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15 Lessons Learned on how to Leverage Analytics in Your Agency

We’re still cranking away on live blogging the Next Generation of Government Training Summit. Take a look at all our great coverage here. Below is a brief overview of the Analytics to Decisions session.

Analytics is changing the way we work and think about our organizations. Analytics is the future for improved decision-making, helping organizations find new value from data and transform the way agencies meet mission need. That’s why I was excited to attend the “Analytics and Decision Making” session at the Next Generation of Government Training summit.

The panel abstract states: Government agencies are using the power of analytics to understand government performance as well as analyze key trends, catch fraud, and drive better citizen engagement. In this session, you will learn tips on using data to effectively do your job better. Learn key analytical strategies that will help you become an analytical star within your agency or organization. Speakers:

  • Christine Heflin, Director of Performance Excellence, Office of the Deputy Secretary, Department of Commerce
  • Carter Hewgley, FEMAStat Director, Federal Emergency Management Agency, Department of Homeland Security
  • Jeff Press, Performance Manager, Performance Improvement Council

The panel included some best practices of analytics use in government – and be sure to check out the GovLoop guide we launched earlier this year, lots of great resources on analytics. Below is also a case study, this session was great and tried to capture as much as I could. So brief write up is below:

  1. Integration of information – can’t work in siloes, share information and data across the department
  2. Have a clear vision of analytics applications
  3. Focus on governance for data use
  4. Make insights actionable
  5. Deliver something real to decision makers – make tangible impact, money
  6. Use data to make informed decisions
  7. Build credibility through solid analysis
  8. Create a culture of analytics
  9. Listen to leadership expectations
  10. Deliver an analytical product that truly aids decision making
  11. Verify through field testing and validation
  12. Going smart – think about clear value, mission need
  13. Focus on the customer – what you need to do to get things done, produce new value for consumer
  14. Translate executive expectations into actionable actions
  15. Trust the experts to deliver and support them with resources

Carter provided an outstanding example of using analytics at FEMA during the tragic tornado in Moore Oklahoma. On May 20, an EF5 tornado struck the south suburbs of Oklahoma City; Moore was the most heavily impacted area. Carter shared some before and after images collected by FEMA, and the results were unbelievable. He continued to explain how FEMA responded and helped the victims of the tragedy.

He also continued to explain the process FEMA used after a disaster strike. Previously, much was using paper based documents and manual entry to respond. In Moore Oklahoma, using modern technology, FEMA implemented a more survivor- centric delivery model, called Disaster Survivor Assistance Teams. Each team was given a mobile device, and could track in real time where FEMA staff was to collect data in the area. FEMA analysts rolled DSAT data in a disaster wide dashboard for leadership to view progress and make adjustments in real time; in one event FEMA matched executive expectations to field capabilities – and staff quickly delivered analytical capabilities that will provide benefits on a national scale. The applications of analytics was unbelievable, and a great case study of how to fully leveraging analytics to fit mission need.

A great quote from this session was, “At FEMA we say, go big, go fast, go smart, but that term is applicable to all of you in whatever role you are playing. You need to be adding the analysis of evidence into the decision making mix,” said Carter.

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Keith Collins

Just got to this page by reference of a federal training program. Immediately a pop window wanting personal information. Not likely

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