Like most baseball fans in the nation’s capital, I had my heart broken when the Washington Nationals failed to advance in the playoffs.

While still in mourning, I was shocked by the number of pundits who claimed to have all of the answers to the team’s heartbreaking defeat. They should have pitched Stephen Strasburg. The other pitchers are bums. Davey Johnson, the team’s skipper, made the wrong calls.

Everyone is entitled to their views, but the problem is that most of the post-game analysis was based on uninformed opinion and emotion, not on a careful analysis of all the facts. 

Surprisingly, there’s an analogy to federal leaders’ management of government programs. Particularly during elections, every armchair analyst proclaims which federal programs are essential and which ones waste taxpayer dollars, but they often lack data to back up their claims. With budget cuts (and perhaps even sequestration) looming, every federal program is under the microscope. And every federal leader should be honestly and carefully assessing their programs to gauge effectiveness and improve outcomes.

The problem always has been that federal agencies lack a traditional bottom line, a profit-and-loss statement like those in the private sector. Yet there’s a major change underway within the federal government that my organization, the Partnership for Public Service, and the IBM Center for the Business of Government, have highlighted in our just-published report, From Data to Decisions II: Building an Analytics Culture .

Based on interviews and focus groups with federal employees — managers, program staff and analytics staff — we identified strategies for using data to make informed assessments of how programs are working and how to achieve better results. The report includes profiles of seven agencies that have made these strategies a reality. Here are just a few tips you can take from their examples:

· Start at the beginning. While this may sound a bit too much like one of baseball great Yogi Berra’s famous quotes, the point is simple. An agency, a leader and a team must be clear about what a program is trying to achieve. Only then can you determine what to measure and commit to using the data to inform decision-making. For example, the Internal Revenue Service established an Office of Compliance Analytics to quickly identify operational problems, consider solutions to achieve the agency’s goals, and then develop pilots to fix the issues ASAP. One effort to resolve tax preparers’ errors resulted in $100 million in savings.

· Include analysis in your daily operations. The cliché thatleaders set the example is certainly true as it relates to using analytics to drive improved program performance. The best federal leaders use data to understand what’s working and where problems exist. If the data is not being collected, they work across the organization to find what is available. Then, they roll up their sleeves to work with the team to figure out how to make things better. The Transportation Security Administration, for example, assesses the performance of airport screeners as they are conducting security activities, and then uses the information to decide if individuals or teams need more training.

· Get the people piece right. If you are launching a new analytics initiative, staffing is especially important. Not only do you need to recruit and find a team of analysts, you need program people willing and able to use data to improve performance. And as a leader, you will need to proactively dispel fears that the metrics will be used to punish people rather than improve programs. Sometimes that team will include people from outside of the agency. The Food and Drug Administration’s medical device center worked directly with industry to analyze the process used to review and approve certain types of medical devices. They soon realized that the process was bogged down by unnecessary delays, and they mutually agreed to a number of steps to avoid the bottlenecks, monitor performance and speed review times.

If there’s a bottom line to the report, it’s this: Just get started. Don’t let perfect be the enemy of the good. The benefits are just too substantial.

From a program management perspective, solid data analysis will help make the case for programs during tough budgetary times, while pointing to places where cuts won’t affect results. From a people perspective, you are more likely to engage and energize your team if they see measurable results stemming from their hard efforts. 

While our report features seven case studies using analytics to drive program results, I’m sure there are more we have not yet discovered. Please share your examples in the comment section below or by emailing me at

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