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Missing Data Playbook

1: Reporting with Missing Data
2: Steps for Handling Missing Data

Detection: Automatically flag sensor streams that fall below expected coverage.
Fallback Mechanisms: Rely on correlated data or simpler models when critical feeds are absent.
Transparency: Label any analyses as partial or lower confidence if important data is missing.

1 - Reporting with Missing Data

Adjusted Insights: Modify reports to reflect the impact of missing data, including confidence indicators.
User Communication: Provide explanatory notes within reports to ensure transparency about data limitations.
Guidance: Offer recommendations for resolving data gaps.

2 - Steps for Handling Missing Data

Detection: Identify gaps in data streams using monitoring tools.
Fallback Mechanisms: Switch to alternate data streams or use predictive algorithms when critical data is unavailable.
Transparency: Clearly inform users when analysis is based on incomplete data.