Thursday, December 10, 2020
As part of a recent research project for the IBM Center for the Business of Government, I interviewed experts on inter-governmental data sharing and looked at case studies of successful “silo busting” - leaders and professionals who have built ways to put the public at the center of their work and who have aligned data systems and sources in service to the public.

The forthcoming report describes the value that can be created, and the ways that services can be improved and made more customer-focused by sharing data across agencies, across levels of government, or from government to nonprofit or corporate partners. From this review of success cases, a set of ideas about what can precipitate or accelerate success emerged, resulting in this list of factors that create momentum for data sharing.

1: A crisis demands combining data, and some sharing can become permanent. Data sharing during emergencies is necessary to staunch the loss of life or property and the historical boundaries to data sharing are dropped, as turf interest has to take a back seat to shared sense of purpose. Even before the COVID-19 pandemic, wildfires, earthquakes, terrorist attacks, and natural disaster response all inspired tremendous collaboration across agencies and levels of government in joint responses to crisis. In many of these instances, the data sharing that got started for the emergency remained in place afterward, creating permanent platforms.

One data leader commented that in a crisis, the instinct to resist data sharing is dropped, saying, “It’s a shame we don’t have the ability to do it on a day-to-day basis. It’s just that data sharing takes a lot of resources, and most people feel like they don’t have enough time to do their day-to-day work much less specialized work that is about connecting different code bases.” Miami Chief Data Officer Mike Sarasti commented on how the COVID-19 crisis accelerated change in his city, “With the COVID crisis, we’ve found it’s amazing how much organizational will to make things happen you have when you have death knocking at the door.”

2: Disruptive innovations provide rare “blank slates” for designing data systems. The advent of new phenomena provides a blank slate for government to devise appropriate responses, often intergovernmental and data-driven ones. When sharing economy companies and micro- mobility services emerged, they presented new opportunities for city and state government to work together to find the right regulatory and governance structures to balance public safety interests with the advance of commerce.

For example, in Boston, the emergence of the market for short-term housing rentals required creating from scratch a single data repository with the eligibility of every residential housing unit in the city. This required quickly negotiating a data-sharing agreement with Airbnb, and resulted in a compliance monitoring dashboard that spanned multiple city agencies across addressing and property ownership data sources, and the inspections processes.

3: External, flexible resources can hasten start-up progress. Most government funding sources come with statutory or regulatory restrictions, sometimes restraining the sharing of data across programs. As one expert noted, government rarely makes data sharing a funding priority, and the work is unglamorous and does not produce headlines, as “There’s no ribbon cutting ceremony for a data warehouse.”

Philanthropic sources have often been the engines of innovation in government. For example, the Bloomberg Philanthropies Mayors Challenge and What Works Cities initiatives have created significant innovation and have substantially advanced data-driven approaches. Arnold Ventures has supported cross-silo data sharing and has supported data innovation and policy based on data with their many justice innovation investments and seed funding for Policy Labs. Often government programs are tied to funding streams and sometimes by virtue of funding source cannot be intergovernmental making the power of outside philanthropic funding more even more important. An excellent example is in the Allegheny County Human Services Data Warehouse, which relied on initial philanthropic support for planning and startup costs.

4: Strong executive leadership vision can inspire action and keep data teams motivated. Data sharing is hard work and takes time. A visionary leader can inspire the team to diligently stay on task even when progress seems unlikely. Without this type of leadership, efforts to overcome inertia and turf battles do not succeed. One data leader noted that projects stall when “Everyone has a different vision of what the ‘it’ is we’re working toward.” Examples of clear vision provided by leaders include the governor of Massachusetts who provided strong vision for the opioid data work and frequent attention, and the Allegheny County Human Services Director who was a constant presence in advancing the data warehouse and data analytics work there.

Strong executive leadership can not only be valuable during project implementation but also when the work is done by taking bold steps to use the data insights to drive policy, thus reinforcing the value of the tools and platforms. This can be particularly valuable when persuasion is needed for an entity being asked to contribute data when there is no statutory authority over the entity, such as an independently elected official or a quasi-governmental agency.

5: External accountability pressure can remove roadblocks, and deadlines hasten achieving tangible results. When a legislature or an oversight entity applies pressure, and especially when there is a deadline to demonstrate action, that pressure can inspire staff to stay focused and help move a complex data sharing project along. This was the case when the Massachusetts legislature gave a deadline for answering seven specific data questions about the opioid crisis – a public deadline with clear consequences for failure that compelled 23 agencies to share data in ways they had resisted for years in order to shed light on the opioid crisis.

6: Trusted intermediaries can be “honest brokers” among agencies reluctant to share data. Third party intermediaries can be powerful allies in crossing organizational boundaries – rather than a city or county or state having to hand their data over to a peer government entity, they can each give it to a university or think tank and each rely on the independence of the trusted third party. This has been successful in a variety of contexts. One excellent example is the work accomplished by the Misdemeanor Justice Project on behalf of the City of New York through John Jay College of Criminal Justice – a project so successful it is being replicated in other cites now.

7: Teams that have a diversity of ideas are more creative and productive. Staffing policy and data teams with a diverse combination of perspectives brings new ways of thinking and increases the culture of innovation. This can derive from a diverse set of skills, backgrounds and ways of thinking on a fully public sector employee team, or can derive from staffing teams with a mix of public servants and consultants. As leading data innovation scholar Julia Lane advocates, government should draw on outsiders from the private sector and academia in forging effective new ways of using data to solve important problems, as well as advancing the data skills of those already in government1. This combination of insiders and outsiders has symbiotic benefit, as while outsiders may have fresh ideas, insiders know how to get things done.

8: Persistence and patience are needed because data sharing takes time. It can take up to two years to complete the negotiations and trust building to create a data sharing agreement.

Some data experts note that sometimes an initiative just withers due to fatigue of how long the process can take. The best examples of success in the field are not those who faced no delays or challenges, rather those who succeeded are those who decided not to give up in the face of delays and frustrations.

The purpose of sharing these top 8 keys to accelerating government data innovation was to provide insight that is useful. If you find this useful, let me know about your data success at



1 Lane, Julia. After Covid-19, the US Statistical System Needs to Change, Significance magazine, Royal Statistical Society, August 2020.