In most field programs there is a time lag between data collection and data reporting as only periodic reporting is expected. The field staff compiles it manually at the end of the period and reports up in the hierarchy. There are two types of compilation - aggregation and data filtering.
Some examples of aggregation done are - calculating the number of new houses registered, the number of people tested positive for a disease, so on. These aggregate data elements to be reported periodically may run into dozens. The reporting formats may be at the intervention unit level - e.g. name, age, gender, caste, religion, APL/BPL status of the newly registered. Here the field staff is essentially reporting sub-set of the fields s/he has collected for each intervention unit.
The field teams in the social sector enjoy working with their communities, servicing community members, meeting their program targets etc. However, they do not particularly like entering data into an excel, paper, or a data entry system (MIS). Hence, it is common that one of the tasks of program coordinators is to follow-up with the field team about data submission at the end of the reporting period. On the other hand, the funders and senior leaders want to know about the exact impact being created. Data has become the primary evidence and communication tool. So, nonprofit projects/programs face these two opposing forces, looking for solutions.
Before we think about potential solutions to this, we must look deeper into the issue itself. We can do this by asking a very simple and obvious question. Why does the field team dislike entering data in the first place? The answer is not very difficult to see. Some of the common root causes are:
Our experience suggests that these issues are recognised by all level of field workers, not simply by the more skilled ones. Even community workers at village levels experience this, quite naturally. It’s just that in most cases, they hesitate in expressing their emotions (because of social hierarchy). Upon prodding in a safe-environment one finds the same reasons at play.
If instead of empathising with these circumstances, if the organisation’s response is of economic reasoning, like:
Then, it creates a no-win situation and the status quo continues. Field team procrastinates, data is delayed, data is of poor quality, more time is spent to clean up data - burning down everyone involved.
Our root cause analysis indicates that procrastination and its direct effects present only half the picture. The harm and underlying missed opportunities it hides are far more serious issues for our programs - worthy of our attention.
The root causes listed above themselves, point to the solution. In most cases, a "happier" system looks like the following.
We highly recommend doing 1 and 2, at a bare minimum. 3 is also important if you find yourself spending too much time in, what is referred to as a necessary activity, called "cleaning of data" or "validating data". These are unnecessary tasks and quite stressful because human beings are not good at these tasks.
The fundamental idea that transforms from the state of procrastination to involvement is to make the fieldworkers productive and involve them fully in the data system. It will improve the quality of service provided to clients. The resulting data flow looks rather unimpressive in the diagram. We also recommend reading this article which covers issues around transitioning from paper to digital.
Icon credits: icons8
This article has been republished with permission from Samanvay Foundation. View the original here.