Chapter 28 — Data literacy
Lesson map
Lesson 28 published. Map, example, EX082.
flowchart TD
N0["Learning outcomes"]
N1["28.1 Why literacy matters"]
N2["28.2 Assessing needs"]
N3["28.3 Designing training"]
N4["28.4 Measuring effect"]
N0 --> N1 --> N2 --> N3 --> N4
N4 --> EX["Worked example"]
EX --> Q["Exercise EX082"]
Logical drawing
flowchart LR
N0["Learning outcomes"]
N1["28.1 Why literacy matters"]
N2["28.2 Assessing needs"]
N3["28.3 Designing training"]
N4["28.4 Measuring effect"]
N0 --> N1 --> N2 --> N3 --> N4 --> Q["EX082"]
[Learning outcomes]
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v
[28.1 Why literacy matters]
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v
[28.2 Assessing needs]
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v
[28.3 Designing training]
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v
[28.4 Measuring effect]
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v
[Worked example] --> [Exercise EX082]
Learning outcomes
You will (1) assess data literacy needs; (2) design short training; (3) measure the effect of training.
28.1 Why literacy matters
Many governance problems come from staff not understanding data: keying generic codes, misreading charts or emailing extracts. Amara explains that data literacy means being able to read, work with, analyse and communicate with data appropriate to one's role. Store staff need different skills from analysts. She notes that governance depends on literacy because rules work only if people understand why they exist and how their actions affect data used by others. Amara proposes that the governance group sponsor a literacy programme focused on the most common mistakes.
28.2 Assessing needs
Amara surveys staff in three stores and head office with simple questions: what data they use, what confuses them and how confident they feel. Store staff say they do not know why codes matter. Head office staff say they struggle to interpret percentages and trends. She groups needs by role and identifies three priorities: entry quality for store staff, interpreting reports for managers and safe handling of personal data for everyone. She shares the survey results with store managers and head office team leaders.
28.3 Designing training
Amara designs short sessions of about thirty minutes for each priority. For store staff, she shows how a wrong code at the till leads to wrong stock orders, using a real example with synthetic numbers. For managers, she explains percentages, ranges and misleading charts. For everyone, she covers what counts as personal data and how to share it safely. She keeps sessions practical, with exercises based on staff's daily work rather than abstract examples. Store managers help schedule sessions during quieter hours.
28.4 Measuring effect
Amara measures effect in two ways: short quizzes before and after sessions, and data indicators such as the rate of generic codes in trained stores compared with untrained stores. Generic codes fall in trained stores, though she notes that other changes, such as new barcode scanners, might contribute. Dr. Lindqvist praises her caution and suggests staggering training across stores so comparisons are fairer in future rounds. Amara records her caution in the report, and the governance group approves a staggered second round of sessions for the remaining stores.
Worked example
Amara defines data literacy, surveys needs and sets three priorities, designs thirty-minute practical sessions and measures quiz scores and generic code rates with appropriate caution.
Synthetic case. Surveys and results are synthetic.
Errors and limits
Quiz scores alone do not show changed behaviour; check data indicators.
Exercises
- EX082 — Store priority. What was the store staff priority? Rubric: 3 = entry quality;; 2 = partly correct;; 1 = other;; 0 = none.
- EX083 — Session length. How long are the sessions? Rubric: 3 = about thirty minutes;; 2 = partly correct;; 1 = wrong;; 0 = none.
- EX084 — Fairer comparison. What did the tutor suggest for fairer comparison? Rubric: 3 = staggering training across stores;; 2 = partly correct;; 1 = vague;; 0 = none.
Continue studying — lesson 28 (chapter 28 of 40)
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