What this helps you see
Decompose actual-minus-comparison changes by category and reconcile their sum without adding an invented opening balance.
A practical use case
The example was computed with Datamon’s deterministic analysis function. Download the input to rerun the operation, and use the output CSV to verify what the figure displays. The calculation does not require a provider-backed AI service.
Free example data and exact column mapping
The figure uses the downloadable CSV with the exact column mapping below. Public snapshots retain their data notes and transformations. Constructed teaching scenarios are identified in the method notes; they are not customer results. Editorial layouts may combine comparisons from the same records.
The free example contains 8 observations and these columns: Team, Actual, Target. Keep the header row and preserve one observation per row. The table below shows the first records; the CSV retains the full example.
Download analysis input CSVDownload free example CSV
| Team | Actual | Target |
|---|---|---|
| Research | 34 | 40 |
| Design | 47 | 45 |
| Platform | 92 | 85 |
| Data | 71 | 80 |
| Security | 52 | 50 |
| Growth | 63 | 70 |
How to use it in Datamon
- This example is computed by the Datamon analysis engine. The manual Analysis selector does not currently expose a separate contribution-to-change option. Inspect Actual and Target in the input CSV, then import the prepared output CSV and choose Waterfall / bridge to visualize the signed differences.
- Keep the observation unit and reporting period explicit. Select the figure size before adjusting text, and check the chart at the size your reader will see.
Calculation and result
Category contribution = actual - comparison; total change = sum(category contributions)- Step
- Period / category
- Change / total value
- Contribution to change
How to read the example
Compare positions and values with the table, then inspect the legend or size encoding where used. A visual pattern is a description of the displayed observations. It does not by itself identify a cause, justify extrapolation, or repair missing data.
- totalChange
- -14
| Period / category | Contribution to change |
|---|---|
| Research | -6 |
| Design | 2 |
| Platform | 7 |
| Data | -9 |
| Security | 2 |
| Growth | -7 |
Refine and export the result
In Refine, edit the title, subtitle, axis descriptions, source and supported series settings. Use color to identify a comparison rather than to decorate every mark. Export SVG, PNG, JPEG, PDF or PowerPoint and inspect the actual file; save an editable project backup for revisions.
Limits and common mistakes
The result depends on the observation order, units and selected records. Zero denominators and incomplete pairs need explicit treatment. Do not turn a descriptive result into a forecast, and inspect the original observations before excluding an outlier.
What is free and what needs an account?
Reading this guide and downloading its CSV and chart images are free. Datamon currently has no paid checkout or subscription plans in this deployment. Creating and saving an editable visualization requires Google sign-in. Provider-backed AI, cloud analysis, publishing and video depend on configured services; this guide does not promise unlimited free connected services.
Questions before you start
Can I visualize contribution to change with a free example?
Yes. The CSV and preview images on this page are free to download. Follow the field mapping and steps above. Sign in to create and save the editable project; connected-service availability is separate.
Can I replace the example with my own data?
Yes. Preserve the observation unit and the required numeric, category or geographic fields. Recompute any derived values, update the unit and source, and compare the output with known rows before sharing.
Sources and further reading
Report a correction or an access barrier: Email the editorial contact.
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