Insights Topic
Published INVISOM articles connected to data governance.
9 articles
All insightsTrusted 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.
Enterprise AIOperating Model
Every enterprise has the same argument about whether AI should be centralized or spread across the business. The better question is which decisions should be made where, and how that should change as the organization matures.
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.
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.
Decision IntelligenceFramework
Most dashboards fail at the hand-off, not the chart. Run a decision inventory instead of a report catalogue, and make every metric earn its place by triggering a named owner’s action at an agreed threshold.
Knowledge IntelligenceTechnical Deep Dive
Graph versus vector is the wrong argument. The right one is about which questions your users ask, whether your entities already exist in systems of record, and whether permissions survive every copy of the data.
Trusted DataArchitecture Guide
A single version of the truth is not found by software. It is decided, attribute by attribute, by people with authority to decide. MDM technology only executes those decisions, and fails when nobody has made them.
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.