5c03e07ded
Simpler story (slide decks unread -> three-bullet status email), proper Claude-chat look with avatars and bubbles on both sides. Canonical trigger prompt switched to English everywhere (homepage, architecture, both adapter generators rebuilt) - V7 consistency green, all checks pass. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PDKeXvpT6tENSvyQGLV1Uq
2.8 KiB
esco_uri, esco_label, relation, onet_soc, source, confidence, qa_count, generator, generated
| esco_uri | esco_label | relation | onet_soc | source | confidence | qa_count | generator | generated |
|---|---|---|---|---|---|---|---|---|
| http://data.europa.eu/esco/skill/143769cb-b61e-47d8-a61e-eedfbec1016c | business intelligence | optional | 15-2051.00 | model-knowledge | high | 0 | gemma3:27b (prompt-designed and spot-checked by Claude) | 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).