A data platform is an additional tool that we all should have in our policy toolkit. Usually, it is not the final goal, but one of the means to achieve an objective. As I recently wrote, when deciding whether a data platform is the right tool, keep in mind its value-added: capturing people’s attention, bridging gaps of knowledge, and providing flexibility in the analysis of data. Data platforms take time, effort, and resources. Before embarking on this journey, be sure that the foundations are solid enough. Here we offer a road map for this initial stage.

I have seen many frameworks to take the first step towards building a data platform. Most of them are similar in essence, trying to identify what is at the core of the message that you want to convey. For me, the 3-Question Rule presented in Communicating Data with Tableau captures very well the 3 questions that we, policymakers, should be sure to answer before moving to the building phase. It is simple and to the point.

Between us: This is the step where an interdisciplinary approach is needed the most. As an economist and a policy practitioner, I am equipped to identify policy questions, and I have knowledge on several tools and techniques that can give birth to a data platform, from data engineering to visualization tools. However, I will never be able to replace the value of an interdisciplinary team looking at different angles of the same issue. And here is where I think data platforms face important chances of falling short. Keep an eye on this! Economists, data scientists, and software engineers are as needed in this process as people on the frontline.

The 3-Question Rule

  1. What is the policy question that you are trying to answer?The first step is crafting the question/s that you want your data platform to answer. This may seem straightforward, but it is certainly one of the most challenging tasks. The Opportunity Atlas does a terrific job here.  It starts, from the beginning, with the question that the platform attempts to answer: “Which neighborhoods in America offer children the best chance to rise out of poverty?”. It is an incredibly sophisticated data platform that starts with a simple question, setting the scene for 2 key stakeholders: the team that builds the platform and the users. For the team, this question is like the north start that guides their decisions throughout the product development. For the users, it delineates the type of information that they will encounter. In our case (I led the design and development of the Small Business Equity Toolkit), we were very clear from the beginning that the question “How are black-owned businesses performing?” was the north start of our project. However, this was a not-good-enough question. What do we understand by performance? Where are the Black-owned businesses that we are assessing with our data located? Several iterations left us with the following question: “How do black-owned businesses in your metropolitan area perform in number, size, and sector?” It may look simple, but there is policy and theory behind this question: identifying the importance of assessing these three dimensions (and not two or four) took the team several iterations.
  2. Why do you want users to know this information? In our project, our second question became:  “Why do we want users to know how black-owned businesses are performing in terms of number, size, and sector?”. We wanted to capture and rank the performance of Black-owned businesses to promote a clearer understanding of the small business ecosystem and its gaps, encourage goal setting and provide useful comparison points to economic decisionmakers at all levels. Having this in mind was key to craft the functionalities of our data platform, and the way in which we needed to present data and visualizations. Another good example appears in the Spatial Equity Data Tool from the Urban Institute. This data tool is also great in making very clear the distinction between the “What” and the “Why” questions. The “What” question: “How can cities ensure that resources are equitably distributed to all residents?”. And then, the “Why” explanation: “to help city officials, community organizations, and residents quickly assess spatial and demographic disparities in their cities”.
  3. Who is your intended audience?Data platforms for public good tend to have a broad audience, from policymakers to academic researchers to citizens. Although it may not be possible to identify only one type of user, each audience has its own requirements and, ultimately, your data platform should be tuned accordingly. For example, platforms such as the Growth Lab’s Viz Hub are popular among researchers and a technical audience (e.g., the economic development team of a local government), and many of its features reflect that trend.

Your takeaways: If you decide that creating a data platform is the right path, then start with the 3-question rule: (1) What is the policy question that you are trying to answer?; (2) Why is your question relevant?; (3) Who is the intended audience for your data platform?

If you have feedback or thoughts about this content, please reach out and let us start an exchange of ideas!