Data your teams trust and your AI can use

Reports that disagree, long waits for a new data set and models that never reach production have the same cause, which is data that is hard to find, hard to trust and slow to move. We help you fix that step by step.

What changes for you

  • One trusted view

    Data from your core systems, channels and cloud services is brought together and cleaned, so every department works with the same numbers.

  • Answers in days, not months

    Automated data pipelines replace hand-built extracts, so a new report or data set does not start a new project.

  • Compliance you can show

    You know what data you hold, where it came from and who used it, and you can prove it to an auditor.

  • AI in production

    Models are built, deployed and monitored in one repeatable process, so they reach the business and stay accurate.

How it works

A data factory has four stages

  1. 1Stage 1Ingestion
    • Data is extracted from its sources
    • It is validated and loaded
  2. 2Stage 2Transformation
    • It is cleaned and enriched
    • It is catalogued and modelled for the business
  3. 3Stage 3Computation
    • Analytics and data science
    • Machine learning, in batches or on streams
  4. 4Stage 4Consumption
    • Reports and dashboards for people
    • Services that other systems call
Data engineering, analytics and operations work as one pipeline, so each new data product arrives faster than the one before.

From data projects to a data factory

Most organisations run data work as a series of separate projects. Each one builds its own extracts, its own rules and its own reports, and each one is slow.

We help you move to a continuous process instead, the approach known as DataOps. Data engineering, analytics and operations work as one pipeline with the four stages shown above.

The technology is only part of it. We also help your teams agree on common rules and guidance, so the process keeps improving after we leave.

For banks: Analytics-in-a-Box

Analytics-in-a-Box for Banking is our ready-made data and analytics platform for banks, built on IBM Software Hub (formerly IBM Cloud Pak for Data). It combines a banking industry data model, data governance, integration and data science in one platform, with modules for:

  • customer insights and a 360-degree customer view,
  • financial reporting, planning and forecasting,
  • regulatory reporting and compliance.

Because the models and integration patterns are already in place, you start from a working base instead of an empty warehouse.

In this area

Technologies we work with

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