Collibra - Detailed Review

Here are some frequently asked questions about Collibra, along with detailed responses:

How does Collibra help with metadata management?

Collibra enables organizations to collect, organize, and enrich metadata through its data catalog and governance capabilities. It supports automated metadata harvesting, allowing users to discover and document data assets efficiently. This automation reduces manual effort, ensures up-to-date documentation, and provides a comprehensive view of data assets and their attributes.

What are the key features of Collibra’s data catalog?

Collibra’s data catalog provides advanced search and filtering options, enabling users to find data assets based on metadata, tags, and relationships. It includes features like natural language search, contextual search across all locations and data sources, and filters to narrow search criteria. Additionally, the catalog allows for automated metadata harvesting and suggests descriptions for schemas, tables, and columns using AI-driven text generation.

How does Collibra support data governance and policy management?

Collibra allows organizations to define, enforce, and monitor data policies. Users can associate policies with specific assets, automate compliance workflows, and track adherence to regulatory or organizational standards. The platform also supports certifications, which validate that specific data assets meet quality and governance standards, marking them as trusted for decision-making.

What role does AI play in Collibra’s Data Intelligence Platform?

Collibra has integrated AI capabilities to enhance its Data Intelligence Platform. For instance, Collibra AI uses large language models (LLMs) to automate data quality tasks, such as generating SQL-based data quality rules from natural language and automating the description of data assets. Additionally, AI Governance helps manage AI models by increasing visibility into how they work, documenting risks, and automating governance workflows to ensure compliance and minimize risks associated with AI adoption.

How does Collibra facilitate data discovery and access?

Collibra’s data catalog and search functionalities make it easier for users to discover and access data. The platform includes intuitive and contextual search, which allows users to find data assets quickly across all locations and data sources. It also provides features like data similarity, which helps users find data sets that are similar to the ones they are currently looking at, but with additional required attributes.

How does Collibra support collaboration between teams?

Collibra fosters collaboration through shared glossaries, workflows, and dashboards. Team members can communicate through comments, assign tasks, and collectively manage data governance activities. The platform also allows for the definition of roles and relationships within the operating model, ensuring clarity and consistency in data governance across the organization.

What is the significance of data lineage in Collibra?

Collibra’s lineage visualization provides a detailed map of how data flows through systems, transformations, and usage points. This is crucial for impact analysis, as it allows users to understand dependencies and downstream effects of changes. For example, modifying a source table can impact multiple reports and dashboards. With lineage visualization, stakeholders can assess the scope of such changes, mitigate risks, and communicate with affected teams.

How does Collibra ensure audit readiness and compliance?

Collibra simplifies audit readiness by automating the documentation of policies, workflows, and data lineage. It provides a transparent view of who accessed or modified data, when changes were made, and how data flows through systems. The compliance dashboard highlights adherence to regulations such as GDPR, HIPAA, or CCPA, while workflows automate audit trails and ensure every action is logged and accessible.

How does Collibra manage data quality issues?

Collibra facilitates the tracking of data quality issues through its integrated workflows and dashboards. Issues can be reported manually by users or detected automatically through data quality integrations. Once identified, workflows assign tasks to relevant stakeholders, provide status updates, and enforce resolution deadlines. Collibra dashboards display open issues, trends, and resolution timelines, allowing organizations to monitor and improve data quality over time.

What is Collibra AI Governance, and how does it work?

Collibra AI Governance is a component of the Data Intelligence Platform aimed at managing AI models alongside the controls used to manage and govern data. It increases visibility into how AI models work, automates governance workflows, and documents risks such as privacy, intellectual property, ethics, and compliance. This feature helps customers control access to data, particularly personally identifiable information (PII), and provides a dashboard for executives and legal teams to monitor and track AI model development and risks.

How does Collibra support the end-to-end data lifecycle?

Collibra supports the entire data lifecycle by providing tools for metadata management, data discovery, governance, quality monitoring, and eventual data retirement. From data ingestion to retirement, Collibra’s catalog ensures that metadata is properly documented and associated with governance policies. It enables data stewards to manage data quality and ensures compliance with workflows that enforce validation and access approvals.