Insurance Data — Governance & Analytics

Four years inside a Belgian insurance group — two as a data steward on data quality, governance and GDPR compliance, two as an analyst on data extraction, KPI data preparation and process automation. Currently coordinating a process consolidation project across fifteen people. Specifics anonymised; methodology and outcomes described in detail.

2022 — 2024 · Data Steward

The data steward years were spent on the cases that didn't fit. Records that wouldn't merge across systems. Fields that disagreed with each other. Edge cases the matching rules could not settle. The work was unglamorous and slow, and it taught me more about how insurance data actually behaves than any course could have.

What I learned: data quality is not a problem you fix once. It's a continuous translation between what the business says it wants, what the systems actually capture, and what humans actually do. Automation handles the regular patterns. What stays on a human desk is the texture — and the texture is where the real decisions live.

2024 — present · Data Analyst

The analyst years turn the better-quality data the stewardship years produced into business decisions: pulling customer data out of Salesforce, preparing the datasets behind the KPIs, and SAS extraction of the information behind commission and mass payment runs — work where a wrong field does not produce a wrong chart, it produces a wrong payment. I also automated the team's email workflow in Python, replacing a repetitive manual process.

Current · Coordinating a process consolidation project

My main work now is pulling procedures that were never standardised into one simple workflow, and preparing the data that feeds the KPIs behind it. The coordination runs across fifteen people in different functions, which is most of the difficulty: the technical part is defining a field once, and the real part is getting fifteen people who each had a working local definition to accept a single one.

A KPI is only as good as the agreement underneath it. If two departments count the same thing differently, the dashboard is not reporting reality, it is reporting the disagreement. Most of what I do is making that agreement explicit and then holding it.

Selected case studies

The following case studies are described at the methodology level, with company-specific details and identifying information anonymized in line with confidentiality obligations.

Case 1 · Merging customer records (MDM)

For two years as a data steward my work was matching and merging customer records. The rule is not "the newest one wins": it depends on the contract attached to the record, the source of the information and the retention obligations. An older record can carry the more recent contract, while a newer record may hold only information given verbally. I defined the priority rules, settled conflicts field by field, and identified the cases where a merge must not happen at all.

Methodology — priority by attached contract, field-by-field arbitration, retention rules respected.

Case 2 · The monthly close cycle

At the start of every month around 150 Power BI dashboards reset to red. Getting them back to green is chain work: check that the queries are ready, extract claims and premium data with SAS, prepare the files, load them into Salesforce in bulk with Data Loader, then raise a Jira ticket for the loss-ratio computation once both sides are validated. Two thirds of the chain runs automatically; when a step fails, it has to be found, fixed with IT, and the rest resumed in the right order.

Methodology — data readiness checks, SAS extraction, bulk loading, IT coordination through tickets.

Case 3 · When a number becomes a commission

The ratio produced by that cycle sets the commission tiers paid to brokers. That is what changes the nature of the work: a mistake does not produce a wrong chart, it produces a wrong payment. In the same scope I reconcile broker contracts against the CRM, check broker and agent conditions, and handle data-maintenance requests coming from the claims platform — recording that a payment was made, for instance.

Methodology — contract-to-CRM reconciliation, consistency checks, handling business requests.

What stays on a human desk is the texture — the cases that don't fit. That texture is where most real decisions live.

What this work taught me

• The interesting cases are the ones AI hands back, not the ones it confidently classifies.
• Good data quality looks like a non-event in production. It's noticeable only when it fails.
• Domain knowledge compounds. Four years of working with insurance data is qualitatively different from four months — the patterns you've seen before are what let you spot the new ones.
• Documentation is not optional. The cleanest systems are the ones whose definitions are reconciled and written down.

For role-specific questions about methodology, tools, or anonymized examples, I'm happy to discuss in interview.