How to identify strong data analytics talent beyond the CV
Hiring data analysts has become significantly more complex over the past few years, not because there are fewer candidates, but because there are more of them, and many look similar on paper. Almost everyone lists SQL, dashboards, Python, cloud platforms, and “business insights” on their CV. The real challenge for companies today is understanding how to distinguish a strong data analyst from someone who merely checks the technical boxes.
Based on years of interviewing analysts across different industries, markets, and levels of seniority, several consistent green flags emerge that reliably indicate long-term value, strong decision-making ability, and real impact on business intelligence systems.
1. Clear specialization instead of “I can do everything”
One of the strongest green flags in a data analyst is not breadth, but intentional specialization.
Strong analysts are usually able to clearly articulate:
- their preferred data stack (for example, Power BI, Looker, Tableau, Snowflake, BigQuery, SQL-based warehouses);
- the types of analytical problems they enjoy solving most;
- the industries or business models they understand best.
This does not mean they are rigid or unable to adapt, but rather that they have made conscious choices and understand the trade-offs between different tools, architectures, and approaches. In practice, companies do not hire “a generic data analyst”; they hire someone to solve specific problems within a specific technical and business context. Candidates who understand this reality and position themselves accordingly tend to integrate faster and deliver value sooner.
2. A practical portfolio that shows thinking, not just results
A strong portfolio is another clear green flag, especially in data analytics and business intelligence roles.
For visualization-focused analysts, this means real dashboards that can be discussed and critiqued. For analytical or data science roles, this means notebooks, models, or structured problem-solving examples that demonstrate how the analyst approaches ambiguity, data quality issues, and trade-offs.
What matters most is not visual perfection, but transparency of thinking. A portfolio should show how conclusions were reached, how assumptions were handled, and how insights were translated into business-relevant outputs. Portfolios that allow for discussion during interviews almost always outperform static CVs, because they reveal how an analyst actually works.
3. Intellectual curiosity with direction
Curiosity alone is not enough; direction matters.
A strong green flag is when analysts can name specific sources they regularly learn from, such as industry newsletters, blogs, research papers, or well-known experts in data analytics, visualization, or decision science. This demonstrates that learning is intentional and aligned with their specialization, rather than reactive or trend-driven.
Analysts who invest in continuous learning within a clear domain tend to develop deeper expertise over time, which directly translates into better dashboard design, clearer metrics, and more actionable insights for stakeholders.
Why these green flags matter for businesses
From a business perspective, these signals correlate strongly with:
- faster onboarding into existing BI environments;
- better collaboration with product, finance, and leadership teams;
- more actionable dashboards and reports;
- stronger alignment between data, decisions, and business outcomes.
The most effective data analysts are not those who claim to know everything, but those who clearly understand where they create the most value and how their work supports decision-making.
Final takeaway
If there is one principle that consistently holds true, it is this: Specificity beats generality in data analytics.
Analysts who know their strengths, can show their work, and continuously deepen their expertise in a focused direction tend to outperform broader but less intentional profiles, both technically and strategically.
For companies building or scaling business intelligence capabilities, learning to recognize these green flags early can dramatically improve hiring outcomes and long-term ROI from data investments.