Data Governance Central to Data Management Success

Transcription

1 Data Governance Central to Data Success International Anne Marie Smith, Ph.D. DAMA International DMBOK Editorial Review Board Primary Contributor EWSolutions, Inc Principal Consultant and Director of Education 5/29/2009

2 Why Data Governance Now? New need for Enterprise View of data and information Consolidation efforts in government Consolidation of school districts Consolidation of governors cabinets Consolidation / mega communities Greater transparency demanded New level of data sharing in decision making More cross line of business collaborative information exchange Too many vocabularies Harmonization and optimization of data and information is required Prescription drug fraud prevention, investigation Recovery Act requires new reporting and analysis

3 Information as an Asset Information and knowledge are the primary resources of the knowledge society of the 21 st century. P. Drucker, 1992 Organizations that do not understand the overwhelming importance of managing data and information as tangible assets in the new economy will not survive. T. Peters,

4 What is DAMA? We are.. the world s largest data management organization; with approximately 10,000 members in 40+ chapters in 8 countries around the globe. Vendor Independent Technology Independent Methodology Independent 4

5 DAMA I Mission and Purpose DAMA (Data Association) International is a professional, non profit association dedicated to advancing the concepts and practices of data and information resource management. DAMA s primary purpose is to promote the understanding, development, and practice of managing data, information, and knowledge as key enterprise assets. 5

6 What Is the DAMA DMBOK Guide? The DAMA Guide to the Data Body of Knowledge (DAMA DMBOK Guide) Published by DAMA International and Technics Publishing Available for purchase ISBN 13: Written and edited by DAMA members industry experts An integrated primer a definitive introduction Modeled after other BOK documents: PMBOK (Project Body of Knowledge) SWEBOK (Software Engineering Body of Knowledge) BABOK (Business Analysis Body of Knowledge) CITBOK (Canadian IT Body of Knowledge) 6

7 DAMA DMBOK Functional Framework Version 3 Data Functions Environmental Elements Data Quality Data Architecture Data Development Organization & Culture Technology Activities Meta Data Document & Content Data Warehousing & Business Intelligence Data Governance Reference & Master Data Database Operations Data Security Practices & Techniques Goals & Principles Roles & Responsibilities Artifacts 7

8 DAMA DMBOK Recurring Themes Data Governance Practices and Principles Data Stewardship Business Partnership Data Quality Data Integration Enterprise Perspective Enterprise Information 8

9 (Enterprise) Data Definition: The planning, execution and oversight of policies, practices and projects that acquire, control, protect, deliver and enhance the value of data and information assets. Goals: 1. To understand the information needs of the enterprise. 2. To capture, store and protect data assets. 3. To continually improve the quality of data and information. 4. To prevent all inappropriate access and use of data and information. 5. To maximize effective use and value of data and information assets. Suppliers: Executives Data Creators External Sources Regulatory Bodies Inputs: Business Strategy Business Activity IT Activity Data Issues Functions: 1. Data Governance 2. Data Architecture 3. Data Development 4. Database Operations 5. Data Security 6. Reference & Master Data 7. Data Warehousing & BI 8. Document & Content 9. Meta Data 10.Data Quality Outputs: Data Strategy Data Architecture Data Services Databases Data Information Knowledge Wisdom Consumers: Clerical Workers Knowledge Workers Managers Executives Customers Participants: Data Creators Information Consumers Data Stewards Data Professionals Executives Tools: Data Modeling Tools Database Systems Data Integration & Quality Tools Business Intelligence Tools Document Tools Meta Data Repository Tools Metrics Data Value Metrics Data Quality Metrics DM Program Metrics 9

10 Enterprise Data Data Governance Data Stewardship Reference & Master Data Data Architecture Data Quality Meta Data Data Development Data Warehousing & Business Intelligence Data Mgmt Services Database Operations Data Security Document & Content 5/29/2009 DAMA International

11 Data Organizations & Roles Legislative & Judicial Responsibilities Executive (Administrative / Performance) Responsibilities Data Stewardship Data Governance Enterprise Divisions & Programs Local Corporate & IT Governance Organizations Divisional Councils Chief Data Steward Enterprise Data Governance Council Executive Data Stewards Coordinating Data Stewards Program Steering Committees Data Governance Office Data Stewardship Facilitators Chief Information Officer Data Executive Data Services Organizations Data Architects Data Analysts Data Mgmt Services Subject Area Oriented Data Stewardship Teams (Enterprise & Divisional) Database Administrators Data Integration Specialists Business Data Stewards & Other SMEs Business Intelligence Specialists 11

12 Data Governance Definition: The exercise of authority and control (planning, monitoring and enforcement) over the management of data assets. Goals: 1.To define, approve and communicate data strategies, policies, standards, architecture, procedures and metrics. 2.To track and enforce regulatory compliance and conformance to data policies, standards, architecture and procedures. 3.To sponsor, track and oversee the delivery of data management projects and services. 4.To manage and resolve data related issues. 5.To understand and promote the value of data assets. Suppliers: Business Executives IT Executives Data Stewards Regulatory Bodies Inputs: Business Goals Business Strategies IT Objectives IT Strategies Data Needs Data Issues Regulatory Requirements Participants: Executive Data Stewards Coordinating Data Stewards Business Data Stewards Data Professionals DM Executive CIO Activities: 1. Data Planning (P) 1. Understand strategic enterprise data needs 2. Develop & maintain the data strategy 3. Establish data professional roles & organizations 4. Identify & appoint data stewards 5. Establish data governance & stewardship organizations 6. Develop & approve data policies, standards & procedures 7. Review & approve data architecture 8. Plan & sponsor data management projects and services 9. Estimate data asset value & associated costs 2. Data Control (C) 1. Supervise data professional organizations and staff 2. Coordinate data governance activities 3. Manage & resolve data related issues 4. Monitor and ensure regulatory compliance 5. Monitor & enforce conformance with data policies, standards, & architecture 6. Oversee data management projects & services 7. Communicate & promote the value of data assets Tools: Intranet Website Meta Data Tools Issue Tools Outputs: Data Policies Data Standards Resolved Issues Data Mgmt Projects & Services Quality Data & Information Recognized Data Value Consumers: Data Producers Knowledge Workers Managers & Executives Data Professionals Customers Metrics Data Value Data Cost Achievement of Objectives # of Meetings Held # of Decisions Made Steward Representation / Coverage Data Professional Headcount Data Mgmt Process Maturity 12

13 Objectives of Data Governance Address and synchronize data collection processes Reduce data redundancy Systematically improve: Data Accessibility Data availability Data flexibility Data reuse 13

14 Governance and Stewardship Data governance is the practice of making strategic and effective decisions regarding an organization s information assets Data Stewardship is the role that ensures the execution of data governance principles. (C) DAMA International /29/

15 Data Governance Decision Spectrum Decisions made by business management Decisions made by IT management Business operating model IT leadership Capital investments R&D funding Data governance model Enterprise information model Information needs Information specs Quality requirements Issue resolution EIM strategy EIM policies EIM standards EIM metrics EIM services DB architecture Data integration Architecture DW/BI architecture Meta data architecture Technical meta data 15

16 Data Governance Benefits Enables the organization to produce high quality information which is useful, appropriate, and timely by: Providing one point of accountability Reducing data duplication Increasing confidence in data Improving timeliness and usability of data Establishing a common vocabulary of data to ensure access to the right information Defining enterprise wide (or site / project wide) values for common reference data Providing information and guidance to assist in compliance and regulatory efforts concerning data 16

17 Critical Success Factors Successful Data Governance requires: Adoption of Enterprise Data methodology and standards throughout the organization Strong, continued management support of the governance initiative and use of data stewardship authority given to stewards to enact proper standards and policies, and to enforce them statutory authority proven to be necessary Continued focus on improving data and information quality, through vigilant data and information management Successful, continual interaction among data stewards, data administration, database administration and systems development / integration Establishment of measures to chart progress of the data governance initiative, and the use of data and information management industry standards and best practices 17

18 Data Stewardship Data Stewardship role that enables implementation of data governance principles. Stewards ensure organizational data and meta data meet quality, accuracy, format and value criteria; ensuring that data is properly defined and understood (standardized) across the enterprise or site or projects; ensure that business processes are appropriate and interact correctly with the data Data Steward a person(s) responsible for the definition, content, and quality of a specific data subject area: Involved in defining subject area boundaries Collects feedback and enhancements for specific subject area Resolves data integration issues Acts as the conduit between business and IT Serves as quality control point for the subject area Responsible for business metadata and actual data values Note: one data steward can be responsible for multiple subject areas OR there can be multiple stewards for one subject area 18

19 Data Stewardship Fundamentals Data stewards act as the conduit between IT and the business stewards are the role that execute governance practices Data stewards come from the business / user community and serve as subject area experts Role of data stewardship is mission critical and should be formally recognized in the organization The data stewards MUST have sufficient bandwidth to allocate time to the data stewardship role The data steward organization does not report to the IT organization 19

20 Differences in Roles For the Data Stewards Resolving data integration issues Determining data security needs Documenting data definitions, calculations, summaries, etc. Maintaining/updating business rules Analyzing and improving data quality For the Data Administrators Translating the business rules into data models Maintaining conceptual, logical and physical data models Assisting in data integration resolution Maintaining metadata repository with metadata documented by the data stewards Participating in application development / integration efforts for data requirements For the Database Administrators Generating physical DB schema Performing database tuning Creating database backups Planning for database capacity Implementing data security requirements Managing access to databases by application development and production staffs 20

21 Meta Data in Governance Stewards manage data (instances of data values) and metadata (information concerning the data, including information about business processes) Metadata management is a key technical enabler for the performance of successful governance Difficult to do governance successfully without a managed meta data environment and understanding of metadata management 21

22 Technical Issues in Governance No managed meta data environment (MME) or not accessible to end users Data storage redundant data stored under different names No standard data setup and maintenance procedures, disparate systems use separate procedures No workflow automation tools used No integrated technical architecture All technical challenges can be overcome need attention to each, with alignment of processes and data management 22

23 Sustaining Governance How do we sustain the Data Governance effort? How do we keep it GREEN? Executive sponsorship sustained and active Job description stewards and governance council Performance metrics for program and stewards Rewards tangible and intangible Statutory requirements Community of Practice and Interest (CoP/I) User understanding and communication Feedback loop for DG Council Drive direction for enterprise data management Marketing governance efforts Internal Cross state / cross agency or department 23

24 Assessment Now and Then 1=Minimal Ad hoc effort 2=Building Formal Process 3=Using formal but lacking accountability 4=Using formal process with accountability and executive support Data Governance & Stewardship (driven by the business) Meta Data Business Impact Education Meta Data Data Quality Master Data Technologies Data Quality Business Impact Technologies Education Governance Other Category Master Data Assess where you are now and compare with where you want to be, to plan your roadmap of how you will reach the future desired state. 24

25 Conclusion Data governance is the central function for successful enterprise data management Enterprise data management can enable costs savings in many areas, and can improve services to internal and external constituencies Challenges in data management and governance can be addressed and overcome with knowledge and attention Support and assistance is available seek out experts in data management and data governance Governance can be successful be positive! 25

26 Questions and Discussion 26

27 Contacts Questions Anne Marie Smith, Ph.D. Office: ewsolutions.com 27

28 References DAMA Data Body of Knowledge English, L. P., Improving Data Warehouse and Business Information Quality (1999), Wiley Marco, D. P., Building and Managing the Meta Data Repository (2002), Wiley Redman, T. C., Data Quality: The Field Guide, (2001), Digital Press Tannenbaum, A., Metadata Solutions, (2003), Addison Wesley Real World Decision Support journal Van Gremberger, W., Strategies for Information Technology Governance, (2004), Idea Group Publishing Enterprise Information Institute Insight journal 28