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BUSINESS DESIGN For Big-­‐Data Driven, Social & Mobile Consumer A Decision Management Approach Gagan Saxena (@Gagan_S) October 16, 2012

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Agenda 1. Business coping with Challenges of Big-­‐Data and the Social, Mobile Consumer 2. Not a Technology Issue 3. Business Design – Structure, Processes and Roles 4. “Decision First” Approach 5. Three Steps to Success 2

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Business Squeezed in the Middle Consumer Evoluon Technology Choices 3

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Big Data Challenge • Velocity – More rapid arrival • Volume – Of much more data • Variety – In many more formats – … including unstructured and semi-­‐structured 4

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ACE Enterprises: The Story So Far…. • Established Business in Retail Distribuon • Facing challenges from nimbler Competors • Struggling to keep more demanding Customers • Need to do something. Quickly. • Department Heads go out to develop Responses. 5

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ACE Enterprises Responds to Challenges 1. Marke(ng ramps up Social Media team and ‘buys’ two new Markeng Management tools 2. Sales recommends a Loyalty-­‐Based Discounng 3. Product Development develops Cross-­‐Sell/ Up-­‐Sell packages to maximize Margins 4. Opera(ons develops Rules for more efficient Shipping and for roung Customer Care calls 5. Accoun(ng tries yet another Performance Management Dashboard 6. Technology signs up for more BI, BPM, BRMS and Infrastructure to go with it 6

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ACE Enterprises is Stuck • Too many Projects have been Started • New ‘ad-­‐hoc’ Roles & Procedures have been Created • Time and Money is running out • Daily ops are suffering since Projects have priority • ACE is no bejer off. Things are worse. 7

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A Fresh Look Redesign Consumer Experience And Create a “SMART” System. 8

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What is a ‘Smart’ System? Business Rules Management Analycs Big Data Predicve Modeling Complex Event Processing Natural Language Processing Business Process Management How do we bring these capabilies into the Organizaon? 9

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“Informaon Technology” coined in 1958 (US-­‐BEA). Double every 18 months per Moore’s Law. In 2006, we moved into the Second Half of the Chessboard. “Exponenal Growth has confounded our intuion & expectaons.” 13

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Soluon? “The soluon is organizaonal innovaon: co-­‐invenng new organizaonal structures, processes, and business models that leverage ever-­‐advancing technology and human skills.” Where’s the Manual? 14

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The Classics on Business Process Design. Assume ‘Classic’ (Legacy) Technology. 15

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Management by Objecves (1954) "Objecves are needed in every area where performance and results directly and vitally affect the survival and prosperity of the business." Create Customers! 16

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”Linking Strategy to Operaons” 17

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Designing New Validated Business Models Learning 18

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New Approach to Business Process Design. Decisions, First. 19

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Business is a Series of Decisions Strategic Decisions • Few in number, large impact • Should we acquire this company or exit this market? Tac(cal Decisions • Management and control, moderate impact • Should we re-­‐organize this supply chain, change risk mgt approach? Opera(onal Decisions • Day-­‐to-­‐day decisions that affect one transacon or customer • Best offer for this customer? How risky is this loan? Is this claim fraudulent? 20

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Change in Processing Paradigm Interrupted Processing A B C Process Human Process Human Process Decision Decision Straight Through Processing Automated Decisions A C Manage Rules & B Handle Excepons 21

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Business Rules ...statements of the acons you should take when certain business condions are true. 22

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Business Rule Formats 23

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The Analycs Spectrum Business Intelligence Data Mining Predicve Analycs X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X How do I use data to Who are my best/ How are those learn about my worst customers? customers likely to customers? What has How do I turn my data behave in the future? been happening in my into rules for bejer How do they react to business? decisions? the myriad ways I can “touch” them? Knowledge -­‐ Descrip1on Ac1on -­‐ Prescrip1on 24

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Analycs DRIVE Acon Operational Systems THE CRUCIAL LINK For being Agile & Adaptive Decision Analytic Systems Business relies on Experts to manage this Link CANNOT SCALE! 25

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From Decisions to Data Data Analyc Insight Decisions NOT the other way around! 26

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ACE Enterprises are Ready for the 3 Steps! Discover Build Improve 27

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1. Discover Decisions Hint: Operaonal Decisions are Everywhere 28

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Idenfy & Understand Decisions ✓ Name ✓ Descripon ✓ A queson ✓ A defined set of allowable answers ✓ Key facts like volume, complexity, latency 29

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Decompose Decisions What is required to make Decisions? q Guidelines, Policy Documents q Human Experse q Regulaons q Exisng System Logic q Analyc insight q Data Describing the Case q External Reference Data q Results of other decisions 30

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Analyze Knowledge & Decision Dependencies 31

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2. Build Decision Services Once Decisions have been defined, Build the Technical Components that encapsulate Decisions Straight Through Processing Automated Decisions A C Manage Rules & B Handle Excepons 32

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3. Improve Decisions • Learning from the Results of Decision Execuon – Good Decisions? – Bad Decisions? • Running Experiments – Use experimental configuraon on small parts of the business and compare (A/B Tesng) • Advanced Analycs – Find Pajerns in Data – Extrapolate Data 33

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ACE Enterprises & Decision Management Strategy New Roles & Workflows Aligned with Decision Model Automated Decisions Rules Experts Opmize Technology Rules Raonalized Technology Architecture Automated Run Base & Predicve Processes Analycs 34

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QUESTIONS? 35