A general-purpose analysis writeup: the totals, the cross-dimension heatmap, and the finding.
Total revenue
$76.1k
Orders analyzed
260
Avg. order
$292.53
Strongest cell
Email × Accessories
Revenue by channel and category
Where the money concentrates — darker is more revenue
Key insight
The Email × Accessories cell is the single largest revenue pocket at $6.5k. Cross-dimension views like this are where averages hide the story — the heatmap makes the concentration impossible to miss.
Live render — every number above was computed from the sample dataset, and the same engine rebuilds this structure from your file.
A data analysis report is a document that answers a specific question with data and shows its work: the question, the data and method used, what the analysis found, its limitations, and what to do about it. Unlike a dashboard, it reaches a conclusion. The example above shows the shape on an order dataset — headline totals, a channel-by-category heatmap that surfaces where revenue concentrates, and the finding written out, each block carrying the code that produced it.
Below: the structure that works, a short worked example you can model, how academic and business versions differ, and the mistakes that make a report unconvincing.
A data analysis report is built around one question and its answer. That's what separates it from a dashboard, which monitors many metrics without concluding, and from a raw export, which shows data without interpreting it. If you can't state the question the report answers in one sentence, you have a data dump, not a report.
The reader wants three things in order: what did you find, how confident should they be, and what should they do. Everything in the report serves one of those. A chart that doesn't help answer the question — however pretty — is decoration, and decoration is what gets a report set aside.
The dependable structure is five parts: the question, the data and method, the findings, the limitations, and the recommendations. Business reports front-load the answer with an executive summary; academic ones build to it. Either way, every serious report makes its method and its limits visible — that's what makes the findings trustworthy rather than merely asserted.
| Section | What it answers | What goes in it |
|---|---|---|
| Executive summary | What did we find? | The headline answer in two or three sentences (write it last) |
| Question / objective | What are we investigating? | The specific question and why it matters |
| Data & method | Where's this from, and how? | The dataset, its size and date range, and the analysis approach |
| Findings | What does the data show? | Results as charts and tables, each with a one-line interpretation |
| Limitations | How much should we trust it? | Sample gaps, confounders, what the data can't tell you |
| Recommendations | What should we do? | Actions that follow from the findings, tied back to the question |
Here's the structure in miniature, using illustrative numbers (invented, not real data) so you can see how the parts connect.
Question: Which acquisition channel produces the most valuable customers, not just the most customers? Data & method: 4,200 orders over Q2, joined to the channel that acquired each customer; we compared order count, total revenue, and average order value per channel, then cut revenue by channel and product category. Findings: Paid search brought the most orders (38%) but the lowest average order value ($42); email brought fewer orders (14%) but the highest average order value ($96) and the highest repeat rate. The revenue heatmap showed one pocket — email customers buying the premium category — producing a disproportionate share of margin. Limitations: last-touch attribution overcredits the final channel, and one quarter can't separate seasonal effects. Recommendation: shift a portion of the paid-search budget toward email capture and premium-category promotion, and re-measure next quarter with a longer window.
Notice the shape: a question worth asking, a method a reader could challenge, a finding that averages alone would have hidden, honest limits, and an action that follows directly. That's a complete report in a paragraph — a full one just expands each part with the charts.
The same skeleton serves both, but the emphasis flips. An academic or statistical report is judged on rigor and reproducibility: it details the method, states assumptions, reports uncertainty, and builds toward the conclusion so a peer could replicate it. A business report is judged on decision usefulness: it leads with the answer, keeps the method honest but brief, and ends in an action. Knowing which you're writing decides where the conclusion goes and how much method to show.
| Academic / statistical | Business | |
|---|---|---|
| Conclusion | Built to at the end | Stated first |
| Method detail | Extensive, reproducible | Brief but visible |
| Uncertainty | Reported explicitly (intervals, tests) | Flagged as limitations |
| Success measure | Rigor and replicability | A decision made |
| Length | As long as rigor requires | As short as clarity allows |
Graphs and tables carry a report's points more efficiently than prose, and readers' eyes go to them first — so make each visual answer one question and label it with the takeaway, not just the variable. A chart titled "Revenue by channel" is a description; "Email drives the highest-value orders despite lower volume" is a finding.
A few working habits: pair every chart with a one-sentence interpretation; choose the chart that fits the question (a heatmap for concentration across two dimensions, a line for change over time, a bar for comparison); combine related numbers into a claim rather than listing them; and put the exhaustive tables in an appendix so the body stays about findings. Above all, show the method — a finding a reader can't trace is a finding they can't act on.
The reports that don't persuade tend to fail in predictable ways.
Upload your file — CSV, Excel, JSON, or TSV — and ask the analysis questions in plain English. The engine writes and runs real Python over the full dataset (not an estimate, not a chatbot guessing at numbers) and drafts the report structure and text from the computed results, which you edit from there. Every metric and chart carries a "how was this computed?" that shows the exact code and its output — so the 'show your method' rule above is satisfied by default, and 'where did this number come from?' always has an answer.
It opens live in the workspace and ships as a share link plus a print-perfect PDF; your file is analyzed in the browser and never stored on the server. See the AI report generator for the end-to-end flow, AI data visualization for the chart side, and the guides on analyzing CSV data with AI and the best AI for analyzing data.
Everything you need to know about using AnalyzeData.
Headline statistics, one or two visuals that each answer a question, and findings in plain language — with the method visible. This example includes the actual code that computed each block; click "Verified" in the workspace version to see it.
Upload your file, ask the analysis questions, and generate — the engine drafts the report structure and text from computed results, and you edit from there.
A dashboard monitors many metrics continuously and never concludes; it's for watching. A data analysis report investigates one question, reaches an answer, and recommends action; it's for deciding. A dashboard might show that revenue dipped; a report explains why it dipped and what to do. Teams often use both — the dashboard to spot the question, the report to answer it.
As short as the question allows. A focused business report can be two or three pages plus an appendix; a rigorous academic one runs longer because reproducibility demands it. Length should be driven by what the reader needs to trust and act on the finding, not by a page target. If a section doesn't help answer the question, cut it.
Enough for a skeptical reader to judge and, ideally, reproduce the analysis: the dataset and its source, its size and date range, any cleaning or filtering you applied, and the analysis approach (what you compared, grouped, or modeled). You don't need every line of code in the body, but the method must be visible somewhere. A report whose numbers can't be traced is an opinion with charts.
Not necessarily heavy statistics — many valuable business reports are built on totals, comparisons, rates, and well-chosen cross-tabs, honestly interpreted. What you do need is discipline: a clear question, a visible method, and named limitations. Reach for formal statistical tests when you're claiming a difference is real rather than noise, or generalizing from a sample to a population.
Open the template with sample data, then swap in your own file.
Use this template