Customer research for Data integration

Define the question before collecting names

For data integration, investigate whether a report silently omits records after a source schema changes. Research is useful when it explains a workflow, its owner, and an unresolved constraint. A list of companies in a sector cannot establish that those companies have this problem.

Begin with analysts depending on refreshed source data and check how their work relates to a data engineering lead responsible for reliable reporting inputs. Map the current process around custom export scripts and manual spreadsheet reconciliation. You are looking for the step where the workaround stops being adequate, rather than asking people to validate an abstract product idea.

Search for the language of the problem

Try the queries below in a search engine or the search feature of a relevant professional community. Read the original discussion and its date. Results are starting points for research; these query examples do not claim any particular search volume or the existence of a matching prospect.

Write an evidence note you can revisit

Illustrative source text: “Our nightly import still succeeds but a renamed source field is now empty. We need a way to catch schema drift before reports go out.” Save the actual source URL and date when researching a real example. Keep a short relevant excerpt and your interpretation in separate fields. Do not rewrite your interpretation as something the author said.

The working hypothesis is that a new source or schema revision breaks an established reporting job. The missing information includes whether the author is a data engineering lead responsible for reliable reporting inputs, whether the need is still open, and which implementation requirements remain unverified. Keep this constraint visible: retries, backfills, and schema changes must preserve data identity.

• Observed: the original words, the visible date, the task described, and any explicit requirements.
• To verify: whether the current process is custom export scripts and manual spreadsheet reconciliation.
• Disqualify when the need belongs to buyers requiring connectors or data residency conditions that have not been implemented.
• Next step: one context-appropriate question, or no contact when there is no suitable route.

Use a short discovery interview

Ask about a recent event before showing your product. Let the person explain the sequence in their own words; do not treat agreement with a leading question as validation.

• Which source changes should stop publication rather than fill missing values?
• How do you know the destination has every expected record?
• What would need to be true for you to load a bounded source sample, introduce a field change, and verify the failure is explicit?
• How would you verify that a consumer can reconcile row counts and required fields to the source snapshot?

Turn notes into a research decision

Compare independently sourced accounts of the same task. Keep an example that contradicts your hypothesis, such as this ambiguous scenario: A list of popular database technologies without a data movement problem. It may justify more reading, but it is insufficient to label someone a buyer.

At the end of a bounded research session, decide whether to narrow the audience, ask a specific follow-up, change the pilot, or stop. Keep unknowns visible and store only the information needed for that decision. Research quality is measured by a better decision, not by how many names are collected.