Weaving Intelligence
AI + BI
Before language models, the intelligence in the business intelligence I built was mostly translation, held up by naming standards nobody was allowed to break. Some of that wave was retired. Most of it was absorbed, and today's AI reads the same names and calls the libraries that inherited its…
Read more →MDM + AI
Rules engines, rule-proposing profilers, auto-approve thresholds, automatic classification and routing were the smart governance of the pre-AI era. They mostly kept their promise. What struggled was adoption, the steward's loop never changed, and today's AI pitch repeats the list.
Read more →Profisee MDM
A governance council approves; it rarely proposes and never builds. Its yes reaches Profisee through the enterprise's own release process, not through Profisee's workflow engine, and its evidence comes back as release notes and a health report.
Read more →General MDM
More governance policy will be enforced by the systems themselves. That will not end the argument about adoption, or simply move it upstream: it is the same argument about who fixes the record and who decides what it should be, held at the rule and the exception instead of in the meeting.
Read more →Enterprise Data Governance
Master data governance and analytical data governance serve different people under different conditions, and should be judged differently. Two operating models, joined only at the definitions they share; the user of the data owns it; and a report keeps its own number when it can say why.
Read more →AI + BI
The one AI myth worth naming is that it will end everyone's job. What the evidence says, what is true instead for natural-language query on master data (garbage in, garbage out; people still frame the question and agree the definitions), where the practitioner's own answer goes too far, and what…
Read more →MDM + AI
When the master data system first automates a steward's routine work, the tricky residue takes over the week and time opens up for problems nobody could chase before. AI should repeat that pattern. What it means for skills, for headcount, and for the experience it frees.
Read more →Profisee MDM
Profisee has no federated-governance feature. A federated model with an enterprise core is built from two ordinary ones, the data model and the matching strategy, and from a rulebook about who owns which attribute.
Read more →General MDM
A governance council is usually built on its charter. It lasts on two things the charter cannot supply: a room of people who can actually decide, and a steady supply of decisions it is required to make.
Read more →Enterprise Data Strategy
A data roadmap that gets funded starts with the problems, not the vision: the problems find the sponsor, the sponsor draws the road, and the budget behind it sets how far it runs. In the current market master data rides that road as part of making data ready for AI, and an honest playbook says so.
Read more →AI + BI
A semantic layer built first makes natural-language answers exact, repeatable and traceable; it does not make them true. Why master data quality comes first, why a useful measurement of the gap is already a project plan, and why the machines will write the semantic layer long before anyone agrees…
Read more →MDM + AI
Budget pressure will hand policy enforcement, routing and, last and in pieces, exception handling to AI, often because it is good enough rather than better. What full automation does to an error, and what to keep doing while it happens.
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