Data Storytelling Practice: Explain a Chart Clearly

Explain a chart by stating what was measured, describing the comparison, and separating the observation from its possible causes. In this exercise, monthly resolved-ticket counts rise, but the resolution rate falls. A useful explanation has to account for the denominator before recommending a change.

Inspect the synthetic data

Download the underlying data. For this simplified exercise, resolved tickets refer only to tickets received in that month, measured at a common follow-up interval. This avoids mixing closures from an older backlog into the numerator. The dataset is fictional and contains no real operational measurements.

Create a line chart of the three resolution rates, with months in calendar order and an axis labeled “Resolved within the follow-up interval (%)”. Keep a companion table available. The UK Government Analysis Function recommends clear context and accessible data or text alternatives for charts. Do not make readers estimate exact values from pixels when the table is small enough to show.

Compare three explanations

Explanation A: “The support team improved because it resolved 40 more tickets in June than April.” The count is correct: 120 minus 80 is 40. The claim of improvement is underspecified because incoming demand doubled and the completion share fell.

Explanation B: “Resolution performance fell by 20%.” This is ambiguous. The rate fell from 80% to 60%, a change of 20 percentage points. Relative to the initial rate, the decline is 20 / 80 = 25%. Choose the intended measure and name it.

Explanation C: “Resolved volume rose from 80 to 120 while incoming volume rose from 100 to 200. The resolved share fell from 80% to 60%. We should inspect capacity and ticket mix before deciding why.” This separates the observed quantities from the causal investigation. It is the most defensible of the three.

Write a recommendation with a boundary

Try this original summary: “Check whether staffing and ticket complexity changed as incoming volume increased. Start with June's unresolved tickets and compare their categories with April's. These three months alone do not establish that the team became less efficient.”

A recommendation can be useful without claiming a cause. You might discover that June contains more difficult tickets, a shorter observation window in the real system, an outage or a definition change. Each possibility requires additional evidence. Keep the denominator and measurement window stable before comparing teams or periods.

Recalculate the combined rate

Across all three months, 305 of 450 tickets were resolved, giving approximately 67.8%. The simple average of the monthly percentages is 70%, which gives each month equal weight despite different volumes. Explain why the weighted result answers the combined-ticket question more directly.

As a transfer exercise, change June to 160 resolved tickets. The June rate becomes 80% and the combined rate becomes 345 / 450, about 76.7%. Reword the narrative before changing the chart title. A title written first can tempt you to keep a story after the data changes.

Keep the explanation accessible

Read your summary without the chart. It should still identify the time span, units, direction and main caveat. Avoid color-only labels such as “the green line” and use the series name instead. Practice turning the conclusion into a decision memo, or reconstruct the calculations with the Excel exercise.

Sources and further practice

UK Government Analysis Function: Charts supports the reference principle used here. The exercise, example data and review routine on this page are original Paradigm Study teaching examples.

For a broader workflow, see career upskillers. Bring your attempt and the step that confused you into Paradigm Study for a lesson or focused practice. Start a learning notebook.