What makes business data useful for AI training and evaluation?
Useful business data connects the situation, the information available, the actions taken and a checkable result. Here is how to assess what your records can support.
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Start with the question the data could answer
A useful business dataset gives an AI team enough evidence to understand a situation, identify what information was available and examine what happened next. Its suitability depends on the capability being developed or evaluated: finding a relevant document, reconciling a transaction and resolving a support case each require different records.
Begin with a concrete question. Could an agent identify why an order was delayed? Could it reconcile an invoice against the purchase order and receipt? Could it propose a software change and check that the reported problem was fixed? The question determines which context, actions and outcomes matter.
Six questions to ask about an archive
Review a small connected example before extrapolating from a storage total. These questions help distinguish the contents of an archive from what a researcher could actually do with it.
| Question | Evidence to look for | A limitation to record |
|---|---|---|
| What was the task? | The original request, relevant policy and information available at the time. | The request exists only as a later summary. |
| How do records connect? | Stable case, order, document or project identifiers across systems. | Matching depends on names or filenames that change. |
| What happened? | Dated actions, revisions, handoffs and decisions. | Only the final file survives. |
| Can the result be checked? | A confirmed state, reconciliation, test or documented resolution. | A record says complete without supporting evidence. |
| What does the sample represent? | Date range, selection rule, counts and known exclusions. | Only unusually complete successes were selected. |
| Which uses are permitted? | Documented source authority, restrictions and the agreed purpose. | Access to a system is being treated as permission for every reuse. |
A delivery exception as a connected example
Consider a fictional distributor investigating an order marked delivered when the customer reports a missing item. The order records the expected quantity. A warehouse event records what was packed. The carrier event records the shipment status. The support conversation identifies the disputed line. A later stock adjustment or replacement order records the resolution.
These records serve different purposes. A delivery status alone cannot establish whether every item arrived. A support note alone may not show what the warehouse knew. Linking them by order, shipment and line-item identifiers makes the disagreement inspectable.
For an evaluation task, a designer could expose only the records available before the resolution and ask an agent to investigate the discrepancy. The later records would be held separately to help check the answer. The example illustrates a design possibility; it is not a Metis customer case or a tested benchmark.
Training and evaluation need different decisions
Training uses examples to change a model's behaviour. Evaluation measures how a model or agent performs under defined conditions. The same source history might inform either activity, but its preparation and permitted use need to be established for each.
For a historical evaluation, decide where the task starts and what the agent is allowed to see. If a later resolution is included in the starting context, the task may test whether the agent can find that answer rather than whether it can solve the original problem. Keep linked records from the same case together when separating training and evaluation material, and check whether near-duplicate cases cross the boundary.
An outcome also needs an appropriate check. A factual retrieval task may need a supported answer; an action task may need verification that the requested state was reached. Anthropic's agent-evaluation guide makes this distinction between a transcript and the resulting environment state. The appropriate check depends on the task.
Read Anthropic's explanation of tasks, transcripts and outcomes
Describe coverage and gaps before estimating scale
Record what a count means. Ten thousand rows could represent ten thousand independent cases, many events from a few cases, or repeated exports of the same records. Useful inventory figures identify the unit, time period, duplicate handling and the share with the required linked evidence.
Describe the selection method alongside the sample. A recent month, a whole project and a set of handpicked resolved cases answer different questions. Include failed, abandoned or ambiguous cases when the intended use requires them and the source can supply them. Explain absences instead of inventing missing events.
A short description of provenance, composition, collection and intended use makes assessment easier to repeat. The Datasheets for Datasets paper proposes documenting those dimensions, along with distribution and maintenance. A simple source inventory can adopt that discipline without assuming the data is already a finished product.
Separate useful evidence from permission to share it
Technical usefulness and permission are separate assessment questions. An export can combine company material with customer messages, employee details, supplier information and licensed components. Identify exclusions and unresolved questions before moving the records.
Preparation should preserve the relationships needed for the intended task while addressing the agreed restrictions. Replacing identifiers inconsistently can break links; retaining a combination of fields can reveal more than each field appears to reveal alone. The proposed release needs review as a package.
Keep claims precise. A dataset that has been cleaned has not necessarily been cleared for training, evaluation or onward distribution. Define the actual use and the evidence supporting it before treating the package as eligible.
Prepare a useful first inventory
A first conversation can start with a description of the records, rather than the records themselves. Use the following outline to make the available material understandable.
- Name the business process and the systems that record it.
- List record types, formats and the available date range.
- Describe stable identifiers and relationships across systems.
- Separate total volume from cases with complete context and outcomes.
- Explain the sample selection rule, gaps and exclusions.
- Describe how new records appear and whether later updates are feasible.
- Identify the source owner, known restrictions and permission questions.
Choose the next step from the evidence
If the records answer a useful question, connect reliably and have a plausible permitted use, a scoped sample review is a sensible next step. If the links or outcomes are missing, establish whether they exist elsewhere before asking for a larger export.
The useful result of an initial assessment is a clear account of what the source can support, what preparation would be needed and what remains unresolved. That account gives both the business and the research team a basis for deciding whether to proceed.
Sources and editorial note
Prepared by Metis with AI assistance and checked against the sources above. Examples identified as fictional are illustrative and do not describe customers or measured results. Cover illustration generated with AI.
Discuss your records
Start with a general description of your systems and available history. Explore a data partnership before sharing business records.