|In addition to the search filters above, you may use the general search bar and find resources across the Hub.|
The work of knitting together large government data systems so that they seamlessly connect and provide customer-friendly services to the public is difficult yet achievable and valuable. If this type of data sharing was easy, everyone would be doing it. Instead, there are only a handful of outstanding examples of success and a lot of barriers to achieving it.
The state of data-driven government has advanced rapidly over the past decade, but it has not yet achieved its full potential. Single-agency successes are prevalent, with rapid acceleration of the use of data to solve problems within an agency. Peer-to-peer data sharing across units of government is increasing. This horizontal sharing among peers in one government (city, state, federal agency) is often orchestrated either by the agency itself or by a shared data or IT organization, often to solve a particular and clearly defined problem. This type of data sharing remains far more common than vertical sharing across layers of government, say from city to county and state or to federal. And yet, solutions to the most complex and vexing public problems require data sharing that spans boundaries of government agencies.
As an example, addressing homelessness can’t be solved with just housing data but also requires data about an individual’s situation and needs across employment and education, health and mental health or substance use, and criminal justice sectors. Yet, in each of those service delivery silos, most staff have no incentive to go outside of their sphere of responsibility to get at the root cause of the problem, nor do they typically have authority to access external data sources. As a result, data analytics projects that are cross-departmental require alignment across many factors and are both uncommon and inspiring.
With digital data being created at a dizzying rate with every mouse click, or swipe of a device to enter a building and credit card transaction, the world is awash in data but not yet keeping up with analysis of this data. How is data connected and used to drive action in government? Not nearly enough. And yet, there are some positive points of reference.