From BI to Predictive Analytics

2,249 views

Published on

In an era of Big Data organizations are looking to use analytic insight to improve
their business. Rapidly changing competitive landscapes and the need to evaluate and
adopt new business models is pushing organizations to become more adaptive. How
can these imperatives be reflected in the way we build systems? In response to these imperatives, organizations are increasingly buying or building a new class of systems - Decision Management Systems. Decision Management Systems leverage the growing power of predictive analytics to create agile, analytic and adaptive processes and systems.

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
2,249
On SlideShare
0
From Embeds
0
Number of Embeds
130
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • To ensure that Decision Management Systems are analytic and adaptive you must embed the results of data mining and predictive analytics in them. In this webinar you will learn what can be discovered using data mining and predictive analytic techniques and how this can be applied to the decision-making embedded in Decision Management Systems. The role of analytics in predicting risk, fraud and opportunity and the importance of continuous improvement and learning will also be covered.
  • Find the decisions that matter to your business and understand themDon’t start with your data, start with the decisions you need to improve
  • This future focus for decisions contrasts with what we typically do with BIBI has historically focused on the past – like this chart of the last 9 days of salesThis works, for people, because they can see patterns and extrapolateYou, for instance, would have no difficulty in estimating day 10 as being around 9Enter Predictive Analytics
  • The interest and excitement around predictive analytics is sometimes described in terms of a move away from looking in the rear view mirror to looking forwardAnd doing so with software not with human intelligenceIf humans can extrapolate from the past, why is this necessary?As the road ahead starts to differ from the road behind, as we must decide more quickly or in real-time, and as we need systems to do more of the deciding
  • Data Mining and Predictive Analytics are increasingly importantSome companies are beginning to use data mining and predictive analytics as key elements of their strategy and more will do so over time.There are two main reasons data mining and predictive analytics have become more important recently:First there is the increase in data that most organizations have amassed and the growth in third-party data providers. Secondly processing power has increased and data storage costs have dropped, making this type of technology affordable to more businesses. Add to these trends the large and growing body of mathematical knowledge around the techniques and the stage is set for a massive expansion as witnessed the emergence of some mainstream books on the topic such as Competing on Analytics, Super Crunchers and Numerati. These books, and some more technical references, are listed in the bibliography under data mining.
  • So how do you get from BI to PA?If BI and analytics are about improving decisions then Predictive Analytics must improve how we make decisionsBut what kind of decisions can they help with? And how do we adopt and use predictive analytics? What are the steps we must take
  • Discover and Model DecisionsDesign and Implement Decision ServicesMonitor and Improve Decisions
  • Focus on the day to day decisions that drive operational success.Operational decisions are everywhereThey implement strategyThey affect customersThey are the focal point for risk and opportunityThey multiply for large scale impact
  • What parts will the engineer need to repair this problem?What offer should we make when this person uses the ATM?Is this credit card fraudulent?
  • Strategy mattersBut it must be made real and executed onNot enough to simply say “we will improve customer retention” – to define a strategic intent and measuresMust figure out the tactical approaches to decision-making that will be required and make day to day decisions that will make the strategy happen
  • Risk is not acquired in big lumps but one bad loan, one fraudulent transaction at a time
  • Being customer centric means focusing on each customer and maximizing the value of interactionsMake decisions about a single customer, maximizing the value of that decision for next best action or retention or cross-sell
  • Find the decisions that matter to your business and understand themDon’t start with your data, start with the decisions you need to improve
  • Build independent decision-making componentsManage detailed dataManage the rules of decisions–rules from policies, from dataEmbed predictive analytic modelsBring all three groups together
  • The power of predictive analytics is their ability to turn uncertainty about the future into usable probability
  • [twitter]#decisionmgt systems link analytic systems to operational systems[/twitter]
  • [twitter]#decisionmgt systems are agile, analytics and adaptive[/twitter]AgileChanging CircumstancesComplianceProcess ImprovementAnalyticManaging RiskReducing FraudTargeting and RetainingFocusing ResourcesAdaptiveFinding New ApproachesTesting and LearningManaging Trade-offs
  • Changing ExpectationsReal-Time ResponsivenessGlobal Customers Expect Global ServiceSelf-ServiceThe 24/7 WorldChanging ScaleBig DataEfficiencyTransaction VolumesChanging InteractionsMobile interactionsSocial interactionsDistributed interactionsChanging Organizations
  • Payment methods Bank details Tax numbersWithholding taxBusiness partner roles DUNS numbersVendor returns
  • Risk – credit risk, delivery risk, price risk. Some upside if get right, big downside if get wrongFraud – good fraud decisions really have no effect but bad ones are a loss e.g. credit card fraud or claims fraudOpportunity – not much of a downside but a degree of upside e.g. cross-sell or up-sell
  • Story
  • Story
  • Story
  • Focus on decision-making – the rules, the measures, who makes which decision, how do you tell good ones from bad ones
  • Don’t just predict things, embed those predictions in operational systems
  • Use your data to adapt your response to evolving problems and opportunities.Measure decision performance so you can improve it Good decision making approaches and good outcomes are distinctUse performance management to monitor finance operations and decision makingDecisions change continuously so Decision Management Systems adaptExperimentation helps Decision Management Systems stay effective and become more effective
  • [twitter]#decisionmgt systems test and learn, improving over time[/twitter]Measure decision performance so you can improve it Good decision making approaches and good outcomes are distinctUse performance management to monitor finance operations and decision making
  • Decisions change continuously so Decision Management Systems adaptExperimentation helps Decision Management Systems stay effective and become more effective
  • [twitter]3 steps to #decisionmgt systems – discover, build, improve[/twitter]
  • In a predictive enterprise, analytics are applied systematically to improve operational decisions… (slide 8)Predictive analytics can then take full advantage of all data and know-how and apply intelligence at every transaction.
  • [twitter]Buy the book to learn more about #decisionmgt systems http://bit.ly/n4p25H [/twitter]
  • From BI to Predictive Analytics

    1. 1. From Business Intelligence to Predictive AnalyticsJames 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. The Value of Business Intelligence Improving the quality of decisions ©2012 Decision Management Solutions 2
    4. 4. Patterns Inform Decision-making10 5 0 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10 ©2012 Decision Management Solutions 3
    5. 5. Time To Look Forward ©2012 Decision Management Solutions 4
    6. 6. The Growing Power of Analytics Processing power has increased and data storage costs have droppedThe increased data thatorganizations have amassed Large and growing body of mathematical knowledge ©2012 Decision Management Solutions 5
    7. 7. From BI to Predictive Analytics ? © Decision Management Solutions, 2011 6
    8. 8. 3 Steps to Decision Management Discover Build Improve ©2012 Decision Management Solutions 7
    9. 9. Discover Decisions
    10. 10. Different Kinds of Decisions Strategic Decisions • Few in number, large impact • Should we acquire this company or exit this market? Tactical Decisions • Management and control, moderate impact • Should we re-organize this supply chain, change risk management approach? Operational Decisions • Day-to-day decisions that affect one transaction or customer • Best offer for this customer? Which supplier? How to handle this claim? ©2012 Decision Management Solutions 9
    11. 11. Operational Decisions Are Everywhere ©2012 Decision Management Solutions 10
    12. 12. Decisions implement strategy © Decision Management Solutions, 2012 11
    13. 13. Decisions are the focal point for risk Risk is not acquired in “big lumps” but one transaction at a time © Decision Management Solutions, 2012 12
    14. 14. Decisions maximize customer value © Decision Management Solutions, 2012 13
    15. 15. Decisions scale for large impact Strategic Decision Tactical Decision Operational Decision © Decision Management Solutions, 2012 14
    16. 16. Case study: Cable TVBusiness challenges Solution Benefits1.2M households Predictive analytics to 13-18% cross-sell hitMany single-product predict churn, cross- rate on averagehouseholds sell Up to 40% cross-sellWhole industry suffers Business rules use success rate for somefrom low loyalty and analytics and data to drive dynamic scripts Teams using the20%+ customer churn scripts have moreIncreasing Embedded in call salescompetition and center application to improve decision Reduced churn by 20-changing regulations 30% making ©2012 Decision Management Solutions 15
    17. 17. Advice: Begin with the Decision in mind Discover Build Improve Find the decisions that matter to your business and model them © Decision Management Solutions, 2012 16
    18. 18. Build DecisionManagement Systems
    19. 19. Decision ManagementSystems deploy and apply predictive analytics ©2012 Decision Management Solutions 18
    20. 20. Analytics Must Drive ActionOperational Systems Decision Management Systems link Decision analytics to operational systems Analytic Systems ©2012 Decision Management Solutions 19
    21. 21. Decision Management Systems Agile Analytic Adaptive ©2012 Decision Management Solutions 20
    22. 22. Analytics, Business And IT Business Decision © Decision Management Solutions, 2011 21
    23. 23. ©2012 Decision Management Solutions 22
    24. 24. Manage the rules of decisions Decision © Decision Management Solutions, 2012 23
    25. 25. What Can You Do With Business Rules? Automate ClaimsPersonalize the experience Detect fraud Create loyalty Target Cross-Sells And more… ©2012 Decision Management Solutions 24
    26. 26. Case: Global ManufacturerBusiness challenges Solution BenefitsSupplier onboarding Extract “Validate 50% reduction intime consuming and Supplier” decision supplier onboarding timemanual Automate and manage All local variationsStandard process using business rules supportedacross 175 countries— Genuinely global “Intelligent” self-servicehundreds of local process applicationsexceptions3,000 supplier updates amonth
    27. 27. Three kinds of Predictive Analytics Risk Fraud Opportunity © Decision Management Solutions, 2011 26
    28. 28. Analytics Predict RiskHow risky is thiscustomer’sapplication forservice…And how shouldwe price it? ©2012 Decision Management Solutions 27
    29. 29. Analytics Predict Fraud How likely is this claim to be fraudulent…. and what should we do about it? ©2012 Decision Management Solutions 28
    30. 30. Analytics Predict OpportunityWhat represents the bestopportunity to maximizeloyalty and revenue?And when shouldwe promote it? ©2012 Decision Management Solutions 29
    31. 31. Embed Predictive Analytics ? ? Decision ©2012 Decision Management Solutions 30
    32. 32. Case study: Specialty InsurerBusiness challenges Solution Benefits12,000 claims a Business rules and Loss ratio expensemonth predictive analytics from 14% to 11%Reduce staff by 25% Automatically identify 32% higherin a recession and subrogation subrogation returnslower expenses opportunities $10M/year additionalReduce fraud and Increase Fast Track subrogation returnsimprove subrogation rate from 2% to 22%
    33. 33. Advice: Find decision-making rules Discover Build Improve Analyze and manage the business rules that underpin your operational decisions © Decision Management Solutions, 2012 32
    34. 34. Advice: Industrialize Predictive Analytics Discover Build Improve Decision Become efficient at building and embedding predictive analytic models © Decision Management Solutions, 2012 33
    35. 35. Continuously Improve Decisions
    36. 36. Measure decision performance © Decision Management Solutions, 2012 35
    37. 37. Improve for Increasing ROI ©2012 Decision Management Solutions 36
    38. 38. and for Adaptive Systems ©2012 Decision Management Solutions 37
    39. 39. Experiment To Learn And Adapt ©2012 Decision Management Solutions 38
    40. 40. Case: State dept of taxationBusiness challenges Solution BenefitsPaper tax returns Single central Recovered millions ofincreased costs and taxpayer database dollars from dubiousslowed responses Integrated system tax returnsInformation system Sophisticated real- Increased collectionsilos time predictive of unpaid taxesManual fraud analytics Decreased number ofdetection and return questionable returnsreview Increased customer satisfaction
    41. 41. TAKEAWAYS
    42. 42. To Decision Management Systems © Decision Management Solutions, 2012 41
    43. 43. Successful Predictive AnalyticsPervasive Used in every transaction At the point of contact/delivery In operational decision makingPredictive From reporting to prediction and forecasting Data mining Predictive analytics and scoringActionable Decisions being made, actions being taken Decision Management Systems Decision Support Systems ©2012 Decision Management Solutions 42
    44. 44. Decision Management Systems What if you could make your systems active participants in optimizing your business? What if your systems could act intelligently on their own? Decision Management Systems can do all that and more. This book shows how to integrate operational and analytic technologies to create more agile, analytic, and adaptive systems. Discount Code: TAYLOR4389For more information about this new release, visitwww.decisionmanagementsolutions.com/book © Decision Management Solutions, 2012 43
    45. 45. Questions
    46. 46. Thank You James Taylor, CEOjames@decisionmanagementsolutions.com 45

    ×