Brian Murrow

For several years, IBM and the Partnership for Public Service have partnered to bring lessons learned from industry leaders in the use of Big Data Analytics to our readers. This year we had a series of Conversations on Big Data, a Podcast series of discussions about using analytics in a number of creative and interesting ways to support real-world business goals. These podcast conversations are designed to broaden the perspective about big data and quantitative analytics, share lessons learned about what worked well and what did not, and provide specific examples of benefits gained.

Recently we sat down with Lisa Danzig, Associate Director for Personnel and Performance, Office of Management and Budget. Lisa comes to OMB from HUD, where she was instrumental in leveraging performance data through the HUDStat program. During our conversation, Lisa shared her experience with HUDStat, which started in 2010, and is a quarterly meeting chaired by the Secretary. Each meeting focuses on a discussion of a specific performance goal, what is being done to achieve that goal, and exploring how to more effectively meet the goal. Lisa says that the benefits achieved by HUDStat were realized by:

·         Focusing on an explicit set of goals, which had clearly defined end-result driven metrics associated with them.

·         Reevaluating how performance is measured, which shifted the conversation from a defensive “this is how it has always been done” mindset to a collaborative “there must be a better way” approach.

During our conversation, Lisa shared with us some great stories about how she leveraged Big Data and Analytics to improve performance across multiple agencies within the Department. Here are her top tips:

Choose Smart Goals. Performance management requires a commitment to continuous improvement 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.

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 sets helped foster creative thinking among the banks themselves that resulted in more solutions being offered to help prevent people from losing their homes. Similarly, when planning or reviewing data generation and collection processes, reaching out to those who actually DO the tasks and activities within the process and obtaining their input so that the impact of the process itself on the data is fully understood.

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 that support the metrics also need to be managed properly in order to sustain a high level of data quality. As mentioned above, the business process of collecting data may impact its quality. Lisa cited one example where the process of distributing vouchers limited their availability to some groups within the target population that could not easily obtain access. Another example is to ensure your data has the appropriate level of granularity — some KPIs 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. The metrics for highly populated urban centers will differ greatly than those for isolated rural areas, and that needs to be accounted for.

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, as well as publishing to senior management the benefits derived and return on investment.

This is just one aspect of our conversation with Lisa about performance management and Big Data. Stay tuned for a future blog about her plans for the OMB.