CV gaps
CV gaps for Data Analyst
Data analyst CVs commonly list tools such as SQL, Excel and Power BI without saying what questions were answered, how large the data was, or what decision followed. Readers look for the link between analysis and action.
The 4 gaps to check
Tools without questions
Why it matters: A tool list shows exposure, not judgement.
How to close it honestly: For each main tool add the question you answered with it, such as "Used SQL to find why renewals fell in one region".
No impact on a decision
Why it matters: Reports that change nothing are easy to overlook.
How to close it honestly: State what the analysis led to: a change of plan, a saving, a faster process or a decision someone made, if true.
Data scale and source unclear
Why it matters: Readers want to know if you worked with a spreadsheet or a warehouse.
How to close it honestly: Say what kind of data, roughly how much, and where it came from, in plain terms you can support.
Audience unclear
Why it matters: Analysts are judged on how well they explain findings.
How to close it honestly: Say who read your dashboards or reports, for example "weekly report for the sales leadership team".
Questions that find your real evidence
- What decision was made because of your analysis?
- What was the messiest dataset you cleaned, and how?
- Who used your dashboards without asking you for help?
Only add what is true. If you cannot back a line up with a real example, leave it out.