Chapter 8 — Writing about data

Chapter 8 — Writing about data

Lesson map

Lesson 08 published. Map, example, EX022.

flowchart TD
  N0["Learning outcomes"]
  N1["8.1 Clarity"]
  N2["8.2 Audiences"]
  N3["8.3 Charts"]
  N4["8.4 Plain language"]
  N0 --> N1 --> N2 --> N3 --> N4
  N4 --> EX["Worked example"]
  EX --> Q["Exercise EX022"]

Logical drawing

flowchart LR
  N0["Learning outcomes"]
  N1["8.1 Clarity"]
  N2["8.2 Audiences"]
  N3["8.3 Charts"]
  N4["8.4 Plain language"]
  N0 --> N1 --> N2 --> N3 --> N4 --> Q["EX022"]

[Learning outcomes]
    |
    v
[8.1 Clarity]
    |
    v
[8.2 Audiences]
    |
    v
[8.3 Charts]
    |
    v
[8.4 Plain language]
    |
    v
[Worked example] --> [Exercise EX022]

Learning outcomes

You will (1) write clear reports on data issues; (2) adapt writing to audiences; (3) use charts honestly.

8.1 Clarity

Amara's first report on loyalty data quality runs to ten pages of tables. Dr. Lindqvist asks her to state the main finding in one sentence. She writes: about one in five loyalty records lacks an email address, which limits digital offers. The rest of the report then supports this sentence. She learns that clear writing starts with knowing the main point, and that tables belong in appendices unless readers need them to follow the argument. Her tutor marks the revised report much higher.

8.2 Audiences

She writes two versions. For the marketing manager, she explains the effect on campaigns and suggests collecting emails at the till. For the data team, she details the rules used, counts per store and the script location. Each audience gets what it needs to act. She notes that one report for everyone usually serves nobody well, because managers skip technical detail and technical staff need more precision than summaries provide. Both readers confirm that their version gave them what they needed to act.

8.3 Charts

Amara draws a bar chart of missing emails by store. Her first version starts the vertical axis at fifteen percent, exaggerating differences between stores. A classmate points out that store managers might feel unfairly blamed. She redraws it starting at zero and adds the number of members per store. She learns that charts can mislead even when numbers are correct, and that honest presentation is part of responsible data work. Store managers later thank her for the fairer chart and the added context.

8.4 Plain language

Amara replaces jargon such as completeness ratio with plain phrases such as share of records with an email address. She keeps technical terms where precision matters and defines them once. Her marketing manager reader says the revised report is the first data report he has read to the end. Dr. Lindqvist notes that governance depends on non-specialists understanding data issues, so plain writing is a core skill for anyone working in the field. She keeps a list of plain substitutes for common terms.

Worked example

Amara reduces a ten-page report to one main finding, writes versions for marketing and data teams, redraws a misleading chart from zero and replaces jargon with plain phrases.

Synthetic case. Reports are synthetic.

Errors and limits

Truncated axes exaggerate differences; start bars at zero.

Exercises

• EX022 — Main finding. What was Amara's one-sentence finding? Rubric: 3 = about one in five records lacks an email;; 2 = partly correct;; 1 = vague;; 0 = none.
• EX023 — Chart fix. How did Amara fix the chart? Rubric: 3 = started the axis at zero and added member counts;; 2 = partly correct;; 1 = partial;; 0 = none.
• EX024 — Jargon replacement. What replaced completeness ratio? Rubric: 3 = share of records with an email address;; 2 = partly correct;; 1 = vague;; 0 = none.

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