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This presentation by Eric Hansen, Finance Data Governance Manager at DAMA COC, provides an overview of data governance and master data management at Nationwide, a Fortune 500 company. It covers the need for data governance within finance, the mission and roles of the data governance function, the vision for finance data governance, guiding principles, the overall governance structure, the Finance Governance Board's responsibilities, and the role of data stewards.
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Data Governance and MDMThe Nationwide Experience Presented by Eric Hansen – Finance Data Governance Manager DAMA COC Quarterly Meeting – Dublin, OH April 2009
Topics • Finance Data Governance • Finance FOCUS project & Financial MDM Solution
Nationwide Overview Some basic facts… World Headquarters Two core businesses: Ranked #108 on the Fortune 500 ~ 36,000 employees More than 16 million policies in force • Property & Casualty Insurance • 6th largest auto insurer • 4th largest homeowners insurer • 6th largest total property and casualty insurer • Life & Retirement Savings • #1 provider of defined contribution plans • 7th largest provider of variable life insurance • 15th largest US life insurer based upon assets
Need for Data Governance within Finance • Ensure that metrics & key performance indicators (KPIs) are consistently sourced and reported across the Enterprise • Ensure consistent accounting and business rules applied from transactional systems • Ensure that data quality does not suffer due to inconsistent standards as well as divergent views of the same data • Enforce Data policies and standards • Change Management in complex systems environment • Ensure impacts of changes are understood in advance • Eliminate Business units reporting and processing financial transactions in silos • Centralize Master Data Management Function
Data Governance Function Mission Statement “The quality and integrity of the financial data must meet regulatory reporting requirements and support accurate decision making for Nationwide’s strategic direction.” In summary, Data Governance is responsible for the quality and integrity of Nationwide’s financial data • Provide a mechanism to maintain and control key reference and metadata used in financial processes, reports, and analysis Key Governance Roles • Gatekeeper • Communicator • Advocate • Analyst • Archivist
Finance Data Governance Vision Our processes, controls, and MDM Strategy will enable… • Financial Reporting & Analysis • Enterprise Financial & Risk Management Tools • Better decision-making across the enterprise • Expedite monthly close and consolidations • Measures designed to provide accountability and promote action • Standard processes • Standardized Master Data
Data Governance – Guiding Principles • Consistency & Standardization • Visibility • Control • Efficiency • Collaboration/Participation • Transparency • Accountability • Authority • Strategic Vision
IT/ Architecture Data Governance Finance Governance Board Reporting PMO What is the Finance Governance Board (Judicial Branch)?
Board’s Main Responsibilities & Participants The Board will: • Establish strategies for Financial Systems • Review and resolve cross business unit disputes and issues • Serve as Judicial Board for Finance’s data policies • Review major initiatives impacting the Financial Systems • Define and align performance measures of the Financial Systems Participants include: • Controllership • Business Unit Finance • Finance Information Technology & Architecture • Enterprise Performance Management • Legal Entity Reporting • Project Management Office • Accounting Policy • Actuarial
Finance Data Governance Business Rules/ Interfaces Reporting & Reference Data Demand Management/ Change Control Metadata & Policy Finance Data Governance (Executive Branch)
IT/ Architecture Data Governance Finance Governance Board Reporting PMO Data Stewardship (Legislative Branch) Primary Data Steward Primary Data Steward Primary Data Steward Primary Data Steward Primary Data Steward Primary Data Steward Primary Data Steward Primary Data Steward
Data Stewards Role and Responsibilities • Liaison between their business area and Finance Data Governance • Inform Data Governance of projects that may affect: • Data Quality • Data consumption or sourcing • Assistance in identifying and resolving data issues • Provide input for metadata, policies, definitions, and processes • Request changes • Identify and understand downstream impacts of upcoming changes • Perform User Acceptance Testing (UAT) • Use data according to the standards and policies established • Advocate Finance Data Governance
Online article – Governance Literally The Jan/Feb edition of Information Management (formerly DM Review) cover story describes our Governance model from the perspective of industry experts. Nationwide Article Online www.information-Management.com/ issues/2007_55/10014860-1.html
Pre-FOCUS State Between the various business, FOCUS looked to consolidate: • 14 General Ledgers • 20 Charts of Accounts • 12 Reporting Tools • 17 Financial Data Repositories • 300,000 Excel Spreadsheets • 75% of Finance Resources Dedicated to Transactional Activities • 25% of Finance Resources Dedicated to Analysis and Insight
Pre-FOCUS State Financial Master Data • Ownership Claimed by Multiple Parties • Disparate Definitions • No Formalized Change Control • No Formalized Distribution • Disconnects lead to frequent Errors and Reruns
What was The FOCUS Journey? • World Class Platform • Standard, Widely Implemented Processes • Common, Comparable Financial Information • Talent Transformation • Enterprise Risk Management • Reinvented Organization
The Pillars of Finance FOCUS • People • Finance Shared Service Center • Processes • Data Governance • Technology • Common Use Applications • Information • Single Version of the Truth
FOCUS Vision While all of these goals were not accomplished within a 24 month period, significant process was made in building a solid foundation. FOCUS will build… ..to help finance accomplish… • Enterprise GL • E-procurement • Financial Data Warehouse • Improved interface data for finance • Redesigned financial processes • Enterprise financial tools • Standardized reference data for financial reporting • Financial Reporting • GAAP • Statutory • Performance Measurement • Expedite monthly close and consolidations • Improved financial analysis • Consistent, reliable information for FOCUS stakeholders • Measures designed to provide accountability and promote action
FOCUS Vision FOCUS address the significant improvements needed in the eight core process disciplines of finance to achieve the future vision. World Class • Business Planning • Capital Optimization • Risk Management • Analysis & Interpretation • Accounting & Reporting • Finance Organization Management • Stakeholder Management • Policy Management Reliable & Consistent Efficiently Managed
Enterprise Finance MDM Challenges • People • Business Owner Buy-In • Technology Owner Buy-In • Process • Lack of Institutional Experience • Lack of Clear Industry Definition and Direction • Technology • Vendor Support • Component Integration (Best of Breed Architecture) • Information • Aggregating and Cleansing
Master Data Content How do we define Master Data? What is our Scope? Key Components • Dimensions • Financial and Non-Financial • Hierarchies • Standard Hierarchies • Alternate Hierarchies • Business Rules
business rules hierarchies How we assign dimensional members to inbound data How we organize the data for processing and reporting attributes Additional information about a member that is used to further describe or classify the member Data Governance members How we ‘tag’ data at the lowest level of a dimension Financial Reporting
Master Data Management FunctionStrategic Goals & Business Rqmts Content • One “Book of Record” Maintenance • Single Point of Entry Control • Cross-functional impacts must be understood, agreed upon, & tested • Enforce Required Business Rules Synchronization and Propagation • Timely, automated, & simultaneous delivery of Master Data to downstream systems Flexibility Reporting
Architecture Guiding Principles • Master Data is an Asset • Master Data is Shared • Master Data is Accessible • Master Data Trustee • Common Reference Data Definitions • Common Use Application
Solution Design • How did we realize our vision? • What design met all of our Business Requirements? • How did we align the IT Solution with Business’ Strategic Goals?
Conceptual View of the Finance Systems Architecture Scope of Finance Data Governance
Financial System Tools Informatica Essbase PeopleSoft Analyzer Teradata Websphere Custom PeopleSoft PeopleSoft Portal Maestro Hyperion Harvest Kalido
Master Data Key Metrics Metrics are used to understand our environment, provide visibility to Management, and drive change (where necessary). We work in a Continuous Improvement environment. • Volumes of Master Data Changes per Month • by Dimension • by Business Units • by Type (Standard requests vs. Emergency) • Count of Change Requests received categorized by: • # Approved • # Rejected • # Approved, but implementation delayed due to downstream impacts or IT support required
Master Data Statistics at Nationwide • 84 Dimension Tables • 26 Standard Hierarchies • 50 Alternate Hierarchies • 7-10 attributes per dimension on average • Over 1,100 Reference Data Tables across Financial Systems • 238 unique interfaces • Average of 58 change requests per month • # Accounting Transactions – 1.7 Billion • # Standard Reports - 334
MDM Roles at Nationwide • Producers • Consumers • Master Data Process Owners • Repository Administrator • Change Requestors • Master Data Owners
Key MDM Processes • Operational Processes • Change Management • Enhancements
FOCUS Project – Key Outcomes • The new set of Financial Systems provides Nationwide with an enterprise platform to process and report monthly financials. • Consolidated monthly results available by business day 8 • Enabling a enterprise approach to identifying, assessing, and managing Nationwide’s significant risks • Consistent, timely information to enable better decision-making across the enterprise • Standard processes implemented which has led to greater efficiency and greater data quality
Lessons Learned • Align strong executive leadership to the project • Look end to end • Centralized business rules engine up front • Centralized MDM repository • Establish Data Governance • Strong metadata and policies • Build data stewardship that encourages ownership of data
Future Vision for MDM at Nationwide • Self-Service Maintenance • Workflow • Integration • Improved Reporting • Architecture Evolution • Increasing Pre-publication Data Quality