BayesIQ

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Reliability Score

85

3085(+55 pts)

Is our sell-through rate really 72% or is the data overstating it?

BayesIQ's audit surfaced 2 findings and enabled targeted remediation that improved the reliability score from 30 to 85.

Buying team ordered $500K+ in seasonal inventory based on 71% sell-through — actual rate is 65%, creating excess stock risk

Reported

71.1%

sell through · 2025-12

Audited

64.9%

Overstated 8.6%

Decision Exposure

Buying team ordered $500K+ in seasonal inventory based on 71% sell-through — actual rate is 65%, creating excess stock risk

128 near-duplicate rows detected (identical on all fields except key columns). This suggests duplicate ingestion or recording.

Illustrative Example

BayesIQ Data Reliability Audit

Retail

Audit Period: December 2025

85

Reliability Score

Good — some issues need attention

Illustrative example — representative of BayesIQ audit deliverables

BayesIQ audits a retailer's inventory and sell-through reporting pipeline. The audit identifies 2 findings — 128 near-duplicate transaction records inflating sell-through aggregations and an 8.6% overstatement in the headline sell-through metric. The buying team had been making seasonal procurement decisions based on phantom demand. After remediation, the reliability score improves from 30 to 85.

2 findings1 high1 medium

Key Metrics: Reported vs Audited

MetricPeriodReportedAuditedDelta
sell_through2025-1271.1%64.9%overstated 8.6%

Top Findings

medium

sell_through misreported for 2025-12 (off by +8.6%)

Buying team ordered $500K+ in seasonal inventory based on 71% sell-through — actual rate is 65%, creating excess stock risk

high

Near-duplicate rows detected

128 duplicate transaction records inflating sell-through aggregations and distorting demand forecasts

128 rows affected

Recommended Actions

  1. 1

    Investigate duplicate records. Add dedup logic keyed on non-key fields.

    Owner: Data Engineering|Effort: Mediumhigh
  2. 2

    Investigate root cause of sell_through discrepancy for 2025-12. Check for duplicate events, missing data, or filter logic differences.

    Owner: Data Engineering|Effort: Smallmedium
Prepared by BayesIQ · 2025-12

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