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How Can Funders Avoid Chasing Data as the Latest Shiny Object?

We don’t need to preach to the choir: given the plethora of data-related projects that donors and development finance institutions (DFIs) have supported in the past, they clearly know that data is a powerful tool for advancing financial inclusion. And there is a lot to be excited about, given how digital technologies are accelerating the speed, depth and breadth of available data.

But is data just the latest shiny object of the financial inclusion community? Are data’s powers fully leveraged? Does more data lead to sustainable impact?

Today, we think the jury is out on all three of these questions. Our research shows that more and better data in the hands of providers, regulators, policy makers, investors and customers doesn’t automatically lead to better outcomes. Results often show that these market actors do not use data or have stopped using data after a while.

That’s why, for us, the real excitement lies beyond data. It is about bringing fresh thinking to help donors and DFIs achieve sustainable impact through their data efforts. Based on our research, we have four recommendations:

  1. Do not overestimate data as a driver of change. Increased knowledge with data does not necessarily incentivize behavior change. For example, robust data on women’s demand and need for financial services will not automatically lead providers to improve their suite of products and services. A detailed theory of change (ToC) will help donors and DFIs to clarify the chain of incentives and behavior changes that a data initiative seeks to trigger. Make sure the pathway from data to expected outcomes is clear.
  2. Clarify how other constraints will be addressed. Behavior change often requires more than new data. Constraints can range from regulatory barriers to macroeconomic factors and poor costumer knowledge. Even if your initiative focuses on a data gap, you should be clear about how other constraints will be addressed. If you are not, consider postponing or canceling your data initiative.
  3. Pay attention to all functions in the data value chain. Data generates change only when produced through a well-functioning value chain. This chain includes data generation, collection, validation, aggregation, analysis, sharing, knowledge extraction and use. Each of these functions in the chain requires supporting functions, such as infrastructure, standard setting, regulations, skills building and product development. A whole set of market actors, not just a data provider, makes the value chain work by performing and paying for these various functions. When one or more of these actors are not fulfilling their role, the value chain is broken and the potential of data is undermined. Hence, donors and DFIs need to build their data initiatives on a diagnostic of the entire value chain and develop strategies to have all functions, not just data sharing, in place and performing.
  4. Unlock long-term incentives in the data value chain and the financial sector. Donors and DFIs often mistake their support for the incentive needed to trigger change. But their support is, at best, short term. To sustain a behavior, market actors must be able to see the value for them over the long term. Donors and DFIs should aim to identify the incentives gaps explaining why data are not yet available or being used. They should then structure their support to ensure market actors test new behaviors and improve their value as needed to remain involved in the data value chain over the long term. This approach might take more time for donors and DFIs than simply providing the weak or missing functions. But if we do not start building the value chain, there will always be data gaps.

 

 

Estelle Lahaye, Senior Financial Sector Specialist and Alice Nègre, Lead Financial Sector Specialist

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