Collibra Data Governance: Key Features, Limitations, & Challenges

Updated March 19th, 2025

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How does Collibra support data governance, and what challenges do data teams face while using it? #

Collibra is often recognized for its comprehensive data governance features, making it a strong contender for businesses aiming to strengthen their data privacy, quality, and lineage. However, as data teams evolve, some find Collibra’s setup lengthy and its flexibility limited. How does Collibra support modern data governance, and are there better alternatives?

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Here’s a quick look at Collibra’s features and challenges:

  • Data classification: Manual and automated classification of sensitive data.
  • Data privacy: Supports adherence to GDPR, CCPA, and other regulations.
  • Data quality: Anomaly detection using adaptive rules.
  • Data lineage: Limited to cloud editions, visualizing data flow.
  • Challenges: Long setup times, complex usability, and scalability issues.

If you’re exploring data governance, here’s what to know about Collibra, and why modern solutions like Atlan might be a better choice for your data strategy.

Table of contents #

  1. Collibra data governance features
  2. Data classification
  3. Data privacy
  4. Data quality
  5. Data lineage
  6. Where Collibra falls short
  7. What makes Atlan the best Collibra alternative?
  8. Hear it from our customers: how Atlan transforms data governance
  9. FAQs about Collibra data governance
  10. Related reads

What are the main features of Collibra data governance? #

Entering the market a full decade and a half ago, Collibra is an enterprise data catalog comprising several products and features under the Collibra Data Intelligence umbrella.

In addition to cataloging data assets for your business, Collibra also supports data privacy, protection, quality, and lineage. A few of these include:

  • Data classification . Manual and automated classification of Personally Identifiable Information (PII) and other sensitive data, with an option to create custom data classes.
  • Data privacy. Adoption and adherence to data protection and privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
  • Policy management . Creation of policies for various teams, organizational units, user groups, etc.
  • Data lineage. Representation of the flow of data from one data pipeline step to another, from source to destination.

Data classification #

To help you manage and protect your data, Collibra automatically applies classification to data assets when you set up ingestion from a data source. With pre-built rules and patterns, Collibra classifies your data into various packaged data classes.

Collibra offers a Data Classification Dashboard that helps you understand the data classes defined within your organization. It also provides some popular built-in, packaged data classes. However, you can only utilize these data classes after enabling automatic data classification.

Data privacy #

Collibra offers a managed solution for adhering to data protection and privacy regulations, such as GDPR and CCPA.

Like other standalone Collibra applications, data privacy has its own asset model. The module focuses on three asset types: business processes, data-sharing agreements, and security issues.

The data privacy application also identifies and captures personal and business-related sensitive information, a feature enabled by automatic data classification and guided stewardship. Even with all these guardrails, the risk of exposure is only partially eliminated. So Collibra also helps you manage actual and potential security breaches.

Data quality #

Collibra’s Data Quality (DQ) is a framework you can install as a standalone application on Collibra Cloud, Google Cloud, or AWS.

Collibra detects data anomalies by tracking the behavior of data assets through monitoring and observing statistical changes in the data. This gives an adaptive view of data quality and observability.

Collibra’s adaptivity is driven by rules that Collibra uses to learn what “normal” means as the underlying data changes over a period of time. This data quality framework allows you to write your own tests and rules within Collibra.

Data lineage #

Collibra’s data lineage is another feature for better understanding and governing your data assets. However, this feature is limited to their cloud edition for some reason.

Collibra automatically adds data sources to the lineage graph. It then stitches together all relevant data assets from those data sources - such as tables, views, columns, etc. - to create the lineage diagram. This diagram shows how the shape of data changes through the pipeline - i.e., how it is transformed.

Where Collibra falls short #

While Collibra offers many standard features you’d expect from a data governance platform, its limitations and age are becoming increasingly apparent:

  • Long and complex implementation: Collibra and other older data governance tools often face drawn-out implementation cycles, sometimes taking over a year for large organizations to fully deploy. This extended timeline can slow down business agility and delay time-to-value.
  • User complaints on core functionality: Despite continuous user feedback over the years, issues with fundamental features—like its search functionality—remain unresolved. This makes it challenging to locate and manage critical data assets efficiently.
  • Limited extensibility: Collibra’s rigid, predefined data model restricts flexibility. It relies on separate applications for individual features, adding complexity, increasing costs, and placing a significant administrative burden on teams. Expanding its capabilities often requires additional investments in both time and resources, which many find cumbersome.

What makes Atlan the best Collibra alternative? #

Modern data governance is more than just reporting issues after they’re discovered. It’s about finding and fixing problems before they become problems. That means shifting data governance to the left - i.e., moving it earlier in the data lifecycle - via data democratization, automation, and embedded collaboration.

This is where Atlan distinguishes itself.

  • With design and implementation principles like adaptive and programmatic governance, Atlan makes it easier to shift governance left and detect problems before they become problems.
  • Atlan is one of the few data governance products that doesn’t keep your data locked in a separate tool. Embedded collaboration enables bringing data governance into your favorite tools such as Slack, Jira, Tableau, and many others.
  • Atlan supports DIY setup. Atlan is also super easy to set up. No expensive consultants are required. You can install, configure, and yield value from Atlan in two weeks - not a year.

Atlan has many other practical features, such as popularity and usage tracking, integration with open-source tools like dbt, advanced tag management, and more. It’s designed to provide the best data governance experience for all purposes and personas within your organization.

Hear it from our customers: how Atlan transforms data governance #

Delhivery – Evaluating data catalog solutions with cost and features in mind

Delhivery evaluated Collibra alongside other commercial data catalog solutions like Alation and Lumada (formerly Waterline) when searching for a robust platform. Their primary focus was on two factors: features and total cost of ownership (TCO). While commercial tools like Collibra offered a simpler solution, they ultimately decided against it due to missing non-negotiable features, such as data preview and querying, along with the high cost associated with setup, licensing, and professional services.

“Each product had either missing features or the TCO was too high,” Delhivery reported.

Austin Capital Bank – Controlling data access and security

At Austin Capital Bank, controlling who has access to what data is critical for both security and efficient data use. Atlan plays a key role in making data access repeatable and secure while enabling self-service data discovery. With Snowflake at the core of their data infrastructure, Atlan’s interface ensures clear visibility and governance of data access across the bank’s teams.

“Atlan became a necessity. That’s how we control access in an easily repeatable fashion,” said Ian Bass, Head of Data & Analytics at Austin Capital Bank.

Tide – Ensuring compliance and managing sensitive data (pii)

Tide uses Atlan as the authoritative source for managing personal data across their organization. This includes ensuring compliance with GDPR and providing transparency around who can access sensitive data. With Atlan, Tide’s Legal team blessed the platform as the single source of truth for personal data, giving teams the confidence to manage sensitive information with accuracy and compliance in mind.

“We said: Okay, our source of truth for personal data is Atlan. We were blessed by Legal,” said Michal Szymanski at Tide.

Want to make data governance a business priority? We can help you craft a plan that’s too good to ignore! 👉 Talk to us for a better data governance solution

FAQs about Collibra data governance #

What is Collibra data governance, and how does it work? #


Collibra Data Governance helps organizations manage and govern their data by offering features like data classification, privacy adherence, quality monitoring, and lineage tracking. It centralizes data assets under one platform to enhance compliance and data-driven decision-making.

How long does it take to implement Collibra in an organization? #


For large enterprises, it can take anywhere from 6 months to over a year to fully implement Collibra. The setup involves multiple steps, such as configuring integrations, creating governance frameworks, and training users.

What are the main challenges with using Collibra? #


Some of the common challenges include long implementation timelines, limited flexibility in extending features, and difficulties in search functionality. Users often require separate applications for different features, which can increase both costs and complexity.

How does Collibra handle data quality? #


Collibra’s Data Quality framework uses rule-based anomaly detection to monitor the behavior of data assets. It tracks changes and adapts to evolving data sets, although users have reported mixed experiences with custom rules and observability.

Is there an alternative to Collibra for faster and more flexible data governance? #


Yes, solutions like Atlan offer faster implementation (as quick as 2 weeks), collaborative governance tools, and better integration with popular platforms like Slack and Jira. Atlan provides adaptive and programmatic governance, making it a modern choice for dynamic data teams.

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