Qualify the job before the account
A promising data integration lead needs more than a company in the right sector. Confirm the job: a report silently omits records after a source schema changes. Identify analysts depending on refreshed source data as the possible user group, then find who can authorize a pilot; the working buyer hypothesis is a data engineering lead responsible for reliable reporting inputs.
Ask about the current workaround, custom export scripts and manual spreadsheet reconciliation, and what would make changing it worthwhile. Do not assign a budget or procurement timeline from company size, funding, or job title alone.
Use a category-specific qualification rubric
Use the calculator below to organize your judgment after reading the source. Unknown evidence should remain unknown. Its weights are a transparent prioritization convention, not a prediction of purchase probability.
| Dimension | Evidence to seek | Reason to pause |
|---|---|---|
| Problem | Which source changes should stop publication rather than fill missing values? | The person cannot describe a recent instance of the problem. |
| Product fit | Can you load a bounded source sample, introduce a field change, and verify the failure is explicit? | buyers requiring connectors or data residency conditions that have not been implemented |
| Timing | a new source or schema revision breaks an established reporting job | The proposed change has no confirmed date or relevance. |
| Implementation | How do you know the destination has every expected record? | retries, backfills, and schema changes must preserve data identity |
| Value | a consumer can reconcile row counts and required fields to the source snapshot | The buyer and user disagree on what success looks like. |
Work through an illustrative assessment
Suppose the source says: “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.” That supports investigating the problem, but it does not yet settle purchasing authority or technical compatibility. A reviewer should ask the two discovery questions above and preserve the answers beside the source.
Compare that with: A list of popular database technologies without a data movement problem. This weaker example should not receive the same pain or timing score. A relevant public topic is not evidence of a current evaluation.
Even a strong problem signal should be rejected when the need belongs to buyers requiring connectors or data residency conditions that have not been implemented. Qualifying out prevents a misleading promise and preserves time for a prospect the product can help.
Create a reviewable handoff
A qualified opportunity should contain the original evidence, a summary of the job, confirmed and unknown constraints, the buyer/user distinction, and an agreed next step. Keep a score attached to those facts so another teammate can challenge the conclusion.
Propose a pilot only when the critical gap is addressed: retries, backfills, and schema changes must preserve data identity. Evaluate the pilot through a real observation that a consumer can reconcile row counts and required fields to the source snapshot, and keep that observation separate from the initial qualification score.
About these playbooks and examples
Prepared by DemandBolt with AI assistance as practical planning resources for people building and selling software. The category scenarios and message examples are illustrative, not quotations from real prospects or claims of customer results. Adapt the hypotheses to your product, verify original sources, and review capabilities before making an offer.
The workflow follows our first-customer guide and buying-signal review method. Our PyInvesting and Rae records distinguish actual internal work from targets. Published .