Weaving Intelligence
Profisee MDM
Unworked stewardship tasks usually begin with one person leaving. Rebuilding the work around positions, a daily digest, ageing, service levels and escalation gives management a path to follow, but it does not take the responsibility off the people who use the data. On Profisee the clocks and the…
Read more →General MDM
Federated or centralised is usually argued as a choice of where stewards sit. The better question is whose standard the hub is publishing: each domain masters its own, the hub republishes it, and when two domains will not agree, the column splits and the enterprise chooses.
Read more →Enterprise Data Strategy
Governance frameworks handed enterprise data strategy three habits: write the policy first, score against it, and seat a council to run it. Having stood up all three, the verdict is to keep the policy and the score as one habit and give the council the job it does well, signing off on changes the…
Read more →Deep Dive Investigations
Standing up Profisee and proving it works is the quick part, under ten percent of the work on one practitioner's record; about half of what remains goes on integration, security, authentication and networking, mostly outside the product. This Deep Dive follows the calendar end to end, and this…
Read more →AI + BI
Natural-language query is sold as one thing and built as two: a funding demo that needs only a model and a data store, and a system that needs agreed definitions, an approved semantic model served through one governed route, and mature master data underneath. What each part asks of the people…
Read more →MDM + AI
An AI model's output belongs in the data steward's queue as a suggestion, with the evidence it used, approved before anything is written. The research on decision fatigue is contested, but the case for spending a steward's attention carefully, and for introducing the model as another set of eyes…
Read more →Profisee MDM
Profisee is simpler than the platforms it is sold against, and almost everything it does badly is the price of that simplicity. At mid-market volumes the price is close to nothing; at the top of the Fortune list it lands on whoever builds the integration.
Read more →General MDM
Data stewardship began with the people who noticed errors made somewhere else. Since then the fix has often gone first to whoever the damage landed on, and been pushed on when they could not finish it: that is the honest answer to who owns the data.
Read more →Enterprise Data Governance
Centralised, hybrid and federated governance are not three models to choose between but three tiers of one, with rules of decreasing strictness: a few hard interface rules at the top, a map of who is responsible for what in the middle, and each domain's own stated rules at the bottom. Whether…
Read more →AI + BI
Under the hood, the model is the last step in a chain: mastering decides which records are real, the semantic layer decides what they mean, and the measurements that hold the model fixed show the layer doing the work. What no model can infer is why the business defined a number the way it did.
Read more →MDM + AI
Building AI into master data without a strategy behind it shows first in where the model sits. Keep it out of every stage's default flow and use it as a measured backstop for the records standard processing cannot resolve, or it becomes a cost on every record for as long as the feed runs.
Read more →Profisee MDM
Whether a master data platform is delivering shows in two counts and a completeness score, read as change over time, never as a single reading or a percentage that should reach a hundred. Before the platform exists only the data headed for it can be counted, as a proxy; everything said then about…
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