Sharing a recent update we just rolled out: Agentic Data Intelligence in watsonx.data intelligence.

It's designed to turn enterprise data into trusted context, so AI agents can understand business meaning, lineage, and governance policies, not just raw data.

If you're exploring AI agents or copilots, this is a step toward making those interactions more reliable, explainable, and easier to trust.

Read the full announcement blog

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Diana Toma
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I think we often need a classic data enrichment step at a later stage of the data pipeline.

How did you implement this process in your project? Specifically, how did you handle the manual enrichment, correction, and maintenance of business data, and how were these changes integrated back into the overall data flow?

As a possible approach, Apparo Fast Edit complements watsonx.data with a business-oriented write-back and data stewardship layer. While watsonx.data provides the Lakehouse platform for data storage and processing, and watsonx.data intelligence delivers governance, cataloging, data quality, context, and lineage, Apparo Fast Edit enables business users to perform controlled manual data enrichment, corrections, mappings, and maintenance activities.

The resulting enrichment, mapping, and correction tables can be integrated into the data pipeline and subsequently published as curated datasets or data products for downstream analytics and reporting.

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Cognos Tipps: https://www.cognoise.de
Lernvideos: https://apparo.university (also in english - please PM)

Jens Bäumler
Cognos Analytics, Planning Analytics and watsonx BI
Apparo Group, Germany
www.apparo.de
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