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How-To Create a Productive Data Analytics Shop

In this presentation, Charles Coe from the U.S. Department of Education outlines the key components to create a successful data analytics shop. Leadership commitment is essential, emphasizing the importance of a clear vision and the necessary resources to avoid common pitfalls. The strategy should involve benchmarking against successful organizations and understanding legal and security requirements. Project management must focus on customer needs, effective identification of risks, and the mitigation of false positives, ensuring timely delivery and stakeholder support.

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How-To Create a Productive Data Analytics Shop

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  1. How-To Create a Productive Data Analytics Shop Charles Coe Assistant Inspector General, Information Technology Audits and Computer Crime Investigations U.S. Department of Education CIGIE / GAO 2012 Financial Statement Audit Conference April 11, 2012

  2. Three Major Components • Vision and Commitment • Strategy • Project Management CIGIE / GAO 2012 Financial Statement Audit Conference - April 11, 2012

  3. Vision and Commitment • Leadership is an essential ingredient • executive champion/sponsor who has an agreed upon vision of the value and direction of the implementation of a data analytic project • DON’T start without it! • Set expectations and secure resources • don’t expect to get a Cadillac on VW budget • no free lunch CIGIE / GAO 2012 Financial Statement Audit Conference - April 11, 2012

  4. Strategy • Benchmark other organizations • who have been successful • do NOT forge your own path unless absolutely necessary! • Take advantage of “Lessons Learned” from benchmark organizations • Research legal & IT security requirements (SORN, CMA, C&A, etc…) • Determine skill sets • needed “over time” • establish effective interview and selection methodology – no, you can’t fudge this one! CIGIE / GAO 2012 Financial Statement Audit Conference - April 11, 2012

  5. Project Management • Identify your customer requirements & needs (Audit & Investigations) • Audit – traditional risk models reflecting how best to allocate limited audit resources that focus on critical operational areas • Investigations – data analytical models looking for known fraudulent patterns within key operational areas that have the highest return on investment of investigation resources CIGIE / GAO 2012 Financial Statement Audit Conference - April 11, 2012

  6. Project Management - continued • Seek program experts to help develop your data analytics projects • False positives are your enemy – these can be fatal! • Deliver on time and don’t miss milestones, otherwise you jeopardize losing your “Champion” supporters • Be prepared for unintended consequences/results of creating risk models! CIGIE / GAO 2012 Financial Statement Audit Conference - April 11, 2012

  7. The End Questions? CIGIE / GAO 2012 Financial Statement Audit Conference - April 11, 2012

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