Data Readiness & Governance

Data,
ready for AI

Data quality, classification, lineage and governance structures – so AI models deliver usable, traceable and compliant results.

Starting point

AI does not fail at models,
but at data

Before AI can work meaningfully in production, data must be findable, understood, classified and controlled. We bring your data landscape to the state that productive AI actually needs.

Our approach is pragmatic: no big-bang catalogue, but a prioritised path along the use cases with the highest business value.

Assess your data

Scope of delivery

  • Data Readiness Assessment along prioritised use cases
  • Data classification and protection-need analysis
  • Build-up of data catalogue, lineage and metadata management
  • Data governance: roles, responsibilities, approval processes
  • Operationalisation with clear KPIs and reviews
Building blocks

Four disciplines,
one governance model

We combine technical data work with organisational accountability structures.

Data Quality

Defined quality rules, automated checks and transparent quality KPIs per data domain.

Classification

Protection needs, sensitivity levels and personal-data status systematically captured and enforced in pipelines.

Lineage

Full traceability: where data comes from, how it is transformed, where it is used.

Governance

Data-owner model, approval flows, audit trails and Swiss-FADP-compliant processing, operationally anchored.

Approach

Three steps to a production-ready data foundation

01 — Assessment

Inventory of relevant data sources, classification status and governance maturity.

02 — Build

Implementation of catalogue, lineage and quality pipelines along prioritised use cases.

03 — Run

Operationalisation with clear ownership, KPIs and continuous reviews.

Assess your data state in a structured way

A Data Readiness Assessment shows within a few weeks which use cases are viable and where targeted investment is needed.

Request an assessment