The 911 emergency response service in Washington, D.C.' has been collecting data on its calls for years. In fact it was probably experiencing information overload. It knew when, where, and the time of day for each of 127,000 emergency calls it receives annually. However, only last year did city officials try to make sense out of the data. By analyzing the data, D.C. officials found that just 20 residents accounted for 10 percent of the calls. That averaged out to 635 calls per year by each of these 20 residents -- more than one a day!
An emergency response costs the District about $700 per visit. So the city realized that sending medical workers in vans to regularly visit the top 20 habitual 911 callers would be far more cost-effective. In addition, the city is now able to be more responsive to true emergency calls. But it would not have taken this course of action if it had not analyzed its data, transforming it into actionable information.
This approach to making sense out of an overload of performance data is called "analytics" by some and "business intelligence" by others. Whatever it is called, it refers to "the extensive use of data, statistical, and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions," according to Professor Thomas Davenport, a well-known business scholar. He and a colleague, Sirkka Jarvenpaa, co-authored a report for the IBM Center for The Business of Government called "Strategic Use of Analytics in Government." They examined a series of government program areas to determine how far along government is in applying analytic approaches to its work. In the process, they developed a set of attributes to assess agencies' capacity to effectively use performance information.
Successful program executives develop and use analytic programs with the following characteristics, according to Davenport and Jarvenpaa:
Accessible, high-quality data. Government often has access to a great volume of data; it needs to not only collect and "warehouse" it, but the data must be of high-enough quality to be used to make decisions. The data needs to be current, and it needs to be separate from agencies' transaction systems. Many state revenue agencies, for example, are using commercial data management software to detect tax cheaters.
An enterprise orientation. Oftentimes data are segregated into specific programs and cannot be compared or analyzed across programs. Agencies need to be able to provide a unified face to citizens and users of the agencies' own internal data, such as finance or personnel. Davenport and Jarvenpaa believe the fragmented nature of many government programs is probably the greatest difference between public and private use of analytics.
Analytic leadership. Leaders who recognize and understand the value of analytics are key. A good example is the former undersecretary for health at the U.S. Department of Veterans Affairs, Kenneth Kizer. He understood the value of identifying key health outcomes and using analytics to drive improvements that resulted in veterans receiving higher-quality than at most private sector hospitals.
A long-term strategic target. Closely tied to leadership, having a clear strategic intent is critical. Setting a long-term goal with intermediate targets begins to develop an enterprise-wide common understanding of priorities and innovations to achieve those goals. Employees respond once they realized that it isn't "measurement for measurement's sake."
A cadre of analysts. Having a cadre of trained analysts is important. In cities using a Citi-Stat approach, there is always a small core of analysts. In the federal government, there are federally funded research and development centers such as RAND and MITRE that provide analytic support, especially for the military. State governments sometimes create partnerships with local universities to provide such capacities, as well.
Taken together, these characteristics can be a useful checklist for understanding what you may want to ensure is in place for your agency.
Growing Government's Analytical Capacity
While there are many examples of the successful use of analytics in government, Davenport and Jarvenpaa conclude that government today still lacks the needed elements of leadership, an enterprise-wide orientation and long-term strategic targeting, all of which are important factors in successful analytics programs. For example, only one out of five states use analytics in their tax-collection efforts, even though data show increases of 10-15 percent in compliance in the states that do use these approaches.
To create effective analytical capabilities, government leaders must not only invest in their technology and data, but also in managerial innovations to transform their organizational cultures and business processes, as well as the day-to-day behaviors of their employees. Private-sector companies that have committed to these approaches find that it takes at least five years to make the transition, but that once they do they have increased their capabilities significantly.
