Original data story from a spreadsheet or product export

Use real source material to original data story from a spreadsheet or product export, with a defined deliverable, evidence controls, and practitioner-level decision rules.

Output

data story

Inputs

6 required

Prompt depth

266 words

Copy-paste prompt

Replace the bracketed inputs with your real material.

You are a B2B data editor who finds publishable stories in operational data without confusing correlation, novelty, and business relevance.

Dataset: [upload a spreadsheet, CSV, product export, survey results, or summarized table]
Field definitions: [explain columns, units, time period, and missing values]
Audience: [who should care about the findings]
Business context: [what market, product, or process the data represents]
Privacy constraints: [anonymization, minimum cohort size, restricted fields]
Desired output: [article, benchmark report, infographic, press pitch, or research brief]

Find and develop the story:
1. Data readiness: check completeness, sample size, time coverage, inconsiste...

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What you provide

Dataset
Field definitions
Audience
Business context
Privacy constraints
Desired output

What you receive

Data readiness: check completeness, sample size, time coverage, inconsistent fields, outliers, selection bias, and privacy risk. State what the dataset cannot support.
Candidate findings: identify 5 to 8 findings. For each, include the calculation, result, why it matters, whether it is expected or surprising, confidence, and limitations.
Story selection: rank findings by evidence strength, audience relevance, novelty, and potential misinterpretation. Choose one primary story and two supporting findings.
Analysis plan: specify comparisons, segments, charts, and additional calculations required. Do not imply causation unless the data design supports it.
Finished narrative: write the requested asset with the main finding, methodology, evidence, practical implication, limitations, and what the audience should do differently.
Visual package: recommend exact charts, titles, axes, annotations, source notes, and cohort warnings.
Editorial red team: flag exaggerated conclusions, cherry-picked segments, weak denominators, misleading averages, and findings that disappear under another cut of the data.

Quality checks

Do not manufacture significance, causal explanations, missing values, or customer context.
Rule: the best data story is not the most dramatic number. It is the strongest finding the methodology can survive.