---
esco_uri: http://data.europa.eu/esco/skill/143769cb-b61e-47d8-a61e-eedfbec1016c
esco_label: "business intelligence"
relation: optional
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# business intelligence — Data Scientist

For a Data Scientist, 'Business Intelligence' isn’t about *creating* dashboards (though understanding them is vital!). It's the ability to deeply understand business KPIs, how data relates to those metrics, and then proactively identify opportunities for improvement through analysis. Daily tasks involve translating vague business questions – like ‘Why are sales down in Region X?’ – into specific analytical problems. This means knowing *which* data sources contain relevant information (sales records, marketing spend, competitor data), understanding their limitations, and formulating hypotheses to test. It's less about reporting what *happened*, and more about uncovering *why* it happened and predicting future trends impacting the business.

Tools go beyond BI platforms like Tableau or PowerBI; while you might use them for initial exploration/validation, a Data Scientist leans heavily on SQL for data extraction & transformation, Python (Pandas, NumPy) for deeper analysis, and potentially cloud-based data warehouses (Snowflake, BigQuery) to handle scale.  You'll often be building analytical pipelines *feeding* those BI tools with enriched or transformed data – think calculating customer lifetime value from raw transaction history. A key skill is 'reverse engineering' existing dashboards; understanding how the numbers are derived and identifying potential biases or inaccuracies.

The biggest pitfall? Getting lost in technical complexity without connecting back to business impact.  It’s easy to build a sophisticated model that doesn’t address a real problem, or present insights no one understands. Another is assuming data quality – always validate assumptions about the source data and be aware of potential biases. Good BI competence for a Data Scientist looks like proactively surfacing actionable insights *before* being asked, framing findings in business terms (e.g., 'This change could increase revenue by X%'), and clearly communicating limitations.

Finally, remember that true Business Intelligence isn't static. It requires continuous monitoring of KPIs, iterative analysis as the business evolves, and a willingness to challenge existing assumptions. A strong Data Scientist doesn’t just answer questions; they anticipate them and proactively seek out opportunities for data-driven decision making.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
