As clients begin thinking about Analytics or Business Intelligence, they are oftentimes confused about the terms that are thrown around in the literature. Just try googling data management or data governance and you’ll see white papers and diagrams with those terms together with master data management, data ownership, data standards, data quality, data architecture management and on and on.
Breaking It Down
The two most confusing terms seem to be data management and data governance. Which comes first? Is one a subset of the other? The experts seem to be divided in this area, so let me attempt to simplify with some definitions:
Data Management – The development and execution of policies and practices that control, protect, deliver and enhance the value of data and information assets. It includes the following:
- Date Governance and Ownership: The exercise of decision-making and authority for data-related matters.
- Data Definitions and Standards: Sets security, standards and policies— agreed upon and shared.
- Data Quality and Maintenance: The set of activities—both preventative and reactive—intended to improve organizational data.
Note that the definitions above are all from the business or functional perspective, not a technological one. Once defined, technology assists with the implementation and management of your defined policies and processes.
The Point of It
In its simplest form, data management is all about putting policies and processes in place to ensure that you can trust the data. Without trusted data, any decision based upon the data is suspect. And, since the goal of any Analytics process is to grow your organization’s competitive advantage through the strategic use of data in support of decision-making, lack of data quality is not an option.