Collibra

At its heart it is a data catalog and business glossary that documents the data assets of an organisation. Data can be classified and profiled, and business ownership can be assigned to the various data assets. Policies can be defined and enforced, so for example, workflow can be defined to check the authorisation of changes to a particular business hierarchy. Collibra has visual tools to assist users in navigating their data landscape, including a comprehensive data lineage solution that tracks the flow of data and can be used for impact analysis. This is the company’s own intellectual property, so they no longer rely, as they once did, on a partnership with a third party data lineage specialist. There is also a “knowledge graph” to help visualise the technical and business connections between data.

There is a search capability to allow data assets to be discovered. Collibra has a reputation for having an interface that is digestible by business users, and this will go further in the future versions with a comprehensive set of changes, including the ability to tailor the interface to specific data assets as well as different business roles. Collibra extends its capability to cover data access and security and overlaps to a degree with special security governance products.

The product uses artificial intelligence in several ways, some of which will be more visible in the upcoming releases, for example with the generation of content for data asset descriptions. This is already used in suggesting possible data duplication. The vendor (correctly) believes that although generative AI can be very productive, it still needs human review for generated content, as AI content can sometimes be overly imaginative (the so-called “hallucination” problem inherent in large language models),

Collibra has extensive partnerships, so for example, Collibra can ingest metadata from platforms such as Google Cloud, AWS, Microsoft Azure and SAP. Interestingly, there is what seems to be a quite productive partnership with SAP despite that vendor notionally having its own data governance offering. Additional bridges exist to products like Databricks and Snowflake, with over a hundred connectors provided. Collibra is planning integration to productivity tools like Slack and Teams also, enabling for example alerts in Collibra to be visible in Slack. Collibra has a robust data quality application which can be used standalone or as part of the overall data intelligence platform, and they continue to further integrate these experiences. For example, data quality scores will be made visible within the data catalog when a data asset is being viewed.

Collibra is the current market leader in data governance and a pioneer in the space. It offers a fully functional data catalog and business glossary with powerful search and discovery tools, visuals and knowledge graphs. The vendor continues to attract investment, and so is well positioned to continue to plough money into research and development to continue to improve its product. The scope of the product has increased in recent years as it has developed its own data lineage and data quality capabilities, which at one time it used to rely on partners to provide. This is a natural development in a market that is beginning to mature, with customers often seeking broader solutions to their data management issues. The vendor needs to ensure that the acquisitions that it has made are smoothly integrated within the overall user interface and customer experience, and it is clearly investing in this.

The Bottom Line

Collibra is one of the market leaders in data governance and did a great deal to popularise this field. The vendor continues to expand its customer base and invest in new and improved product areas. As such it is a natural product to be considered for enterprise data governance needs.

Fig 2 - Simplify DQ rules for business users

Collibra’s software can connect to over 40 data sources including Snowflake and Oracle, and scan data where it resides.  The software does data profiling while also classifying and discovering sensitive data. It is also able to detect schema changes and identify anomalous outliers in the underlying source data. It allows for the writing of custom data quality rules via a SQL editor including a new SQL assistant based on generative AI to help business users do this. While there is the ability to detect potential duplicates and run workflows for data remediation, there is no direct data cleansing, merge/matching, or data enrichment capability natively in the UI.

Collibra Data Quality and Observability competes with other data quality products such as those from Informatica, Ataccama, Experian and other specialist vendors like Monte Carlo Data, Bigeye and Anomalo.

As part of data catalog and data governance powerhouse Collibra, the data quality component of Collibra’s software suite will naturally get attention from the large Collibra customer base. Data quality is tightly linked to data governance, and so Collibra customers will naturally consider whether this adds value for them. The software does not have as full a range of data quality functionality as some competitors, but its predictive data quality rules ability will certainly be of interest to many who have existing data quality programs and are aware of how much effort goes into manually producing data quality rules.

The bottom line

Collibra customers will naturally want to address data quality as part of their data governance efforts, and so will want to consider Collibra’s own data quality offering.