What is the difference between a data dictionary and a glossary?
A data dictionary defines data elements, their meanings, and their allowable values. A data glossary is enterprise-wide and should be created to improve business understanding of the data they produce and use.
What is a business data dictionary?
What is Data Dictionary? Data Dictionary is a detailed definition and description of data sets (tables) and their fields (columns). This specification includes information such as data type, size, allowed values, default values, constraints, relations to other data elements and meaning/purpose of data set and field.
What is the difference between a data catalog and a data dictionary?
A data catalog differs from a data dictionary in its ability for searching and retrieving information. While business terms, found in a data catalog, can be also found in business glossaries, a data catalog looks more like a directory. Data catalogs assume users already know or have easy access to business definitions.
What is the purpose of a business glossary?
A business glossary is a compilation of unique business terms that are clearly defined, along with other useful attributes. The core purpose of this glossary is to serve as a reference guide that provides employees with common verbiage used in a business.
What is the meaning of data dictionary?
A Data Dictionary is a collection of names, definitions, and attributes about data elements that are being used or captured in a database, information system, or part of a research project. A Data Dictionary also provides metadata about data elements.
Why do you need a data glossary?
A data glossary serves the same purpose for all the data assets in an organization. It contains business terms, phrases, and concepts that help define the data. Apart from providing context, a data glossary can help organize and thus make it easier to discover data assets.
What are the main benefits of using a data dictionary?
A data dictionary promotes clearer understanding of data elements; helps users find information; promotes more efficient use and reuse of information; and promotes better data management.
How do you write a business glossary?
Managing the Business Glossary
- Definition must be stated in the present tense.
- Definition must be stated in a descriptive phrase or sentence.
- Definition should avoid acronyms and abbreviations.
- Definition must not contain the words used in the term (tautology)
What is a business glossary in data management?
A business glossary is a key artifact that a data governance program produces to demonstrate that the organization has an agreed-upon understanding of key business concepts, business terms and the relationships between them. It is also used to demonstrate adherence to data policies and regulations.
What is the difference between a data dictionary and a business glossary?
Data dictionaries usually come in the form of schemas, tables, columns, etc. whereas a business glossary provides a unique definition for business terms in textual form. A business glossary cross references terms and their relationships whereas data dictionaries do not. What is the relation between a data dictionary and a business glossary?
What’s the difference between a data catalog and a glossary?
A data catalog is the pathway—or a bridge—between a business glossary and a data dictionary. It is an organized inventory of an organization’s data assets that informs users—both business and technical—on available datasets about a topic and helps them to locate it quickly.
What should be included in a business glossary?
It is the entry point for all organizations that have any kind of data initiative in play. A business glossary is the red thread that connects the business terms and concepts to policies, business rules, and associated terms within the organization. When creating a business glossary, you should have:
How to reconcile data dictionary and business glossary?
The first way to reconcile the discrepancy between a Data Dictionary and a Business Glossary is manually. Many organizations have attempted this but the task can be extremely expensive and time-consuming, and the results may be prone to errors. This is typically performed by analyzing data values in the physical columns.
