Oracle Enterprise Information Management 2011 (†542)Enterprise Information Management: Best Practices in Data Governance (Oracle, May 2011).
- data governance (p. 3): The specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of data and information. It includes the processes, roles, standards and metrics that ensure the effective and efficient use of data and information in enabling an organization to achieve its goals. (†889)
- data governance (p. 3): Data governance is not meant to solve all business or IT problems in an organization. The main goals and objectives of data governance include the following. · To define, approve, and communicate data strategies, policies, standards, architecture, procedures, and metrics. · To track and enforce conformance to data policies, standards, architecture, and procedures. · To sponsor, track, and oversee the delivery of data management projects and services. · To manage and resolve data related issues. · To understand and promote the value of data assets. (†890)
- data governance (p. 5): Some of the key deliverables for data governance include: ¶ Data policies are a collection of statements that describes the rules controlling the integrity, security, quality, and use of data during its lifecycle and state change. ¶ Data standards are more detailed rules on how to do it. Sample data standards include naming standards, data modeling standards, and other data architecture standards. ¶ Resolved issues . . . procedures of addressing data related issues including data quality issues, data naming and business rules conflicts, data security issues, and service level problems. ¶ data management projects and service development effort across the organization. As a result, it drives better data management projects to have higher success rate, deliver more value, and reduce time to deliver and cost to implement. ¶ Quality data and information . . . with improved quality, easier access, and managed and auditable security. Quality data and information, as a result, is the core deliverable of the data governance function. $para;Recognized data value . . . data as an asset. A key output of data governance is to valuate core enterprise data assets – what business processes they support, how critical are these processes, how critical are these data elements in support of these processes, what are the ramifications and risks to the organization if they are unavailable or incorrect. (†893)