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.
26. Case: Global Manufacturer
Business challenges Solution Benefits
Supplier onboarding Extract “Validate 50% reduction in
time consuming and Supplier” decision supplier onboarding time
manual
Automate and manage All local variations
Standard process using business rules supported
across 175 countries—
Genuinely global “Intelligent” self-service
hundreds of local
process applications
exceptions
3,000 supplier updates a
month
32. Case study: Specialty Insurer
Business challenges Solution Benefits
12,000 claims a Business rules and Loss ratio expense
month predictive analytics from 14% to 11%
Reduce staff by 25% Automatically identify 32% higher
in a recession and subrogation subrogation returns
lower expenses opportunities $10M/year additional
Reduce fraud and Increase Fast Track subrogation returns
improve subrogation rate from 2% to 22%
40. Case: State dept of taxation
Business challenges Solution Benefits
Paper tax returns Single central Recovered millions of
increased costs and taxpayer database dollars from dubious
slowed responses Integrated system tax returns
Information system Sophisticated real- Increased collection
silos time predictive of unpaid taxes
Manual fraud analytics Decreased number of
detection and return questionable returns
review Increased customer
satisfaction
46. Thank You
James Taylor, CEO
james@decisionmanagementsolutions.com
45
Hinweis der Redaktion
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
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]