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Acord 2016 Data Governance & Analyitcs - Perfect Together

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Joint presentation made at ACORD 2016 by Sandi Perillo-Simmons, Hartford and Pat Saporito, SAP

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Acord 2016 Data Governance & Analyitcs - Perfect Together

  1. 1. ACORD2016 Data Governance & Analytics: Perfect Together! Pat Saporito, SAP Sandi Perillo-Simmons, The Hartford
  2. 2. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 2Public Data Trends & Growth Analytics & Data Strategy Framework Business Value of Data & Data Governance The Hartford Case Study Key Takeaways Agenda
  3. 3. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 3Public What’s Happening New Business Models, Customer Experience & Value Paradigms Uber The world’s largest taxi company, owns no vehicles. Alibaba The most valuable retailer, has no inventory Airbnb The world’s largest accommodation provider, owns no real estate. LEADERS REQUIRE A VISION TO LEAD IN THE DIGITAL ECONOMY DATA & ANALYTICS ARE THE OIL OF THE DIGITAL ECONOMY! Lemonade Wants to become the world’s first peer-to- peer property and casualty insurance provider Meteo Protect Offers a new type of insurance to meet the needs of those affected by climate change and adverse weather conditions Discovery Uses thousands of analytic models to classify and underwrite risk, adjust claims and help improve member’s health and wellness
  4. 4. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 4Public Living in a Digital Economy The need to provide analytics to everyone Reduce cost and complexity Automate analytics in the age of the algorithm Travel in light years with enterprise speed and scalability HYPERCONNECIVITY IS DRIVING NEW CHANNELS AND CONNECTED ECOSYSTEMS DATA AND ANALYTICS ARE THE OIL FOR THE DIGITAL ECONOMY 90% of the world’s data has been generated.1 72% of insurers are forming new distribution partnerships2 212 Billion “Things” will be connected 3 43% of insurers plan to or have acquired innovators/startups for new innovation capabilities2
  5. 5. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 5Public Digital core Omnichannel network The Internet of Things network Service and supplier network Workforce network Front office Middle office Back office SAP HANA platform Property Life Health Real Time Data & Analytics Digital Framework for Insurers Data & Analytics is the life blood of the Digital Insurer Personalized experience Reduce FTE costs Engaged partners & suppliers Optimized risk management Operational AgilityDigital Orchestration Full analytic transparency Transactional & Analytic Data Platform
  6. 6. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 7Public BI/Analytics Projects Failure Causes Technology is only One Consideration Between 70% to 80% of corporate business intelligence/analytics projects fail (to meet expectations), according to research by analyst firm, Gartner. “Organizations tend to throw technology at BI problems. You could have the right tool, but it could be doomed to failure because of political and cultural issues, an absence of executive support so the message doesn't get out, and poor communication and training.”
  7. 7. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 8Public Data Challenges Challenges • Data is everywhere • Multiple data types • Data integration • Analytic tools • Analytic skills Impact • Data silos • Data credibility • Data access • Lack of data use • GIGO – “Junk” analytics
  8. 8. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 9Public Enterprise Analytics: Strategy to Execution Execution Feedback to Strategy Strategy Link & Align Strategy to ExecutionBusiness Driven Strategically Aligned User Access & Usability Improved Decisioning Analytics Strategy & Roadmap Corporate Strategy New Data / Capabilities Real Time Analytics Operational Reporting Exploration & Visualization Predictive Analytics Ad Hoc Reporting Corporate Data Strategy & Metrics Performance Management Governance, Risk & Compliance Business Strategy Analytics Strategy Data Strategy
  9. 9. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 10Public Building Blocks of a Rock Solid Analytics Strategy SAP Analytics Strategy Framework Objectives Business Needs Business Benefit Technology Organization Background and Purpose Current State and History Analytics Objectives and Scope Summary of Analytics needs Envisioned To-Be State Priorities and Alignment Value Proposition of Analytics Expected Benefits – Future State KPI Business Case Information Categories Architecture and Standards Applications & Tools Governance Structure Program Management Roadmap and Milestones Measurement Education / Training Support Critical Success Factors: • Business driven analytics strategy, supported by a data strategy • Executive sponsorship
  10. 10. © 2014 SAP AG or an SAP affiliate company. All rights reserved. 11 Why is it important to manage your company’s data effectively? Poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits Gartner, Inc., Measuring the Business Value of Data Quality, October 2011
  11. 11. © 2014 SAP AG or an SAP affiliate company. All rights reserved. 12 These are the top business issues where you need better information governance metadata efficiency legal-hold SFA application-integration SEC content-management regulation business-process SOX business-user roadmap big-data qualityaudit HANAworkflow shared-services enterprise-standard traceability compliance CRMcost organizational-change acquisition analyticsmarketing security strategy Dodd-Frank FCC ERP multi-use-technology controls migration risk embedded-UI breach HCM conversion
  12. 12. © 2014 SAP AG or an SAP affiliate company. All rights reserved. 13 What is Information Governance? Information Governance A discipline that includes people, processes, policies, and metrics for the oversight of enterprise information to improve the business value High Value Information Optimized Business Processes Smarter Business Analytics Timely Mergers and Acquisitions Compliance with Laws and Regulations Process People Policies & Standards Metrics
  13. 13. Copyright © 2016 by The Hartford. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford. DATA GOVERNANCE: The Hartford Case Study
  14. 14. Copyright © 2016 by The Hartford. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford. 15 Recognition of Need for Enterprise Data Management Premise: Data is an important strategic asset that must be effectively leveraged to support growth strategies and expense management objectives. The complexity of our data is high due to siloed legacy systems and data repositories. Internal Forces • Business strategies & investments with significant data requirements • “Siloed” legacy systems and data repositories • Cost/time to complete projects within the current environment External Forces • Regulatory reporting requirements • Heightened compliance environment • Data driven companies continue to use data to enter markets and dominate • Customers & producers expect high-quality service and increased value Enterprise Data Management must be a multi-faceted approach to address our data challenges which includes:  Understanding business information needs (strategies, competition, data pain points)  Understanding and improving the deep information (ever-changing) landscape (quality, tools, governance)  Supporting information-centered business strategies Targeted Outcomes:  Common data definitions  Easy access to consistent, high-quality data  Enterprise view of data  Standards and guiding principles  Enterprise planning of data initiatives Benefits Expected:  Increase revenue  Manage Cost and Complexity • Improved portfolio implementation • Eliminate data reconciliation efforts and work-arounds • Reduce operational and defect management costs  Reduce Risk and Support Corporate Compliance
  15. 15. Copyright © 2016 by The Hartford. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford. 16 Data Management Policy Scope: The policy applies to “structured data” only meeting the following criteria: Policy Topic Description of Policy Identification & Definition of New Business Data • New data must be named and defined according to standards Enterprise Critical Data Requirements: • Critical data must be identified, defined, and named • Employees must ensure that data integrity is maintained Data Governance & Compliance • Business Data Standards must be followed • Data Certification will be required Documentation of Data Issues & Problems • Data issues must be documented • Data Stewards will be the primary contact to remediate and escalate documented data issues Data Artifacts • Projects and maintenance efforts must complete the appropriate artifacts as defined by the engagement model and weDeliver methodology Enterprise Metadata Repository • Metadata must be captured and uploaded into the enterprise tool • Metadata must be maintained for high quality Data Type Data Type Description Compliance Timeframe Enterprise Critical Data Data that is integral to The Hartford’s most critical business functions 3 months from identification Domain Critical Data Data that is identified as being critical to a domain’s primary business functions 6 months from identification New Data Data that is being introduced to Hartford systems for the first time At implementation when new data is being introduced to Hartford systems for the first time Existing Data Data that exists in a current Hartford system that will now be stored on a new system or a rewritten version of the existing system. At implementation when existing data is stored on a new system or a rewritten version of the existing system. The policy includes roles, responsibilities and the following policy topics:
  16. 16. DATA GOVERNANCE FRAMEWORK Executive Leadership Team (ELT) Enterprise Data Governance Council (DGC) Third Party Data (3PD) Enterprise Data Stewardship • MANDATE & AUTHORIZATION • CORPORATE OWNERSHIP • FIDUCIARY RESPONSIBILITY • REGULATORY RESPONSIBILITY Chief Data Officer Business Domain Owners • AUTHORITY & ACCOUNTABILITY • DIRECTION SETTING • STANDARDS COMPLIANCE • POLICY SETTING • DATA OWNERSHIP • PROCUREMENT & DELIVERY • RELATIONSHIP MANAGEMENT • STANDARDS COMPLIANCE • POLICY DEVELOPMENT 3PD Review Team 3PD Relationship Managers • OVERSIGHT OF DATA DOMAINS • STRATEGIC LEVEL • STEWARDSHIP INITIATIVES & DECISION MAKING Data Stewardship Council (DSC) Domain Data Stewardship Group (DDSG) Architecture Review Board (ARB) Project Teams • STANDARDS COMPLIANCE • PROJECT ACCOUNTABILITY • PROJECT ACCOUNTABILITY • DATA-CENTRIC PROJECT EXECUTION • DEVELOPMENT & DELIVERY OF CORE ARTIFACTS Collective Membership Project Architects Data Stewards Business SMEs Database Admins App Developers Business Analysts Data Modelers Data Architects Business & Technical Leaders IT Consultation Domain Data Governance Council (DDGC)• DOMAIN DATA ACCOUNTABILITY • DAY TO DAY DIRECTION • OVERSIGHT TO DATA GOVERNANCE FUNCTIONS Cross-Functional Domain Leadership Domain Data Stewards Data Owners Lead Data Stewards EnterpriseDomainProjects Project Governance ENTERPRISE STRATEGIC DATA SERVICES (ESDS) Program Management for: • Data Governance • Data Stewardship • Data Quality • Metadata Advisory Project Scores Advisory Advisory / Chair Support Advisory / Chair Support ----------------- Support for: • Data Management Policy • Enterprise Business Data Standards • Variance Request Process & Repository • Data Management Scorecard • Critical Data Element Lists • Decision-Making Models • Integration with SDLC
  17. 17. Copyright © 2016 by The Hartford. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford. 18 Metrics Strategy Business Factory How Is data impacting results ? How is business leveraging data? How is our Data Factory operating? • Improvements in UW results • Increased customer retention and up-sell • Reduced claim fraud • Sales force effectiveness • Improved producer results • Faster executive reporting • Data Confidence • Business Ready • Data Protection • Data Consumption • Information Usage • Time to deliver • Throughput • Availability • Data Quality • Regulatory & Compliance
  18. 18. Copyright © 2016 by The Hartford. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford. 19 Data Management Operating Model · Data Mapping · Overall Requirements · Consults on all artifacts · Sets and champions broad policies · Enforces execution · Approves exceptions · Publishes score results · Has long-term ownership · Grows steward community · Scores data assets · Reports to governance · Certifies business data · Maintains business metadata · Constructs data assets · Connects to enterprise · Solves system data needs · Follows stewardship guidance · Maintains technical metadata · Designs data movement patterns · Constructs logical model · Profiles and maps data elements · Follows design standards · Maintains technical metadata · Constructs data assets · Ensures system needs are met · Implements physical model · Optimizes performance · Maintains technical metadata
  19. 19. Copyright © 2016 by The Hartford. Confidential. For internal distribution only. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford. What is Metadata? 20 Type Who uses it? Metadata is used to: Business Definitions / Glossary Everyone Build a common language Data Ownership Everyone Know which people make decisions and answer questions Databases and Tables Analysts, IT Find data Data Lineage Analysts, IT Understand movement/transformations and for impact analysis Valid Values Everyone Understand what data should be Data Models Analysts, IT Understand data design Business and Data Quality Rules Analysts, IT Create confidence in data Known Issues Analysts Avoid and/or correct problems Definitions / Glossary Owners ReportsDatabases Tables Data Models Valid Values Data Quality Rules Metadata Repository Data Lineage Metadata is data about data. It summarizes basic information about data, which can make finding and working with particular instances of data easier. Known Issues
  20. 20. Copyright © 2014 by The Hartford. Confidential. For internal distribution only. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford. Current Focus: Enhanced Governance from Source to Use with Improved Business Ownership and Accountability Outcomes Initiatives Data-Driven Culture • Shifted focus from project-driven to data asset-driven • Improved business ownership and accountability Data Strategy / Roadmap • Creation of a data strategy and roadmap / Clear sense of purpose • Determination of key data initiatives that can transform our company • Coordination of capital projects and alignment/compliance to a reference architecture • Alignment of Data Governance Authority • Improved authority to govern • More local governance Resources • The right people with the right skills (including data owners) • Funding Improved Execution • End-to-end application and right-sized from source through use • Faster, improved execution / agility • A scalable governance & stewardship model (Gold, Silver, Bronze) Quality Data and Information • Well-defined data quality framework • Next generation data quality practices: Tools, automation, continuous monitoring for critical data • Resolved data issues Recognized Data Value • Communication to “sell” the value of governance & stewardship • Training and education to improve data literacy Measurable Success • Metrics that show how our data efforts help us achieve our business goals. • Data Quality dashboard 21 The Goal: Manage data as a trusted source of information and accelerate our usage of data and information for competitive advantage.
  21. 21. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 22Public SAP Analytics Maturity Model Levels of performance along the best practice framework Level 1 Level 2 Level 3 Level 4 IT Driven Analytics Governance Requirements driven from a limited executive group KPIs/Analytics are identified, but not well used KPIs/Analytics are identified and effectively used KPIs/Analytics used to manage the full Value Chain Business Driven Analytics Governance Evolving Business Governance with Competency Center Developing Enterprise-wide Analytics Governance with Business Leadership Do not exist or are not uniform Exist and are not uniform Uniform, followed and audited Analytics “Silos” for each Business Some Shared Analytics Applications Consolidating and Upgrading Robust and flexible Analytics architecture Evolving effort to formalize Information and Analytics Governance Standards and Processes Application Architecture Information Chaos Information Oversight Information Democracy Information Empowerment
  22. 22. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 23Public IDMA Workshops www.idma.org IDMA offers the following One-Day Training Workshops for insurance industry professionals:  Claims Data Management (ClaimsDM)  Data and Analytics Strategy (D&AS)  Data Management for Insurance Professionals (DMIP)  Data Governance and Stewardship (DG&S)  Enterprise Data Management (EnterpriseDM)  Insurance Data Quality (IDQ)  Strategic Data Management (StrategicDM)  Tools for Managing Data Effectively (TMDE)
  23. 23. © 2015 SAP SE or an SAP affiliate company. All rights reserved. Thank you Pat Saporito, CPCU, FIDM Sr. Dir., Global COE for Analytics SAP Labs +1 (201) 681-9671 pat.saporito@sap.com Twitter: @PatSaporito Author Applied Insurance Analytics Amazon http://amzn.to/1mfmYiC Sandi Perillo-Simmons, MBA, AIDM, ACE Director & Data Governance Lead Enterprise Data Office The Hartford Financial Services Group 860-547-3461 sandi.perillo-simmons@thehartford.com

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