Data for Action - the drivers and the action pathway

Recently, I have been engaged in multiple conversations on data and its management for program evaluation, monitoring, and impact. I read this wonderful case study from PNG on the Real-time Monitoring of Vaccination Campaigns (RT-VaMA) toolkit. The RT-VaMA initiative in Papua New Guinea (PNG) has been applied in PNG to enhance vaccination coverage and data management through real-time technology.

In our data conversation, I always begin with what good data stands for in modern times. Good data is digitally accessible (and) timely actionable information. For good data, there are two sets of drivers – the #R-drivers and the #T -drivers in modern times. The R -drivers are the #reliability, #relevance, and #replicability factors of the data. Is the data source reliable? How much effort has gone into ensuring that the data is clean and validated? The reliability is more about the instruments used for data collection, and analysis and the platforms to host the data. It is quite different from the trustworthiness of the agencies which has now become a major bone f content between countries and institutions. The relevance piece is important. Too much data that does not add any significant value to the analysis is counterproductive to meaningful interpretation of the data. And the replicability of the data. Will data that has been collected from a certain population in Rajasthan be valid for the same interpretations for a population in Jharkhand? One must clearly understand what factors drive the data and what factors are universal when studying the health of populations.

The T-drivers are the #timeliness, #technology, and #trustworthiness of the agents generating the data. The timeliness part is easily understood. Data delayed is data wasted. Technology is well understood too. The various digital technologies, old, new, and in the pipeline, all have their significant role in data generation, analysis, and interpretation in modern times. But the most important thing in recent times is the issue of trustworthiness. Your data is not my data because I was not involved with your data collection, collation, and analysis. This seems to be the motto of the day. The only way to solve this is that if you want me to act on the data that you generate, involve me transparently from the beginning. We are all aware of the many confrontations that governments have had with international agencies on data on immunization, infectious diseases, and the nutrition status of children. This is mainly because the countries were not included in the teams that generated the data. The 4th T- teamwork is imperative for action on data by responsible and designated agencies.

This brings is to the final point. What do we do with data? This is where we at the Quality, Equity, Inclusiveness Initiative have come up with our pathway of data for action.

#Data leads to a #diagnosis of the problem.

With Diagnosis, we #design interventions.

With interventions designed, we #develop plans.

With plans developed, we #demonstrate (with our meager resources) programs.

We #document the demonstration.

The documentation is then #disseminated.

(If found valuable), agencies like governments and other funding agencies can support further #diffusion (scale up) of the demonstration.

Reading this excellent post from PNG on the effective use of real-time data for measuring and enhancing immunization coverage is a real-time example of what we wish to accomplish with digital data in our work.

The RT-Vama story is not just about the application of the data but about its genesis through good data governance, partnership, and effective action.