Who can argue with the idea that good decisions should always flow from good information?
This seemingly obvious logic raises questions about how we collect, access and sort information, and, ultimately, use it to make decisions. How can we be sure that we have the right data at the right time, and that it is organized and interpreted in the right way to support decision-making?
Consider, for example, that the Institute for Health Metrics and Evaluation (IHME) at the University of Washington produces a tremendous volume of robust data on population health and health systems on a regular basis. What would happen if funders and other stakeholders effectively used these data regularly to inform decisions and take action? Consider also the work of Hans Rosling, a professor of global health at Sweden’s Karolinska Institute, and his ability to present datasets in seductive, compelling and understandable ways. What would happen if more data from all sectors were presented in such dramatic and effective ways?
The potential of taking the IHME data to Rosling-like conclusions is apparent, but the steps in between are mind-boggling. Since discovering the volume of data produced by a group like the IHME and the inspiring data visualizations of someone like Hans Rosling, I understand, in a new way, the potential power of data to change minds and inform decisions. I have also discovered that complex processes are involved with data work, from its collection to using it to explore choices and make decisions.
David Mimno from Cornell University compares data collection and analysis to woodworking. According to Mimno, working with data involves joining information together as well as selecting and pruning. It is “like building a data chair,” Mimno says. “You turn a dataset on the data lathe, and then glue it to the appropriate slot in another dataset. Carpentry has all these aspects, from selecting and shaping to careful joinery.” This analogy reminds us not only to honor the power of data but also that we, as collectors and users, have a responsibility to make careful decisions – joining, selecting and pruning.
Funders should take a leadership role in advocating for the better use of real-time data to drive our decision-making. We should be leaders in collecting, verifying, analyzing, sharing and reporting our data. We must also advocate for a more systematic and consistent collection of data. As we reach the end of the MDG commitments, I see a critical need for the effective use of data to help drive our goals around health for the next generation of global health targets.
Health systems decision makers and advocates for the health workforce – human resources for health – have a long way to go in effectively and collectively using data to drive decisions around defining, hiring, developing and deploying health workers. We know, for example, that the availability of birth attendants is linked to reductions in maternal and infant mortality. We do not, however, adequately train and deploy enough attendants to reach those who need health services. We have also seen promising research linking management training to better health outcomes. Yet resources for management development are neither properly allocated nor universally included in project budgets.
I believe in the promise of better managed health facilities and systems as a result of well-trained health workers. I also believe that the responsible use of data and technology will accelerate the adoption of proven practices to improve health systems. As we approach a new era of global goal-setting, let’s take stock of the data that we have and how we are using it to make decisions. Data have given us the power to be selective and have provided us with a great opportunity to collaborate in supporting the global health workforce. After all, don’t we all agree that good decisions should flow from good information?
Photo credit: Chris James White/PSI.
