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data-governance-compilation's Introduction

Data-Governance-Compilation

Federal, State and Local governments across the United States are all now beginning or in the midst of major reforms and transformations to the way that they approach the data they manage. Modernization projects can be seen popping up across nearly every governing body in the United States and an incredible body of knowledge, learned expertise, and tools have emerged from those who were the early adopters of these reforms. There are now many models, resources, and communities of practice that agencies can and should rely upon when building out their governance bodies. There is no need to re-create the wheel. We should eagerly stand on the shoulders of those who have gone before us. To that end, the federal data strategy has begun compiling and pulling together a library of resources for practitioners to take from.

This toolkit includes:

  • Model charter language

  • Pre-built Data Governance templates of agendas, customizable project timelines, project plans, and more.

  • Actual data governance handbooks and policies implemented by other organizations

  • A library of maturity models that can be used to conduct self-assessments for your organization

  • A compilation of research, whitepapers, and reports on data governance topics

  • A list of known communities of practice (listserves, slack channels, max.gov communities) where you can connect with other practitioners directly

Please visit the Library for the full toolkit. Contributions welcome!

Definitions

  • City of San Francisco: Data governance is about the exercise of authority, control and shared decision-making over the management of data assets. It is the process by which a company manages the quality, consistency, usability, security and availability of its data. A data governance system is a set of approaches for how we obtain/produce, manage, share, and use data. That system decides who is responsible for what data, what is expected of those who are responsible for managing data, and what are the systems and roles that support the technical management and dissemination of data

  • NIST: A set of processes that ensures that data assets are formally managed throughout the enterprise. A data governance model establishes authority and management and decision making parameters related to the data produced or managed by the enterprise

  • The Data Governance Institute: 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 MDM Institute: The formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset.

  • Forrester: The process by which an organization formalizes the fiduciary duty for the management of data assets critical to its success.

  • Oracle: 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.

Templates

  • The library has pre-built Agendas, timelines, project plans and more! Why recreate the wheel?

Governance Handbooks and Organizational Policies

  • A compilation of different handbooks and policies from different types of organizations from around the country. Great for inspiration!

Maturity Models and self-assessments

Education and Research

Living Models (actual Governance Bodies in the real world)

Communities

Additional Lists

  • Open Source Governance Models โ€“ GovEx labs collection of topics that governance boards should consider supporting, executing, and/or developing policy around.

Related

  • Data Federation Project - A project focused on tools and best practices to supported federated data collection efforts

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