Big Data Analytics; The value of the right action. April 1 st, 2014 Edwin Steenvoorden VP Business Analytics & Information Strategy

Transcription

1 Big Data Analytics; The value of the right action April 1 st, 2014 VP Business Analytics & Information Strategy Better intelligence, smarter decisions

2 Introduction Better intelligence, smarter decisions

3 Big Data Value of the right action 3

4 Einstein (on Analytics??) Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted. Everything should be made as simple as possible, but not simpler Big Data Value of the right action 4

5 Peter Hinssen / Jer Thorpe It is no longer about technology but about the application More things are becoming data Big Data Value of the right action 5

6 Better intelligence, smarter decisions

10 The BI Landscape Better intelligence, smarter decisions

11 Big Data Analytics, what is all the fuss about? But the hype is over! Better intelligence, smarter decisions

12 Trough of disillusionment. Better intelligence, smarter decisions 12

13 Big Data Value of the right action

14 Putting information to work is an organizational capability! Big Data Value of the right action 14

15 Smarter Decisions Big Data Value of the right action 15

16 The Bat and the Ball A bat and ball cost $ The bat cost one dollar more than the ball. How much does the ball cost? Daniel Kahneman; Thinking Fast and Slow Big Data Value of the right action 16

17 Data and its place in the technology clusters Big Data Value of the right action 17

18 5 (Big) Data trends Big Data Value of the right action 18

19 Better intelligence.smarter Decisions Digital Channels WCM Predictive Analytics DIGITAL CHANNEL SERVICES RADICAL COST REDUCTION BIM Maturity APS Data Science INFORMATION TO BUSINESS VALUE Socail INFORMATION STRATEGY PAM Bay of value ENTERPRISE INFORMATION FOUNDATION INFORMATION AS A SERVICE In memory Databases Business Value Better intelligence, smarter decisions Mainland of Legacy

20 Product Development & Maintenance SAS CLS BI AUI BI CLS AUI Policy Data Capture Customer Information Mgmt Channels (Exclusive Agents, Independent Agents, Direct, Affinity) Underwriting Rules / Exceptions (Reg & Comp) Policy Editing & Validation Rules Forms Management Rate Development & Maintenance / Rate Algorithm Dev Regulatory Approval Management Actuarial Support Inquiry Customer Information Maintenance CLS CLS MIS Customer Servicing Regulatory Compliance BI Product & Policy Audit Audit Repository Statutory Reporting Marketing & Sales Support BI Distribution Channel Management Marketing & Sales Support CLS Actionable Insight Report Development CLS AUI AUI Underwriting Quote Inspection & Audit Tier Placement Underwriting Rule Execution Case Review CAT Exposure Management CLS Management & Control (Business Intelligence) CAT Exposure Development Mgmt Policy Life-Cycle Management AUI CLS Policy Validation Policy Storage & Retrieval Policy Updates Rating Execution Policy Issuance Document Production Scheduled Activity BI AUI External Business Relationships CLS MIS Report Production Business Integration AUI Bill Issuance & Payment Collection 3 rd Party Interaction Payment Plan Development Payment Collection & Application Bill Calculation and Summarization Commission Calculation and Summarization Bill Format & Production Cash Disbursement CLS BI Claims / Benefits Disbursement Operations AUI AUI BI Business Activity Monitoring Operational Statistics Notification & Intake Fraud Identification & Investigation Policy & Coverage Verification Product & Bus. Rules Management Settlement Loss Cost Management Loss Estimation Recovery End of CY 2007 H H H Initial ING Access infrastructure E2E element monitoring events, requirement thresholds and s defined responses (tools, identified events, thresholds, responses, people) Org training & communications plan defined Create a repository Procedures for for creating monitored Procedures and events, and templates maintaining thresholds for E2E inventory of and requirements monitored threshold gathering events, responses developed thresholds and threshold responses E2E monitoring requirements defined for top 20 apps (tools, events, thresholds, responses, people) Correlation relationship definitions for top 20 apps defined (including Top 5 apps) Execution of E2E plan, build and run processes (people, process & technology) E2E requirements and standards refreshed to align with tool portfolio E2E activities integrated with SDLC Command Center Operational for E2E Run H Processes updated to map to new E2E tool suite E2E design, build and test checkpoints established for all new Wave deployment E2E integrated projects of 20 apps into with future E2E tools & plan state Svc Mgt for future apps in processes 2009 Enterprise correlation engine implemented End User monitoring implemented Domain-level correlation implemented OLA requirements and E2E activities integrated 3 rd party implications with existing Service defined Implement E2E Mgmt processes tool (HW & SW) E2E integrated into Top 5 Apps Deployed on Tower and app team security & risk review E2E (interim state) accountability established for process E2E (interim OLA definitions) E2E tool suite training developed Governance process for managing tool & initiated portfolio implemented E2E controls Decide on and purchase E2E tool portfolio established in PAR new E2E tool suite E2E Manager E2E Support Team rationalized Hired/Appointed Established Interim tool standards defined Financial Accounting Control Infrastructure Performance Management Talent Development Budgeting & Forecasting Realign OOMC controller reporting relationship Define financial and operating reporting requirements to identify data requirements Define OOMC finance competency model Establish 360 review process, policies, and questionnaire. Define joint HRB and OOMC Forecasting & Budgeting Requirements and identify data requirements Input (Artifacts) Offer Letter Trading 407 Letter Accounts Report Holdings Report Process Output (Artifacts) Owner: HR Owner: HR Owner: HR Offer Letter Owner: Compliance Owner: Brokerage /IT STRUCTURE AND STABILIZE OPERATIONALIZE AND STANDARDIZE CONTINOUS IMPROVEMENT Business Complete data map Define & implement data ownership structure Define process control mechanisms and accountabilities Create and source OOMC performance management role Conduct 360 review of OOMC finance employees and complete gap analysis Define talent acquisition/development strategies Design supporting process Implement manual process fixes where feasible Implement process control mechanisms Implement standardized reporting Identify required control & diagnostic metrics for FA processes Assign accountabilities for control & diagnostic metrics for FA processes Define strategic process/product metric model Define career path options & succession planning Create and communicate a targeted development plan for each employee Hyperion Implementation Identify process improvement projects Define baseline and target metrics for FA processes Define strategic process/ product metrics Assign accountabilities for process / product metrics Design individual performance measurement model Assign performance metrics Ongoing continuous improvement Implement individual performance management model within Finance Owner: Compliance Owner: LOB Mgr YES Trading Policy Sign-Off Owner: Compliance 407 Letter Accounts Report Holdings Report Owner: LOB Mgr Holdings Non- Compliance Report Auditable Process /Artifact Location HR Employee File PeopleSoft HR HR Employee File Dataware Compliance Employee File Validate Employee Record in PeopleSoft HR & Employee Folder Owner: HR Owner: Compliance Validate disclosure Reports in HR Employee File and /against Brokerage Holdings data In Dataware Reports Owner: Compliance The Intelligence Enterprise Roadmap Intelligent Enterprise (IE) Roadmaps allow us to ensure that information management capabilities and initiatives are aligned with an organization s strategic business objectives. Needs of the Business Architecture Documentation Stakeholder Interviews Decision Support Reports Process Documentation Inputs Large volume of Error Logs information logs 41 % 130 MB prevent events from being sent to MLIF database Informational Logs in a timely 12 % Warning Logs manner. Impacts 47 % 38 MB troubleshooting 151 MB efficiency. Statistics are based upon provided Application event logs which are sent to MLIF database Level 3 Workflow Processes are Elaborated into Auditable Level 4 Input /Output Controls & Deliverables Policy Sign-Off Current State Assessment Future State Definition Blueprint & Roadmap Outputs Needs Analysis Quick Hits & Tactical Improvements CLS Documerge Service Point WAG Actuarial Workstation Excel ecms-c eagent / WAG Truck and Work Comp Mainfiles AION Field Data iaccess CLS Documerge Std Billing Pred Model Pred Model SQL,SAS Report New Billing System CLS ACS EASYPAY / Std Billing ACE EASYPAY/Std Billing Target Environment Definition CRN/FACT ( no work comp) WCS Requirements Definition Process Development Process Integration Organization & Governance Technology Transformation Roadmap Initiative Priorities & Dependencies 30 Days* 90 Days* 180 Days* 9 Months* One Year* Case Governance Model Implementation Plan Big Data Value of the right action 20

21 5 Critical Deliverables Vision & Principles Big Data Value of the right action 21

22 Observations on Business Analytics Better intelligence, smarter decisions 22

23 Big Data Value of the right action

27 Big Natural Disasters Big Data Value of the right action 27

28 Big Data Deployment : From Satellite to Mobile device Big Data Value of the right action 28

29 Big Data Deployment : 7 examples Big Data Value of the right action 29

30 Case Study: Bank Cyber Crime ($ 45 mio) Source: Cyberrisk in Banking, SAS Whitepaper Big Data Value of the right action 30

31 Case Study: Target CI Analytics Forbes magazine Big Data Value of the right action 31

32 Conclusion Better intelligence, smarter decisions

33 Twain vs. Einstein "It is difficult to make predictions, especially about the future more than the past I am interested in the future, because that is where I plan to live Big Data Value of the right action 33

34 Change.. With a vision in small increments Big Data Value of the right action 34

35 Change.. With a vision in small increments Wave 1 Fixing the Basics Wave 2 Growing Wave 3 Expanding Business Adoption Business Performance Management Define KPI s, Threats, Rules, etc. Data Catalogue 3.0 Data Catalogue Assess 2.0 satisfaction & awareness Create awareness of Data BI & Analytics Catalogue 1.0 at users and Analytical API Big Data stakeholders Governance setup Capabilities Development Demand Platform & implementation BI Maintenance Business Business Management & Support Enablers Relationship setup setup Data Coherence Management BI Delivery Informatio Engage Enterprise setup Data Integration Development Management n Strategy Architecture Privacy Catalogue setup setup Officer Lifecycle Technical Information Management Design Application Architectu Architecture setup setup Authority Architecture setup re setup setup Information Strategy BI Competency Center BI Service Center BI Technology Big Data Value of the right action 35

36 With: Data, Data & Data Big Data Value of the right action 36