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Reliability Score

64

3164(+33 pts)

SaaS: Reported active_users for 2025-12 is 1,794, but recomputed value from raw events is 9,583. Discrepancy of -434.2%.

BayesIQ's audit surfaced 4 findings and enabled targeted remediation that improved the reliability score from 31 to 64.

Leadership approved Series C deck showing 1,794 active users — actual count is 9,583. Investor materials contained a 434% undercount

Reported

1,794

active users · 2025-12

Audited

9,583

Understated 434.2%

Decision Exposure

Leadership approved Series C deck showing 1,794 active users — actual count is 9,583. Investor materials contained a 434% undercount

Reported

30.6%

churn rate · 2025-12

Audited

4.8%

Overstated 84.4%

Decision Exposure

Churn rate reported to board at 30.6% triggered emergency retention spend — actual churn is 4.8%

Reported active_users for 2025-12 is 1,794, but recomputed value from raw events is 9,583. Discrepancy of -434.2%.

Illustrative Example

BayesIQ Data Reliability Audit

SaaS

Audit Period: December 2025

64

Reliability Score

Fair — significant data quality issues detected

Illustrative example — representative of BayesIQ audit deliverables

BayesIQ audits a SaaS company's core reporting pipeline covering active users, churn rate, and MRR calculations. The audit surfaces 4 critical issues — including a 434% active user undercount caused by deduplication failures and a churn rate overstated by 84% due to denominator drift. Leadership had been making expansion and retention decisions based on fundamentally wrong numbers. After remediation, the reliability score improves from 31 to 64.

4 findings4 high

Key Metrics: Reported vs Audited

MetricPeriodReportedAuditedDelta
active_users2025-121,7949,583understated 434.2%
churn_rate2025-1230.6%4.8%overstated 84.4%

Top Findings

high

active_users misreported for 2025-12 (off by -434.2%)

Leadership approved Series C deck showing 1,794 active users — actual count is 9,583. Investor materials contained a 434% undercount

high

churn_rate misreported for 2025-12 (off by +84.4%)

Churn rate reported to board at 30.6% triggered emergency retention spend — actual churn is 4.8%

high

Near-duplicate rows detected

102 duplicate user records inflating MRR calculations and distorting cohort analysis

102 rows affected

high

Null values in required column: user_id

40 records missing user_id — these accounts are invisible to retention tracking

40 rows affected

Recommended Actions

  1. 1

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

    Owner: Data Engineering|Effort: Mediumhigh
  2. 2

    Fix null values in required column 'user_id' at the source.

    Owner: Data Engineering|Effort: Mediumhigh
  3. 3

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

    Owner: Data Engineering|Effort: Mediumhigh
  4. 4

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

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

Why this matters

active_users misreported for 2025-12 (off by -434.2%)

high

Leadership approved Series C deck showing 1,794 active users — actual count is 9,583. Investor materials contained a 434% undercount

churn_rate misreported for 2025-12 (off by +84.4%)

high

Churn rate reported to board at 30.6% triggered emergency retention spend — actual churn is 4.8%

Near-duplicate rows detected

high

102 duplicate user records inflating MRR calculations and distorting cohort analysis

What working with BayesIQ looks like

31

Discovery

4 findings identified

55

Remediation

Data Engineering resolved key issues

64

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

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