What Makes Data Valuable for AI Training?

From text to tasks

Early large language models learned mostly from public text: web pages, books, articles and open code. That made them good at writing and answering questions. The current focus is on models that can carry out tasks: writing and fixing code, operating software, following procedures and checking their own work. Teaching those skills requires a different kind of data.

For a model to learn how to complete a task, it helps to see many examples of the task being completed well, with the steps visible. Public text rarely shows that. Business systems do, because they record work as it happens.

This shift explains why AI developers are interested in records that most companies treat as routine by-products: ticket histories, review threads, runbooks, version history and process documents. None of these were created with AI in mind, which is part of what makes them useful. They show how people really work under real constraints, not how a textbook says the work should be done.

The four parts of a complete workflow

The most valuable data captures the complete workflow. When reviewing a data source, look for whether each of these four elements is present and linked together.

• Objective: what was the goal? A feature request, a customer complaint, a reconciliation task, a design brief.
• Tool use: which systems and actions were involved? Commands run, files edited, queries made, forms filled.
• Decision process: why was one approach chosen over another? Review comments, discussion threads, design notes.
• Verifiable outcome: how do we know it worked? Passing tests, a closed ticket confirmed by the customer, a reconciled ledger, a shipped release.

Why verifiable outcomes matter so much

A record of someone attempting a task is useful. A record that also shows whether the attempt succeeded is much more useful. Verifiable outcomes let AI developers tell good examples from bad ones, measure whether a model is improving, and build environments where a model can practise and be checked automatically.

Software is rich in verifiable outcomes because tests either pass or fail. But other fields have them too: a support ticket marked resolved without being reopened, an invoice matched to a payment, a contract clause that survived legal review. Wherever your systems record a clear result, that data gains value.

Scarcity and domain depth

Data that is hard to find elsewhere is worth more. That includes work in specialised domains such as insurance claims, clinical documentation, tax preparation, logistics planning or industrial engineering, where the knowledge is held inside companies rather than published. It also includes older or less common technology, such as legacy enterprise systems, where public examples are thin.

Depth matters as well. A long, continuous history in one domain shows how practice evolves, how edge cases are handled and how mistakes are corrected. That is harder to replicate than a large volume of shallow records.

What this looks like across different teams

The four-part test applies well beyond software. In each case below, the valuable record is the one that connects the request, the work and the result, rather than any single document on its own.

• Engineering: a feature request, the PRD, commits, code review comments and the passing test run that confirmed it worked
• Customer support: the customer's message, the agent's investigation steps, the knowledge-base articles used and a resolution that was not reopened
• Finance: a month-end close checklist, the reconciliations performed, the adjustments made and the final balanced ledger
• IT operations: an incident alert, the runbook followed, the commands executed, the root-cause note and the confirmation that service was restored
• Legal: a draft contract, the redlines exchanged, the reasons given for each change and the signed final version
• Design: a brief, early sketches, feedback rounds in comments and the approved design that was built

Context, structure and cleanliness

Records gain value when they are connected. A Jira issue linked to a pull request, linked to a design document, linked to a release note forms a chain a model can follow. Isolated files without links lose much of that meaning. Consistent structure, such as standard ticket fields or templated PRDs, also helps, as does data that can be exported in a usable format.

Cleanliness does not mean perfection. Real work is messy, and some of that mess is useful because it shows how people recover from mistakes. But duplicated, auto-generated or corrupted content adds little.

A quick way to judge your own data is to pick ten random records from a source and ask whether someone outside your company could follow what happened in each one: what was asked, what was done and how it ended. If most can be followed, the source is likely in good shape. If most are fragments, look for the linked systems that hold the missing context.

Rights and trust

Finally, data is only valuable if it can be used. That means you need to own it or have the right to share it, and any personal information needs to be handled lawfully, usually by removing or replacing it through anonymization before further use. Data with clear provenance and documented rights is easier to work with and therefore more valuable.

If you are unsure how your data measures up, the initial value assessment with Momentra is designed for exactly this: a discussion of data type, completeness, scarcity and the tasks it could support, before any data is shared and with no obligation.