Business Friendly Data Mining


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  • The power of predictive analytics is their ability to turn uncertainty about the future into usable probability
  • Goals and measures of success are clear
  • Business, IT and Analytics team can collaborate effectively
  • [twitter]#decisionmgt systems link analytic systems to operational systems[/twitter]
  • AgileChanging CircumstancesComplianceProcess ImprovementAnalyticManaging RiskReducing FraudTargeting and RetainingFocusing ResourcesAdaptiveFinding New ApproachesTesting and LearningManaging Trade-offs
  • Business Friendly Data Mining

    1. 1. Business Friendly Data MiningJames Taylor CEO
    2. 2. Your presenter – James Taylor CEO of Decision Management Solutions Works with clients to improve their business by applying analytic technology to automate & improve decisions Spent the last 9 years championing Decision Management and developing Decision Management Systems ©2012 Decision Management Solutions 1
    3. 3. Data Mining and Predictive Analytics WORK ©2012 Decision Management Solutions 2
    4. 4. Success Requires A Clear Target ©2012 Decision Management Solutions 3
    5. 5. Success Requires Deployment ? ? Decision ©2012 Decision Management Solutions 4
    6. 6. Success Requires Collaboration Business Decision ©2012 Decision Management Solutions 5
    7. 7. Business-Friendly Data Mining
    8. 8. Decision Management is the proven approach used tomanage decisions, set goals for predictive analytic efforts, apply and deploy predictive analytics effectively ©2012 Decision Management Solutions 7
    9. 9. Step 1Be clear which decisions you are improving ©2012 Decision Management Solutions 8
    10. 10. A Decision A choice or selection Based on facts That ends uncertainty And results in action ©2012 Decision Management Solutions 9
    11. 11. Discover High ROI DecisionsRisk decisions Customer decisions How risky is this deal How profitable might this and what should we do? customer be? Predict risk of a bad Predict response, outcome opportunity, potential Rigorous, regulated Rapidly evolving Fraud decisions How likely is this to be fraudulent? Predict risk of fraud “Black box” ©2012 Decision Management Solutions 10
    12. 12. Use Questions to Define Decisions For each decision Name Description Question and a defined set of allowable answers Any other results to be returned with the answer ©2012 Decision Management Solutions 11
    13. 13. Step 2Understand how those decisions impact the business ©2012 Decision Management Solutions 12
    14. 14. Target Decision-making On KPIs Strategy defines KPIs KPIs measure operations Operational decisions affect KPIs The link between decisions and KPIs is critical ©2012 Decision Management Solutions 13
    15. 15. Define The Business Context Key Performance Business Processes Indicators What processes does this How is success measured business execute? in this business area? Which business processes How to tell good decisions will need decisions made? from bad ones and the value of focusing on a Business Events decision What key business events trigger business behavior? Organizations When will decisions be What is the basic required and in what organization structure context Teams, departments, Existing Systems groups not individuals What are the core systems Who cares about the for this business area? decisions How will you have to deploy decision making? ©2012 Decision Management Solutions 14
    16. 16. Put Analytics In Context Processes Know which business Activities require decisions processes will be improved by your analytics Events Trigger decisions Know when your analytics might be Systems calculated Implement decisions Know how you will have Organizational Units to deploy your analytics Make decisions Own decisions Know who cares about Are impacted by decisions your analytics and who will have to believe them ©2012 Decision Management Solutions 15
    17. 17. Step 3Decompose decisions to understand them ©2012 Decision Management Solutions 16
    18. 18. Decompose the decisioning What is required to make decision? Guidelines, policy documents Human expertise Regulations Existing system logic Data describing the case External reference data Predictive Analytic Models Data Mining Results The results of other decisions ©2012 Decision Management Solutions 17
    19. 19. Case study: InsurerBusiness challenges Solution BenefitsUse analytics to Model and Find the decisionsimprove decompose that could beunderwriting decisions impacted and Map decisions to refocused analyticEmbed analytics in effortclaim processing systems andapplication organizations Constrained analytic effort to ensure successful implementation
    20. 20. Decision Models Are Revealing ©2012 Decision Management Solutions 19
    21. 21. Decision Models Are Revealing 2 ©2012 Decision Management Solutions 20
    22. 22. Step 4Deploy analytical decisions to the systems andprocesses that need them ©2012 Decision Management Solutions 21
    23. 23. Build decision-making components Operational Systems Decision Analytic Systems ©2012 Decision Management Solutions 22
    24. 24. Create Decision Management Systems Agil e Analyti c Adaptiv e ©2012 Decision Management Solutions 23
    25. 25. Questions?
    26. 26. Takeaways
    27. 27. Decision Management Establishes a clear target Enables rapid deployment Creates a collaboration framework Business Decision
    28. 28. Thank You James Taylor, CEOjtaylor@decisionmanagementsolutions.c om