Forrester Wave Report 2026 for Data Quality: how to read it and why it matters | Ataccama

A global pharmaceutical and diagnostics company replaced a multi-year Informatica IDQ deployment with Ataccama. The move happened alongside a transformation program that retired the company’s SAP ERP system. Today the program runs on Snowflake, covers four business units, and is delivered through a defined process that involves business teams end to end, from business pro...

Enterprises are facing immense pressure to deploy AI agents at machine speed, but senior data leaders are – at least for now – reluctant to enable fully autonomous execution. Not a single participant in Ataccama’s recent virtual Data Think Tank said they believed AI agents were ready to act without huma...

A clear architecture has formed beneath every enterprise AI agent, and the industry has quietly agreed on its shape: a semantic layer that settles what data means, and a context layer that governs how meaning gets used. However, another critical component beneath both is a trust layer that decides whether the data was ever true to begin with.

As data platforms like...

From ad-hoc fixes to systematic excellence. Chart your organization’s path to data management maturity.

Every data management function runs the same core procedures such as raising a data quality issue, requesting access, connecting a source, or defining a business term. What separates a Level 1 organization from a Level 4 one isn’t which procedures...

AI agents need an authoritative view of the customers, suppliers, and products they reason about. MDM establishes that view by resolving fragmented records into governed entities, giving agents a consistent understanding of who or what the data represents.

In this Gartner® report, explore where MDM fits in the context layer for AI agents and how governed master data...