The concept of big data is a major trend in the IT world, and public-sector IT is no exception. In fact, data exists at the core of many emerging concepts in IT, including cloud computing and social media, according to Bill Briggs, chief technology officer of Deloitte Consulting. On Tuesday, Aug. 20, Briggs spoke on big data during GTC West – a two-day executive event hosted by e.Republic Inc., Government Technology’s parent company.
But with exploding quantities of data reaching new levels – terabytes and petabytes – predictive analytics is key for helping government agencies structure the way they do business. Briggs noted that in Pennsylvania, predictive analytics is used to help determine the likelihood of a noncustodial parent paying child support.
Through the Pennsylvania Child Support Enforcement System, the state can analyze a series of nearly 40 variables and identify if a parent is likely to become noncompliant in making child support payments on time. The system allows staff to identify appropriate preventative actions, conduct proactive intervention and improve intervention efficiency.
The system deployment helped the Pennsylvania’s Bureau of Child Enforcement increase collections to nearly twice their total before implementing predictive analytics. In addition, they were able to improve operational and process efficiency and improve outreach and defendant relationships.
Briggs said other agencies can duplicate the success of Pennsylvania’s predictive analytics program, achieving similar wins for their organizations.
Before addressing a big data project, Briggs said it’s important to recognize the need for specialized talent – namely, data scientists. Briggs offered a few suggestions for successfully taking on a big data/analytics project.
1. Narrow Focus
Analytics, according to Briggs, can be a broad topic, so government agencies shouldn’t set out to tackle big data just to tackle it. He recommends finding a specific area in the enterprise that could by improved by the integration of big data and analytics. For example, agencies can utilize big data to help with fraud detection.
2. Set Expectations
When planning a project using big data, Briggs also recommends setting clear expectations. It’s important to “anchor” and establish what success means, he said.
3. Stay on Target
Briggs said when deciding within your organization how to implement an analytics and big data project, it’s important to stay on target. You may have an idea of what the success of the project will look like, so stay focused on that specific idea, rather than keeping goals open-ended.