From raw data to decision-ready information
Data can come from systems, documents, forms, machines or manual records. Before use, the meaning, timeliness, units, access controls and authoritative source must be clear.
The data chain
01
Collection
Retrieve only the data fields and documents that the workflow requires.
02
Validation
Check formats, duplicates, missing values, timestamps and units.
03
Context
Link data to case, project, customer, equipment or document version.
04
Output
Show source, data quality and basis of calculation in report or app.
Reporting with traceability
Calculations and KPIs should be performed in deterministic code. AI can help describe documented changes and formulate questions for unexplained deviations, but should not invent causes.
Governance from the start
- Define authoritative sources and data owners
- Preserve version, time and lineage
- Restrict access by role, case and purpose
- Agree on retention, deletion and logging
- Test for leaks between roles, departments and customers
From raw data to decision-ready information
Raw data alone is not decision-ready. Numbers without units, definitions, timestamps and context cannot support either reporting or an AI agent. The same number can mean three different things in three systems, and without a common definition, the disagreement first becomes visible in the management report.
That is why we work with defined key figures: one owner per data source, unique entities, known update times and a documented path from source to report. Then a result can always be traced back to the data it is based on.
Data quality is an ongoing process
Data quality is not a one-off project. Sources change, fields are reused, and integrations fail silently. Validation during loading, validation against permitted value ranges and alerts for deviations make quality measurable in operation.
Access and retention are included: who can see what, how long it is stored and what is deleted. It is clarified based on the sensitivity of the data and the consequence of errors, not as a standard template.
Typical questions
Should data be gathered in one new system?+
Not necessarily. A solution can read from existing sources whose ownership, quality and access can be handled properly.
Can AI calculate KPIs?+
The calculation itself should normally be in testable code. AI can explain the result and point out missing context.
Can machine data be used?+
Yes, when signal meaning, timestamps, units, quality and OT access are clarified.