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May 16, 2011 By Sarah Rich
Public-sector leaders struggle with overwhelming volumes of data and have difficulty effectively analyzing it, according to a new study by IBM’s Institute for Business Value. The study, released in March, addresses the value of analytics and why analytics discipline should be implemented in management practices in the public sector.
The Power of Analytics for Public Sector surveyed 100 leaders in federal, state and local governments, and non-governmental and nonprofit organizations worldwide. The public sector has seen an “information explosion” in recent years, the report said, with more information available today than ever before.
The overwhelming amount of data is more than needed for effective decision-making, according to the IBM Global CEO study. In fact, the information explosion was cited by public-sector executives as the main driver for increased complexity.
Executives also reported having little insight available on this data, which has created a “data paradox.” Public-sector analytics professionals reported spending 47 percent of their time collecting and organizing data and less than a third of their time on extensive analysis.
“The glut of data creates challenges in getting the potential value from massive amounts of data that organizations collect, store and manage,” the report said.
The combination of the data paradox and the information explosion creates challenges and complexities for effective information reporting in the public sector. Twenty-four percent of leaders surveyed said the data paradox is the greatest barrier for analytics adoption and use.
Some organizations have been deemed successful with their approach to improving analytics within their agencies. The report highlighted two major government agencies that have successfully deployed analytics tools on high volumes of information.
The Alameda County, Calif., Social Services Agency created a “lifecycle” view to track customer interactions with county and state social service resources. From this new approach, the agency was able to identify $11 million in fraud and waste in the first year of implementation.
The New York State Department of Taxation and Finance deployed a data analytics system to predict how likely a tax return is to be questionable, which allows the system to prioritize which cases are least likely to be eligible. The system then rejects ineligible refund requests to prevent them from going through the audit process. The state has saved more than $889 million while processing tax refunds faster.
Lynn Reyes, the government lead for the IBM Institute for Business Value and one of the study’s authors, said public-sector leaders need to develop an analytics competency within their organizations. According to the study, analytics competency is an “organization’s capacity to use analytics in an expanded, systemic manner and advance it as an enterprise skill.”
“[Analytics competency] requires an analytics talent and analytics capabilities, and those are certainly tools, technology, techniques and analytics leadership,” Reyes said. “In essence, government organizations need to ‘go pro’ in this area so they can use this information.”
Four key steps for obtaining analytics competency, according to the study, include focusing on outcomes rather than current issues, orientating the management of information (as opposed to only collecting information), using analytics-enabled insights to meet objectives, and modeling and embedding analytics discipline into management practices.
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http://www.govtech.com/education/IBM-Too-Much-Data-Stymies-Effective-Analysis.html
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The need for analytical capacity in public agencies runs afoul of chronic staffing shortages and low wages, as well as the cultural orientation of agencies towards near-term problem solving. It's bigger than just strategic planning among management (although that certainly helps to create the conditions to advance analytical capacity and make it useful). I say this as a former public agency analyst who now works on contracted projects with such agencies as an outside consultant. I sympathize with my old officemates and new partners in their frustration.
I totally concur with the observations of Thubley. Although most managers appreciate and know how to use analytics to forecast and plan, most do not have te staff or the time to contribute to it. Most of the time, we are engrossed in the dat-to-day exercise of putting out fires as the crop up. It's very frustrating indeed.
In addition to the reasons noted by Thubley and Gary why agencies often can't maintain an effective analytical capability, there are systemic reasons for government agencies to collect lots of data. A government agency administering a program will eventually be asked an analytical question concerning the program by a top-priority client, such as a governor or legislator, that requires that they have already collected data beyond the minimum they would need to administer the program. Also, if the program is operating successfully, the same clients will add functions to it, and expect that the implementation will be simple and low-cost, since the basic program is operating already. For these reasons, agencies with experience tend to design data systems that are more complex and collect more data than the absolute minimum they would need to administer their programs. Otherwise, they will appear incompetent or unresponsive when they can't answer apparently simple analytical questions concerning the program, or if they estimate a large cost to add another function to it. There is nothing unreasonable about the kinds of requests agencies receive. Policy-makers need the information, and it's prudent to design systems that can be modified and added to. But IBM's findings aren't surprising.
I was particularly taken by Tate's comment as it relates to my position as Tax Assessor in a South Carolina County. Historically, assessors offices have been one of the largest generators of data in local government. As data management capabilities have grown, the number and accuracy of analytic functions have also grown. However: in line with the comments of THubley, the wage and staffing constraints often restrict our ability to perform at the levels both state and local officials expect. In the search for reduction of the "analytics gap" I would suggest that serious looks be taken at the levels of expertise that already exist in the marketplace of CAMA (Computer Assisted Mass Appraisal) and property tax administration vendors. They are doing pretty wonderful stuff, but the applicability of their software is limited by the level of expertise of the users (local assessment jurisdictions). In short, these vendors are providing software that allows data to be gathered and manipulated so as to make analysis easier. We don't have the people and the time to work through successful implementation as often as we wish.