Report

A reliable database as the foundation for AI applications

Before AI applications go live, we check whether the underlying data is sound – and specifically address any critical gaps.

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Background

Companies embarking on AI or automation projects often encounter data issues: duplicates, inconsistent formats or a lack of standards, which prevent reliable results.

Our Approach

  • 1

    Analysis of the data sources relevant to the AI use case and their quality.

  • 2

    Root cause analysis of key quality issues (including duplicates and missing standards).

  • 3

    Prioritising the measures that have the greatest impact on the AI application.

Result

A verified, reliable database and a roadmap that enables the most critical quality gaps to be targeted and closed – as the foundation for the productive use of AI (see our AI & GenAI Advisory Solution).

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