For the past several decades, major success stories from within the social services sector have been somewhat scarce.
The complexities of intergenerational poverty, aging populations, addiction, abuse, unemployment, homelessness, rising health care costs and tightened budgets have exceeded the capacity of existing programs.
New initiatives driven by big data and advanced analytics are shedding light on entrenched social challenges—resulting in significant breakthroughs and new frameworks for what’s possible.
Central to the array of analytics-enabled approaches is the ability to dig deeper into data than ever before in order to gain an integrated perspective on individual, family and community issues. This requires drawing upon information that had previously been separated by organizational boundaries, for purposes of:
Gaining insights from patterns,
Coordinating and collaborating among all agencies with which the individual or the family is involved, and
Shifting from a reactive to a proactive approach.
In Alameda County, Calif., coordination among the previously siloed data that had been maintained within five social services agencies has served as the foundation for a transformational Social Services Integrated Reporting System. The system has empowered case workers with a comprehensive, dashboard view of clients across all programs. Among the advantages is the ability to assess the effectiveness of various interventions; pinpoint problem areas; understand connections among clients in the system based on phone numbers, addresses and a range of other data; inform clients of additional services for which they might be eligible; and receive alerts concerning clients who are not complying with the terms that they have agreed to.
This ability to better understand connections and identify gaps at a glance is creating a depth and breadth of new perspectives among the 2,200 employees of Alameda County’s social service agencies and the populations they serve. The most recent estimate of anticipated cost savings resulting from increased efficiencies and reduced fraud: $11 million.
In the UK, the Medway Youth Trust is using predictive analytic models to reverse the downward spiral of youth unemployment by working to identify youth who are most at risk for dropping out of school or losing their jobs. With 6.2 percent of young people between the ages of 16 and 19 in the Medway area neither in school, training or employed, and realizing the staggering societal costs over the course of a life for a youth who slips through the cracks, the Medway Youth Trust set out to identify and then intervene on behalf of youth most at risk.
Central to the initiative is a propensity model that’s based on more than 1,000 factors that combine to impact youth unemployment. Any youth that’s identified with a greater than 60 percent chance for ending up neither employed or in education or training is targeted for intervention based on documented analysis of root causes. One year into the program, positive outcomes were reported for 51 percent of the 723 individuals initially identified as at risk.
Early findings revealed a 250 percent improvement in the accuracy for identifying at-risk youth versus the previous and time-consuming method of manually reviewing records. While the Trust is continuing to refine and sharpen its focus on the types of interventions that are most effective, the model has produced evidence that interventions work and that the earlier in a person’s life the interventions occur, the more effective they are.
Download the US or EU version of the interactive whitepaper to learn more about potential of advanced analytic technology to transform social services outcomes.
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