Do You Really Need Clean Data?

Question: How likely will a ProcureTech solution implementation be successful without clean data?

Rob Handfield, PhD (November 2017)

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Quantifying Success Likelihood Without Clean Data

No direct study isolates ProcureTech success rates without clean data, but we can infer from the following industry benchmarks.

Industry Benchmarks:

• McKinsey (2020): 70% of digital transformations miss goals, often due to data quality, suggesting ProcureTech’s similar fate without clean data.
• Standish Group (2020): 31% of IT projects fail outright, 35% are challenged (partial success), with data issues as a top factor. ProcureTech, a subset, likely mirrors this, with ~30-40% partial success possible if workaround processes compensate.
• SNS Insider (2023): 70% of businesses use procurement software effectively, implying 30% struggle, largely due to data. Without clean data, effectiveness drops sharply.

My Take: Many Procurement Insights articles stress continuous self-cleaning data as vital, citing DND’s success (2004-2007) where human-AI synergy cleaned supplier data for 20% savings. Without such mechanisms, failures dominate, as static data can stale “within a relatively short period of time.”

Practical Estimate:

• Full Success (10-20%): Rare without clean data, requiring exceptional workarounds (e.g., manual corrections) or simple use cases (e.g., tail spend). Even then, ROI is limited, risking churn.
• Partial Success (30-40%): More feasible if teams tolerate errors or focus on narrow functions (e.g., basic sourcing), but adoption and scalability suffer, capping CAGR.
• Failure (60-90%): Most likely, as dirty data breaks analytics, integrations, and trust, aligning with the 50-70% failure rate.

Question: What is the ranking of the following ProcureTech solution providers from best to worst when it comes to data cleansing?**

** NOTE: These are the “Elite Eight” solution providers from Joël Collin-Demers 2025 ProcureTech Cup.

Ranking from Best to Worst

The evaluation criteria are based on the Agent-based Metaprise :

• Outcomes: Reducing errors, boosting adoption, and delivering savings, tied to CAGR.
• Data Extraction/Harmonization: Collecting and standardizing data from diverse sources (e.g., ERPs, suppliers).
• AI/Automation: Self-learning algorithms for error detection and continuous cleaning, as in DND’s success.
• Human Oversight: Practitioner validation for contextual accuracy, per Metaprise’s model.
• Enrichment/Scalability: Enhancing data with external insights and handling complex datasets.

Ranking Model #1

Below is a table ranking the ProcureTech solution providers Anvil Analytical, BeNeering, Trust Your Supplier, Aerchain, Crown Procurement, Axiom, Fairmarkit, and ORO Labs from best to worst for data cleansing, based on their ability to extract, harmonize, enrich, and maintain clean data, aligned with my continuous self-cleaning data concept (Procurement Insights, April 15, 2025). The table summarizes their strengths, human-AI synergy, outcomes, weaknesses, and ranking rationale, reflecting the analysis from the previous response.

Rank	Provider	Cleansing Strengths	Ranking Rationale
1	Anvil Analytical	AI-driven spend analytics, CO2 tracking	Top AI cleansing, scalable, Hansen-aligned
2	Trust Your Supplier	Blockchain supplier data, AI compliance	Strong supplier cleansing, Metaprise fit, narrower scope
3	ORO Labs	GenAI orchestration, real-time harmonization	Enterprise-grade cleansing, scalable, less specialized
4	BeNeering	AI sourcing, spend transparency	Robust sourcing cleansing, less proven at scale
5	Aerchain	AI supplier data validation	Effective sourcing, limited enterprise fit
6	Fairmarkit	AI tail spend cleansing	Good for tail spend, lacks breadth
7	Axiom	Basic tail spend cleansing	Minimal cleansing evidence, narrow scope
8	Crown Procurement	No ProcureTech presence	Irrelevant, no cleansing capability

Notes:

• Alignment of the table with the Metaprise model, emphasizing human-AI synergy for continuous self-cleaning data, as in DND’s success. Top providers (Anvil, TYS) mirror this, reducing the 50-70% failure rate and boosting CAGR via retention.
• Crown Clarification: As discussed, Crown Procurement lacks ProcureTech relevance, ranking last due to no evidence.

Ranking Model #2

Based on publicly available information regarding data cleansing capabilities, the following table ranks the specified ProcureTech solution providers:​

Rank	Provider	Data Cleansing Capabilities
1	Anvil Analytical	Offers comprehensive procurement analytics with rule-based data cleansing, automated category-level classification, and decision support tools. Their platform enables rapid setup and delivers actionable insights for spend management. Anvil AnalyticalAnvil Analytical
2	ORO Labs	Utilizes Generative AI to process and enrich large volumes of vendor data, identifying duplicates and parent-child relationships to ensure a clean and accurate master vendor database. ORO BlogSilicon Review
3	Trust Your Supplier	Provides a centralized platform that unifies supplier data from various sources, eliminating data silos and ensuring accurate, up-to-date supplier information. Trust Your SupplierTrust Your Supplier
4	Aerchain	Offers AI-powered procurement solutions with features like continuous monitoring to keep supplier information updated, though specific details on data cleansing processes are limited. Aerchain
5	Fairmarkit	Acknowledges the importance of clean procurement data and is exploring AI-driven solutions to enhance data cleansing and categorization, with preliminary tests showing promise. Fairmarkit
6	BeNeering	Provides cloud-based procurement solutions focused on seamless ERP integration, aiming to eliminate redundant configurations and data duplication. Specific data cleansing functionalities are not detailed. Spend Matters
7	Crown Commercial Service	Engages in data cleansing and quality assurance processes to maintain the integrity of procurement information, primarily within the UK public sector. Public Sector Experts
8	Axiom	Offers procurement services with advanced sourcing tools, but specific information regarding data cleansing capabilities is not readily available. Axiom Technologies Group

This ranking is based on the extent and specificity of publicly available information concerning each provider’s data cleansing functionalities.​

Ranking Model #3

Summary Table

Provider	Data Cleansing Ranking
Anvil Analytical	1 (Best)
Trust Your Supplier	2
ORO Labs	3
Fairmarkit	4
Axiom	5
BeNeering	6
Crown Procurement	7
Aerchain	8 (Lowest)

Rationale:

Anvil Analytical stands out for its comprehensive, automated, and innovative approach to data cleansing, especially in supplier and spend data. Trust Your Supplier is strong in unifying and validating supplier data, focusing on compliance and risk, but less on deep legacy cleansing. ORO Labs and Fairmarkit offer workflow and process automation with some data validation, but not at the same depth. Axiom provides best practices and automation advice but is not a dedicated cleansing platform. The remaining providers lack sufficient evidence of advanced data cleansing capabilities in the available sources.

Ranking Model #4

This evaluation reveals significant variation in how ProcureTech providers approach data cleansing, with clear differentiation between leaders and followers:

1. Anvil Analytical – stands out with the most comprehensive and advanced data cleansing capabilities, particularly in rule-based cleansing and external data enrichment.

2. Fairmarkit – demonstrates excellence through its AI-driven approach and high user satisfaction ratings, confirming the effectiveness of its solutions.

3. BeNeering – offers effective AI tools specifically designed to enhance procurement data quality.

4. Axiom – provides reliable methodologies for data cleaning and integration, though with less specialized focus than the top providers.

5. Trust Your Supplier – emphasizes supplier data accuracy but lacks comprehensive cleansing capabilities across all procurement data types.

6. Aerchain – incorporates automation-based cleansing features within its broader procurement solutions.

7. Crown Procurement – focuses primarily on analytics functionality with limited attention to specialized data cleansing.

8. ORO Labs – shows minimal emphasis on data cleansing, focusing instead on workflow automation.

Summary:

Organizations selecting a ProcureTech provider should carefully consider the importance of data cleansing to their procurement objectives and evaluate providers accordingly. Those requiring high-quality, reliable data for advanced analytics and strategic decision-making should prioritize solutions from the top-ranked providers in this assessment.

Philip Allouche, CEO (April 2025)

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NOTE: In my next post, I will analyze the research on which of the above solution providers you are likely to find ProcureTech initiative success.