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FCW : November 15, 2012
owchart documenting what disaster recovery entails for the organization, and even today some of the core num- ber-crunching is done in Excel. Tech- Stat and PortfolioStat are both built around structured in-person meetings, with the resulting data owing into an OMB-provided template. Some metrics and analysis, however, beg for visualization, particularly when they involve location data. Matt Gen- tile, Deloitte s principal for geospatial analytics, contended that "the map is becoming the dashboard." Geospatial tools allow for more sophisticated anal- yses, he said, and enable agencies to blend multiple layers of data as they measure impact and make decisions. He added that a wide variety of com- mercial and open-source mapping options are available, many of which can be plugged into broader analytics systems or used on a stand-alone basis. At the U.S. Agency for International Development, for example, geoanalyt- ics were crucial to election-monitoring efforts in Afghanistan. The results --- which combined vote tallies, incidents of violence and signs of fraud --- were eventually published for all to see, but the original and primary purpose was to create "an intranet connecting D.C. to Kabul," said MapBox CEO Eric Gundersen, whose rm built the open- source system. "Staff on the ground could ag potential problems and eas- ily send them around for all to see." The hurdles According to Dolan, the biggest chal- lenge for most agencies is simply getting started. There is a misconception that analytics must make sense of every- thing, he said, but "the best way to eat the elephant is one bite at a time." Bethann Pepoli, chief technology of cer of EMC s State and Local Gov- ernment Division, agreed. "Instead of reacting to every situation and every request, we re suggesting that you be more proactive in identifying challenges and the solutions you want," she said. Lucas said agencies should resist the strong temptation to create a strategy around the data they already have rather than the questions they need to answer. "There s probably a little too much bias toward what you have in your pocket," he said, but real insights often require sources from outside the agency. "We know that any real breakthrough...is always some kind of a mashup." Failure to clearly explain those goals and get departmental buy-in can also cripple a project. As a government of - cial in the "From Data to Decisions" focus groups put it, "There s still very much a culture of fear of metrics --- fear that the data can be used against your program." Similarly, analytics projects must be designed so that they can actually ExecTe c h be put to use; a system that requires the agency to reinvent its operations will likely face an uphill climb. A recent Harvard Business Review article warned that analytics can run into the same prob- lems that plagued big customer relation- ship management projects in the 1990s: "The systems remained stubbornly dis- connected from how...frontline managers actually made decisions, and new demands for data management added complexity to operations." And nally, there is the data being ana- lyzed, which must be right on three dif- ferent levels: factually accurate, properly structured and relevant to the goals being pursued. The analytics team can handle the data structure, but the other two elements require close collaboration with program staff and subject-matter experts. ■ 30 November 15, 2012 FCW.COM Next steps: How to plan and launch an analytics project Identify a tightly de ned, mission-critical goal. It should be important enough to justify the effort that analytics will require and ensure that senior executives are invested in the process, but it should not be so big or broad that people are afraid to tackle it. Determine the questions whose answers could help reach that goal. Successful analytics projects don t develop a dashboard based on the data available. Instead, they gather the relevant data and develop metrics to meet a clearly de ned goal. Inventory the data you already have available and identify any that you must start collecting. Look beyond your agency --- and beyond government, for that matter --- when building your dataset. Pick the appropriate tools to capture and analyze the data and share the resulting analytics with decision-makers. Tools can range from simple spreadsheets to full-blown enterprise systems, but the nal presentation should be simple and clear. A data scientist might be required to develop the analytics, but managers and executives should not have to be statisticians to understand and use them. Revisit and re-evaluate the questions and metrics on a regular basis. Getting the right statistics can be dif cult, and framing the objective properly is even harder. Be prepared to ne-tune as you go along. 1 2 34 5
October 30, 2012
November 30, 2012