An augmented data catalog is crucial for all data-driven organizations. According to Gartner, who coined the term, an augmented data catalog is a data catalog that uses machine learning to automate the manual tasks involved in cataloging data, including metadata discovery, ingestion, categorization, curation and enrichment. An augmented data catalog is a must have for data and analytics leaders.
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Why do organizations need an augmented data catalog?
Organizations need an augmented data catalog as part of their overall data management and analytics strategy. The rapid growth and diversity of data sources, data types, users and deployment models make it difficult for organizations to identify and inventory their data. Many organizations rely on manual spreadsheets and other manual data management tools to catalog their data. But as data continues to grow in amount and importance, organizations can no longer rely on manual cataloging. In addition, many data and metadata management tools lack business focus, and therefore, do not help organizations derive value from their data.
An augmented data catalog solves these pains by automating the cataloging process and enabling users to discover, understand and access data. Leveraging ML capabilities, augmented data catalogs automate the process of discovering, inventorying, profiling, tagging and creating semantic relationships between distributed and siloed data assets. Automating these data cataloging tasks enables IT, data stewards and business analysts to spend more time on strategic initiatives and less time manually cataloging and searching for data.
What are the key capabilities of an augmented data catalog?
The foundational feature of an augmented data catalog is its ability to automate manual tasks through machine learning. But on top of machine learning-powered capabilities, augmented data catalogs include numerous other capabilities that help organizations to discover, understand, govern, collaborate and consume their data. Augmented data catalog features include:
- Native connectors: Scan for and extract metadata from the most popular data sources such as, enterprise data warehouses, data lakes, operational databases, enterprise applications, cloud data stores and non-relational data stores
- “Google-like semantic” search: Allows users to find, browse and filter for the best and most relevant data sets
- Automated, end-to-end data lineage: Use end-to-end lineage for governance and compliance use cases, as well as for impact analysis
- Business glossary: Define business terms and policies throughout the organization and assign relevant business terms to the metadata
- Certification of data assets: Certify data sets, metrics and reports based on quality and trustworthiness
- Integrations with BI tools: Allow users to understand context and lineage of data used in reports and to catalog reports and dashboards for easy shareability and reuse
- Rest-based APIs: Enable users to integrate the data catalog into their environment and consume cataloged content across different applications
- Embedded governance: Establish policies, assign data owners and certify data accuracy with appropriate governance processes and controls
How to increase business value with an augmented data catalog?
According to Gartner, the biggest challenge that most organizations face is finding and inventorying data that is distributed across the organization. With distributed data management and analytics, organizations struggle to deploy a data governance solution that can manage the data deluge. An augmented data catalog solves this issue by providing an easy, automatic way to inventory and contextualize data and make it accessible for use. Data catalogs free up IT, data stewards and business analysts’ time so they can focus on more strategic initiatives.
And a data catalog should not be a standalone tool. Rather, an augmented data catalog should be the foundation of a broader metadata management strategy. Organizations looking to enable their business to discover, understand, govern, collaborate and consume data should look to invest in an augmented data catalog with enterprise-grade capabilities, like Collibra Data Catalog.
Become data driven with Collibra Data Catalog
Collibra Data Catalog is an augmented data catalog using machine learning to automate data classification, data curation, data stewardship and compliance to build and update an active metadata graph. Together, our ML capabilities enable customers to catalog, organize, govern, curate and add business context and policies to data assets at scale — improving the productivity of business analysts, data stewards and data scientists and reducing time to insight. With Collibra Data Catalog, customers empower all their data citizens with trusted data to make impactful, data-driven business decisions.
