Overview: Data Health Check Methodology
This case study shows how a rigorous data health check scored 93.17 across 3.56M distributor transactions, establishing the AI-ready foundation required before any serious pricing or revenue growth analytics can be trusted. The five-dimension data health check methodology evaluates completeness, accuracy and validity, consistency, timeliness, and matrix conformance — giving leaders a single, defensible number for the quality of the dataset feeding every downstream decision. In mid-market environments where decisions often rest on spreadsheets pieced together from multiple ERPs, a data health check is the difference between analytics leadership acts on and analytics that stays in a deck. See the full case study below, or read our related case study on Engineering a Trustworthy Data Foundation.
A disciplined data health check also changes the tone of the analytics program itself. Instead of endless disputes about whether the numbers can be trusted, commercial and finance stakeholders share a common scorecard that makes quality visible, measurable, and improvable — the foundation on which any serious AI analytical engagement is built.
Client Situation
Before AI and Machine Learning analytics can deliver reliable insights, the underlying data has to be trustworthy. The distributor came to Revify with 3.5MM transactions spanning three years — a large, business-critical dataset of unknown quality.
Without a rigorous assessment, any downstream insight risked being undermined by bad inputs: outliers inflating volume metrics, duplicate rows inflating revenue, and inconsistent SKU-to-brand mappings distorting category-level reporting.
The Revify Approach
Assess — Five-Dimension Data Health Check
- Scored the dataset across five weighted dimensions (Completeness, Accuracy & Validity, Consistency, Timeliness, Matrix Conformance).
- Produced transaction-level evidence files (CSVs) for each issue class, so every score was traceable to the specific rows driving it — not an abstract percentage.
Diagnose — Targeted Findings by Dimension
- Accuracy & Validity (Score of 85.00%): quantified outliers — 258,197 rows of unusually high quantities, 123,562 rows of high invoiced sales, 149,019 rows of high costs — each with a 1% threshold exceedance.
- Consistency (Score of 87.50%): surfaced 4,550 SKUs tied to multiple brand types and 124 customers with conflicting ZIP codes, both material distortions for category and geographic reporting.
- Completeness (Score of 100%) and Timeliness (Score of 100%): confirmed no gaps in required fields or time coverage.
- Matrix Conformance (Score of 93.33%): validated 21 of 27 optional fields present, providing strong analytical depth.
Recommend — Prescriptive Remediation
- Delivered specific, prioritized remediation steps with detailed CSV support files, categorized by impact on analytics reliability.
- Leveraged AI to cleanse and complete the dataset for Revify onboarding at the ‘Excellent’ tier with no critical blockers.
Key Findings & Results
The final dataset achieved a 93.17 / 100 Overall Data Health Score — the ‘Excellent’ tier and the minimum standard for Revify onboarding.
Over the three-year window assessed, the data covered 67.7M units, $296.2MM of net revenue and $67.0MM of gross profit — a scale at which even sub-1% data errors have material financial implications.
| IMPACT DIMENSION | QUANTIFIED BENEFIT |
| Overall Data Health Score | 93.17 / 100 (Excellent) |
| Transactions assessed | 3,563,731 |
| Net revenue covered by assessment | $296.2MM (2022–Q1 2025) |
| Gross profit covered | $67.0MM |
| Critical blockers for onboarding | Zero |
| Issue-specific CSV evidence files delivered | 14 |
Why This Matters
| Every dollar of analytics value compounds on the quality of the data beneath it. A 93-out-of-100 score is not a vanity metric — it is the difference between a client acting on signal and chasing noise. |
Conclusion
With a trustworthy, auditable data foundation in place, the distributor could confidently pursue pricing, discount governance, retention and cross-sell analytics knowing that outcomes reflected real business dynamics — not data artifacts.
The Data Health Check is now the repeatable on-ramp for every client Revify onboards, ensuring the analytics engine is always fed with data of proven quality.
Related Case Studies
- From ‘Good’ to ‘Excellent’: Engineering a Trustworthy Data Foundation Across 1.3M Transactions
- Beyond Historical Reporting: Deploying Predictive & Prescriptive Analytics for Mid-Market Pricing Decisions
Further reading
For broader industry perspective on revenue growth management and pricing analytics, see McKinsey’s Growth, Marketing & Sales insights.
