Enterprise Data Governance

DATA GOVERNANCE Enterprise Data Governance Strategies and Approaches for Implementing a Multi-Domain Data Governance Model Mark Allen Sr. Consultant, Enterprise Data Governance WellPoint, Inc. 1

Introduction: Mark Allen is a senior consultant and enterprise data governance lead at WellPoint, Inc. Prior to WellPoint, Mark was a senior program manager in customer operations groups at both Sun Microsystems and Oracle Corporation. At Sun Microsystems, Mark served as the lead data steward for the customer data domain throughout the planning and implementation of Sun s customer data hub. Mark has more than 20 years of data management and project management experience including extensive planning and deployment experience with customer master initiatives, data governance, data integration projects, and leading data quality management practices. Mark has served on various customer advisory boards focused on sharing and enhancing MDM and data governance practices. Mark is also co-author of the book: Master Data Management in Practice: Achieving True Customer MDM (John Wiley & Sons, 2011) WellPoint, Inc: WellPoint is one of the largest health benefits companies in the United States: Revenue: $60 billion (2011), Net Income: $2.6 billion (2011) Approximately 37,700 Employees Approximately 34 million members in its affiliated health plans Approximately 62 million individuals served through its subsidiaries. 2

Presentation Topics Defining a Multi-Domain Data Governance Program Three Must Haves, Why This Must Be Business Driven. Defining and Prioritizing Your Domains Domain Types, Definition, Prioritization Setting up a Data Governance Program Management Office Guiding Principles, PMO Setup, Cross-Domain Interaction Coordinating Budget, Tools & Resources Across Domains Budget Planning, Managing Tools and Resource Maturing, Measuring, and Communicating Data Governance Defining a Maturity model, Measuring and Communicating. 3

Consider These Recent Findings From the TDWI 2012 Best Practices Report Next Generation Master Data Management 61% of organizations surveyed have already deployed MDM solutions, and more than one-third practice multi-data-domain. Few organizations practice MDM with an enterprise scope. Most start with a few silos and then connect the silos later, suffering much rework in the process. Data governance is a desirable practice, but not when done ad hoc in the middle of an MDM project. Instead, user organizations should seek to simultaneously mature the master data program with a data governance initiative, using an evolutionary program plan that delivers incremental tactical value. - Quote in the report from David Loshin, President, Knowledge Integrity, Inc. 4

Defining a Multi-Domain Program Three must haves for the data governance program: Must have alignment with a company s strategic initiatives and be able to provide tactical value in one or more of these areas: Risk Mitigation, Quality Improvement, Process Improvement, Cost Reduction, Revenue Growth Must be business driven with an Executive Sponsor who has C-Level influence and sufficient reach across the business model to drive data accountability and stewardship. Must have a Data Governance Program Management Office (DGPMO) type function with sufficient full-time dedicated resources and IT alignment to drive the program and facilitate the build out the governance domains. 5

Defining a Multi-Domain Program Why this must be a business driven model: Data Value: The value of data is dependent on how accurately the data is captured and how relevant that data remains in context to its usage throughout the data life cycle. Process Improvement: Most data defects can be attributed to business process issues. The business needs to own and drive process improvement needs associated to data quality improvement initiatives and new technical solutions. Data Stewardship: The concept and practice of data stewardship needs to be firmly embedded in the operational areas where active involvement and management of policies, standards, rules, and quality control can directly occur. 6

Defining a Multi-Domain Program Management Structure Example Executive Sponsors Strategic Management Tactical Management Execution Roles Executive Sponsors Executive VP - Business Executive VP - IT Steering Committee Executive Sponsors Domain Executives/Governors DGPMO Representative Program Management DGPMO Team Domain Data Stewards Domain Architects Project and Task Management Project & IT Team Liaisons Business Analysts and SMEs Functional Groups and Users Responsibilities Program Sponsorship Ensure C-Level Support Ensure Alignment with Corporate Values Funding and Resource Support Oversee Steering Committee Direction, Strategies, Decisions DG Vision, Design, Priorities Define Organizational Representation Policies, Standards, Approvals Own and Oversee DG Domains Manage Process and Activities Drive Program & Domain Maturity Manage Intake, Issue/Resolution Process Ensure Data Architecture Alignment Provide Input to Steering Committee Implement & Execute Implement DG Solutions Apply Standards and Quality Controls Bring issues, requirements, or other needs into the DG process 7

Defining a Multi-Domain Program Multi-Domain Functional Model Example Business Opportunities Business Drivers Business Issues ENTERPRISE DATA GOVERNANCE Enterprise Governance Charter Governance Steering Committee (Cross-Domain Council) DG Program Management Office Program Management & Communication Domain Team Data Governance Domains Domain Team Domain Team Governance Process Tools & Services Policies & Standards Metrics & Reports Data Stewardship Quality Management Process Control Business Terms Data Management Projects and Initiatives 8

Defining and Prioritizing Your Domains Domain Types: The type of governance domains that are implemented will be highly influenced by a company s industry orientation, business model, system and data architecture, and dynamics between master and operational data. Examples: Sales and Manufacturing Domains: Customer, Products, Items, Orders, Materials, Partners, Supplier. Health Care Domains: Members, Providers, Products, Claims, Care Management, Pricing, Underwriting Financial Services Domains: Accounts, Customers, Products, Service, Contracts, Actuary, Partners 9

Defining and Prioritizing Your Domains Governance Domain Definition: Various artifacts can be used or created to clearly define and distinguish which data, systems, processes, and users are involved with each domain. Data Models: Conceptual and Logical models that document and organize the business data for communication between technical and business areas. Data Dictionary: Provides a centralized repository of information about data such as meaning, relationships, usage, and format. Operational Business Architecture: depicts how organizational and operational functions associate to the data model. This will help identify what Data Stewards and Subject Matter Experts are needed. Functional Architecture: depicts how systems and process relate to a master repository or data domain. Source System Mapping: Provides field level mapping between the logical data model and source system applications Data Life-Cycle: Depicts the data flow and Create, Read, Update, Delete (CRUD) aspects of data elements in the domain. 10

Defining and Prioritizing Your Domains Prioritizing: Domain priority and implementation order will be influenced by strategic initiatives and solution implementation plans relating to data and process integration, decommissioning of legacy systems, and business reporting requirements. Examples: If implementing an enterprise data warehouse, the data governance model will be highly influenced by the design of the enterprise data model design and subject areas, how this aligns with the operational business model, and priorities related to business reporting or analytics. If implementing an integrated business application suite, the data governance model will be highly influenced by new end-to-end process design, data and process migration order, and any master data hub implementation plans associated with integrated business initiative. When considering priorities, focus on near team objectives that can establish quick wins and be executed within a reasonable time frame. Longer term objectives can change and are often influenced by the execution and success of the nearer term objectives. 11

Setting up a Data Governance Program Management Office (DGPMO) Guiding Principles: A DGPMO should always be cognizant of how to continually coordinate and enable domain specific governance needs while avoiding over-management and a threatening approach. Bob Seiner s article on Non-Invasive Data Governance provides a great perspective on this approach: http://www.tdan.com/view-articles/12639 The DGPMO needs to enable a bridge between the business view of data and the technical view of data. Business typically sees data from a business context perspective as fields and views in relation to a process. IT typically sees data more from a data model perspective as entities, attributes, and elements that have structure and relationships. A governance team needs to understand and appreciate both perspectives. If an existing domain or operational area already has a successful data governance practice underway a new DGPMO should acknowledge this and guide it gently into the enterprise data governance model. In a multidomain model a DGPMO needs to ensure that the initial domain implementations develop best practices that other domain areas can leverage to accelerate their governance implementations. 12

Setting up a Data Governance Program Management Office (DGPMO) DGPMO Setup: Define a solid charter and design approach for developing and implementing the governance process. Clearly distinguish the purpose and role of the data governance program office and what services it provides. Create high level process flows projecting how the governance process will work, how domain and cross-domain dynamics will be facilitated, and how data governance will interact with other business functions or steering committees where data governance awareness and involvement is needed. Allow flexibility in your governance process to accommodate different types of needs, engagements, and deliverables that can be expected from the various domains and touch point areas. Ensure there is good engagement with IT and that a combination of program management and analytical skill sets exist in the DGPMO team. 13

Governance Planning & Implementation Example Planning & Design Phase Implementation & Maintenance Phase Establish Program & Charter Policies, Standards, & Controls Process Readiness Implement Maintain & Improve Distinguish the Data Governance Charter Agreement Goals & Objectives Define DGPMO, Program Model, and Domains in scope Identify Roles & Responsibilities Set Top Priorities Committed Budget & Resources Communicate Program Plan Define Key Policies, Big Rules & Quality Standards Establish Key Metrics, Monitors, & Improvement Targets Identify Program Maturity Model Establish Quality & Service Level Agreements Define Metadata Management Plan Define and Communicate Implementation Plan Address readiness needs for Domains, Tools, Processes. Complete Baseline Measurements Needs Complete Training & Launch Plans Launch the Process. Begin Data Governance Meetings Manage Priorities, Activities, and Issues Resolution Needs Review Key Metrics & Performance Indicators Communicate Status of Projects & Improvements Keep Sub-Teams Actively Engaged Complete Key Improvement Initiatives Identify and Address Negative Quality Trends Monitor & Control Data Life-Cycle Manage Risk & Compliance Needs Build a Quality Culture Mature the Teams and Processes 14

Governance Process Flow Example Data Governance Intake Process Data Governance Domain Team Issues Data Quality Policies Standards Processes Support Compliance Metadata Monitoring No Processes Services Control Metrics Data Management Quality Management Data Governance Charter Risk Management Qualified Request? Yes Cross- Domain issue? No Governance Review Engage Steering Committee? No Decision Yes Yes Cross- Domain Governance Engagement Executive Steering Committee Engagement mark.allen@wellpoint.com 15

Cross-Domain Interaction Example Cross-Domain Data Governance Council Member Member Member Member Member Other Key Members Governors Stewards Customer Domain Governors Stewards Product Domain Governors Stewards Financials Domain Governors Stewards Other Domains Change Control Team Enterprise Data Warehouse Example: Finance, Legal, HR, Privacy DGPMO Advisors Data Governance Intake Process, Issue Tracking, Meeting Facilitation Project & Program Areas 16

Governance Interaction with Solution Design Solution Design Process Initiative Planning Discovery & Requirements Design & Develop Test & Verify Implement Control & Maintain DGPMO Identifies needs for data governance involvement. Creates DG engagement plans as needed. Participation in discovery and requirement sessions. Identifies any data impacts. Responds to needs for data analysis, standards, general guidance, metrics, etc. Engaged in data modeling, data integration plans, validation rules, and data policy decisions Involved in test and verification efforts. Governance team sign-off of data quality and integrity. Assist with readiness plans and facilitates resolution of data related issues Involved in data quality control, metrics, monitoring, and change management Enterprise Data Governance Process 17

Coordinating Budget, Tools and Resources Across the Domains Budget Planning: DGPMO should consider budget for data quality orientated tools, services, consulting help. Many existing programs and process areas will be interested in data quality improvement but haven t budgeted for the tools and services needed. Various IT oriented services such as metadata management, data analysis, data integration, data cleanup, or development of reports can create competing demand across multiple domain practices. Work with project areas to plan for budget assistance where governance benefits their needs. Create a DGPMO cost center that internal IT resources can bill when IT services are needed with data governance deliverables. 18

Coordinating Budget, Tools and Resources Across the Domains Managing Tools and Resources: The DGPMO must work closely with the domains and IT resources to coordinate and prioritize cross-domain demand for the tools and resources. Ensure that the tools and services are as scalable and extensible as possible in order to manage this demand as economically and efficiently as possible. Work closely with third-party vendors so that they understand your enterprise data governance use cases and can continue to provide tools and services that will meet your maturity needs. Document the value proposition and ROI being provided by the tools, services, and resources so that strong business cases can be made to justify retention of these assets, for renewal of vendor licenses, and for expansion of the program. 19

Maturing, Measuring, and Communicating Defining a Governance Maturity Model: Many maturity model concepts and examples already exist that can be adapted to your needs. A good reference point is http://www.nascio.org/publications/documents/nascio- DataGovernancePTII.pdf Choose meaningful maturity stages with achievable milestones that support the progressive build out of the governance model. These milestones should represents a value chain that enables data governance to become a core competency. The maturity model should express how data governance teams, processes, and technologies are developing as enterprise assets. Show how data ownership, stewardship, quality management, and cross-functional collaboration emerge as part of the overall data governance strategy. The maturity model should reflect how governance improves from a reactive to a proactive state. 20

Maturing, Measuring, and Communicating Measuring Governance Maturity: A robust maturity model needs to measure the advancement and achievement of data governance practices throughout the enterprise. Data governance milestones need to be measureable and consistent across domains, although each domain may have different priorities and time frames for achieving these milestones. Maturity should include measurement of an active governance state including intake statistics, meetings, responsiveness, decision making, and average meeting attendance. Measurement should include statistics associated with the use and growth of data governance orientated tools such as a metadata repository, a business glossary, collaboration tools, and the modeling and mapping of data associated to each domain area. Maturity needs to express progress with plans, improvement, and management of data quality. 21

EDG Maturity Phases and Definition Example Level 1: Marginal & Reactive Level 2: Defined & Initiated Level 3: Sustainable & Proactive Level 4: Optimized & Integrated Data governance is at best a marginal and nonformalized practice that occurs in silos. EDG strategy, charter, and objectives have been defined. DGPMO has been initiated. The DGPMO and data governance practices are gaining traction across the enterprise. Data governance is a core competency throughout the enterprise. Data governance type decisions occur mainly in project teams, ad hoc forums, or via management escalations. IT has the primary responsibility for Data Management. No enterprise-wide data quality improvement strategies or initiatives exist. There is need for a more formal data governance structure program plan. An EDG operating model and domain structure has been defined. Governor and Data Steward roles and responsibilities have been defined. EDG awareness and process training has been initiated. One or more data governance domain team has been initiated. Ability to measure data governance activity and focus on quality improvement exists. Policies, standards, and needs are being actively addressed by the domain governors and stewards. Domains are engaged in various discovery, design, integration, and sustaining support processes. DGPMO is facilitating cross-domain needs. Data governance and quality management activity is now measureable against planned objectives and improvement targets. EDG interactions and control measures are well embedded in the business and IT processes. There is consistent focus and measureable improvement with data quality due to EDG initiatives. EDG practices are contributing to improve master data management, risk management, and compliance needs. 22

Maturing Level Milestones & Tracking Example 23

Maturing Level Tracking Dashboard Example EDG Quarterly Update : Customer Domain remains on track at Level 3. Has initiated data quality improvement plans. Product Domain has recently entered Level 3 and is completing build out of business terms. Sales Domain has completion of their Data Quality Assessment Plan. Ready to move to Level 3. Financial domain still waiting on completion of their conceptual data model to progress in Level 2 Launch of the Partner Domain has been delayed due to Partner Management reorganization. Level 1: Marginal & Reactive Level 2: Defined & Initiated Level 3: Sustainable & Proactive Level 4: Optimized & Integrated Customer Product Sales Financial Partners Self-Governed Data Domains 24

Maturing, Measuring, and Communicating Communicating: Establish a communication model outlining the Who, What, How, Where, and When. Use aids such as a RACI chart to clearly outline communication roles and responsibilities. Regularly communicate updates, roadmaps, activities, key decisions, major achievements, and topical events. Ultimately, recognition that EDG is working will come down to: Visibility of the value and consistent practices that the data governance process, tools, and artifacts deliver. Clear evidence of effective data quality improvement and management. Ability to recognize a cohesive enterprise data architecture and how the data governance model is aligned with that. 25

Enterprise Data Governance Needs to be Constantly In Motion People Process Enterprise Data Governance Services Technology Data Management Quality Management Data Domains Risk Management Data Stewardship 26

And It s All About Discipline DISCIPLINE Whenever the people are well-informed, they can be trusted with their own government. Thomas Jefferson PLAN GOVERN IMPLEMENT 27

Thank You! Questions? 28