data governance

The initial step in the implementation of a data governance program involves defining the owners or custodians of the data assets in the enterprise. A policy must be developed that specifies who is accountable for various portions or aspects of the data, including its accuracy, accessibility, consistency, completeness, and updating. Processes must be defined concerning how the data is to be stored, archived, backed up, and protected from mishaps, theft, or attack. A set of standards and procedures must be developed that defines how the data is to be used by authorized personnel. Finally, a set of controls and audit procedures must be put into place that ensures ongoing compliance with government regulations.

Getting started with data governance

To explore how data governance is used in the enterprise, here are some additional resources:

Data quality management and governance tutorial: This is a complete guide to data quality management concepts, strategies and technologies. Get best practices and technology considerations for maintaining accurate, current and concise data.

Master data management must start with data governance: An effective MDM project starts with managing people and processes, including data governance. The technology is secondary.

Data governance: How to get started and find success: Data governance, or lack thereof, can make or break enterprise programs. Learn data governance program success factors and get advice in this podcast with expert Jill Dyché.

Why data governance projects fail: Expert Rick Sherman discloses the top reasons why 90% of data governance projects are doomed for failure.

Read more about data governance: - Duffie Brunson writes about "Data Quality and Data Governance: The Basics." - Dennis Wenk and Christophe Bertrand discuss data governance. - The Data Governance Blog covers issues related to running a successful data governance program.

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