Let’s assume you are the CIO of a public enterprise — one typical in that it is led by a committed but financially stressed elected official. You can see through the morass of new technologies great opportunity for the jurisdiction, but not one without startup expenses and changes in IT governance. What might your message be as you approach the boss and other agency heads to make your case for how mobile tools, data mining and cloud software can be combined to improve responsiveness? It might include the following elements:
- The Value Proposition. We now have the tools to dramatically enhance public services. Through prediction, we can solve problems before they occur, redirect resources to where they make the most impact, hire and promote the best people, help those citizens most in need and save the most money. Technology advancements have produced startling breakthroughs.
- The Team. A leader with strong executive support is critical, because a successful data operation will require cooperation from throughout the enterprise. An executive order from the relevant elected official is often the easiest and most expedient way to begin. The leader should bring a creative, generalist lawyer in from the beginning to work with agency lawyers around data use. Someone with budget authority is also essential to help calculate and justify the savings realized through analytics. A working group with a representative from each agency should meet regularly to socialize successes.
- Piloting. Start with key agencies to build enthusiasm and validate the model, rather than seeking a blanket mandate. If the analytics team demonstrates the value of data by successfully solving problems, the pilot agencies will become the advocates of data. The team should work with the agencies to identify a problem statement first, collect the data second and identify gaps in the data third. This approach is far more effective than ordering all data to be shared or included in a central place before any value is proven.
- Transparency and Security. Include transparency about the policies for data use and access from the beginning. Of course, protecting privacy and data confidentiality is crucial, but one needs to be open about the related policies. Data security requires a range of rules — including deletion of personal identifiers, archiving and access — so that field workers and others throughout government will have access to the data needed to solve problems with analytics.
- Funding. Discretionary funding enables a faster start. In some cases, this may be through philanthropy. If that funding is not available, including a leader with budget authority on the project team is essential, like the Office of Management and Budget director. To justify an investment, look at it as a way to more effectively spend existing money, rather than as a new expenditure. As successful use cases emerge that demonstrate efficiency and savings, making this argument will be easier.
As we have observed governments starting a data office or team, these keys to success have remained constant across geographies and levels of government.