Data as the foundation of AI solutions
An AI solution processes the organisation’s data, and its results are as reliable as the data it works with. That is why we assess data readiness before development begins, in relation to the specific application.
The assessment gives the organisation a clear picture: which sources are ready for use, which need preparation and how much effort that will take. The implementation decision is then grounded in facts.
The value of an AI solution is determined by the data it works with.
Data quality
We assess quality across the dimensions that determine the reliability of results:
- Availability — where the data resides, how it is accessed and on what basis the solution uses it.
- Completeness — coverage of the information needed to perform the task.
- Consistency — agreement of the same information across systems.
- Timeliness — a refresh frequency matched to the needs of the process.
- Lineage — the ability to trace where each piece of information comes from and what transformations it has undergone.
- Harmonised terminology — shared definitions of key terms across all departments that use the data.
Data security
Data entering AI solutions is subject to the same protection rules as in the source systems. During preparation, we:
- classify data by sensitivity level, including personal data and trade secrets,
- carry over permissions from the source systems, so that each user receives answers based solely on the data they are authorised to access,
- apply minimisation, pseudonymisation and anonymisation wherever the application requires it,
- define where data is processed and stored, together with retention rules,
- provide access logging and a complete audit trail.
Organisations that process data in areas requiring heightened protection combine this work with a private AI environment.
Delivering data to solutions
Prepared data reaches AI solutions in a repeatable, controlled way. We design the flows from source systems, the transformations, the search indexes used by the models and a shared definitions layer used by both analytics and AI.
We record agreements between data producers and data consumers as data contracts. We monitor quality continuously: completeness, timeliness and schema conformance are checked on an ongoing basis, and deviations are routed to the data owner before they affect results.
Ownership and development
Every data source has an owner who is responsible for its quality and decides on changes. The ownership established at this stage becomes part of AI governance and oversight.
With each new application, the demands on data grow: new sources, greater granularity, more frequent refreshes. That is why we manage data readiness as a process built into the solution life cycle and developed alongside the solutions.
Deliverables
The organisation receives:
- a data readiness assessment for the selected applications,
- data classification and a permissions model for AI solutions,
- a shared glossary of business terms,
- data flows feeding the solutions, with quality monitoring,
- assigned ownership of data sources.