Builder's Notes · · 8 min read
From Raw Registry Data to Advisor Intelligence
Public data becomes useful only after identities, dates, product roles and company relationships are interpreted consistently.
By Jan Kluz, Founder, Emable
Jan builds data and AI products for European financial distribution, with a focus on advisor intelligence, decision validation and responsible use of regulatory data.
Public registers contain an extraordinary amount of information. They also contain almost none of the managerial meaning people expect when they first open them.
A register can tell you that an authorisation exists, when it began, which product role it covers and which regulated entity it connects to. A distribution manager asks different questions: Who is active? What experience do they have? Which product areas do they genuinely cover? Is a change a career move, a product transfer or an administrative correction?
Turning the first set of facts into the second set of answers is the real work behind advisor intelligence.
Start by preserving the source
The original record should remain immutable in the analytical pipeline. Cleaning must not mean erasing the evidence.
Emable stores the raw meaning first, then builds interpreted layers above it. This makes it possible to revisit an assumption when a role code changes, an identity match improves or the regulator corrects a record. Without that separation, every cleanup silently rewrites history.
The same principle is useful beyond regulatory data: preserve what the source said, document what you inferred and never confuse the two.
Resolve people without pretending certainty
Names are not unique. Addresses change. Diacritics and company naming conventions vary. A person can appear through multiple product relationships. Identity resolution therefore cannot be a single “same name equals same person” rule.
A defensible process combines several signals and produces a confidence level. Strong identifiers can support an automatic match. Ambiguous cases should remain separate or be queued for review. The cost of an unresolved duplicate is usually lower than the cost of merging two different people and building decisions on the combined history.
This is especially important when the output influences recruitment. A polished profile built from the wrong identity is worse than an incomplete profile that clearly states what is missing.
Separate authorisation movement from career movement
One advisor can operate through different companies for different products. If an insurance authorisation changes while credit and investment relationships remain unchanged, the register shows a real event. But it does not necessarily show that the advisor physically left one organisation for another.
Emable keeps at least three analytical concepts distinct:
- a record-level authorisation change;
- a product-company relationship change;
- a person-level career transition supported by broader evidence.
Managers can then choose the concept that fits the decision. Recruitment teams may care about career stability. Product leaders may care about changes in specialisation. Market analysts may care about the flow of authorisations between entities.
Make time explicit
“Current” is a query parameter, not a permanent property. A profile must state the observation date and the rule used to classify activity. Historic analysis needs equally clear start and end boundaries.
This prevents a common failure: comparing a current snapshot with a historical cumulative universe and treating the difference as market growth or decline.
We also monitor structural breaks in the source. A large batch of imported history, a new reporting interface or a revised code list can create an apparent market event. The pipeline must detect these changes before a chart or AI summary turns them into a story.
Build explanations into the output
Intelligence should not end with a score. A useful advisor profile explains which records support product experience, how tenure was calculated and which attributes are missing or inferred.
For a manager, that changes the conversation from “the system says 82%” to “the candidate matches the region and product requirements, has a stable documented history, and has one unresolved company relationship worth checking.”
That is actionable, reviewable and compatible with human judgement.
The journey from registry to intelligence is therefore not primarily a data-volume problem. It is a definitions, lineage and evidence problem. Once those foundations are correct, AI can make the information faster to explore. Without them, it only makes an unreliable interpretation arrive sooner.
Methodology and limitations
Emable resolves regulatory records into person, company, product and time dimensions. We preserve the original record, document exclusions and avoid treating product-authorisation changes as physical job moves without supporting evidence.