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Data quality, governance, master and reference data, and preparing data for AI.
11 articles
The EU Data Act’s access-by-design rules now apply and cloud switching charges end in January 2027. Here is how to turn the obligations into a working data governance program.
Swift’s deferral of the structured address deadline is not a reprieve for payments data. It is a window to build governed reference data for addresses, LEIs, vLEIs and ISO 20022 code lists.
AI agents that act on customers, products and suppliers need to know which records are real and authoritative. Here is how MDM becomes the context layer for agentic AI.
A 26% drop in the UK electricity factor, a simplified EU Taxonomy and the NACE Rev. 2.1 switch show why sustainability numbers depend on governed, versioned reference data.
LLMs and AI agents can write quality rules, triage alerts and propose fixes. This playbook shows where they help, where they hurt, and how to keep humans in control.
Data observability has moved from a nice-to-have to a prerequisite for AI. Here is what changed in 2026, what to monitor, and how to roll it out without drowning in alerts.
Embeddings and LLMs promise better matching of customers, suppliers and products. This guide shows where they beat rules, where they do not, and how to combine them safely.
Most RAG and agent failures trace back to messy documents, not models. This playbook shows how to make unstructured content AI-ready, from inventory and permissions to monitoring.
When nobody trusts the numbers, dashboards go unused and AI projects stall. This 90-day plan focuses on the data that matters most and makes quality visible, owned and measurable.
Synthetic data can unlock AI projects blocked by privacy, scarcity or imbalance, but only with disciplined evaluation. Here is when to use it, how to test it and where it fails.
AI agents are becoming both the biggest new data consumers and a new kind of data steward. Here is how to govern data for agents, and with agents, using active metadata.
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