Question

1. What is a data dictionary? What is its importance in ensuring data quality? What type of information would be listed in a data dictionary?

Answer 1

What is a data dictionary?

Answer:

Data dictionary is a collection/ conglomeration of data objects, items, elements in centralised repository with standardized data definitions and forms the functional basis of businesses whose operability depends on the data model.

It can be considered as a super catalogue or a codebook/ master guide for all the data definitions across different functions in an organisation organised into a singular centralised database providing information about different operable data models within the organisation with complete information about the data definitions, the data origin, the data quality, the data relationships and data operability.

The role of a data dictionary in any organisation is to ensure the streamlining of the process of data management and data organisation such that the technical and operational teams have access to a singular organised database that can enable standardized data sharing and analysis providing data consistency required for the organisations/businesses operability and functioning.

What is its importance in ensuring data quality?

Answer:

Data dictionary is very important in ensuring the consistency of data at different levels of data access in the organisation.High quality data is data which is complete, consistent ,valid, accurate and ensures timeliness.The maintenance of high quality data requires clear data definitions and strong data organisation and management.

Data dictionary is very important in ensuring data quality as it provides a tool for organisation and systematized management of large objective data relevant to business processes by providing clear data definitions and consistency among multiple users and across multiple functions..The importance of data dictionary in ensuring data quality is due to its key role in providing the clearly defined,comprehensive data collection which is very important to maintain data consistency and thus data quality.

The data dictionary ensures

1.The provision of a complete comprehensive database thereby providing data completeness

2.Incorporation of clear and defined, concisely explained standardized data definitions and relationship definitions thereby providing data accuracy and consistency.

3.Easily searchable format for data retrieval and data processing across multiple databases thereby ensuring data timeliness

4.Simplified format for accessing data for building advanced data models thereby improving the data functionality and operability.

5.Maintenance of data integrity across multiple databases without any data redundancy providing data validity.

6.Provision of uniform database and standardized data definitions for use in multiple data functions across organisations thereby providing data consistency.

Thus,the data dictionary by providing standardized data definitions ensures access to comprehensive, consistent, accurate,valid data with timeliness and proves to be important in maintaining data quality

What type of information would be listed in a data dictionary?

Answer:

The data dictionary is data in the centralized organised format that gives the information about the various datasets,database tables,the data definitions used in the organization.

It is dictionary about all the data in the organisation like the data variables, data tables,data columns, various databases and so on

The type of data that would be listed in a data dictionary include data objective listings/variables/attributes, the different types of data, the data definitions, data element properties such as data identity/the unique identity/unique identifier number, missing data and quality indicator codes, reference data, data relationship to other objects, data ownership,data variables definitions.

It a super catalogue or a codebook that is referred to access data objects/ elements definitions providing consistent, accurate,valid ,high quality data for various data processes including data analysis.