Data lives in different systems
BI, ERP, the warehouse, exports or spreadsheets hold parts of the context separately.
Use case 00
Data foundation for AI workflows
The data exists, but it isn't ready for automation. We map the sources, quality, ownership and relationships so that reporting, a workflow or AI can be built on top of them.
Where the problem arises
You have data. A solid foundation, not.
Typical signs that a company has data, but can't reliably build a workflow or AI on top of it.
Same metric, different result
Teams use different sources, definitions or calculations for a single number.
Data ownership isn't clear
It's not obvious who owns the source, the metric, a change or the final decision.
Input quality is uncertain
Data is incomplete, out of date or unverified - and it's unclear what to trust.
From a map of sources to a usable foundation
The goal is a practical output you can build reporting, a workflow or an AI pilot on.
We map the data sources
Where data is created, how it flows, who works with it and in what format.
We assess input quality
Completeness, timeliness, consistency and trustworthiness of data across the sources.
We find duplicates and gaps
Different versions of the same numbers, missing links and places where context gets lost.
We define ownership
Who owns the source, the metric, a change and the sign-off.
We design the target state
What to unify, complete or set up so that a workflow can be built on top of the data.
Impact
What improves after the foundation phase
The foundation isn't the goal. It's a shorter and safer route to reporting, a workflow or an AI pilot.
A clearer data foundation
Reporting and the workflow don't rest on the assumption that the data is fine.
A shorter path to a pilot
The pilot starts on verified data, clear links and a smaller risk of surprises.
Greater trust across teams
Metrics have a clearer meaning, source and ownership.
When to start
When data holds up the next step
A foundation makes sense the moment you want to build reporting, a workflow or AI, but it's not clear what to trust in the data.
Reporting shows different numbers
Dashboards or exports contradict each other and teams argue over which version is correct.
Data is split across systems
The context is scattered between tools, spreadsheets and teams.
You're planning an AI workflow or a rebuild
Before investing, you want to know what you are really building on.