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9 articles
All insightsData ArchitectureTechnical Deep Dive
The open table formats have converged on the same features. What now decides whether your data stays open is which features you switch on, which engines can still read them, and which catalog protocol brokers access.
Trusted DataFramework
A data product is not a dataset with a new label. It is a promise to consumers, written down as a contract, owned by a named person and measured in public. Without the promise, nothing has changed.
Knowledge IntelligenceArchitecture Guide
Models are now interchangeable; context is not. Organizations that build context once, as a governed shared layer with permissions attached, will reuse it across every assistant and agent. Those that wire it per project will rebuild it every time.
Cloud and PlatformsArchitecture Guide
AI programs fail on data and integration, which is exactly what enterprise architecture is meant to govern. The fix is not more framework. It is a handful of living artifacts that make data and AI decisions explicit, traceable and reusable.
GovernanceImplementation Guide
The NDMO standards tell you what must be true. DAMA-DMBOK tells you how to make it true. Entities that confuse the two end up with 191 documents and very little managed data.
GovernanceArchitecture Guide
Lineage drawn by hand is out of date by the next release. Lineage captured from the systems that move data becomes the evidence layer that supervisors, auditors and AI governance all depend on.
GovernanceOperating Model
Governance that lives in committees grows with the number of meetings. Governance that lives in pipelines grows with the data. Most programs fail because they chose the first and expected the results of the second.
Trusted DataImplementation Guide
Most data quality programs measure everything and fix little. Start from the few dozen data elements that decisions depend on, write rules against them, and run quality as a service with thresholds, owners and resolution times.
Trusted DataExecutive Guide
Enterprise AI rarely fails at the model. It fails on data nobody certified for the job. The fix is not a clean-everything program but trusted data, earned one use case at a time.