Seller field guide · 3 min read
How should you prepare a sample for a data buyer?
Prepare the smallest approved sample that answers the buyer’s stated evaluation question. It should represent the relevant workflow, preserve necessary context and disclose its limitations. Sample sharing is still a data transfer, so agree the purpose, recipient, handling and scope before sending records.
Ask what the sample needs to prove
micro1 separates discovery, contracting, delivery and acceptance in its published process. Clarify where a sample fits in the proposed buyer workflow and what decision it will support. Initial interest, technical compatibility and final acceptance are different questions.
Write an evaluation question such as: can these permitted records show the steps from a customer problem to a resolution? Avoid sending a broad archive simply because the buyer has not specified a sample. Ask what record types, relationships and outcome evidence are necessary before choosing the population.
Source notes: micro1: operational data partnerships and process
Represent the workflow rather than cherry-picking wins
A useful sample should make its selection method clear. Include the relevant case types and known limitations instead of selecting only unusually detailed successes. Where failed or unresolved cases are included, label their actual outcomes and explain what information is missing.
This proposed sampling table helps a seller connect the evaluation question to a bounded package. It does not prescribe a statistically representative sample size or a buyer’s acceptance rule.
| Sample choice | What to decide | What to disclose |
|---|---|---|
| Population | Which permitted workflow and period? | Excluded customers, systems and records |
| Variation | Which routine and exception cases matter? | Why those cases were selected |
| Context | Which linked records are essential? | Missing attachments or broken relationships |
| Outcomes | How is success, failure or uncertainty recorded? | Cases without a verified final state |
| Handling | Who can access the package and for what purpose? | Agreed use, retention and removal process |
Source notes: NIST: AI Risk Management Framework
Review the sample as an independent package
Have a reviewer inspect the actual export rather than approving only the selection query. Check embedded content, attachments, filenames and metadata as well as the main text. Confirm that identifiers are handled consistently without exposing the key used to reconnect them to people or accounts.
Attach a short manifest with the package version, selection method, known limitations and approved evaluation purpose. Keep the final licensing decision separate. If the buyer requests a larger or different sample, repeat the scope review rather than treating the first approval as unlimited permission.
Source notes: NIST: Privacy Framework
Illustrative scenario · fictional company
A fictional support team samples an escalation workflow
Fir Software is asked whether its internal support history contains enough reasoning for a proposed evaluation. It selects approved cases spanning routine escalation, an unsuccessful attempt and an unresolved issue. Restricted customers and attachments are excluded. The package notes that unresolved cases lack a final outcome and explains why each case type was included. The buyer receives only the reviewed sample for the agreed purpose, with no implied permission to use the full archive.
When to pause or narrow the scope
Client work and customer files are excluded from this starting scope. Assess company-owned internal records separately from personnel, health, privileged or NDA-restricted material.
- The buyer has not explained the intended evaluation purpose.
- Sensitive information cannot be separated from the proposed sample.
- The sample would materially misrepresent the coverage or quality of the full candidate dataset.
Your preparation checklist
- Write the evaluation question and permitted purpose.
- Define the candidate population and selection method.
- Include meaningful variation with accurate outcome labels.
- Inspect the actual exported content and references.
- Deliver a versioned package with limitations and agreed handling terms.
Start with a description of the records. The quote form does not ask you to upload a dataset.
Discuss your dataSources and further reading
- micro1: operational data partnerships and process · Checked October 26, 2026
- NIST: AI Risk Management Framework · Checked October 26, 2026
- NIST: Privacy Framework · Checked October 26, 2026
This is general information, not legal advice or confirmation that a buyer will accept your records. Published buyer ranges describe their programs, not an offer for your dataset. Buyers may pay QuoteMyData when a deal closes; see how we are paid.
