Data mining and predictive analytics will make social service agencies more effective.
No single area of innovation promises as much public value as the rapidly evolving areas that allow government officials to utilize data across agency and IT silos. These technologies, whether data mining or sophisticated middleware, produce three transformative changes — they can improve the ease with which citizens can access services, facilitate field worker problem solving and produce a foundation for answering big, predictive questions through data analytics.
Five years ago, New York City launched an initiative, HHS-Connect, to collect its social service data in one place. The idea is to allow clients to walk into different social service agencies without having to re-enter their information and complete duplicate paperwork. “We have a vision of a client walking into, for example, a homeless shelter and not having to reapply your information if you had already been to the public welfare office or to the Administration for Children’s Services,” said Kristin Misner, chief of staff to the deputy mayor for health and human services.
Yet this first step should quickly facilitate further actions that make government benefits easier to obtain for those who qualify and more difficult for those whose actions produce waste, fraud and abuse. Data mining and predictive analytics will help overburdened social service agencies detect fraud and better provide and target services. In Los Angeles County, the Department of Public Social Services uses a series of algorithms to analyze its systemwide data and uncover possible anomalies. The department’s system offers a risk analysis of potential fraud within California’s child-care program, allowing investigators to prioritize their caseload. The technology means the agency can take a proactive stance against fraud, as opposed to solely responding to tips on hotlines.
Big data also allows governments to target their social services to those most in need, a crucial goal as budgets have grown tighter amid the economic downturn. Buffalo, N.Y., has used data analysis to expand Operation Clean Sweep, a collaborative community program that provides a range of services at once, from graffiti removal to health care, to particular neighborhoods. City officials analyze 311 and 911 calls, as well as economic and neighborhood data, to identify candidate neighborhoods and understand what pressing issues must be addressed first.
Finally, mobility puts usable information in the hands of those who do the real work of government. Silo busting not only allows them to see information and complete forms in the field but soon will provide decision support as well. Indiana child welfare services, for example, will soon provide guided insights to welfare workers faced with very tough decisions in the homes of challenged children.
Public officials, so long restricted by vertical IT systems and vertical work, now can chart a path to much more effective services thanks to breakthroughs in the use of data.