Explore a Live Engagement
This is what a governed analytics engagement looks like end-to-end. Select an industry to walk through real findings, board reports, and governance workflows.
Reliability Score
85
30 → 85(+55 pts)
Retail: 128 near-duplicate rows detected (identical on all fields except key columns). This suggests duplicate ingestion or recording.
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.
BayesIQ Data Reliability Audit
Retail
Audit Period: December 2025
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.
Key Metrics: Reported vs Audited
| Metric | Period | Reported | Audited | Delta |
|---|---|---|---|---|
| sell_through | 2025-12 | 71.1% | 64.9% | overstated 8.6% |
Top Findings
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
Near-duplicate rows detected
128 duplicate transaction records inflating sell-through aggregations and distorting demand forecasts
128 rows affected
Recommended Actions
- 1
Investigate duplicate records. Add dedup logic keyed on non-key fields.
Owner: Data Engineering|Effort: Mediumhigh - 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
Deliverables
Why this matters
sell_through misreported for 2025-12 (off by +8.6%)
mediumBuying team ordered $500K+ in seasonal inventory based on 71% sell-through — actual rate is 65%, creating excess stock risk
Near-duplicate rows detected
high128 duplicate transaction records inflating sell-through aggregations and distorting demand forecasts
What working with BayesIQ looks like
Discovery
2 findings identified
Remediation
Data Engineering resolved key issues
Steady State
Metrics verified and trustworthy
Your Data
This could be your story
What makes BayesIQ different
Findings connected to business meaning
Operational and executive surfaces produced
Remediation governed and tracked
Evidence and decisions preserved
Metric confidence legible over time
Book a Retail Data Diagnostic
A focused $7,500 engagement. We audit your retail data, score your metrics, and deliver a remediation roadmap in 2 weeks.
Book a diagnosticMonthly Metric Reliability Program
Ongoing monitoring, governed corrections, and executive-ready reporting. Starting at $2,500/month.
Learn more