2023Unpublished venueRequires access

Data Governance and Data Management

Mary Anne Hopper

Open publisher page 1 citations

Abstract

Data Governance and Data Management need to work hand-in-hand. Data Governance provides the oversight, measurement, and communication while Data Management provides the tactical operations to achieve desired outcomes. It is important to understand how Data Governance and Data Management align in support of larger business goals. It is important to understand how Data Governance and Data Management align in support of larger business goals. A great first step to gaining this understanding is to examine an overall framework of a program and its components parts. They can be broken down into Data Governance, Data Management, Data Stewardship, Business Drivers, Solutions, and Methods. The SAS Data Management Framework breaks down each of these components. Data architecture policies include statements about data models, data movement, data sharing, data integration, data standards, ETL standards, data access, and service level agreements. Data life cycle policies will pertain to the management of data from its creation to its eventual destruction.

About this research paper

What this paper is about

Data Governance and Data Management need to work hand-in-hand. Data Governance provides the oversight, measurement, and communication while Data Management provides the tactical operations to achieve desired outcomes. It is important to understand how Data Governance and Data Management align in support of larger business goals. It is important to understand how Data Governance and Data Management align in support of larger business goals. A great first step to gaining this understanding is to examine an overall framework of a program and its components parts. They can be broken down into Data Governance, Data Management, Data Stewardship, Business Drivers, Solutions, and Methods. The SAS Data Management Framework breaks down each of these components. Data architecture policies include statements about data models, data movement, data sharing, data integration, data standards, ETL standards, data access, and service level agreements. Data life cycle policies will pertain to the management of data from its creation to its eventual destruction.

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Available abstract

Data Governance and Data Management need to work hand-in-hand. Data Governance provides the oversight, measurement, and communication while Data Management provides the tactical operations to achieve desired outcomes. It is important to understand how Data Governance and Data Management align in support of larger business goals. It is important to understand how Data Governance and Data Management align in support of larger business goals. A great first step to gaining this understanding is to examine an overall framework of a program and its components parts. They can be broken down into Data Governance, Data Management, Data Stewardship, Business Drivers, Solutions, and Methods. The SAS Data Management Framework breaks down each of these components. Data architecture policies include statements about data models, data movement, data sharing, data integration, data standards, ETL standards, data access, and service level agreements. Data life cycle policies will pertain to the management of data from its creation to its eventual destruction.

Key concepts: Data governance, Data management, Data virtualization, Enterprise data management, Information governance, Corporate governance, Stewardship (theology), Data warehouse

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