risk identification
Differences in the understanding of business indicators by different members may lead to biased query results and affect the quality of decision-making.
protective measure
- Establishment of a unified data dictionary: Define clearly in Bilbo the caliber of calculation of key indicators
- Predefined query templates: Create standardized query templates for common business scenarios
- Setting up data alerts: Adding explanatory labels to confusing fields
Specific implementation programs
- Sort out 20-30 core metrics commonly used by teams
- Add explicit definitions to Bilbo's "Data Context" on a case-by-case basis
- Creating Sample Queries for Complex Indicators
- Organize regular query review sessions to calibrate understanding
- Setting up a training mechanism for newcomers
Effectiveness Verification
After 3 months of implementation, the effectiveness can be assessed by spot-checking the consistency of query results and feedback from business units, which usually reduces understanding bias by more than 601 TP3T.
This answer comes from the articleBilbo: a smart tool for querying and visualizing data using natural languageThe
May not be reproduced without permission:AI productivity tools " How to avoid understanding bias in data queries when used by teams?
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