What does it really mean to make data driven decisions in child welfare?

Technology Systems are not well understood and often do not adequately support business processes. Systems tend to be designed for data collection. Reporting and data usage is often an oversight and typically the last... [ view full abstract ]

Technology Systems are not well understood and often do not adequately support business processes. Systems tend to be designed for data collection. Reporting and data usage is often an oversight and typically the last priority. Having “Lots” of data does not always equate to solving social problems. Big data does not always mean big solutions.

In this session, participants will:
• Learn how to establishing a sustainable structure of analytic maturation
• Understand the Importance of identifying sources and consumers of data
• Learn how to create a data literate workforce
• Gain techniques for identifying actionable data scenarios

We will challenge the participants with a dialogue about the underutilization of data and discuss analytics challenges including the lack of system compatibility, independent islands of automation and the sometimes insurmountable problems of data sharing restrictions.

Data analytics methodology will be discussed, including the guiding principles of data analytics, giving conference attendees the tools to think about how data analytics, business intelligence, and predictive analytics fit within their domain.
Our presentation will provide concrete examples of where data analytics has been used in human services agencies to gain efficiencies, reduce cost, and improve outcomes for children and families. We will present the business problem, how it was solved, and the results of those efforts.

In the world of child welfare, where time and cost have major implications, data analytics is the only way to make informed and insightful decisions that can improve outcomes and enhance business operations. This presentation will strip away the complexities and fears associated with building and operationalizing data analytics, and approach it from an achievable and realistic perspective.