Without a data dictionary, one has to rely on siloed, undocumented tribal knowledge or spend lots of time familiarizing with codebases, SQL queries, and logs.
This is where data dictionaries greatly help in documenting your database.
The following are the broader benefits of having a data dictionary:
- Helps to understand the overall database design, structure, relationships, and data flow
- Facilitates building a common vocabulary and hence shared understanding amongst data users
- Helps detect errors and anomalies in data
- Enables crowdsourcing data quality and data integrity checks
- Helps save time on data discovery and enables reliable analytics and reporting
- Assists in managing data quality, consistency, and security for compliance audits
- Helps enforce database management and programming standards
- Makes it easier to onboard new analysts/data engineers into the team
Later in the post, we’ll explore 6 of these benefits in detail and explain how having a robust data dictionary is a crucial step toward true data democratization.
What is a data dictionary?
A data dictionary provides complete information about an organization’s data assets. This information can include:
- Column description
- Distinct values, missing values, and frequency of each value within a column
- Data type
- Classification and glossary terms
Here’s an official data dictionary definition from the IBM Dictionary of Computing:
“A data dictionary is a centralized repository of information about data such as meaning, relationships to other data, origin, usage, and formats.”
Meanwhile, according to the DAMA Dictionary of Data Management, a data dictionary is:
“Software in which metadata is stored, manipulated and defined – a data dictionary is normally associated with a tool used to support software engineering.”
What are data dictionaries used for?
Traditionally, an enterprise data dictionary was an IT-owned document. It acted as a database dictionary built to store the definition or meaning of all columns in a data table. This helped catalog the structure and content of data with meaningful descriptions at the column level.
For a modern data stack, that’s not enough. We now need data dictionaries equipped to help with active metadata management — learn about the definition as well as the transformations, classifications, tags, and more. Besides technical metadata, active metadata management also covers operational, business, and social metadata.
What does an active data dictionary do? Here’s an active data dictionary definition from Gartner:
“An active data dictionary is a facility for storing dynamically accessible and modifiable information relating to midrange-system data definitions and descriptions.”
So, data dictionaries for the modern data stack should also provide information to understand everything about the data as well as everything that happened to it. This includes full context with data definitions, standards, rules, classifications, and metrics for quality assessment — mean, median, missing values, and more.
Now let’s look at the top benefits of a modern data dictionary.
Six key benefits of a data dictionary
- Spot data anomalies quickly
- Improve data quality
- Get access to trustworthy data
- Foster transparency and collaboration
- Facilitate regulatory compliance
- Enable fast and accurate data analysis
Let’s explore each benefit in detail.
1. Spot data anomalies quickly
A modern data dictionary tracks column-level metrics such as minimum and maximum values, unique values, frequency, mean, and median.
Combined with business context and tribal knowledge, these data elements help you spot bad or missing data and anomalies at a glance.
2. Improve data quality
Organizations should engage key stakeholders from various departments when building data dictionaries so that everyone agrees upon standard data definitions, rules, procedures, tags, and classifications. These standards reduce data chaos and make data easy to understand and use.
Additionally, modern data dictionaries let you create business rules, column, and row-level permissions, and quality checks to ensure data quality and consistency.
They send real-time quality alerts and notifications and auto-generate quality reports to help you keep track of updates and ensure data quality.
3. Get access to trustworthy data
A modern data dictionary maintains:
- A history of all revisions made to a data set
- Details of the person making the edits
- Data source
- Other discussions related to the data set
This centralized repository of all essential information regarding data sets helps you track data lineage and gauge whether the data quality standards are met.
Whenever you can’t verify the credibility of a data set, modern data dictionaries enable you to create discussions around that data and instantly share it with the relevant people.
4. Foster transparency and collaboration
According to data governance coach Nicola Askham, an organization can have multiple data dictionaries as it primarily contains details of the systems hosting or holding data assets. However, multiple data dictionaries can lead to siloed data, chaos, and mistrust in data uploaded by other teams.
So, if you standardize data definitions, rules, and other attributes, as mentioned earlier, you can ensure that data teams collaborate and work together to ensure complete transparency of data-related processes and preserve data integrity.
5. Facilitate regulatory compliance
Complying with regulations such as the GDPR or CCPA requires enforcing a robust data governance program. One of the benefits of a data dictionary is that it facilitates compliance by allowing you to auto-classify PII data and set up granular access permissions and controls. As a result, only the people with the right credentials can access sensitive data.
The meticulous, real-time logs ensure that you’re aware of all the changes happening to data and the details of the person making those changes.
6. Enable fast and accurate data analysis
Another key benefit of a data dictionary is faster analytics and BI. Both technical and business teams are involved in setting up and managing a modern enterprise data dictionary.
The data dictionaries auto-classify sensitive data, auto-generate row-level metrics, standardize format and definitions, and offer context that goes beyond technical metadata. They’re also built to empower business users to perform advanced analytics without any support from IT.
This simplifies the process of finding relevant data sets to work with to solve a business problem.
To summarize, the benefits of a data dictionary include faster detection of data anomalies, improved data quality, availability of trustworthy data, greater transparency within data teams, better regulatory compliance, and faster analytics.
To know more about setting up an enterprise data dictionary for your organization, check out Atlan’s data catalog, which goes beyond the traditional dictionary and provides a complete profile of the data.
Related reads on data dictionary benefits
- What is a data dictionary: Purpose, importance, and why do you need one?
- What are the differences between a data catalog and a data dictionary?
- What is a business glossary? How can you build one with crowdsourcing?
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