1 How to Define Your Project Objectives
2 How to Identify Your Data Requirements
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
3 How to Determine Your Method of Data Collection
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.
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.
Organizing a data collection plan doesn’t have to be painful if you’re working with your team.
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.
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