The document discusses data governance and data quality, noting that data governance involves establishing roles and procedures around data acquisition, maintenance, and use. It states that data governance becomes important when individual managers can no longer independently make decisions related to data. The document outlines some key aspects of effective data governance programs, such as defining data requirements and assets, as well as common challenges to implementation like gaining business commitment and treating data governance as an ongoing program rather than a single project.

1. Technology Evaluation Centers From Data Quality to Data Governance Jorge García, Research Analyst ComputerWorld Technology Insights, Toronto , 10/2011. www.technologyevaluation.com

2. Technology Evaluation Centers 1. Introduction No, I don’t seeanyproblemwiththe data! Source: www.wolaver.org

3. Technology Evaluation Centers 1. Introduction (What is Data Quality?) The totality of features and characteristics of data that bears on their ability to satisfy a given purpose.

4. Technology Evaluation Centers 1. Introduction (What is Data Quality?) Data Quality Management: Entails the establishment and deployment of roles, responsibilities, and procedures concerning the acquisition, maintenance, dissemination, and disposition of data.

5. Technology Evaluation Centers 1. Introduction (Data Quality features) - Accuracy - Reliability - Completness - Appropriatness - Timeliness - Credibility Ideal features of Data

6. Technology Evaluation Centers 1. Introduction (From Data Quality to Data Governance) At what level of Data Quality is your organization? Incidental Data Quality Proactive prevention Optimization Limited data analysis Addressing root causes Data profiling, Data cleansing, ETL Continuous DQ process improvements Repairing source data and programs Enterprise-wide DQ methods & techniques

7. Technology Evaluation Centers 1. Introduction (From Data Quality to Data Governance) At what level of Data Quality is your organization? Incidental Data Quality Proactive prevention Optimization Limited data analysis Addressing root causes More - Management complexity - Cross Functionality - Security concerns

8. Technology Evaluation Centers 1. Introduction (From Data Quality to Data Governance) Data Management Data Quality Data Quality Business Process Data Governance Policy People Technology Governance comes into play when individual managers find that they cannot – or should not – make independent decisions.The Data Gov. Institute

10. Data cleansing

11. Extract, transform and load data (ETL)

12. Data warehousing

13. Database designData governance can be applied to these disciplines, but is not included in any of them.

14. Technology Evaluation Centers 1. Introduction (From Data Quality to Data Governance) Data Rules Business Rules Policy DQ BPM DG

15. Technology Evaluation Centers 1. Introduction (From Data Quality to Data Governance) Data Rules Business Rules Policy DQ BPM DG A data stewardshipstrategy can help data to become a corporateasset

18. Quality

20. Technology Evaluation Centers 2. Some Facts (Initiatives priorities) Source: Programs or Initiatives, Initiate Data Governance Survey Report

21. Technology Evaluation Centers 2. Some Facts (Company Size) Source: Company Size, Initiate Data Governance Survey Report

22. Technology Evaluation Centers 2. Some Facts (Industry) Source: Industry, Initiate Data Governance Survey Report

24. Reduces corporate data redundancy

25. Encourages control over valuable data and information assets

26. Assists in making more effective use of data assets.

27. Transforms and manages data more effectively and securely

28. Improves business decisions by the provision of accurate data

30. Define all necessary data requirements

31. Define cross-functional initiatives

33. Technology Evaluation Centers 4. DG - Challenges Source Board or Council, Initiate Data Governance Survey Report

35. Encouraging commitment to keep the program alive and moving

36. Effective Collaboration

38. Lack of senior-level sponsorship

39. Underestimating the amount of work involved

40. Long on structure and policies, short on action

41. Lack of business commitment

42. Lack of understanding that business definitions vary

43. Trying to move too fast from no-DG to enterprise-wide- DGSearchDataManagement.com

44. Technology Evaluation Centers 5. DG- Tips (Call to Action) Place DG as a priority initiative. 2. Consider DG as part of the larger scope of knowledge asset management. 3. Understand DG must be properly planned and chartered. 4. Leverage a maturity model for planning manageable phases in DG. 5. Engage the business side of government in DG.

45. Technology Evaluation Centers 5. DG- Tips (Starting point) Begin now to develop expertise and governance for managing data 2. Begin to build awareness through communications 3. Understand the scope of data governance 4. Ensure that DG has appropriate representation from business stakeholders Implement DG within existing enterprise and data architecture practice. Start with a limited scope initiative.

46. Technology Evaluation Centers 5. DG- Tips (Drivers) Source: Data Governance Part III: Frameworks – Structure for Organizing Complexity, NASCIO

48. Clarifyconstraints and drivers

50. Adhering to requirements and standards

52. Provideguidelines and standards

53. Participategovernancerequirements

55. DG is a program, a permanent work in progress

56. DG policies are made by humans, for which has an imperfect element