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Investigative Analytics Data science for everybody Curt A. Monash, Ph.D.

Investigative Analytics Data science for everybody Curt A. Monash, Ph.D. President, Monash Research Editor, DBMS2 contact @monash.com http://www.monash.com http://www.DBMS2.com. Agenda. Six aspects of analytic technology Investigative analytics Uses Tools Pitfalls.

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Investigative Analytics Data science for everybody Curt A. Monash, Ph.D.

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  1. Investigative Analytics Data science for everybody Curt A. Monash, Ph.D. President, Monash Research Editor, DBMS2 contact @monash.com http://www.monash.com http://www.DBMS2.com

  2. Agenda • Six aspects of analytic technology • Investigative analytics • Uses • Tools • Pitfalls

  3. Six things you can do with analytic technology • Make an immediate decision. • Plan in support of future decisions. • Research, investigate, and analyze in support of future decisions. • Monitor what’s going on, to see when it necessary to decide, plan, or investigate. • Communicate, to help other people and organizations do these same things. • Provide support, in technology or data gathering, for one of the other functions.

  4. Investigative analytics

  5. Investigative analytics defined Seeking patterns in data via techniques such as • Statistics, data mining, machine learning, and/or predictive analytics. • The more research-oriented aspects of business intelligence tools. • Analogous technologies as applied to non-tabular data types such as text or graph. where the patterns are previously unknown. Source: http://www.dbms2.com/2011/03/03/investigative-analytics/

  6. Analytic progression • Trends  • Correlations  • Decisions Source: http://xkcd.com/552/

  7. Core drivers for investigative analytics • The big three • Make a better offer • Make a better product • Diagnose a problem • And more • Trading, inventory, logistics, science …

  8. Make a better product (or service) • Discover what people care (or don’t care) about • Uncover flaws (and their root causes) • Test, test, test

  9. Detect and diagnose problems • Manufacturing (classic) • Manufacturing (modern) • Customer satisfaction • Network operation • Bad actors • Terror • Fraud • Risk

  10. And more • Inventory optimization • Distribution planning • Algorithmic trading • The risk analysis revolution • Science

  11. The prerequisite -- capturing data • Transactions • Loyalty cards • Credit cards • Logs • Sensors • Communications metadata • Communications content Data is the food for analytics

  12. Two aspects of investigative analytics • Monitoring and sifting data • Exciting because it’s Fast, Fast, Fast!!* … • … and has cool visuals • Serious math • Geek supremacy *See also “Big, Big, Big!!!”

  13. Monitoring and sifting data • Cool dashboards • Drilldown and query from those cool dashboards

  14. Serious math • Statistics, which overlaps with … • … machine learning • Graph theory • Monte Carlo simulation • Maybe more?

  15. Investigative analytics concerns • The future may not be like the past • Don’t ignore what you can’t measure • Privacy

  16. Illumination? Or just support?

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