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Collibra vs Informatica: What's Better for Data Cataloging and Governance?

Book a demo and try the most trusted and efficient data catalog for an AI-ready future. 

Collibra and Informatica are both well-known data governance platforms for automating data management and improving workflow. They provide a centralized platform for data quality, cataloging, and stewardship. 

Collibra’s features make it easier for businesses to understand, find, and trust their data assets across the organization. Whereas, Informatica differentiates itself in data integration and quality by providing Extract, Transform, and Load (ETL) capabilities. It caters to businesses looking to consolidate and transform big data from various sources.

While Collibra focuses on data governance and understanding, Informatica provides the technical strength to cleanse, integrate, and quality-check data at scale. Both tools have advanced features, but they take different approaches to data management. 

Let’s compare Collibra and Informatica in detail to learn the differences between their features.

Collibra: Do more with trusted data

Collibra is a data intelligence platform that enables organizations to master the intricacies of data management. At its heart, the platform zeroes in on enhancing data governance, data quality, and ensuring data privacy.

Collibra's main solution is its data governance platform. The governance platform comes equipped with an array of tools designed to set, manage, and uphold data policies, standards, and procedures. Through these measures, Collibra assists organizations in adhering to both internal guidelines and external legal standards, promoting a unified data language throughout the entity to unify data understanding and application.

Moreover, Collibra offers a comprehensive data catalog, facilitating the exploration and understanding of data assets by teams. This catalog benefits from advanced metadata management features, allowing users to label and record data assets for enhanced clarity and utility. The metadata will provide details on the data’s origin, content, configuration, and its interconnections with other data assets.

As part of its offering, Collibra delivers channels for managing data privacy, ensuring adherence to an array of legal and regulatory mandates. Through the automation of compliance tasks and providing transparent oversight on data utilization, Collibra plays a pivotal role in helping organizations minimize the risks associated with data breaches and regulatory non-compliance.

Users seem to agree that Collibra offers a more user-friendly interface, when comparing Collibra vs. Informatica.

Features & benefits of Collibra

AI governance

  • Streamlined automation of workflows, processes, and guidelines specific to AI governance

  • Seamless integration with existing data and AI frameworks

  • Evaluation of viability and clear definition of AI applications, including data usage, model selection, and intended outcomes

Data governance

  • Extensive repository of business terminology

  • Assignment of stewardship roles and responsibilities

  • Unified data across systems for enhanced reporting accuracy and analytical insights

  • Unified management of governance policies

  • Dedicated support for addressing, overseeing, and solving data-related queries

Data catalog and metadata management

  • Enriched insight by linking business, technical, and privacy metadata with quality indicators and detailed lineage at the column level

  • Intuitive search capabilities

  • Ready-to-use services

  • Automated processes for classifying and organizing physical data resources

Data quality and stewardship

  • Compatibility with a wide range of over 40 databases and storage systems

  • Comprehensive monitoring of data integrity and pipeline dependability

  • Pre-established, sector-specific validation rules for immediate application

Data lineage

  • Comprehensive tracking of data provenance across various sources

  • Automated lineage collection from SQL dialects, ETL, and BI tools

  • Interactive diagrams displaying simplified lineage from origin to endpoint

  • In-depth analysis of lineage including tables, columns, transformations, and SQL queries

  • Insight into both direct and indirect data relationships and flows


  • Simplified, code-free method for deploying policies to cloud environments

  • Use of metadata and contextual understanding to guide data access decisions

  • Sophisticated algorithms for identifying sensitive information, enhancing precision, and efficiency

  • Standardized evaluations to identify and mitigate risks in data handling processes

What are the drawbacks of Collibra?

Although Collibra may be a top choice for many organizations, others feel that it falls short of several key business requirements. We analyzed dozens of reviews of Collibra customers to understand where they faced issues.

Challenging implementation

  • Users and deployment teams experience a challenging initial learning period

  • Fully grasping Collibra's capabilities demands a significant investment in time and training effort

Complex UI

  • Collibra is among the more established, older solutions available

  • It's not particularly recognized for providing the best user experiences, especially for specific user types

  • It may exhibit slow responsiveness

  • Its versatility might lead to complexity: an abundance of choices and customizable options can intimidate newcomers

  • Users have indicated that Collibra frequently does not share its product roadmap, leaving them unprepared for updates or changes that affect new releases and improvements


  • Lacks queryable data and metadata via the platform 

  • Users report a lack of visualization and reporting capabilities

  • Non-technical users don’t understand how metadata is structured

Limited AI functionality

  • Lacks AI-assisted search and guided research via generative AI 

  • Difficult to power data discovery and analytics

Data quality functionality is not mature

  • Lacks key functions: security administration, connectivity, and user-friendliness

  • Less data observability maturity than the competition

Lacks user support for custom builds

  • Customers don’t always receive sufficient support when creating custom connections

  • Asset characteristic changes don't reflect straight away, but adding them again will cause duplications

  • No chat functionality

Security issues

  • Some reviews have noted that transferring data from one system to another system leaves data open to vulnerabilities 

  • Users report that security-wise, other solutions in the market are much better

Cost and flexibility

  • Collibra is considered a premium solution and can be expensive for smaller organizations

  • Organizations may become reliant on Collibra's technology, which can make transitioning to another solution challenging

Collibra Pros & Cons



  • Renowned for its comprehensive data governance capabilities, making it a valuable solution for organizations that prioritize data governance, compliance, and data quality

  • Resource-intensive to implement and maintain, may require dedicated resources including skilled data governance professionals to fully leverage its capabilities

  • For metadata management, offers a centralized data catalog and metadata management features that simplify data discovery, data lineage tracking, and data asset organization

  • Relatively costly for smaller organizations or those with less complex data governance requirements

  • Provides tools and features for data profiling and quality assessment, helping organizations maintain data accuracy and reliability

  • Vendor lock-in as organizations become reliant on Collibra's technology, which can make transitioning to another solution challenging

Informatica: AI-powered cloud data management

Informatica is an enterprise data catalog solution that collects data from different sources to help businesses make informed decisions. It allows organizations to control the full potential of their data by keeping it accessible, clean, and secure. 

Informatica focuses on helping businesses increase efficiency while cutting costs, based on the ability to understand data right from the start of the analysis process. Both in the cloud and on-premises data is managed and analyzed through Informatica, including the data’s structure and relationships.

Informatica also uses an AI engine, CLAIRE, to automate routine management tasks, making them more efficient. Instead of relying on error-prone custom coding, CLAIRE handles tasks like data integration, data quality, and governance for you. 

Unlike Collibra, which primarily focuses on data governance, Informatica excels in data integration and ETL processes. It helps businesses gain insights from multiple data points to inform their strategy. 

When compared to Collibra, users felt that Informatica is easier to set up and has better ongoing product support.

Features & benefits of Informatica

AI-powered data automation

  • Organizations use CLAIRE (the AI and machine learning engine) to automate data management tasks 

  • Reduces data classification time 

  • Self-service data discovery and dataset recommendations 

  • Apply privacy policies easily to APIs to protect data according to compliance regulations

Master data management

  • Connects and synchronizes master data across diverse applications and repositories, regardless of location

  • Speeds up deployment

  • Business-oriented user interfaces


  • Informatica works well for taking data from one source to another, no matter the source: database, flat file, mainframe, Unix, Windows, etc. 

  • Data rules can be built in a centralized location and then applied to numerous data sources, making it easier to update as business rules evolve over time

Data lineage

  • Extract data lineage information automatically, like detailed and summary views of data movement across pipelines

  • Derive lineage from code in SQL scripts, stored procedures, and AI/ML code

  • Track data flow from system to column-level for report on impact analysis

Cloud connectivity

  • Choose from hundreds of no-code cloud connectors to connect your data and applications in minutes

  • Transfer data securely between applications 

  • Reduce Total Cost of Ownership with connections by building pipelines more efficiently

Data quality

  • Integrate data cleansing, standardization, and address verification

  • Auto-generate common data quality rules

  • Understand your data health with observability functionalities

  • Identify and resolve data issues before they impact business downstream

What are the drawbacks of Informatica?

Like any other data management tool, Informatica may not be the perfect fit for every team or organization. We analyzed dozens of reviews of Informatica to understand where users and customers saw room for improvement:

Lacks powerful data collaboration and discovery capabilities

  • Does not address the growing need for accessible and collaborative data analysis

  • Challenging to extract insights from data directly within the platform

Difficult to use

  • Complex interface to understand, particularly for new users

  • Some users noted that powerful hardware is required to use Informatica because it requires a lot of compute resources 

  • There is sometimes a lengthy process required for running workflows 

  • Less accessible for teams who want to access their datasets quickly

Expensive for some teams

  • Expensive for small to mid-sized organizations or teams with limited budgets

  • Beyond licensing fees, Informatica may incur additional costs for add-on features

Steep learning curve

  • Some users noted a steep learning curve with setup and training before the Informatica data quality tool provides value

  • Some users found that documentation could be outdated 

  • The platform may lack adequate training for new or non-technical users


  • Complex transformations are difficult to setup and debug 

  • Transformation speed and optimization could be better, according to some users

Informatica Pros & Cons



  • Comprehensive data integration capabilities like ETL, data replication, migration, and real-time integration across many data sources and formats

  • Expensive for smaller organizations as it has complex pricing and licensing structures

  • Data profiling and cleansing tools help implement data accuracy, consistency, and reliability, which helps organizations maintain high-quality data

  • Requires significant hardware and infrastructure resources that add up to overhead costs

  • Governance tools for policy management help organizations establish and maintain governance frameworks

  • Time-consuming to learn for beginners due to its complex UI, may require specialized training to fully harness the capabilities The data catalog built for your AI future

Collibra and Informatica are traditional data catalogs that require complex infrastructure setup and maintenance—-which pushes new businesses away because of these complexities. Whereas handles scaling, updates, and infrastructure seamlessly to improve your data catalog, governance, and dataOps experiences.’s cloud-native architecture has no hidden costs and provides top-tier, AI-primed data management capabilities that set it apart from conventional data platforms. In addition to these features, is powered by a knowledge graph architecture, which can connect to any data resource in your ecosystem and expand the possibilities of what you can do with a data catalog. 

The hybrid architecture support lets users find and use data from anywhere, whether on-premises or in the cloud. That way, users run cross-platform queries and receive real-time alerts while easily leveraging the entire data ecosystem, regardless of its physical location. has more powerful, AI-ready data discovery and collaboration features, and is easier to use when compared with Collibra and Informatica.  For more information, check out a full comparison of Collibra vs

Top Features of

These are the top features that tend to push users toward over Collibra and Informatica:

AI-driven data discovery

  • AI-assisted data discovery with natural language search capabilities helps users quickly find relevant data assets regardless of their expertise level

  • Guided ideation and exploration through AI-enriched metadata and suggested research questions 

  • Powerful knowledge-graph-based search delivers context-specific results so users can discover relevant information

Enterprise data governance and trust

  • Automated workflows streamline governance, enforce policies, and engage stakeholders

  • Comprehensive data lineage visibility across the analytics ecosystem

  • A single source of trusted, well-governed data products mitigates risks

Data mesh architecture with a federated data platform

  • Manages data like a product in a ‘storefront’ environment while empowering domain-driven data teams

  • Utilizes a knowledge graph to provide flexibility and agility so each enterprise domain has its metadata model and ownership over data structure and usage

  • Federated query capabilities across disparate data sources and domains that prevent data silos and provide a comprehensive view of all enterprise data

Operationalizes data trust and collaboration with dataOps

  • Provide a centralized platform for data producers and consumers to collaborate, communicate, and share metadata about complex data pipelines and applications

  • Automate and streamline communication about data pipeline updates, issues, and improvements

  • Equips data users with context, trust signals, and lineage information about data sources to make data-driven decisions

Cloud-native architecture for smooth migration

  • Accelerates cloud migration by cataloging assets, evaluating relationships, and prioritizing critical data transfers

  • Strategic cloud data setup by providing insights to plan migrations, move only necessary data and refine pipelines

  • Facilitates collaboration through task assignment, documentation, and an easy-to-use interface

Enterprise-wide data understanding with a semantic layer

  • Represents business terminology as concepts and relationships understandable by humans and machines—powering semantic search and discovery

  • Model any entity—-data, policies, metrics, people—by adhering to W3C standards like RDF, OWL, and SPARQL

  • Technical extensibility to expand the data model to new sources and apply business context without costly reconfigurations

Powering the world’s leading data teams

  • is the most-used data catalog on the global market, with 2+ million users and counting

  • Serving the leading data teams worldwide, including The Associated Press, OneWeb, Prologis, and others The data catalog built for tomorrow's data teams

To sum it up, is the only data catalog built on a knowledge graph architecture, ensuring that its data queries deliver accuracy levels 3x higher than those of traditional data catalogs. Compared to Informatica and Collibra, is the better choice for teams looking to incorporate AI into their business models, or to justify the business case for AI. 

To learn more about why the leading data teams choose, schedule a demo today.

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