Friday, January 6th, 2012 - 17:36
Wednesday, January 4, 2012 - 17:24
In the future, it will no longer be enough to analyze historical data. In these past two posts, we are offering three principles government agencies should use in consideration when moving to the future of analytics.
In our last post, we introduced the first principle government agencies should adopt in effort to dramatically cut costs while simultaneously driving improved performance; the importance of government agencies to understand their current environment. Today we will wrap up the two other principles to predict future outcomes and learn how changes impact those outcomes.
Principle 2: Predict Future Outcomes
Analytics algorithms will continue to advance their ability to perform predictive analysis on future outcomes. These predictions are continuously growing more accurate as more complete data is available to analyze. They are also providing more immediate feedback based on changing real-time conditions.
Example: The Fire Department of New York (FDNY) is involved designing a new, centralized inspection-tracking system. In the current environment, FDNY schedules building inspections based on the type of building and the time since the last inspection. FDNY intends in the future to use predictive risk algorithms to identify the buildings that are most in need of inspections. The same information will also provide vital real-time information to firefighters as they are responding to fires.
Principle 3: Learn How Changes Impact Outcomes
Analytics will help organizations test the impact of changes by providing immediate feedback about how a change affects the world around it. Agencies will use analytics to test their hypotheses about how to solve key mission goals. The analytics of the future will increase the speed of the feedback gained during hypothesis testing and enhance the ability to learn the impacts of agency policies before full-scale implementation.
Example: The Department of Transportation National Highway Traffic Safety Administration (NHTSA) developed the “Click It or Ticket” campaign by using analytics to learn the approach that would have the biggest impact. The NHTSA needed to determine how best to promote the use of seatbelts. The agency leadership prescribed an approach using controlled experiments and data analysis to determine the best way to achieve their goals. The agency found that through focusing on compliance and high-visibility enforcement it was able to dramatically increase seatbelt use. These highly successful results persuaded jurisdictions across the nation to also put evidence-based, data-driven analysis at the core of how they decide upon the best course of action.
Achieve the Vision
These few examples illustrate how the future of analytics will enable dramatic changes in the way government performs. Successful implementation of analytics requires visionary leaders who recognize the potential benefits (which we will talk about later in this blog series). There is still much work to do for agencies to organize vast amounts of data and to establish policies that encourage data-driven decision making. Many visionary government leaders recognize the potential of analytics to cut costs, improve performance, and drive government to be more citizen-centric.
Coming up next
Join us next time as we discuss the current federal analytics strategy.
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Mr. Treworgy has over 20 years of analytics and project management experience. In addition to his primary focus on United States departments and agencies, he also has carried out work for a number of government organizations in Europe and Africa. A thought leader in the area of strategy and information analytics, Mr. Treworgy publishes frequent articles, presents often at conferences, and has provided expert witness testimony on several occasions, including at a joint Senate / House of Representatives hearing. He graduated with a BA in Economics from Williams College and an MBA from Harvard University.
David Treworgy (email@example.com)
Greg Greben is Vice President and Market Leader for Business Analytics and Optimization (BAO), IBM Global Business Services, US Public Sector.