Adopting a Process-Driven Approach to Master Data Management
From DQ to DG
1. Technology Evaluation Centers From Data Quality to Data Governance Jorge García, Research Analyst ComputerWorld Technology Insights, Toronto , 10/2011. www.technologyevaluation.com
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
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
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
56. DG policies are made by humans, for which has an imperfect element
57.
Editor's Notes
“Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” (The Data GovernanceInstitute)“Data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise. Data governance ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality..” (Wikipedia)
Shortening the compilation of data for business decision-making purposes Corporate reduction in data redundancy Gaining control over valuable data and information assets Assisting in making more effective use of data assets. Transforming and managing data as a valuable organizational asset Improving business decisions by guarantying the provision of accurate data from all original sources Increasing end user trust in data stored within all organization's data repositories.
A DG initiativemust:Define, monitor and manage policies to control how data assets are used Define all necessary data requirements for decisions at all levels: operational, tactical and estrategical. Define cross-functional initiatives in order to promote awareness of how data is used within all areas of the company Define and managetheproperdocumentation for managing data acrosstheenterprise and promoteitsadoptiontoimprovedailyoperations in allareas
Call to ActionPlace data governance as a priority initiative.2. Understand data governance as part of the larger scope of knowledge asset management. 3. Understand data governance must be properly planned and chartered. Start with a limited scope initiative.4. Leverage a maturity model for planning manageable phases in data governance.5. Engage the business side of government in data governance.
Begin now to develop expertise and governance for managing data, information and knowledge assets.2. Begin to build awareness through communications and marketing initiatives.3. Understand the scope of data governance.4. Ensure that data governance has appropriate representation from business stakeholders, i.e., the real owners of the information. 5. Implement data governance within existing enterprise and data architecture practice.
Data Governance role is to enhance data quality management strategies to act as part of the specific business in order to serve the needs of all data consumers.Data governance is a program, a permanent work in progress that needs to be improved progressively. Data governance policies are made by humans, for which has an imperfect element , which has to be reviewed constantly in search for improvent.Data Governance initiatives will need to have 100% support from all levels of leadership (strategic , tactic and operational) in order to improve chances of success.
