes on an individual or group. This approach is often used when direct observation is available but not direct contact either for reasons of inaccessibility or fears of bias.

Document / Record Review

Often used when information is needed from the past, document review allows for secondhand observation of sources when firsthand observation is unavailable.

How to choose

As you can see, the method of data collection that is right for your program is entirely dependent upon your program objectives and data requirements. Some programs might even call for a combination or hybrid of approaches to collect all the information you need.

As you evaluate your options, always make sure that their strengths map back to the objectives of your program and account for the characteristics of your sources. Once you make your choice, it’s time to put all the pieces together.

Determine How To Select A Data Collection Method

4 How to Organize Your
Data Collection Plan

A data collection plan is just that – a plan for how the information your program hopes to collect will flow from its source all the way to the actionable insights you hope to glean from it. The process of developing this plan will reveal things about where your data comes from, who has access to it, and how it is collected and stored – all of which are key pieces of information that will inform the design and implementation of any new system you choose.

Two approaches to a data collection plan

There are two primary methods of organizing a data collection plan that we typically use.

An information workflow diagram is more visual and diagrams the components and their connections throughout your process. They typically start with what data is being collected and follows through from how it is being collected to where it is stored and how it is shared.

On the other hand, data collection plan outlines are more analytical, applying a standard set of criteria to the process for you to fill out in a way that makes sense for your program. This approach helps organize each variable you are collecting by source, method of collection, timeline, where it is stored, and how it is analyzed and shared.

Each approach has its own strengths and weaknesses, but they share the goal of documenting your data collection plan in a way that can be shared, analyzed, and improved.

Learn How To Design A Data Collection Plan

Organizing a data collection plan doesn’t have to be painful if you’re working with your team.

Additional considerations

As you develop your plan, it’s not uncommon to begin to consider aspects of your program you hadn’t thought about before. This is an intentional aspect of the process. It’s much better to head into the design and implementation phase of your program aware of these facets, than it is to retroactively build them into a program.


Often, these planning frameworks don’t include the dimension of time. Consider ways you might incorporate it to account for how often your frontline workers will head into the field to collect data or how often you will lead them in refresher training sessions.

Approvals and consent

Depending on the type of data you collect and how it flows through your program, you may need to request consent from beneficiaries or approvals for data integration from another organization. Think about how you might note these potential bottlenecks on whatever method of planning you choose.

Why use a data collection plan?

The most important reason to use frameworks like workflow maps and data collection plan outlines is that they help you to understand the stakeholders, data sources, and points of connection that will reveal areas for improvement and strengths to take advantage of.

The effort to review every aspect of an existing process and map their interactions makes for a better final product. This is not a coincidence. These projects are made up of interacting components, and if you can understand how each variable relates to the others in your data collection process, you can build a map that provides you strong insights for improvement and tells you precisely where to focus your efforts.

Learn How To Design A Data Collection Plan

Ready to start your data collection plan?

A well-organized data collection plan is the culmination of all your other work in clarifying your project objectives, defining your data requirements, and selecting a method of data collection. This plan will serve as the basis of your efforts to actually design and build a program that precisely serves the needs of your project and its beneficiaries. It will help when you bring on new team members and when you need to apply for new funding. The structure that your data collection plan provides will help at every step of the way from here on out and, if acted on correctly, should improve the sustainability of your program as a whole.

If, after following this process, you find that mobile data collection is the right avenue for your program, we have some good news for you. We have developed a comprehensive guide to take you from the process of selecting the right mobile data collection tool through designing your surveys, building your tool, and ultimately maintaining its sustainability. This guide is a collection of advice from our partners and Global Services team, aimed at increasing the use and impact of mobile data collection tools worldwide.

To learn more, check out our Guide to Mobile Data Collection.

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