Data Governance

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This document outlines the key concepts and components of establishing an effective data governance program, including typical business drivers, high-level goals, foundational principles, stakeholder collaboration, operating models, and organizational structures like a steering committee, council, and data stewardship team to develop strategy, tactics, and operations.

Technology

Data Governance Core Concepts
Principles
Typical Business Drivers
High Level Goals
Establishing Data Governance
Foundations
Key Components
Inputs, Activities, Outputs
Stakeholder Collaboration
Data Governance Operating
Models
Operating Models & Summary
Considerations for selection
DG Organization
Metrics and Measures

Data Governance Steering
Committee
Data Governance Council
Data Governance
Office
Data Stewardship
team
Strategy
Tactical
Operational

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Treat data as an asset and gain a competitive advantage. Your Challenge Despite the growing focus on data, many organizations struggle to develop an effective strategy for their data assets. This is due to their intangible nature and varying use across the business. Data Management is a business process managed by IT. This creates a challenge for IT as it is required to create and manage complex systems of operations that link closely to integral business operations. Our Advice Critical Insight Data Management is not one size fits all. Cut through the noise related to Data Management and create a strategy and process that is right for your organization. Have the business drive your Data Management project. It all starts and ends with Data Governance. At a minimum, invest in Data Governance initiatives. Impact and Result Coordination between IT and the business will create a Data Management strategy that understands and satisfies the data requirements of the business. Data Management requirements and initiatives will be derived from the following: business goals and strategic plans, current capability assessments, business drivers for data, understanding of market and technology opportunities, and a clear understanding of the business’s drivers regarding data. Creating a clear Data Management Strategy and developing a roadmap of initiatives will allow IT to create a plan for how to bridge the gap between IT and the business and create a Data Management framework that supports the business’s immediate and long-term data requirements.

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You can watch the replay for this Geek Sync webcast, Data Architecture and Data Governance: A Powerful Data Management Duo, on the IDERA Resource Center, http://ow.ly/95yL50A4rZg. Batman and Robin. Han Solo and Chewbacca. Mario and Luigi. Just like these famous pairings, so it is for data architecture and data governance — they’re aligned to support each other in a variety of ways. Like data governance, data architecture as a practice can be leveraged to identify and enforce standards within the systems landscape to support business objectives. And data architecture certainly benefits from sound business oversight and stakeholder influences inherent in a successful data governance program. Join Kelle O’Neal to learn about the critical aspects of aligning data architecture and data governance, with a specific focus on: -Why aligning data architecture and data governance is important -The key intersections of people, processes and technology between data architecture and data governance -How data architecture and data governance work together to enforce standards -The capabilities that data governance can apply to data architecture without interfering -How your project and development methodologies can help drive alignment

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Data Governance

2. Data Governance Core Concepts Principles Typical Business Drivers High Level Goals Establishing Data Governance Foundations Key Components Inputs, Activities, Outputs Stakeholder Collaboration Data Governance Operating Models Operating Models & Summary Considerations for selection DG Organization Metrics and Measures

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Data Governance

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