How to assess · For hiring teams
How to Assess Data Visualisation Skills When Hiring
The test formats that actually work for Data Visualisation, what a strong answer looks like, sample questions and a scoring rubric you can use as-is.
The short answer
Assess Data Visualisation with a task, not a conversation: chart critique and redesign, visualise a finding, ai-scored assessment (e.g. cohesyve) or dashboard review. Score it against written criteria you fix before you see any submissions, and weight the criteria that the role actually depends on.
- Chooses the chart form from the question — comparison, distribution, relationship, trend — not from habit
- Uses honest scales, baselines and encodings, and can spot a misleading chart
- Removes clutter: gridlines, legends, colours and labels that do not carry meaning
- Leads with the point: a title that states the finding, annotation where the eye should go
Paste a job description; Cohesyve generates a role-specific assessment and rubric. Ten candidates free, no card.
Data visualisation is the last step in analysis and the one where most of the value is lost. A correct analysis presented in a chart that misleads, clutters or buries the point produces the same outcome as a wrong analysis. The skill is choosing the form that answers the question, removing everything that does not, and being honest about what the data can support. It is not tool-specific and it is rarely tested. This page covers how to assess data visualisation for analyst, BI and data science roles: chart choice, honesty, clarity, and design for the reader.
Why Data Visualisation is worth testing
Poor visualisation costs decisions. A truncated axis exaggerates a change; a dual axis implies a relationship that is not there; a dashboard with thirty widgets tells the reader nothing. Testing shows whether a candidate designs for understanding, and that determines whether their analysis changes anything.
What strong Data Visualisation looks like
- Chooses the chart form from the question — comparison, distribution, relationship, trend — not from habit
- Uses honest scales, baselines and encodings, and can spot a misleading chart
- Removes clutter: gridlines, legends, colours and labels that do not carry meaning
- Leads with the point: a title that states the finding, annotation where the eye should go
- Designs dashboards with a reading order and a decision in mind
- Uses colour with intent, including for colour-blind readers
- Matches the level of detail to the audience
Ways to assess Data Visualisation
Chart critique and redesign
Provide three flawed charts — a truncated axis, a pie with twelve slices, a dual axis implying causation — and ask the candidate to explain what is wrong and sketch the fix.
Pros
Cons
Best for Any level.
Visualise a finding
Give a small dataset and a finding to communicate to an executive. Ask for one chart and a title. Thirty minutes.
Pros
Cons
Best for Stakeholder-facing analysts.
AI-scored assessment (e.g. Cohesyve)
Generate a data visualisation task from the job description — a chart critique, a design brief, a dashboard review — with a rubric. Each candidate receives a different variant; the reasoning is scored alongside the work.
Pros
Cons
Best for Screening an applicant pool fairly before interview time is spent.
Dashboard review
Show a cluttered dashboard and ask what to remove and why.
Pros
Cons
Best for BI roles.
Cohesyve
Run a Data Visualisation assessment on your next opening
Cohesyve generates a unique Data Visualisation task per candidate from your job description, with the scoring rubric attached. Questions are different for every applicant, so they cannot be shared or looked up.
What to test
Chart choice
Whether the form fits the question.
Honesty
Whether the chart tells the truth.
Clarity
Whether the reader gets the point.
Design for audience
Whether it serves the reader.
Sample Data Visualisation questions
When is a bar chart better than a line chart?
EntryLook for Categorical comparison versus continuous trend; lines imply continuity.
What is wrong with a bar chart whose axis starts at 80 instead of zero?
EntryLook for Exaggerates differences; bars encode length; lines can sometimes start elsewhere with care.
A dual-axis chart shows revenue and headcount rising together. What does a reader conclude, and is it justified?
MidLook for Implies a relationship; scales are arbitrary; consider two charts or an explicit ratio.
You have a finding and an executive with thirty seconds. Design the slide.
MidLook for One chart, a title stating the finding, one annotation, nothing else.
A dashboard has thirty widgets and nobody uses it. Fix it.
SeniorLook for Start from the decisions it should support, keep what serves them, establish reading order, remove the rest.
Red flags
- Chooses charts by appearance
- Does not notice a truncated axis
- Titles are labels rather than findings
- Every chart has a legend, gridlines and six colours
- Cannot say who a dashboard is for
Scoring rubric
| Criterion | Weight | What strong looks like |
|---|---|---|
| Chart choice | 25% | Form follows question. |
| Honesty | 30% | Scales and encodings are truthful; misleading charts are caught. |
| Clarity | 25% | The point is immediate. |
| Audience design | 20% | Detail and layout fit the reader. |
Mistakes hiring teams make
- Judging on visual polish
- Not including a misleading chart to catch
- Testing tool features rather than judgement
- Accepting a dashboard with no stated purpose
- Assuming analysts can communicate because they can analyse
Roles that need Data Visualisation
Common questions
Is visualisation worth assessing separately from analysis?
Yes. Strong analysts with weak visualisation lose their findings at the last step. A thirty-minute critique exercise covers it.
What is the best single visualisation question?
Show a truncated-axis bar chart and ask what is wrong. Honesty awareness is the foundation.
Does the assessment need a specific tool?
No. Judgement is tool-independent; sketches and critique are enough.
How long should a visualisation assessment take?
Thirty minutes for a critique; thirty for a design exercise.
Cohesyve · Skill assessments for hiring
Test Data Visualisation before the first interview
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