#2 Make your data and analytics open, accessible, and transparent
Agencies are required to make their data accessible and support their policymaking with statistical evidence, but many still use opaque analysis processes. They may be working with data that is old or incomplete and may also struggle to produce the data that was used in an analysis when pressed to do so. Not only does an opaque approach risk errors in understanding that can lead to suboptimal decisions and poor data quality, but it is also slow—when data is not open, accessible, and clearly versioned, agencies can lose time reproducing work or delay their decisions due to a lack of confidence in the underlying data.
By contrast, an open, accessible, and transparent approach to data and analytics allows anyone who’s involved in the process to see the data that’s being used, to reference the analysis that took place to understand the decision—and to also provide a starting point for the next analysis.
And transparency builds trust. An agency’s ability to defend a decision with quality data is essential—as the public has grown accustomed to doing their own research and digging into the data, data transparency helps build and maintain trust with the people they serve.
#3 Prioritize user-friendly tools and upskilling as needed
Giving mission teams access to the data is only part of the solution; you must also provide them with everything they need to understand and use the data effectively. Adopting a Data-as-a-product approach empowers agencies to package their data in a well-defined interface rather than serving up raw datasets. This is key: Mission employees and analysts have a range of data skills, so it’s important to equip them with the self-service tools they need to interrogate and interpret the data in a user-friendly way. Interactive BI dashboards and visual data manipulation tools can engage decision-makers in the analytics process.
In addition to delivering user-friendly data experiences, agencies must match employee skills to the mission need that the data is serving. This might require introducing new people who can bridge the gap between policy and data by bringing high-level analytics skills to a policy background. Don’t be afraid to embrace organizational change in pursuit of innovation, as the two go together. For a program or center to reach its full data-driven potential, there may also be upskilling required—an opportunity that moves existing employees to higher value work while allowing agencies to attract new employees with data skills to the mission.
While arming mission teams with the data they need to make informed decisions is foundational to mission success, it's also key to start with the end in mind. What problem are you trying to solve, and how can we design a solution with this desired outcome in mind? Learn how we used a federated data governance approach and data analytics, modeling, and rapid simulations to help the Centers for Medicare & Medicaid (CMS) modernize their regulatory impact analysis process.