Creating usable spend data

Procurement teams are under pressure to optimize costs, improve supplier performance, and ensure compliance. Central to meeting these objectives is spend analysis, a systematic approach for evaluating an organization’s historical expenditure data. Equally important within this domain is data classification, the act of organizing procurement transactions into meaningful categories delivery usable spend data and drive actionable insights. This blog post provides buyers in training with an accessible overview of spend analysis in general, followed by an in-depth exploration of data classification practices. Key terms are defined throughout to support understanding and application.

This blogpost support EFFSO’s course about Spend analysis.

What Is Spend Analysis?

Spend analysis (or spend analytics) involves the collection, cleansing, classification, and examination of expenditure data with the goal of uncovering cost-saving opportunities, enhancing procurement efficiency, and ensuring compliance with organizational policies and contracts. Simply put, it answers critical questions such as:

What are we buying?
How much have we paid and in what quantities?
Who are our suppliers and who within the organization is driving the spend?
On what terms are goods and services procured? (sievo.com, en.wikipedia.org)

According to Hansjörg Fromm, spend analysis “is an umbrella term used to capture various strategic activities that are important in shaping the company’s purchasing strategy.” (onventis.com) At its core, spend analysis transforms raw financial and operational data—often scattered across multiple systems—into a coherent “purchasing map.” This map serves as a foundational tool for category management, budget planning, supplier negotiations, and forecasting future volumes.

Key Terms and Concepts

Before diving deeper, let’s define several essential terms:

Spend Data: Information about expenditures on goods and services from external suppliers. This includes invoice lines, purchase orders, P-card transactions, and expense claims.
Data Cleansing: The process of detecting and correcting (or removing) corrupt, duplicate, or inaccurate records within a dataset.
Data Classification: Assigning each transaction to a meaningful category within a procurement taxonomy, enabling aggregation and analysis at various levels.
Spend Taxonomy: A hierarchical structure—often three levels deep—that groups purchases into mutually exclusive, collectively exhaustive (MECE) categories aligned with business needs.
Spend Cube: A multidimensional view of spend data, typically organized by category, supplier, business unit, and time period. (sievo.com)

Understanding these definitions is vital for effective spend analysis and classification initiatives.

The Spend Analysis Process

A robust spend analysis typically follows four core steps:

Data Collection
Gather expenditure data from all relevant sources: ERP systems, procurement platforms, P-card exports, expense management tools, and even ad-hoc spreadsheets. (p-i.com.au)
Data Cleansing
Standardize supplier names, currencies, units of measure, and dates. Remove duplicates and correct erroneous entries. Methods include string-matching algorithms and reference to master data. (p-i.com.au)
Data Classification
Map each transaction to a category within a spend taxonomy using manual rules, machine learning (ML) models, or a hybrid approach. Ensure at least 90–95% classification accuracy for reliable insights. (p-i.com.au, comprara.com.au)
Reporting and Visualization
Create dashboards and reports—often based on a spend cube—to highlight spending patterns, supplier concentration, contract compliance, and savings opportunities. Visual tools facilitate executive-level decision making. (onventis.com, sievo.com)

By following this structured process, procurement teams can move from data chaos to strategic clarity hence creating usable spend data.

Benefits of Spend Analysis

Effective spend analysis delivers tangible value across the organization:

Cost Savings: Identifies maverick spend, duplication, and opportunities for supplier consolidation, which can drive 5–11% savings when ≥90% of spend is classified. (p-i.com.au, sievo.com)
Improved Supplier Management: Highlights top-spend suppliers, underperformers, and areas for renegotiation.
Risk Mitigation: Detects concentration risk by exposing overreliance on single suppliers or regions.
Enhanced Compliance: Monitors adherence to negotiated contract terms, reducing off-contract purchases.
Budgeting and Forecasting: Provides historical baselines that improve the accuracy of future spend projections.

These benefits collectively strengthen procurement’s role as a strategic partner to finance, operations, and the C-suite.

What Is Spend Data Classification?

While spend analysis describes the overall process, spend data classification zeroes in on the crucial step of mapping raw transactions to a structured taxonomy. As defined by the Purchasing Index series, spend classification “is the process of mapping every transaction, invoice line, P-card swipe, [or] expense claim to a clear, business-friendly category.” (p-i.com.au) A well-designed classification scheme replaces generic GL codes with categories tailored to sourcing strategies, enabling precise analytical outcomes.

Why Data Classification Matters

Even the most comprehensive data collection effort is futile without accurate classification:

Executive Dashboards: Require meaningful categories to tell coherent cost-management stories to leadership.
Savings Identification: Unclassified or poorly classified spend often lives in “Miscellaneous,” obscuring true savings potential (e.g., a CFO discovering AUD 18 million under ‘Miscellaneous Services’). (p-i.com.au)
Benchmarking: Custom taxonomies, when aligned with standard codes (e.g., UNSPSC), facilitate external comparisons without sacrificing internal relevance.
Governance and Trust: High-accuracy classification (>95%) builds stakeholder confidence and encourages data-driven decisions. (p-i.com.au)

Without precise classification, every downstream analysis—whether for cost-reduction initiatives or supplier negotiations—starts on shaky ground, hence not creating usable spend data.

Common Spend Data Challenges

Messy and Fragmented Sources

Data often resides in multiple systems—ERP, procurement apps, facilities management tools, ad-hoc spreadsheets—and may lack standardized fields. A comprehensive source mapping exercise is essential to identify all “money out” data streams. (p-i.com.au)

Inconsistent Supplier Naming

Vendors may appear under varying names (e.g., “Acme Corp,” “Acme Corporation Pty Ltd,” “ACME”), inflating supplier counts and skewing concentration analyses.

Currency, Date, and Unit Discrepancies

Mixing AUD, USD, and EUR without proper FX conversion—or litres with gallons—leads to flawed variance analyses. Standardization techniques are critical for apples-to-apples comparisons. (comprara.com.au)

Over reliance on GL Codes

General-ledger accounts focus on accounting rules, not sourcing strategy. They often lump strategic and non-strategic purchases together, hindering category-specific insights. (p-i.com.au)

Taxonomy Drift

Without governance, new products, suppliers, or M&A activities can render a taxonomy obsolete. Regular reviews and drift-detection KPIs keep classifications current. (p-i.com.au)

Addressing these challenges upfront ensures that classification efforts are creating usable spend data.

Classification Methods

Manual Rule-Based Classification

Procurement analysts write IF-THEN rules (e.g., supplier contains “OfficeMax” → Office Supplies). While transparent, this approach struggles to scale and adapt to new vendors or SKUs.

Machine Learning (ML) Approaches

Supervised learning models classify transactions based on features such as item description, supplier name, and price. These models improve over time but require labeled training data and ongoing retraining to prevent drift. (comprara.com.au)

Hybrid Human + Machine

Combines ML predictions with human review for transactions that fall below confidence thresholds or deviate significantly from typical price-variance rules (e.g., ±30% from category average unit price). This hybrid approach balances scalability with precision. (comprara.com.au)

Choosing the right mix of methods depends on data volume, complexity, and organizational tolerance for classification error.

Designing an Effective Taxonomy

A robust spend taxonomy guides accurate classification and intuitive analysis. Key principles include:

MECE (Mutually Exclusive, Collectively Exhaustive): Each transaction fits into one category, and all real-world spend is covered.
Three-Level Hierarchy:
- Level 1: Broad categories (e.g., Facilities, IT, Professional Services).
- Level 2: Sub-categories (e.g., Maintenance Services, Hardware).
- Level 3: Granular buckets (e.g., HVAC, Laptops). (p-i.com.au)
Alignment with Business Structure: Categories should reflect how sourcing teams and budget owners think and operate.
Standard Code Mapping: Map internal taxonomy to external standards (e.g., UNSPSC, NAICS) to support benchmarking and regulatory reporting.

When creating usable spend data, early involvement of procurement, finance, and IT stakeholders ensures taxonomy buy-in and long-term relevance.

Common Pitfalls in Classification

Based on industry experience and published best practices, avoid these errors:

Treating Taxonomy as Finished: New products and suppliers demand ongoing governance. (p-i.com.au)
Skipping Tail-Spend: Ignoring petty cash or field-office P-card data can mask compliance risks, even if it represents only 5% of total spend. (comprara.com.au)
No Feedback Loop: ML models drift; without monthly retraining using corrected classifications, accuracy declines. (comprara.com.au)
Over-engineering With Granular Codes: Excessive category depth can overwhelm users; aim for 8–12 Level 1 buckets. (p-i.com.au)
Relying Solely on GL Codes: As noted, they lack strategic alignment and often conceal real spend patterns. (p-i.com.au)

By proactively addressing these pitfalls, procurement teams can maintain high classification quality and creating usable spend data.

Best Practices for Spend Analysis and Classification

Conduct a Comprehensive Data Audit
Document each data source’s ownership, update frequency, and quality issues. Use a checklist to capture hidden feeds like SaaS auto-renewals. (p-i.com.au)
Standardize and Cleanse Early
Implement supplier-name normalization, currency conversion at transaction date, and unit harmonization before classification. (comprara.com.au)
Iterate Taxonomy Design
Pilot categories with a subset of transactions, gather user feedback, and refine before full rollout.
Adopt a Human-in-the-Loop Model
Use ML for bulk classification, but route low-confidence or anomalous transactions to analysts for review, ensuring ≥95% precision. (p-i.com.au)
Govern and Monitor
Establish a lightweight governance committee to oversee taxonomy changes, review drift metrics quarterly, and authorize updates. (p-i.com.au)
Leverage Visualization Tools
Build dashboards that slice spend by category, supplier, business unit, and time period. Visual cues—heat maps, bubble charts—accelerate insight discovery.
Embed in Procurement Strategy
Align spend analysis outputs with category plans, supplier negotiations, and budgetary reviews to ensure insights translate into action.

Technology and Tools

A growing ecosystem of solutions supports spend analysis and classification:

Spend Analysis Platforms (e.g., Sievo, Onventis): Provide end-to-end workflows from data ingestion to dashboarding. (sievo.com)
Data Preparation Tools (e.g., Alteryx): Facilitate complex ETL and cleansing tasks.
ML-Enabled Classification Engines (e.g., Rosslyn Analytics, Mintec): Accelerate taxonomy mapping with AI.
Business Intelligence Suites (e.g., Power BI, Tableau): Offer rich visualizations for executive reporting.

When selecting tools, consider integration capabilities, ease of taxonomy customization, AI accuracy, and governance features for usable spend data.

Implementation Roadmap and Governance

To turn theory into practice, follow a phased rollout:

Discovery & Audit: Identify data sources, stakeholders, and key use cases.
Pilot Program: Test a small dataset with initial taxonomy and classification methods.
Scale & Automate: Expand to full dataset, implementing ML/hybrid classification and process automation.
Governance & Continuous Improvement: Establish roles, responsibilities, and cadence for taxonomy reviews and model retraining.
Business Integration: Embed spend insights into category management cycles, supplier performance reviews, and budgeting.

A lightweight governance model—comprising procurement leads, finance representatives, and data stewards—ensures taxonomy relevance and classification accuracy remain high over time.

Conclusion – creating usable spend data

Spend analysis and data classification are foundational capabilities for modern procurement organizations. By systematically collecting, cleansing, and categorizing expenditure data, buyers gain the clarity needed to negotiate better contracts, mitigate risk, and drive sustainable cost savings. Although challenges abound—from fragmented data sources to taxonomy drift—following proven frameworks and leveraging hybrid human-machine approaches can yield classification accuracy north of 95% and tangible savings of 5–11%. Armed with robust spend analytics, procurement professionals transition from back-office transaction processors to strategic business partners, unlocking opportunities that resonate across finance, operations, and the executive suite.