This post originally appeared on The Huffington Post
Since the launch of the “data revolution” in international development in 2013, the field has been abuzz with the potential transformative effect of leveraging data in many forms — big, open and citizen-generated — to deliver more targeted and impactful development outcomes for the world’s poor. This quickly led to the launch of an umbrella organization dedicated to marshaling the world’s interest in the data revolution, the Global Partnership for Sustainable Development Data (GPSDD), which has helped steer development toward the consensus that using data isn’t just for monitoring and evaluation, it’s for all development practitioners.
Data is often held up as development’s next silver bullet, providing the insights and evidence necessary to finally crack the nut of development’s toughest challenges. But lost in the many conversations and conferences surrounding the data revolution has been the crucial role of what we call “data sherpas” — the translators and infomediaries whose job (explicitly or implicitly) is to shepherd the right data to the right problem at the right time, and to help policymakers and practitioners take action on key problems using the right information. Based on new research, it turns out that the efficacy of data sherpas may be just as important, if not more, than the quality of the actual data in moving from insights to action to development impact.
The inefficient data revolution
The traditional narrative of how the data revolution can transform international development goes something like this: provide analytical tools and data repositories for policymakers and practitioners to access (ideally with mapping and geo tools); sprinkle in an Application Programming Interface (API) or two through which third-party apps can pull newly liberated data sets; and — voila! — development insights will emerge and change in the world will occur.
This narrative explains why many first-generation data revolution deliverables are online build-it-and-they-will-come tools that hope, expect or need data users to first discover the existence of the tool, put it to use on their own, and (hopefully) accelerate data-driven policymaking, evidence-informed advocacy, and smarter program design. Good examples include Global Forest Watch, Ending Rural Hunger, and GPSDD’s own Data4SDGS Toolbox, just to name a few examples.
But these important tools and approaches run the risk of being insufficient absent better dissemination and uptake efforts. For instance, it’s unrealistic to expect a resource-constrained official in a low-income country to spend hours poring over data sets in the hope of finding one key insight. To that end, understanding the role of data sherpas provides an important way forward.
Embracing the messenger
Much of the data revolution’s theory of change (more data + better use of data = improved development outcomes) emerged from the literature and practical experimentation around “evidence informed policymaking” (EIP). Within that experience and knowledge base are nascent intellectual frameworks for understanding how knowledge and data actually make their way (or not) into policy decisions.
Flagged in the EIP literature but often lost in data revolution initiatives is the paramount role of evidence “translators,” or what we call data sherpas. These are the individuals, institutions and actors who analyze, package, and present data to decision-makers as they engage in policy and programmatic design and implementation. Data sherpas might take the form of journalists and think tanks, but also political parties and religious leaders, key staff who support decision-makers, and public thought leaders whose messages and ideas are transmitted via traditional and social media. In short, these sherpas provide intellectual transport for raw data and information to make their way into the brains of those tasked with putting that data to use.
It’s crucial for data revolutionaries to understand what makes certain sherpas more or less effective. With many potential data sherpas available in a given sector or context, who should we bet on to get data into development decision making? Some emerging research sheds light on those answers.
Our organization, Results for Development, has been assessing what makes data sherpas effective by testing theorized traits through observational research of two real-time reform efforts: one in Ghana focused on reforming the national health insurance scheme, the other focused on implementing a new Right to Information law in Buenos Aires, Argentina. We want to understand who decision-makers are listening to, and what data and information they find compelling and why.
What makes an effective data sherpa?
Coupled with recently-published companion research on data uptake produced by collaboratives such as the Governance Data Alliance and AidData, we’re collectively beginning to understand what makes for a more effective data sherpa. Here are a few takeaways:
- Perceived gravitas. Data sherpas who come from “established” and “reputable” institutional backgrounds likely have more influence with policymakers and program planners.
- People trust their peers. Data sherpas who are perceived as peers by those they are trying to influence are often more effective.
- Political will matters hugely for sherpas’ effectiveness. Time and again, decision-makers cite “space from the top,” or freedom of action, as crucial for whether they are able to make use of the data that Sherpas present to them…or not.
- Decision-makers trust sherpas more when their data validates decision-makers’ priorities. We all have biases, and the research suggests that decision-makers will more aggressively gravitate toward data and insights that confirm pre-existing opinions rather than those that upend them.
- Timing (and format) is key. In our work on health data through the Primary Health Care Performance Initiative, we have found that policymakers and health systems managers are often flooded with tons of data from different sources. But this data rarely is used effectively. They have told us that the right data needs to get to the right decision-maker, in the right format, at the right moment in time. Better summary composite metrics can help tell a story. So can well-designed dashboards that are targeted to country-level decision-makers. Knowing when and how to deliver data to the appropriate person is essential.
While none of these individual traits and contextual dynamics are revolutionary, taken together they remind us of both the need for a messenger and the importance of who that messenger is. For data revolutionaries to be truly effective in the future, it’s not just about the data or its message; it’s about who’s delivering that message.
Gina Lagomarsino is president and CEO of Results for Development (R4D). Throughout her career, Lagomarsino has focused on expanding health coverage to low-income populations. As a managing director at R4D, she was instrumental in developing several key partnerships, including the Joint Learning Network for Universal Health Coverage, the Center for Health Market Innovations and the Primary Health Care Performance Initiative.
Nathaniel Heller is the executive vice president of integrated strategies at R4D, where he oversees the organization’s cross-cutting practice areas, including R4D’s work in governance and social accountability, adaptive and collaborative learning, adapting and scaling up innovation, and initiatives aimed at increasing access to vital products and services.
Photo Credits: Government Data Alliance ; We Love Reading ; R4D.