What Is Your Data Collection Process?

July 10, 2018


Part two in our series on getting starting with mobile data collection focuses on why and how to examine and outline your existing data collection process.


 

Before you can consider tools and tricks to optimize your data collection program, the key first step is analyzing your program as it exists today. This process will reveal things about where your data comes from, who has access to it, and how it is collected and stored–all key pieces of information that will inform the design and implementation of any new system you choose. Most mobile data collection tools will claim to speed up the process and make it more efficient, and that’s probably true – but only when you know which parts of your current process could benefit from those improvements the most.

When you have to map out your project from square one, it opens your eyes to gaps you didn’t see earlier and that technology may not be able to fix,” explains Gillian Javetski, COO & Co-Founder of TecSalud, an ICT4D company in Bogotá and Cambridge. “All of a sudden, the conversation may shift from ‘What do we want this technology to do’ to ‘Wait, actually, is the problem in our workflow?’”

 

A training leader explains the basics of a workflow map to a team of health workers in Zambia.

A training leader explains the basics of a workflow map to a team of health workers in Zambia.

There are a number of different methods of performing this type of analysis. Some are visual, some are more text-based, but they all look to outline each component of a data collection program from its source to how it will be shared. Whatever your method, the idea is that the practice of mapping out how your data is being collected and used should help to reveal opportunities for improvement in your data collection process overall. The deeper in detail you go, the more opportunities for improvement you’ll find.

The two main ways field teams at Dimagi review projects before they begin are with workflow maps and data collection plan outlines. Let’s look at the benefits and distinctions between these two approaches to review your current data collection process.

 

What is workflow mapping?

A workflow map is a diagram of components and their connections throughout a particular process. When used correctly, workflow maps can increase program efficiency, reduce errors, and improve outcomes. When designed well, they portray a clear beginning and end. After reviewing a workflow map, someone unfamiliar with the program should be able to explain it clearly from start to finish.

Workflow maps actually represent a number of different types of flow diagrams–from organizational hierarchies to application workflows and information flow diagrams. An information flow diagram is most often what we use when mapping how information flows through an existing data collection process. It typically starts with what data is being collected (e.g. quantitative data vs qualitative data) and follows through from how it is collected (e.g. paper forms vs mobile device) to where it is stored and how it is shared from the bottom to the top of your organization (e.g. reporting presentation vs online dashboard).

 

This is a very basic version of an information flow diagram based on a typical CommCare project.

This is the most basic version of an information flow diagram. Data from beneficiaries is collected by community health workers (or other data collectors) using a mobile data collection tool that wirelessly sends data to the cloud. There, it is accessed by a program manager or analyst on a desktop platform. Of course, this version doesn’t include what type of data it is or how that program analyst shares reports with their superiors, funders, or the government. However, that is exactly the type of information that is covered in more complex information flow diagrams.

Two key questions to ask when designing your information flow diagram are (1) What are the major milestones that occur in this process? And (2) What are the major component types (e.g. actions/activities, documents, decisions, etc.)? The answers to these questions are like the pieces to your puzzle. Once you collect them all, start with the outside and work your way in. In other words, begin with your data source and your final output and then fill out the pieces in-between.

The beauty of an information flow diagram is that you can read the same diagram from bottom-to-top and notice different things about your data collection process than if you review top-to-bottom. The change in perspective will help reveal something about the way your data is used and potential means for improving it.

Bonus tip: We like to use a platform called Draw.io to create our workflow maps. Check it out here!

 

What is a data collection plan outline?

Another method of analyzing your data collection process is to fill out a data collection plan outline. It helps organize each variable you are collecting by source, method of collection, timeline, where it is stored, and how it is analyzed and shared.

 

The first few rows of a data collection plan outline from Americorps.

Compared to the information flow diagram, which looks to map data through your entire program in a visual way, a data collection plan outline typically summarizes relevant characteristics of each variable in a table or chart. This approach does not quite measure up to an information flow diagram in terms of viewing your program’s strengths and weaknesses from a high level, but it’s great for organizing detailed notes on how each variable is collected, who has access to what, and even how it might be analyzed. In fact, you can often find some insightful trends by reviewing each row in the chart together. For instance, in the chart above, you can see how the source of your data might differ between data points #1, #2, and #3, by reading across row #2 (“source of data”).

One reason we like the Americorps version of this outline is that it ends with “How will the data be used for program improvement?” It is a good reminder that, regardless of your data collection program objectives, you can always examine your results in a way to improve your final output. Addressing that question early and deliberately is a good way to make it a habit and improve the sustainability of your program.

 

Why does it matter?

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.

For example, in an analysis of the time between the collection of data and the submission of that data to the server in the 20 countries with the highest CommCare usage, a careful observation of the clinical workflow helped the Dimagi Data Science team determine that 75% of CommCare users were using their application as an offline data collection tool. By understanding how the data flowed in these low- to no-connectivity environments, it was then possible to optimize surveys, flows, and general user experience accordingly.

More recently, one of the most important uses of an information flow diagram has been to assess privacy risks related to data protocols. The EU’s General Data Protection Regulation (GDPR) forced millions of organizations working with user data from the EU market to examine their data flow to uncover any potential violations of the regulation before it went into effect.

In each of these cases, the effort to review every aspect of an existing process and map the interactions between them made for a better final product. It is not a coincidence. These projects are made up of interacting components, and if you can understand how each variable interacts with 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.

 


Once you understand how your current data collection process works, the next step in our starter’s guide to mobile data collection is to understand what data you actually need to collect: “What are your data requirements?”


 

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Dimagi

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