Data management
Context
Your organisation's data is spread across several systems, with duplicates, inconsistent fields and different definitions from one team to another. Decisions are then made using data whose reliability or definition is not always clear, and every new report requires manual work to reconcile the different sources.
My role
I start by clarifying what the data actually represents and what decisions it needs to support. I structure the data so that it reflects how teams actually work with and use information, not just how it happened to be recorded originally.
I put practical governance in place, with clear rules on who enters what, which source is the reference, where information should be stored and how a correction is traced back to the source rather than remaining isolated in a report. I also build dashboards that provide a reliable, shared view instead of one-off extracts that differ from person to person.
A concrete example
On my personal project LudoExplorer, focused on board game recommendations, I turned a large and inconsistent public dataset into a structured and enriched database, with a classification designed to match how users actually search for information. This structuring work came before any automation or AI functionality.
Let's talk. Your data exists, but its reliability, definition or origin is open to question? Describe the problem and we will look at how to make it genuinely usable.