8 Top Data Catalog Use Cases Intrinsic to Data-Led Enterprises

Last updated on: February 28th, 2023, Published on: June 8th, 2022
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The data catalogs of today can be used in many different ways to help data teams work more efficiently and intuitively.

Top Data Catalog Use Cases

Some of the top data catalog use cases include:

  1. Crowdsourcing data curation
  2. Boosting team productivity
  3. Unifying all data context
  4. Simplifying employee onboarding
  5. Maintaining data accuracy
  6. Speeding up root cause analysis
  7. Streamlining security and compliance
  8. Maximizing the business value of data

First- and second-generation data catalogs (those of the 2000s and 2010s) were essentially inventories of data/metadata built for IT users and data stewards. While useful in theory, such tools were notoriously cumbersome to use and siloed from the rest of the data stack.

Today, third-generation data catalogs are harnessing the power of active metadata by serving as a collaborative workspace for all data users to seamlessly leverage data inside their existing workflows.

Table of Contents:

  1. Top Data Catalog Use Cases
  2. Effortless data discovery at scale
  3. More efficient data curation
  4. Enhanced data team productivity
  5. Understand the relationship between all data assets
  6. A unified metrics repository
  7. Advanced business glossaries
  8. Smarter onboarding and training
  9. Increase trust in the data at hand
  10. Ensure data accuracy
  11. Perform rapid root cause analysis
  12. Conduct intelligent migration management
  13. Gain visibility into external data
  14. Stronger security and compliance management
  15. Use data to its full potential
  16. Foresee the downstream impact of potential changes
  17. Stay in control of third-party data enablement
  18. Executive enablement and alignment
  19. Data catalog use cases: Related reads

Let’s look at some of the most common ways in which data teams use data catalogs to discover, understand, trust, and use data.

Effortless data discovery at scale

IBM had once famously cited that the 80/20 rule applies to how data scientists work as well. They spend 80% of their valuable time finding, cleansing, and organizing data, leaving only 20% to perform the actual analysis.

This indicates an urgent need to make data discovery faster and less complex.

A data catalog greatly increases speed to insight by providing a single repository of data users can use to easily discover high-quality data for their work. Here’s how.

More efficient data curation

Modern data catalogs make data curation easier by uniting data from across the business data ecosystem that data curators can then organize and maintain.

Instead of having data curation activities remain limited to a select group of people, a third-generation data catalog lets you capture tribal knowledge and vital business context from all types of data users. A data catalog enables greater knowledge and context sharing with key features such as:

  • A data dictionary / business glossary
  • Assignable owners/experts
  • Automatic tagging and classification
  • Custom metadata (e.g., freshness)
  • READMEs and key documentation

This crowdsourced approach makes data curation comprehensive yet flexible, bridging the gap between data and users for greater understanding. Put another way, a data catalog serves as a single location for employees to access and share information about data.

How does a data catalog system look like: An example

How does a data catalog system look like: An example | Source: Atlan

Enhanced data team productivity

Here are some specific ways in which a third-generation data catalog can help your data team spend less time finding data and more time working with data to drive results.

Analysts: Data catalogs improve analysts’ efficiency by reducing time to context with features such as searchable glossaries, asset profiles, and visual query builders.

Engineers: Data catalogs make engineers’ lives easier by significantly reducing data downtime using features such as automated data quality profiling, data lineage construction, and programmatic pipeline monitoring.

BI teams: Data catalogs enable more effective analytics for business intelligence teams by centralizing dashboards and automating the reporting process.

Imagine if your data users could turn to a simple, Google-like search experience to easily find a wide variety of data assets such as tables, databases, SQL queries, and BI dashboards. That’s exactly what a data catalog platform does couple with filter-rich browsing (think: Amazon) that makes discovering data as fast and intuitive as possible.

Understand the relationship between all data assets

Having a genuine single source of truth for data across all applications is the holy grail of data management. As IBM’s Jay Limburn said when discussing data fabric, “[Businesses] want a single source of the truth for data — one that’s easily accessible, responsibly governed, works with current systems, integrates across a disparate data estate, and isn’t too costly.”

Today’s data catalog is capable of serving as a knowledge base that provides end-to-end visibility across all data assets and allows everyone in the organization to stay on the same page when it comes to data.

A unified metrics repository

A data catalog serves as a centralized repository for all of your diverse data sources and associated metrics. This includes notes on data set structure, quality, definitions, and usage. It is a single access layer that lets users query all available data within the business.

This means no more confusion around questions like, “Which column in a table should I use for analysis?” A data catalog would have the answer, as it contains all column descriptions plus metrics describing the characteristics of the column such as mean, median, missing values, etc.

A data dictionary is a documentation for all the data assets in a database. Source: Atlan

A data dictionary is the documentation for all the data assets in a database. Source: Atlan

Advanced business glossaries

A data catalog leveraging active metadata allows organizations to continuously and automatically add crucial context to data by auto-classifying assets and auto-generating business glossaries.

Such business glossaries go beyond simple definitions to include synonyms, antonyms, categories, classification types, linked assets, and much more. This means you can instantly answer questions like, “What does this data asset mean?” or “How do I know what Y in this report stands for?”

Business glossary: A collection of business terms and definitions. Source: Atlan

Business glossary: A collection of business terms and definitions. Source: Atlan

Smarter onboarding and training

A data catalog helps new hires get up to speed quickly through its searchable glossary, saved queries, and more. It allows everyone to understand what every detail within a data set means, reducing dependencies and helping teams use data in the same way.

This allows you to scale knowledge throughout the organization as you grow so employees can quickly pick up the terms and processes relevant to their roles. This is much more efficient than relying on dependencies with other team members and crossing your fingers that there will be no timing mishaps related to departmental shifts or turnover.

A Demo of Atlan Data Catalog Use Cases

Increase trust in the data at hand

Trust is a must when dealing with data. Proper data governance ensures users can trust the quality and accuracy of information to be able to use it effectively in their daily work.

“You can have all of the fancy tools and you can have a million data scientists, but if the quality is not good or not sufficient, then you’re nowhere,” says Veda Bawo, former Director of Data Governance at Raymond James.

Ensure data accuracy

Through automated data quality profiling and lineage construction, third-gen data catalogs improve data quality, accuracy, and, consequently, user trust. A data catalog powered by machine learning will “auto-magically” execute quality edits and custom data checks so your employees can spend less time on tedious inspections and more time collaborating to solve more complex problems.

Perform rapid root cause analysis

“Why is our report broken?” is a painfully familiar question for most data teams. With a modern data catalog, an analyst can look at the lineage for the report and identify the anomaly or data quality issue themselves. Whether the problem lies in the workflows transforming data or the source data itself, a data catalog helps you address the root cause so you only have to solve an issue once.

Data lineage helps track transformations across the data life cycle. Source: Atlan

Data lineage helps root-cause analysis by tracking transformations across the data life cycle. Source: Atlan

Conduct intelligent migration management

A data catalog helps data teams recognize the impact of a data transfer to ensure a smooth transition. It allows a thorough analysis of current processes and analytics so as to better understand how and where data should be transferred.

In a cloud migration situation, this could help greatly reduce associated risks and costs. For example, you could examine data usage insights to arrange optimal timing for the migration of frequently used assets (reducing risk) as well as ensure only relevant data assets are migrated (reducing cost).

Gain visibility into external data

It’s just as important to be able to trust second and third-party data as the data generated by your business. A data catalog helps businesses gain visibility into the full context of external data so they can decide whether or not to utilize it within their environment.

You likely know that enriching outside data with existing data sets can be used to enhance marketing and sales. A data catalog would also give you the ability to perform automated checks for integrity and accuracy so you know whether or not to trust external data sources.

Stronger security and compliance management

Data catalogs enable effective security and compliance management using auto-classification of personally identifiable information (PII), creation of tag-based access policies, column-level access controls, and more.

In a world where security and compliance mandates are becoming increasingly stringent, this makes it easier to continuously track and monitor sensitive data to ensure that it meets the requirements of laws such as CCPA, HIPAA, and GDPR.

Data Governance Benefits - Data democratization

Example of implementing tag-based access policies. Image by Atlan

Use data to its full potential

Your business likely collects a great deal of data, but are you using it to its full potential? According to the How to Win in Today’s Data Economy report from Snowflake, only 6% of global businesses say they are able to fully use the data they collect.

Foresee the downstream impact of potential changes

A third-gen data catalog allows businesses to maintain awareness of how data pipelines or downstream processes will be impacted if they choose to change a given data asset.

For example, a data catalog could alert your users about the specific data tables or columns that would be affected before letting them make any schema changes to an asset.

Data lineage helps predict the possible downstream impact of a transformation. Source: Atlan

Data lineage helps predict the possible downstream impact of a transformation. Source: Atlan

Stay in control of third-party data enablement

Your business is likely familiar with the delicate situations that go along with giving third parties access to your data. Data catalogs give you the ability to provide selective access to key assets and functionality.

This is also known as data enablement. Data Enablement enables the rest of the organization and associated third-party partners to become data-driven… It focuses on automation, team productivity, and supporting the data-adjacent roles and functions inside a company.

A data catalog gives you the ability to design custom access policies based on 1) the persona accessing the data and 2) the purpose they are fulfilling by accessing the data. You can even have multiple types of access policies in place and once and grant/deny access for each persona and purpose.

Executive enablement and alignment

A data catalog gives executives the ability to fully comprehend the state of their business data ecosystem to help drive strategic priorities. It more accurately portrays data as an asset so leaders can get a better idea of the potential ROI of data management decisions.

Some of today’s most successful enterprises such as Netflix, Uber, and LinkedIn put a great deal of effort into optimizing their metadata management because they recognize that metadata holds the key to fully understanding the value of a data asset. For example, LinkedIn charged more than $60 per user profile when they were acquired by Microsoft a few years ago.

The third-gen data catalog will power the use cases of tomorrow

Third-generation data catalogs powered by active metadata already help businesses discover, trust, understand and use their data assets more effectively than ever before. What’s really exciting is that these data catalogs are also capable of driving many more use cases that have yet to be imagined.

For example, you might leverage past usage metadata from BI tools to see which dashboards are used the most and when. Or you could automatically determine who the owners and experts are for a given data table or dashboard based on SQL query logs.

Most common data catalog use cases. Source: Atlan

Most common data catalog use cases. Source: Atlan

With a virtually unlimited number of use cases on the horizon, data catalogs have the potential to unite every user’s toolset and serve as a gateway to the data stack of tomorrow — a truly intelligent data system.

See how Atlan customers have been using our third-generation data catalog to bring their data to life.

Evaluating a data catalog platform for your organization?  Do take Atlan for a spin. Atlan is a third-generation modern data catalog built on the framework of embedded collaboration that is key in today’s modern workplace, borrowing principles from GitHub, Figma, Slack, Notion, Superhuman, and other modern tools that are commonplace today.

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