Audit Data Readiness Across 4 Dimensions: Volume, Quality, Label, and Freshness
Overcome the 'we have data' illusion across 4 audit pillars, detect latent historical bias, and avoid the treacherous Proxy Label Trap.
Audit Data Readiness Across 4 Dimensions: Volume, Quality, Label, and Freshness
When evaluating AI feasibility, hearing "We have plenty of data" from engineering or data teams is frequently the precursor to expensive product failures. Having gigabytes of logs in a data warehouse and possessing data that meets production readiness criteria for training or grounding AI are fundamentally different realities.
Running Example: FinTrack Logistics aiming to build an AI model that predicts at-risk delivery delays to trigger proactive rerouting. The data team reports: "We have 5 years of logs covering 20 million shipments."
Data Illusion: 'We have 5 years of logs'
4-Pillar Data Readiness Audit
Audit Data Feasibility: 4 Non-Negotiable Data Criteria
Only 120 historical fraud cases (severe class imbalance ratio 1:10,000).
No objective ground truth; CS tagged dispute tickets with 30% label inconsistency.
Adversarial fraud tactics evolve rapidly; models trained on 3-month-old data fail.
Requires exporting raw device fingerprints and biometric traces to third-party endpoints.