Using data to drive action!


Hi, my name is Nick Amland, and I’m a field fellow for Dimagi.  I spent the last 8 months in Tanzania working on a CommCare project, and now I’m back in the US leading our Active Data Management (ADM) initiative (  This post will describe our current approach and recent progress.

Our motivation for ADM stems from our experience over the last few years, in which we see the most successful projects utilizing active feedback loops where somebody actively uses the data we collect to improve field operations.   We’ve also seen a lot of data we collect go unused—not surprisingly because most of the health programs we work with are not designed to expect real-time data.   ADM strives to help partners make active use of the data collected and achieve evidence-based change.

Part of my desire to drive this initiative comes from my work in Tanzania, where we had a fully employed and incentivized CHW workforce, our own employed supervisors, and still struggled with how best to utilize all of the data we had available.

Approach:  Monitoring and follow-up action are two distinctly different elements of one process.  Monitoring refers to reviewing reports and data analysis which informs follow-up action.  Both rely on the other, and they must be closely linked for evidence-based change and performance improvement to occur.  But, we identified a significant gap creating a barrier to effective or optimal performance monitoring and follow-up action.

Numerous reasons create this gap including poorly designed reports and data requirements, busy project managers, and unclear prioritization and expectation of effort.   The foundation of our approach concentrates on addressing this gap by making it easier for a project manager to access, correctly interpret, and act on data using short, routine reports that more explicitly link analysis to follow-up action and working with partners to integrate this process into their existing supervisory structure.

To talk a little more about our effort to link reporting analysis to follow-up action, we tackle this problem in primarily three ways: simplifying report design, establishing clear follow-up, and setting metric targets.

Design Simplicity: In each of our report prototypes, design simplicity is paramount.  Everyone knows complexity kills, but our working environment emphasizes this effect.  If a project manager needs more than 2 minutes to interpret a report, we believe the chance of follow-up action decreases.  Simplifying a report’s design increases the chance of a project manager reviewing and acting on the information presented.

Clear Follow-Up:  In addition to design simplification, we provide a list of follow-up items.  This helps to more explicitly link the information to action.  For example, a graph or chart presenting aggregated data might be insightful, but the step from this graph to action isn’t direct enough.  It is a step removed.  A graph or chart requires both more effort and a correct interpretation to lead to meaningful action.  We believe this one step decreases the chance a project manager will act.  The name of an individual CHW with specific follow-up action is the most actionable piece of information.  As the primary output of our report prototypes, we generate a list of CHWs with associated follow-up actions as a way to incrementally address weak performance.

However, we realize that identifying specific follow-up items accomplishes only half of this process.  We intend to track these items from open to close in order to confirm action follows the identification of these issues.

Metric Targets: Monitoring is severely limited without setting an expectation or target and leads to unclear and potentially misguided follow-up action.  We’re strongly encouraging our partners to set targets for the level of activity they expect (e.g. designate CHWs to visit x number of clients every x days).  This adds context to data analysis which project managers can use for an easier, more appropriate, and meaningful interpretation of the data.

Next Steps: The next major milestones for ADM are report standardization and automation.  All CommCare projects have slight differences, but the concept of mobile CHWs managing clients and submitting forms remains an underlying congruence.  We want to standardize the metrics to enable a level of autonomy.  So, we’re currently testing our report prototypes with partners in India, Africa, and the Middle East.  Through an iterative process, we have started to refine our prototypes to respond to project manager feedback.

Once we validate our approach, we want to automate it.  We plan to automatically generate and disseminate our reports on a weekly and/or monthly basis.  To accommodate different goals from project to project, we’ve started designing a user configurable report builder.  The report builder is designed around a simplified user interaction and focuses on a low number of inputs about the metrics a particular project manager value’s and associated metric targets.

I recently posted about our ADM initiative on a Google Group of ICT contributors.  This post and discussion that follows can be found here:





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