Friday, July 23, 2010

Data Governance - An Abbreviated Glossary of Terms

As I write more articles for you, I realize that I use a lot of terms for which you might find definitions valuable. I will not stand on my head and scream and shout if anyone disagrees with the definitions I’ve provided. This is not mean to be the be-all and end-all Data Governance glossary, although I might tackle that one in the near future since it sounds interesting (well . . . as interesting as Data Governance topics go, right?)

As always, please feel free to let me know if and how you think I’ve gone awry with anything I’ve listed here. I’m always interested in your feedback.

Data Governance:
The formal processes by which Key Corporate Data Assets are protected and maintained in alignment with the vision and mission of the business.

Data Stewardship:
The processes, policies and workflows implemented to ensure that Key Corporate Data Assets are managed in a consistent compliance manner in accordance with the Policies and Procedures of the Data Governance Office.

Data Governance Office:
The DGO is the office sponsored by the DG Governance Board and is accountable for the data quality of Key Corporate Data Assets, ensuring standardization of nomenclature related to those assets and generally governing the data in an active manner.

The DGO is responsible for ensuring that the guidelines policies and procedures established with the business and approved by the DG Governance Board are properly implemented and followed to ensure high-quality consistent data that is relevant and available when required to make critical decisions about and for the business.

Master Data Management:
The program(s) implemented to ensure Key Corporate Master Data Assets are managed consistently and appropriately.

Conditional Master Data:  Originates as a transaction then becomes relatively static meeting the rules for master data; it is a key corporate asset or is used to create other transactions. Examples include Contracted Products, Quotes and Contracts
Master Reference Data:  Data used to populate attributes associated with master data. Often called lookup/reference tables or list of values tables. This data directly affects the quality & integrity of the master data therefore it is managed & controlled in the same manner as master data. Examples: Cost centers, Industry Class/sub-class, profit centers, etc…
Meta-data:  Data about data.  Meta data is used to describe data and provide additional information about the data itself. Meta data defines such aspects as:
    • Creation date, time and user – used to keep track of relevance
    • Ownership, or other responsible parties – used for stewardship
    • Purpose of the data – what system(s) use it, when, how often, for what. This is often documented in a data dictionary
    • Business rules that apply or some level of scope of use for the data, etc…

Key Corporate Data Asset:
Assets within an organization which provide substantial value to the organization, its mission and its success in the industry. These can be Master Data Assets (see below)

Master Data Assets:
Data sets used to describe objects or entities that are part of the organization. (I.e. customers, products, materials, vendors, employees, regions, markets, etc…) Other properties include:
  • Considered a key corporate asset
  • Changes infrequently.
  • Used to categorize & define hierarchical & referential relationships between transactions.

Enterprise Data:
"Enterprise" data includes, but is not limited to - shared (or potentially shared) data about managed entities, interests, finances, employees, resources, customers, providers, business affiliates, best practices, operating procedures, etc.

Data Integrity:
Business requirement that data in a file or message traversing the network remain unchanged and/or that the data that was received matches exactly that which was sent.

Data Integrity is the process of preventing malicious or accidental changes to data or message content as it is moved and/or updated throughout its life cycle.

Business Intelligence:
The processes by which data throughout an organization is brought together to provide support for making business decisions.

BI provides a company with the ability to glean both tactical and strategic insights into its operations to create a competitive advantage in the market or event to help define new more lucrative or otherwise advantageous markets in which to compete.

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