Enterprise Data Governance

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1 Enterprise Aligning Quality With Your Program Presented by: Mark Allen Sr. Consultant, Enterprise WellPoint, Inc. 1

2 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 operation 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 in Practice: Achieving True Customer MDM (John Wiley & Sons, 2011). Contact or visit WellPoint, Inc: WellPoint is one of the largest health benefits companies in the United States: Revenue: $61.7 billion (2012), Net Income: $2.6 billion (2012) Employees: 43,650 Nearly 36 million members in its affiliated health plans Nearly 68 million individuals served through its subsidiaries. 2

3 Quality Presentation Topics Aligning with IT and Project Building Quality within the Program Creating and Maintaining an Enterprise Footprint Governing the Analysis and Quality Improvement Process 3

4 Quality Aligning with IT and Project Building Quality within the Program Creating and Maintaining an Enterprise Footprint Governing the Analysis and Quality Improvement Process 4

5 Quality Stuck in Neutral Companies can struggle with achieving their data management and quality management objectives because of fundamental organization and process alignment issues, such as: Not having data governance aligned well with IT governance and project governance programs Organizational changes that can slow momentum or fragment governance and quality management focus Not working collectively to correct root causes of data issues and randomly applying point fixes Different groups having different systems of record or using different reporting and analytic solutions From the TDWI Best Practice Report Next Generation Master (2012), among the top responses from users surveyed regarding their challenges to MDM success were: - Lack of cross-functional cooperation - Coordination with other disciplines (DI, DQ) - Poor data quality 5

6 Quality Alignment Issues and Frustrations I work in our Customer Service. We are continually challenged because our customer data is inconsistent and not centralized. This clearly impacts the quality of service we deliver to our customers but we can t seem to get connected with our other groups who can help address this. I spent weeks creating a quality dashboard for our product marketing group. When I previewed this with our Sales and Finance teams they disagreed with a number of my calculations and results. They pointed me to other data and metrics but I can t get clear answers about that data and those calculations. You would think that there should be just one common version of account codes or country codes, but instead each of our source systems seems to have its own versions causing me a lot of extra time each month to normalize and recheck this data for executive reports I deliver. We have hired a consulting firm to do a master data management assessment. As project manager I need to quickly pull together a current view of our data architecture and data flows but I am finding it hard to get our data architects to commit enough time for this. I have expensive consultants ready and waiting. (images and captions for illustrative purposes only) 6

7 Quality Lack of Alignment IT Quality? Project Relationships and handshakes between the various governance functions are not well established. A program exists but lacks sufficient authority and reach. IT is not transparent and occurs through various boards and architecture review committees. Project functions are distributed across business units resulting in planning inefficiencies and overlaps. There is no common data quality management strategy and framework. Business terms, metrics, data models, and metadata are inconsistent. 7

8 Quality Companies are trying to address the alignment issues with more integrated data governance and quality management strategies From the Information Difference Research Study How links Master and Quality (Aug 2010) involving 257 world-wide companies: 58% indicated that their plans for implementing data governance will be part of a broad data management initiative involving data quality. Better quality and faster decisions making was the top response when asked what are the main benefits they expect to deliver. From the Kalido White Paper The Role of Quality Monitoring in (Feb 2011) prepared by Jim Harris, Obsessive-Compulsive Quality: governance provides the framework for a proactive approach to data quality, which requires going beyond reactive data cleansing projects, and establishing a pervasive program for ensuring that data is of sufficient quality to meet the current and evolving business needs of the organization. From the TDWI Best Practice Report Next Generation Master (2012), the top reasons for implementing MDM were: 1. Complete views of business entities 2. Sharing data across the enterprise 3. -based decisions and analyses 4. Customer Intelligence 5. Operational excellence 8

9 Quality Key to Building Alignment and Collaboration Use as the aligning function with the IT and Project areas. Work with IT management and the project planning teams to define clear charters, roles, and engagement opportunities. Leverage cross-functional collaboration where it already exists in projects and programs. Bring attention to where good governance and quality management practices are occurring. Build from best practices. Be persistent, be opportunistic, but have patience. 9

10 Quality An Aligned State IT Quality Project Charter: IT Strategies, Investments, Tools, Infrastructure Information Architecture, System Architecture Technical Review and Solutions. Technical Support Metadata Support Engaged in and Project Decisions Charter: Coordination of Enterprise Practices Quality Polices and Standards Defines Roles & Responsibilities Controls Enterprise Business Terms and Rules Engaged in IT and Project Decisions Charter: Project Review, Plan, Budgets Project, Strategies, Roadmap Resource Allocation Project Testing, Delivery, and Support Support of Quality Requirements Engaged in IT and Decisions functions have formal relationships and clear charters. Actions and decisions are coordinated through common strategies, processes, and forums. stewardship is a core competency with support from IT and Project resources. Quality is driven through but supported by IT and Project governance functions. Quality requirements are defined and applied to IT and Business projects. WellPoint, 2013 June 2013, 2012, DGIQ Conference 10

11 Quality Aligning with IT and Project Building Quality within the Program Creating and Maintaining an Enterprise Footprint Governing the Analysis and Quality Improvement Process 11

12 Quality Building Quality within the Program Quality Needs To Be A key discipline and function of data governance Expressed with clear, measureable milestones in a data governance maturity model Supported through data governance and quality requirements in the solution design process Supported by data stewards and IT associate who are members of the governance team Supported by consistent business processes and IT solutions 12

13 Quality DQM As A Function Guiding Principles Model Measurement Our program has been established to define an enterprise-wide data governance foundation and on-going program strategy. Our data is a strategic enterprise asset with people accountable for its management, quality and integrity. Our policies, standards, and quality requirements will be key factors in our Program Office (PMO) and Solution Design (SDLC) processes. ship Functional Model Metadata Processes Services Controls Metrics Charter Quality Adoption & Maturity Metrics Intake and Decision Metrics Quality Metrics Metadata Metrics WellPoint,

14 Quality DQM In A Maturity Model Level 1 Marginal & Reactive: governance is at best a marginal and non-formalized practice. There is need for a more formal structure. Level 2 Defined & Initiated governance has been defined and implemented within an EDG domain structure, process, and context. Level 3 Sustainable & Proactive governance is an ongoing practice with active processes engaged in the data and project life cycles for the data domain. Level 4: Optimized & Integrated governance is a core competency throughout the enterprise providing a key role in the EIM and MDM strategies. governance type decisions are occurring in an ad hoc manner in different decision areas ownership is unclear and quality improvement efforts are disorganized. Business terms lack standardizations and control Code sets do not have consistent ownership and management Some data models exist but are not complete or maintained Enterprise data management strategies or projects are indicating need for data governance Audit or compliance issues suggest need for formal Marketing data governance DG Audits raise data issues and mitigation plans Sales DG A formal data governance charter exists ownership and steward roles are defined DG processes and collaboration sites have been implemented DG communication and Training exists model s exists for each data domain Business Glossary and Metadata Finance Repository DG solutions exists engagement exists in key projects activity metrics and maturity measurements exists quality management orientation plans and processes are underway quality analysis has been initiated Product DG Leads and sub-teams are in place Consistent use and control of business terms and reference data models and dictionaries are maintained and updated life cycle flows exists source to target mapping is well defined Key data entities and elements have been identified quality is measured and actively reviewed quality improvement initiatives are ongoing governance and quality management policies are cataloged and quality requirements are part of SDLC process Customer DG ownership is well established and data stewardship is a core competency governance is well integrated with enterprise data architecture and information management strategies, plans, and investments Master practices exists with data governance and quality management as key disciplines quality improvement roadmaps are well described within an organization s continual improvement plans Risk is well managed with HR, Legal, Compliance, and Privacy Office actively engaged in the DG process 14

15 Quality DQM Supported In A Solution Design Process Solution Design Process Initiative Planning Discovery & Requirements Design & Develop Test & Verify Implement Control & Maintain Identifies needs for data governance involvement. Creates data governance engagement plans as needed. Participation in discovery and requirement sessions. Identifies any data impacts. Responds to needs for data analysis, standards, general guidance, and quality metrics. Engaged in data modeling, data integration plans, validation rules, and data policy decisions. Involved in test and verification efforts. 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. 15

16 Quality DQM Supported By s & Analysts Domain Team Structure Strategic Focus Domain Trustee Governor Governor Governor Tactical Focus Architect & Analyst Operational Focus Metadata Sub-Team Quality Sub-Team Compliance Sub-Team WellPoint,

17 Quality DQM Supported By Processes and Solutions Domain Trustee Quality Index 12 Month Trend Domain Trustee Architect & Analyst Governor Governor Domain Governor Trustee Architect & Analyst Governor Governor Governor Governor Governor Governor Quality Dimension Completeness Validity Consistency Duplication Accuracy Definition Quality Status Detailed Reports Architect Metadata Quality Compliance & Analyst Sub-TeamMetadata Sub-Team Quality Sub-Team Compliance Sub-TeamMetadata Sub-Team Quality Sub-Team Compliance Sub-Team Sub-Team Sub-Team Rules, policies & procedures Requirements from driver(s) Analyze and review requirements Evaluate compliance of requirements Y Violation? Y N Request data analysis and profiling Drive the design of solution(s) Submit request to DG for amendment Amendment possible? N Requirements cannot be fulfilled Evaluate compliance of solution(s) Rules, policies & procedures Violation? N Y Submit request to DG for amendment Amendment possible? Y N Requirements cannot be fulfilled Review solution(s) and obtain approval Assign work to proper team for execution Obtain final signoff Requirements delivered WellPoint,

18 Quality Aligning with IT and Project Building Quality within the Program Creating and Maintaining an Enterprise Footprint Governing the Analysis and Quality Improvement Process 18

19 Quality Creating and Maintaining an Enterprise Footprint Ensure there is a common enterprise-wide process for data governance intake, item tracking, and decision response. Ensure there is an Executive Council or Board of Trustees forum for addressing enterprise data governance and quality management strategies as well as other crossdomain items or issues. Create a user friendly collaboration site and use common enterprise platforms to support the processes and artifacts of data governance and quality management Define a communication model and RACI matrix for driving consistent communication 19

20 Quality Process Flow Example Intake Process & Item Tracking Log Issues Quality Policies Standards Processes Support Compliance Metadata Monitoring No ship Functional Model Metadata Charter Quality Processes Services Controls Metrics Qualified Request? Yes Cross- Domain issue? No Domain Review Engage Executive Council? No Decision Yes Yes Cross- Domain Engagement Executive Council Engagement WellPoint,

21 Quality Cross-Domain & Quality Cross-Domain & Quality Council (Board of Trustees) Trustee Trustee Trustee Trustee Members Other Key Members Governors s Customer Domain Governors s Product Domain Governors s Finance Domain Governors s Other Domains Analytics Teams Analytics IT, Finance, Legal, HR, Privacy DG PMO Leads Intake Process, Issue Tracking, Meeting Facilitation Program, Project, and Consumer Areas 21

22 Quality Common Sites and Platforms Supporting & Quality Process Collaboration Site Intake Process & Item Tracking Log Issues Quality Policies Standards Processes Support Compliance Metadata Monitoring No Functional Model Metadata Quality ship Processes Services Controls Metrics Charter Enterprise Platforms Qualified Request? Yes Cross- Domain issue? No Domain Review Engage Executive Council? No Decision Yes Yes Cross- Domain Engagement Executive Council Engagement Enterprise Users 22

23 Quality Define a Communication Model and RACI Matrix for Driving Consistent Communication Communication Model Target Audiences (Who to communicate to) How to Communicate (Communication Channels) What to Communicate When to Communicate Why to Communicate Executives Sponsors Trustees Stakeholders Governors s Business Users Subject Matter Experts IT IT Teams Project Project Teams New Employees Newsletters SharePoint Wiki Bulletin boards communications Project meetings Departmental meetings Tailored messages for targeted audiences FAQ DG Charter and Objectives DG Structure and Teams Accomplishments Program News and Announcements DG Intake Process Other relayed processes Metrics and Dashboards Policies, Standards Meeting and decision info Training material FAQ Relevant Industry artifacts about DG Daily Weekly Bi-Weekly Monthly Quarterly Semi annually Annually As needed Gain trust Keep them informed Maintain a presence To invoke feedback Receive suggestions Value of DG and RACI Matrix 23

24 Quality Aligning with IT and Project Building Quality within the Program Creating and Maintaining an Enterprise Footprint Governing the Analysis and Quality Improvement Process 24

25 Quality Governing The Analysis and Quality Improvement Process Focus on data analysis and quality improvement efforts that have the most benefit to business operations and analytics. Define, maintain, and publish enterprise standard data validation rules, quality dimension definitions, and scorecard formats. This will minimize variations in quality measurement and format. Reduce overlapping solutions, resources, vendor tools, and consulting engagements. Time and effort well spent with developing data governance will translate over time into better data quality management and less data quality issues. 25

26 Quality Focus on improvements that have the most benefit to business operations and analytics High Purchase Quality Analyzer Tool Country Code Cleanup Duplicate Customer Merge Address Validation & Cleansing Product Description Cleanup Cost 8668 Parts Taxonomy Consolidation Customer Name Standardization Parts Code Standardization Account Code Cleanup Duplicate Contact Cleanup Service Code Analysis Business Term Standardization Quality Training Low Low High Benefit 26

27 Quality validation rules using standard quality dimension definitions and scorecard formats Quality Index 12 Month Trend Item Item Title Requirement Description Measurement Criteria DQ Dimension 1 Zip Code Entry and format of zip codes are in source Measure overall zip code field Completeness systems is inconsistent causing zip code completeness and consistency issues in the completeness Measure zip code field Completeness enterprise data warehouse. More complete, consistent and accurate capture of zip codes completeness from each EPDS v2 data source data is required. This will have many benefits Measure zip codes for valid format Validity including creating more accurate location Measure zip code field accuracy Accuracy Quality Dimension information for many business and customer within source systems. Accuracy Completeness services. should using the USPS database as the correct zip code reference source. Validity Consistency 2 Tax ID Each legal business DQ entity Dimension in US should Enterprise have a Each Definition business entity has one and Uniqueness Duplication unique tax id. Need Completeness to ensure these are Completeness unqiue only is one the measure tax id. of missing data. and valid ids. Accuracy 3 Consistency Consistency Each is the tax measure id has a of valid the expected format. data values Validity in one data set being equivalent Definition with values in Quality another data set. Facility Type Need facility type code of each hospital. Each Each facility should have valid Validity Accuracy Accuracy is the measure of how correct the values agree with an identified reference source of information. Code facility should have valid facility type. facility type. Definitions should Definitions should Referential come from CMS (e.g., Referential acute come Integrity from is CMS the measure (e.g., acute of the facility, condition that exists when all intended references from the data in one facility, SNF, VA hospital) Integrity column of a SNF, table VA to hospital) data in another column of the same or different table is valid. 4 D&B DUNS The D&B service provide Uniqueness DUNS numbers Uniqueness that Percentage is the measure matched, of when percentage no entity exists of Accuracy more than once within the same data set. Reference represent unique Business Entity information variance, and percentage below Duplication Duplication is the measure of duplication existing within or across systems for a particular field, record, or data set. to validate or augment a company's customer match confidence level. information. Need Timeliness to use DUNS numbers Timeliness as a is the measure of the degree to which data is available for use in the time frame in which it is expected. cross-reference to ensure accuracy of customer data. Currency Currency is the measure of the degree to which information is current with the real world that it models. How "fresh" the data is in relation to possible time related changes. Has been refreshed within a specified period of time. Validity Validity is the measure of how well the data conforms to attributes associated with the data element such as its data type, precision, format patterns, range, or expected list of values for the field. Accessibility Accessibility is the measure of being able to access data when it is required. Status Detailed Reports Credibility Credibility is the measure of the enterprise users trust and confidence in data. 27

28 Quality Benefit of aligned strategies and practices + Effort Quality Issues Time + 28

29 Quality In Summary, It s a Journey I work in our Customer Service. We are continually challenged because our customer data is inconsistent and not centralized. This clearly impacts the quality of service we deliver to our customers but we can t seem get connected with our other groups who can help address this. I spent weeks creating a quality dashboard for our product marketing group. When I previewed this with our Sales and Finance teams they disagreed with a number of my calculations and results. They pointed me to other data and metrics but I can t get clear answers about that data and those calculations. You would think that there should be just one common version of account codes or country codes, but instead each of our source systems seems to have their own versions causing me a lot of extra time each month to normalize and recheck this data for executive reports I deliver. We have hired a consulting firm to do a master data management assessment. As project manager I need to quickly pull together a current view of our data architecture and data flows but I am finding it hard to get our data architects to commit enough time for this. I have expensive consultants ready and waiting. Looking back, I am happy to say that we have gotten much better at addressing our data issues since we have aligned our data governance and quality management programs. (images and captions for illustrative purposes only) 29

30 Thank You! Questions? 30

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