Decide · Build · Data strategy and foundations

One version of the truth, before you ask AI to use it.

AI is only as dependable as the data underneath it. In most large organisations that data lives in many systems that disagree with each other. We find out where it lives, agree what is true, and build the pipelines that keep it that way.

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When you need it

Sound familiar?

  • The same product, part or customer is described differently in each system
  • Reports that need days of spreadsheet work before anyone trusts them
  • An AI project stalled because nobody could say which data was right
  • Rules on where data may be stored and processed, such as GDPR
What you get
  • A data strategy that the business and IT both sign up to
  • Pipelines from source systems to the services that need the data
  • Data quality rules that run on their own
  • A foundation ready for analytics and AI
How we work

Four steps, one team.

  1. 01

    Find the data

    With IT and the business, map which systems hold what, and where they disagree.

  2. 02

    Agree what is true

    A data strategy: shared definitions, ownership, quality rules and where data may live.

  3. 03

    Build the pipeline

    Clean, reconcile and enrich the data on its way from source systems to the tools people use.

  4. 04

    Keep it right

    Automatic checks that catch problems as data changes, so quality does not drift back.

Where mistakes are expensive

An AI system built on data that disagrees with itself will be confidently wrong. Fixing the data is less visible than building the model, and usually worth more.

From our work Alfa Laval Decades of service and repair data reconciled across systems, behind a customer self-service portal. See the case →
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Other ways we help

Not sure this is your situation?

Tell us where you are. We will be straight about whether and how we can help.

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