12 – Data Dictionary

Level : Medium
Number of people : 1
Duration : 1h


Like a journal or index, a data dictionary gives relevant information about databases you are using. It guides you through a vast amount of data and helps you to easily find the ones compatible with your needs. It is part of a database management system (DBMS).

It documents standard definitions of data elements, their meanings, and allowable values. It also enables a common interpretation of data elements.

It provides all stakeholders with relevant information and a shared understanding of the format.

A data dictionary takes the form of a table with at least 4 columns.

  • Name: It must be unique.

  • Code/Alias: Other name known by stakeholders.

  • Type/value/meaning: Type of data, acceptable values of it.

  • Comment/description: Information about the element in the context. 

The next columns can contain calculation rules, management rules, length/size of the data, examples, or any information that helps understanding the data.

Data dictionaries area (sometimes referred to as metadata repositories) contain information about owners, any particular security constraints, the date and time data was created at first, and the positioning of data in the database. As organizations adopt data mining and more advanced analytics, it may provide the metadata required by more complex scenarios.

Why use a data dictionary?

A data dictionary allows you to keep your data in an orderly manner. This way you can easily access required information.

Using a data dictionary reduces the risk of your data being lost, damaged, or leaked.

A well-organized dictionary brings better control over your data. These will stay relevant and never be redundant, even after numerous uses.

Analysis is simplified through a dictionary. Required data is quickly accessible and make the most specific analysis easier.

As data is more relevant, well organized, and quickly accessible, decision-making will become more successful and comfortable.

How to manage a data dictionary

It can be maintained manually (passive) or via automated tools (active).

1) Standard relational database (Active)

Each piece of information about your database is automatically added to your dictionary through your software. Each new software, website, or database management system has its own automated data dictionary and is maintained through the operator. Every change or addition to the data is automatically updated in it.

2) Spreadsheet or text file (Passive)

Here, the data dictionary is not created automatically through the software. You have to create a dictionary for yourself as well as for the others who wish to use your data. Moreover, this type of data dictionary needs continuous updates about any changes in your data to be accurate and useful.

Using a Data Dictionary in Business Analysis

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The dictionary focuses on business concepts and interpretations. Data dictionary used in business analysis contains interpretations of business concepts and methodologies in contexts they are needed by the analysts. It makes business easy and interpretable for business analysis. 


  • A data dictionary is like an index allowing you to find relevant data according to the context.

    • Name: It must be unique.

    • Code/Alias: Other name known by stakeholders.

    • Type/value/meaning: Type of data, acceptable values of it.

    • Comment/description: Information about the element in the context. 


  • Data control

  • Data integrity

  • Data consistency

  • Data analysis

  • Decision-making efficiency

2 types of operating

  • Active: standard relational database

    • Each change or addition is automatically integrated to your dictionary.

    • Modifications are managed by software.

  • Passive: spreadsheet or text file

    • The dictionary must be manually created.

    • It must be regularly updated.


  • Adaptation

    • Data is raw.

    • A dictionary allows choosing the appropriate data according to context.

  • Interpretation

    • A dictionary contains business concepts and methodologies useful to business analysis.

    • It simplifies business interpretation for business analysts.

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