Impacts of Bad Data
Even the best dashboards crumble when data integrity fails. Understanding how corrupted shipment or delay records distort insights is a core skill for analysts.
This audit-driven approach compares the cleansed dataset with the raw intake, hunting for duplicate loads, impossible values, and sudden structure shifts. Any divergence between the baselines signals corrupted records that need fixing before the visual reaches stakeholders.
Image source: generate_visuals.py
Key takeaways
- Duplicate or misloaded records can fabricate performance gains and erode trust.
- Side-by-side views of clean versus corrupted data expose anomalies immediately.
- Build manifest cross-checks into the workflow so errors surface before executive reviews.