Extreme Convergence: Fusing IT and Business in a Leaner, Global, Virtualized World

Transcription

1 Extreme Convergence: Fusing IT and Business in a Leaner, Global, Virtualized World The role of Appliances in The Travelers Data Warehouse Platform Strategy ComputerWorld Premier 100 IT Leaders Conference March 7 th, 2011 The views expressed in this presentation are those of the author and do not necessarily reflect the views of The Travelers Companies, Inc. or any of its subsidiaries. This presentation is for general informational purposes only.

2 Agenda - About The Travelers - Extreme Convergence: Enterprise Business Intelligence & Analytics - Business Intelligence & Analytics Roadmap: Fusing IT and Business - BI&A Capabilities - Example: Data Warehouse Appliance Technology - Implications of Appliances on Data Warehouse Platform Strategy - Summary

3 TRAVELERS AT A GLANCE Travelers offers a wide variety of insurance and surety products, as well as risk management services, to numerous types of businesses, organizations and individuals. Our products are distributed primarily through U.S. independent insurance agents and brokers.

4 Travelers Company Overview Second-largest writer of commercial U.S. property casualty insurance Second-largest writer of U.S. personal insurance through independent agents Total assets of approximately $105 billion, shareholders equity of $27 billion and total revenue of $25 billion, as of December 31, 2010 Number 98 on the Fortune 500 list of largest U.S. Companies Approximately 32,000 employees Representatives in every U.S. state, Canada, Ireland and the U.K. Represented by approximately 14,000 independent agencies and brokerages countrywide

5 Enterprise Business Intelligence & Analytics The Enterprise Business Intelligence and Analytics business unit was formed in 2008 under a high level premise: There is a valuable business opportunity to view business intelligence more holistically across the enterprise. The business unit is comprised of business and technology resources, getting direction from: A Governance Group (LOB Business Intelligence leaders) A Sponsor Board (LOB Presidents, CIO, senior Exec s) Loose Mission Statement : Develop an enterprise perspective on Business Intelligence, and create value and leverage through the more efficient use and sharing of technology, data, and analytics.

6 Maturity Model for Business Intelligence & Analytics Proactive Measure Understand Anticipate Optimize Execute OPERATIONALIZING HOW to MAKE it Happen? INFLUENCING WHAT do we WANT to Happen? ANALYZING WHY did it happen? PREDICTING WHAT WILL happen? Scenario Building, Data freshness Disintermediation and optimization of routine decisions Reactive REPORTING WHAT happened? Batch Reports Ad Hoc, BI Tools Predictive Modeling Intuitive Evidence-Based

7 Observations: Each BU Has Similar Business Intelligence Framework Business Intelligence Embedded in Business Processes Claim Underwriting Product Management Marketing Financial Management Sales Implementation will vary by business needs and processes Analytic Capabilities Optimizing Products/Resources Decision Support Information Optimizing Sales/Profit Counting, Measuring and Monitoring Specific analysis unique to business technology consistency is less important Focus on speed & flexibility over standardization Foundational Investments Sandboxes (test and learn) Data Warehouse/Marts Data Platforms, Infrastructure, & (Data Oriented) Tools Data Management Disciplines Near Real-time Databases Balancing immediate business needs with longer term vision Foundational Investments should be consistent across enterprise

8 Guiding Principles X Business Unit Implications Governance Process Highly Optional ---- Insignificant x business unit implications Business Unit / IT Decision Making Process Benefit from Sharing Best Practices Short term x business unit implications Strong Bias Towards Consistency Long term x business unit implications Governance Group To Determine Process Sponsor Board Approval for Material Deviations

9 Enterprise Business Intelligence & Analytics: Vision & Strategy To maximize shareholder value by integrating analytic insights into our business decisions enhancing our competitive advantage. Evolve BI & A along the Maturity Model to deliver value to the front lines in support of our strategic and tactical business objectives.

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11 Business Intelligence and Analytics Enablers Foundational Enablers Analysis People Practices Data Management Technology Definition and Value Statement Effectively utilize internal/external information for better business insights, decisions and processes throughout the organization. Ensure Travelers has the best analytic talent to compete in the P&C industry through: (1) sourcing and recruiting focus; (2) talent development; (3) knowledge management; (4) community building. Establish best practices which create, enhance, and manage analytics and insights to continually improve our business execution. Establish and adopt a common data management strategy to create high quality information. Create a scalable and highly available business intelligence technology infrastructure and continually evaluate emerging technologies to drive our industry leading analytics.

12 Capabilities-Based Frameworks: Capabilities Required in the Technology Enabler Data Management Infrastructure Data Warehouse/ Mart Platform Infrastructure Analytics Infrastructure Information Delivery Infrastructure Technology that assists in the preparation, cleansing, and integration of data to be used by the analytics community The technology used to store information for use by the business intelligence and analytics communities The technology used to assist organizations in developing insight into a broad class of business problems The technologies required to deliver insight to the various decision makers across the enterprise Infrastructure: Capabilities; Stability/Availability; Price Performance Processes: Simplify IT process to support time to market of BI solutions Connections To: People, Practices, Data Dependencies On: Analytics

13 Capabilities-Based Technology Framework: Technologies Required to Support Enterprise Analytic Strategies Data Management Technology Data Warehouse/ Mart Platforms Analytics Infrastructure Information Delivery Technology Data Quality Data Profiling Data Integration & Cleansing Name & Address Standardization Individualization Householding Metadata Integration Geocoding Data Governance/Stewa rdship Master Data Mgmt DW Platform Data Freshness Geospatial Support In-DB Scoring Unstructured Data Time-Series Support Semantic Layers Real-time Analytics support Data Mining Predictive Modeling Text Mining Model life-cycle management Embedded Analytics architecture Web Analytics Social network Analysis Production Reporting Interactive Reporting Bus Query & Reporting OLAP Dashboards Scorecards Performance Mgmt Adv d Visualization Mobile BI Processes: Quick Provisioning of Analytic infrastructure; Analytic Workstations; Evaluate Emerging Technologies

14 Each Capability has a sample Baseball Card : e.g., Data Warehouse/Marts: DW Platform DESCRIPTION The ability to store, retrieve, filter, join, and aggregate extremely large amounts of information through a structured query language Business Interest Timeframe Q2 Q3 Q4 Q1 Q2 Q3 Q4 Bond Business Insurance Claim Corporate Personal Insurance BUSINESS UNIT A. Bond B. Business Insurance C. Claim D. Personal Insurance E. EBIA Initiatives: Business Intelligence & Analytics DW Build Various on-going DW initiatives Continue to add subject areas Add funding for underlying data support, New subject areas Rationalize DW Appliances Infrastructure Application Investment N/A N/A N/A Strategy/Imperative Supported Bus strategy 1 Bus strategy 2 Bus strategy 3 ASSUMPTIONS Performance and price-performance are important considerations Some workloads will take priority over others; technology should support prioritization of workloads Over time, business will need near-real-time business intelligence H/A and DR will become critical as analytic insights are integrated into business processes EXAMPLE TECHNOLOGIES Teradata Netezza Oracle Exadata EMC Greenplum Aster Data (since acquired by Teradata) VISION / NEEDS The Enterprise has a scalable, highly available, cost-effective technology that is used as a single-version of the truth for the information needs of all constituents of business intelligence and analytics. ISSUES / RISKS Marketplace is in flux with small vendors and interesting solutions coming to market in disruptive ways. Need to rationalize appliance vendor solutions relative to our DW/DM Platform Strategy

15 Data Warehouse Appliance Evaluation Objective Definition Data Warehouse Appliance: Hardware and software designed for storing and accessing large volumes of business intelligence data, that is easy to install and use. Desire to reduce platform expense Common Themes Much larger data volumes Much larger number of users More sophisticated workloads Better data freshness Focus Area Evaluate Data Warehouse Appliances and determine their role in the Business Intelligence technology strategy Need to prioritize workloads 15

16 Benchmark Methodology - Execution Create a benchmark that is representative of real-life workloads and expectations of growth Use real data, real queries Copy of a sizeable subset of an existing data warehouse database 100 s of representative, actual ad-hoc queries (ranging from simple to complex) Significant number of intensive batch jobs (derived data) 1,000 s of operational business intelligence queries (insert/update/select) Workload chosen to be: Representative of real requirements Challenging Test scenarios were based on multiple LOBs Data volumes, concurrency, data freshness Data scalability should cover all potential BI spaces From single data mart up to consolidated Enterprise Data Warehouse

17 Business Intelligence Maturity Model Proactive Measure Understand Anticipate Optimize Execute OPERATIONALIZING HOW to MAKE it Happen? INFLUENCING WHAT do we WANT to Happen? ANALYZING WHY did it happen? PREDICTING WHAT WILL happen? Scenario Building, Data freshness Disintermediation and optimization of routine decisions Reactive REPORTING WHAT happened? Batch Reports Ad Hoc, BI Tools Predictive Modeling Data Warehouse technology is foundational to various levels of maturity Intuitive Evidence-Based

18 Market Dynamics: DW Appliance vs. Enterprise Class DW devices Proactive Reactive Measure Understand Anticipate Optimize Execute DW Appliances -Scale Data -Scale Workload -Scale Users REPORTING WHAT happened? Batch Reports ANALYZING WHY did it happen? Ad Hoc, BI Tools PREDICTING WHAT WILL happen? Predictive Modeling INFLUENCING WHAT do we WANT to Happen? Scenario Building, Data freshness OPERATIONALIZING HOW to MAKE it Happen? Disintermediation and optimization of routine decisions Enterprise Class DW Devices $$$ -Scale Data -Scale Workload -Scale Users -Manage Workloads -Support Operational BI Intuitive Evidence-Based

19 Traditional Data Warehouse Silos Business Unit 1 Business Unit 2 Business Unit 3 Production Business Unit 1 Production Business Unit 2 Production Business Unit 3 Model Office Business Unit 1 Model Office Business Unit 2 Model Office Business Unit 3 Test Business Unit 1 Test Business Unit 2 Test Business Unit 3 Development Development Development Sandbox Emergency Fix No Disaster Recovery

20 Revised Environments DW Approach w/ Silos Shared Appliance Approach Significant expense savings over 3 years Fewer environments to manage and support Performance benefits in lower environments 1 1 Due to inconsistent peak utilization by LOB in lower environments

21 In Summary Conference Theme: Extreme Convergence: Fusing IT and Business in a Leaner, Global, Virtualized World Enterprise Business Intelligence and Analytics: focusing at the intersection of business strategy and its key technology enablers Data Warehouse Appliances: A key enabling technology in support of our business strategy A platform that provides significant cost saving opportunities in an expense conscious business environment