What makes business data valuable for AI?

Begin with the task a buyer needs to solve

A large archive can still be a poor match for a specific project. Before asking what a dataset is worth, ask what someone could learn or test with it. Can it show how a case was resolved, how an exception was handled, or how a decision changed when new information arrived?

For an initial internal review, describe one potential task in a sentence. For example: “These records could help evaluate whether a system chooses the correct next step when a delivery is delayed.” That gives your team a concrete hypothesis to investigate.

Look for depth, context, and outcomes

Compare two hypothetical collections. One contains thousands of ticket titles with no replies. The other includes the original issue, relevant context, the sequence of actions, and a reviewed outcome. The second gives a reviewer more information about the work, even if it has fewer records.

Ask whether timestamps are reliable, attachments are available, and records can be linked across systems. Note where a field changed meaning after a software migration. Context that seems obvious to your employees may be impossible for an outside team to reconstruct.

Use seven questions to qualify the asset

1. Usefulness: What concrete task could these records support?
2. Distinctiveness: What experience or domain detail would be difficult to reproduce?
3. Completeness: Can a reviewer follow the beginning, actions, and outcome?
4. Coverage: Which periods, situations, and business units are represented?
5. Quality: What is missing, duplicated, inconsistent, or unverified?
6. Permission: Who can authorize the proposed use, and what restrictions apply?
7. Preparation: How much effort is needed to produce an approved, usable package?

Explain limitations as carefully as strengths

An archive collected during one unusual season may not represent normal operations. A dataset of only successful cases cannot show how failures unfold. Keep those limitations visible when discussing possible uses.

The research paper Datasheets for Datasets proposes documenting how datasets are assembled and how they should be used. For a business owner, the practical lesson is straightforward: a clear description makes it easier for someone else to judge fit.

Separate a planning estimate from a negotiated price

Headcount can help start a conversation about operational scale, but it does not reveal how much eligible data exists or what a buyer needs. A commercial discussion also has to address scope, permitted uses, exclusivity, preparation effort, and acceptance requirements.

Build an opportunity brief before building a revenue forecast. List the evidence you have, the questions still open, and the next person responsible for resolving them. That creates a better basis for an assessment than multiplying employee count by an assumed payout.