AI data preparation: getting legacy data AI-ready

The most valuable data your organisation owns may be the data it stopped using.

Every AI and analytics initiative runs on history: what was bought, made, sold, maintained and paid, over as many years as the organisation can supply. For most enterprises the deepest history is not in the shiny new platform – it is locked in legacy systems, in proprietary formats, behind licences kept alive purely to preserve it.

AI data preparation is the work of turning that history into something models and analysts can actually use: accessible, clean, governed and – above all – kept in context. This paper sets out why legacy data fails those tests, why the data lake is not the shortcut it appears, and how decommissioning doubles as the preparation step.

The first barrier is access: the data sits inside applications that were never built to share it, reached through interfaces nobody uses and licences the business resents paying. You cannot prepare data you cannot get at.

The second is context. Enterprise data is deeply relational: a single product’s master data can span twenty to thirty tables, a supplier can sit nine or ten layers deep, and a purchase order deeper still. The meaning is in the schema – strip it away and what remains is columns without answers.

The third is governance. Historical estates hold personal data, retention obligations and unknown quantities of duplication. Feeding ungoverned data to AI does not just produce bad answers – it reproduces compliance problems at machine speed.

The tempting move is to dump the legacy estate into a data lake or warehouse and call it done. But moving relational data into a lake loses the schema – the relationships that make a supplier a supplier and an order an order – leaving teams to reassemble meaning by hand, table by table, if they still can. Storage is not preparation.

A lake full of tables is not a source of truth. It is a jigsaw with the picture thrown away.

An ungoverned copy also doubles the compliance surface: the same personal data now sits in two places, one of which has no retention rules at all. Preparation has to add governance, not shed it.

Done properly, AI data preparation for legacy estates is four disciplines applied at extraction – once, not per project:

• Extract with context. Take the data out of the source system with its schema and business meaning intact – structured and unstructured, at whatever depth of history is worth keeping.
• Clean on the way through. De-duplicate, standardise and filter at extraction, so every downstream consumer inherits the same cleaned foundation rather than repeating the work.
• Govern from day one. Retention rules, disposal, legal hold and granular access applied to the historical estate – so the AI programme runs on data the organisation is actually allowed to use.
• Keep it queryable. Hold the result somewhere people and tools can reach it – searchable, reportable, with familiar interfaces rather than a raw dump.

This is exactly what decommissioning into Cella does. Legacy data is extracted with its full business context and held in one governed platform for every retired system, SAP and non-SAP alike – where it stays searchable and reportable through familiar interfaces. Freed from the old system but kept in meaning, the history becomes ready for AI-powered natural language search and analytics across your data – clean, governed and queryable – while the systems that held it captive are switched off, taking their cost with them.

600+ systems’ data brought under management, across SAP and non-SAP estates

40+ legacy system types decommissioned – anything with a database qualifies

30+ years of legacy data experience behind the platform

It is the foundation work – context, cleanliness, governance – that makes AI valuable to these datasets.

Start with an inventory: which systems hold meaningful history, how far back it runs, what state it is in, and what governance applies. Most organisations discover their richest training data is exactly where their highest infrastructure waste is – the systems kept alive only for the records inside them.

That overlap is the opportunity: the same project that prepares the data removes the cost of the systems holding it. Talk to our team about making decades of history usable – and cheaper to keep.