Data analysts should have access to the raw data layer for several important reasons:
1.
Data Integrity & Transparency
Access to raw data allows analysts to verify the accuracy and completeness of processed or transformed data. It provides transparency into how data has been cleaned, aggregated, or filtered, which helps identify potential issues in upstream transformations.
2.
Flexibility in Analysis
The raw layer provides full granularity, enabling analysts to explore data in ways that might not be possible with curated layers. This is crucial when:
- Investigating anomalies
- Building custom reports
- Performing exploratory or ad hoc analysis
3.
Root Cause Analysis
When data issues arise, analysts often need to trace the data lineage back to its origin. Raw data access enables them to perform root cause analysis without relying entirely on data engineers.
4.
Creating Custom Logic
Standard transformations might not meet every analytical need. Analysts might need to apply custom logic or business rules not captured in the semantic layer or data marts.
5.
Speed & Agility
Waiting for engineering teams to expose new data points or fix curated datasets can delay insights. Direct access to raw data allows analysts to move faster, especially in fast-paced or data-driven environments.
Caveat:
While access to raw data is valuable, it should be governed properly:
- With read-only permissions
- Clear documentation
- Training to ensure responsible use
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