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

Build a 5-layer architecture that validates, governs, and certifies data before any autonomous agent executes a process. 

The Modernization Bottleneck: Many organizations move legacy workloads to the cloud to gain scale for AI, but moving “messy” data faster only creates faster AI failures. The gap isn’t in the model; it’s the missing Data Trust Layer required to ...

Most SAP data quality implementations take months before they produce a single usable result, and the delay rarely comes from writing rules. It comes from rebuilding knowledge about SAP’s data model that the industry already has, one engagement at a time. Every new project spends time defining business rules for each of the SAP domains like material, customer, vendor, enterpr...

Expanded partnership helps enterprises build trusted data foundations for AI faster as agent adoption accelerates across South Asia

Boston, Mass – July 28, 2026 – Ataccama, the data trust company, today named Emisha its authorized Value-Added Distributor (VAD) across India, ...

An AI agent capable of querying your Snowflake environment is not, by that fact alone, an agent worth trusting with what it finds there. Snowflake has spent the past year closing the first problem, and closing it well. Horizon Context builds semantic views that agents read from a single, shared definition, and that context increasingly travels outward too, reaching Claude, Po...

Every AI initiative, analytics dashboard, and regulatory report rests on the same quiet assumption: that the data underneath can be trusted. Yet the volume and velocity of enterprise data have long since outgrown the way most organizations manage quality — one hand-written rule at a time. “Agentic data quality” has become the phrase everyone reaches for to describe what comes...