Find data in context to help you deliver results

On your journey to make use of the right enterprise data to deliver on data and AI initiatives, it's common to encounter some critical blockers to success. Reliance on manual processes, low enterprise-wide data literacy, on top of a continuous increase in volumes and shapes of data sources and data ingested across an evolving business make delivering growth and creation of new business models a challenge.

When there's a DataOps practice in place to deliver continuous, high-quality trusted enterprise data, focused on enabling collaboration across an organization, you'll be better positioned to drive agility, speed and new initiatives at scale. Root to the practice will be a data catalog tool that will put data in your hands with automated organization and on-boarding of content, consistent definitions and self-service management of enterprise data.

Learn about the capabilities that make IBM Watson® Knowledge Catalog the right fit for your data and business analyst team.


Improve data quality

  • Data quality analysis: Measure the quality of your data using 11 dimensions out-of-the-box, and further customize across every value of every row of every record to reflect a column's quality for your business purposes and compliance.
  • Data lineage: Track where data was originated, and how its consumed, allowing for more trust when accessing data across a large number of supported sources and destinations.
  • Reference data management: Standardize common values used across applications using a drag-and-drop feature to make mapping columns from .csv files to the reference dataset's code, values and description fields simple.

Regulatory compliance

  • Regulatory accelerator: Use natural language processing to extract key terms, available definitions, policies and controls from the regulatory taxonomy to interpret regulation, then map those regulatory terms to your business terms and data assets.
  • Policy management and enforcement: Create policies to describe how sensitive data needs to be handled and automate across the business through data protection, data quality and automation rules.

Govern data lakes

  • Business glossary: Ensure a common terminology is used across the business by leveraging the glossary to support term hierarchies, synonyms, relationships with technical metadata, and any other custom attributes and relationships the organization needs.
  • Automated metadata generation: Reduce the need for users to annotate data manually by using built-in data discovery algorithms that use machine learning to automatically classify the contents of each data set, including names, addresses, zip codes and social security numbers.
  • Discovery: Streamline the process of finding, importing, analyzing and cataloging new data from different sources making it easier to search for, govern and use the data.

Self-service discovery and analysis

  • Workflow: Elevate corporate accountability by allowing data stewards to create, update, review and approve assets, while providing domain expertise to keep users informed of progress at all times.
  • Role-based catalog: Surface business-ready data for data and business analysts to find the right enterprise data available across all databases or applications, search based on their needed context and use data for their projects.

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Try Watson Knowledge Catalog

Take advantage of machine learning and AI to analyze your data. Catalog your data to make it easy to find and use.