Wednesday, October 8, 2014
Lisa Danzig, associate director for performance and personnel at the Office of Management and Budget, shares some insightful stories on leveraging big data and analytics to improve performance across multiple agencies within the Department.

Previous to her experience with OMB, Ms. Danzig worked with the Department of Housing and Urban Development (HUD) and helped develop and lead its acclaimed HUDStat program. Prior to that, she led strategic planning for New York City’s housing programs. She has an MBA and is a former community organizer. She shares her four top tips on creating and using analytics, based on her combined experiences on data and analytics. They include: Tip 1: Choose Smart Goals. Performance management requires a commitment to the continuous improvement of best practices. Goals should be simple, clearly defined, and focused. Smart goals are not written in stone — they should be refined as needed. For example, when addressing goals related to reducing the number of homeless through public housing programs, Lisa and her colleagues had to really get down to basics. The first challenge was to define an “occupied rental unit” and how that definition impacts the metric “occupancy rate.” Another challenge is that the homeless population is not a homogenous sub-section of our society. Different strategies are required for the “chronic” homeless population as opposed to those whom are homeless simply through unfortunate circumstances. In the end, the goal was restructured to address those populations separately and provide more focus. Tip 2: Leverage Best Practices & Collaboration. The challenge of transforming data into useful information that is a valuable enterprise-level asset is not new — so why re-invent the wheel? Understanding best practices and partnering with other organizations that are struggling with the same challenges to pool resources, experiences, and results that have worked well has many positive results. For example, establishing benchmark data from multiple banking sources not only established parameters that were used to help homeowners during the foreclosure crisis, but the examination of differences among the results helped foster creative thinking among the banks themselves, which resulted in more offered solutions to help prevent people from losing their homes. Similarly, when planning or reviewing data generation and collection processes, reach out to those who actually DO the tasks and activities within the process and obtain their input so that the process and its impact on the data is fully understood. Tip 3: Understand the Data. It is important to take a good, hard look at the data. Most analytics programs have minimal choice regarding source data, so accept that you have to work with what you’ve got. Data quality issues must be addressed so that the metrics produced have integrity. Master and reference data also need to be managed properly in order to sustain a high level of data quality. To ensure your data has the appropriate level of granularity — some Key Performance Indicators must be collected at an agency level but others at a program level. Identifying trends within the data that impact your goals is another way to increase effectiveness. Lastly, the metrics for highly populated urban centers will differ greatly than those for isolated rural areas, and that needs to be accounted for. Tip 4: Celebrate Success. Performance management is a long term process. It is important to foster camaraderie and trust among participants along the way. Celebrating each small success incentivizes all involved to keep plugging away. Also, keep senior management informed of the benefits derived and return on investment. To listen to Ms. Danzig’s complete podcast and to read excerpts from her interview, visit the “Conversations on Using Analytics to Improve Mission Outcomes” page. In my next blog, I will highlight the insights gleaned from an interview with Malcolm Bertoni, Assistant Commissioner for Planning at the Food and Drug Administration.