In a recent New York Times op-ed, performance-data pioneer Beth Blauer and epidemiologist Jennifer Nuzzo labeled the state of the nation's COVID-19 data as "a mess." Eight months into the pandemic, they wrote, "there is still no federal standard to ensure testing results are being uniformly reported." Efforts like Johns Hopkins' Coronavirus Resource Center try to standardize where possible, but overall the national numbers are inconsistent. This hinders policymakers' ability to smartly allocate resources, such as vaccines, to where they will do the most good.
The pandemic demonstrates why investing in collecting, standardizing and sharing data among levels of government — not just pandemic data, but data of all kinds — has moved from a nice thing to do to an urgent priority for states and localities.
A new study, authored by Harvard's Jane Wiseman and published by the IBM Center for the Business of Government, highlights the value of intergovernmental data sharing more broadly. The study points to four types of value that come from sharing data among agencies and levels of government:
• Rapid response to emergencies. Almost a decade ago, the state of Virginia began a state-local data-sharing effort, starting with an opioid data project in one community. Its success in helping reduce deaths led, in 2018, to the formalization of the role of a statewide chief data officer and creation of the Commonwealth Data Trust. These foundational data-sharing efforts paved the way for the state's ability in early 2020 to quickly stand up a COVID-19 dashboard, which gives state leaders near real-time information about hospitals in need of supplies, and locations with the largest COVID outbreaks.
• Improved service quality. Allegheny County, Pa., built a data warehouse of social, health, justice and education data that enables Pittsburgh-area caseworkers to prioritize the delivery of services where they are most needed. Individual-level data, for example, has led to the development of risk models for the delivery of child-welfare services. The county's Allegheny Family Screening Tool analyzes hundreds of data points from various data sources and is used by frontline caseworkers to predict the long-term likelihood of out-of-home foster-child placements and whether to investigate a call about potential child endangerment.
• Better allocation of resources. The city of Louisville, Ky., taps into a private-sector data-collection vendor to generate a real-time, citywide view of the use of rented "micro-mobility" vehicles, such as electric scooters. This data is collected from a number of different private-sector micro-mobility providers using a common data-sharing standard called the Mobility Data Specification. The standard is used by more than 80 localities and is now administered by the nonprofit Open Data Foundation. Its use improves both traffic safety and efficiency.
• Seamless customer experiences. No state or locality in the U.S. has yet developed the ability to provide seamless customer-service experiences to the extent that one small, unitary city-state government has. Singapore offers a potential vision of the future in data-sharing. Its strategy is to share government-created administrative data behind the scenes and simultaneously invest in a digital infrastructure that allows seamless customer experiences with services such as SingPass, which enables Singaporeans to easily access an array of services.
Wiseman's report also identifies key factors for successful data-sharing initiatives:
The first is committed leadership to create a clear and compelling vision. For example, Allegheny County's Marc Cherna has been the head of its Department of Human Services since the mid-1990s, when he launched the county's now-renowned human-services data warehouse initiative.
The second is the team and the specific skills and judgment to make quick decisions and broker data-sharing agreements. Virginia's Commonwealth Data Trust, for example, is staffed by a Data Governance Council, an interagency network of data analysts and champions.
The third is creating a repeatable process. The process of creating intergovernmental data-sharing platforms can take a long time, but once in place they can be readily shared and replicated. For example, Los Angeles' Mobility Data Specification defined a process for collecting, sharing and reporting data on the use of e-scooters, bicycles and other shared micro-mobility vehicles. Once this process was defined, it was readily replicated and adopted in communities around the country, including Louisville.
Last but not least is the data itself. As noted by Blauer and Nuzzo, data analysis and data-sharing efforts are only as good as the underlying data, which takes time and effort to develop. That is why investing in a data-sharing initiative with the capacity to collect, share and analyze data shouldn't be an afterthought.
Undoubtedly, as Wiseman notes in the study, the most important success factor to achieving effective intergovernmental data sharing is leadership. We hope this is taken as the urgent priority it is.
Governing's opinion columns reflect the views of their authors and not necessarily those of Governing's editors or management.