Data Analysis
Data analysis is the skill of turning a dataset into a decision. It is assessed on whether someone notices what the data cannot tell them — the biggest analytical failures are confident conclusions from data that never supported them.
Also called: analytics, quantitative analysis, data interpretation
What is data analysis?
Data analysis covers framing a question quantitatively, choosing appropriate cuts and comparisons, distinguishing correlation from causation, quantifying uncertainty, and communicating a finding with its caveats intact.
Every organisation has more data than analysis. The scarce skill is not querying — it is knowing which question the data can actually answer and saying so when it cannot.
What do the levels of data analysis mean?
Four levels, each defined by observable behaviour rather than by years. This is the definition employers set their bar against, and the one every proof is scored to.
- L1Aware
Can summarise a dataset and produce accurate descriptive statistics.
Evidence: Correct summary analysis with clear presentation.
- L2Working
Segments data sensibly, compares against a baseline, and spots obvious data quality problems.
Evidence: Segmented analysis with a baseline comparison.
- L3Independent
Identifies confounds, quantifies uncertainty, and states clearly what the data cannot support.
Evidence: An analysis that names a confound and bounds its conclusion accordingly.
- L4Leading
Sets analytical standards, designs the measurement itself, and is trusted to overturn a popular conclusion.
Evidence: Analytical standards adopted by a team; a reversed organisational decision.
| Level | Label | People proven here |
|---|---|---|
| L1 | Aware | 0 |
| L2 | Working | 1 |
| L3 | Independent | 0 |
| L4 | Leading | 1 |
How is data analysis assessed?
Candidates receive a dataset with a plausible but misleading pattern in it. Scoring rewards identifying the confound, quantifying uncertainty, and stating explicitly what the data does not support.
Which roles require data analysis?
| Role | Level required | Status |
|---|---|---|
| Product Manager | L2 Working | Nice to have |
| Growth Analyst | L3 Independent | Required |
How do you prove data analysis?
Data Analysis: questions people ask
- Do I need SQL or Python?
- The data is provided in a readable form and you may use any tool, including AI assistance. The rubric scores interpretation and honesty about limits, not query syntax.
- What is the most common reason candidates score low?
- Confidently stating a causal conclusion the data cannot support. Naming the confound scores higher than a cleaner-looking answer that ignores it.
- How does this relate to experiment design?
- Analysis reads data that already exists. Experiment design creates data that can answer a question. Roles that need to prove causality usually require both.