Written by Farah Ravat, Data Analyst at SpendQube
Key Takeaways:
- Clean data is the foundation: Without accurate, structured spend data, even the most advanced machine learning (ML) algorithms will fall short
- Machine learning accelerates insights: From automated categorisation to anomaly detection, ML makes spend analysis faster, smarter and more reliable
- Practical steps drive results: Applying clear taxonomy, automated checks and explainable models leads to better spend visibility and measurable savings
Picture this: You’re running a business, juggling suppliers, budgets and contracts. Money flows in every direction, but do you really have spend visibility? If you’re unsure, you’re not alone. Many organisations struggle with spend analysis, and that lack of insight can get expensive fast.
Read on for my practical, battle-tested tips that I use as a data analyst to help companies like yours structure their data, uncover hidden risks and make smarter spending decisions.
The Struggle with Messy Spend Data Management
Spend data rarely arrives neatly packaged. It comes from multiple systems, in different formats, and with varying detail. Supplier names might be spelled five different ways. Descriptions can be vague, like “services.” Inconsistent categorisation hides the real story.
Without structure, even simple questions become tricky to answer…
- Are we too reliant on some suppliers or spreading spend too thinly?
- Where could we negotiate better rates or consolidate vendors?
- Is spending aligned with strategic priorities?
Without these answers, inefficiency, overspending and missed opportunities follow.
How I Turn Spend Chaos into Clarity
I start by imposing order on the data so it becomes usable. Every transaction is categorised in a clear hierarchy:
- Level 1: Broad categories like “Technology” or “Professional Services”
- Level 2: Subcategories such as “Cloud Infrastructure” or “Management Consulting”
- Level 3: Specific suppliers, products or services
Clients often provide raw, uncategorised and inconsistent spend data. Using taxonomy tables tailored to each company’s typical transaction patterns, I methodically map their spend into this hierarchy. I work closely with each client to understand their unique context and ensure the structure reflects their business priorities.
For example, a payment to a payroll software provider might be categorised as Level 1: Software, Level 2: Payroll Software, Level 3: PaySuite.
This structured categorisation gives companies better spend visibility into where their money is going, helping them spot areas of high spend and identify opportunities to reallocate resources more effectively.
I also verify that descriptions align with what suppliers provide and flag discrepancies, unusual amounts or duplicates. This builds a foundation of accurate and consistent data, ensuring trustworthy insights.
Once the data is clean, machine learning takes the analysis further.
How Machine Learning Elevates Spend Analysis
Machine learning doesn’t replace human insight; it makes it faster and smarter. Here’s how I use it:
- Smarter categorisation: ML models learn from past data to automatically classify transactions by supplier names, descriptions and patterns
- Anomaly detection: Models flag spend that deviates from the norm, like high-value invoices or suspicious suppliers
- Trend forecasting: Predictive models identify upcoming spend patterns for better budget planning
- Continuous learning: Models improve as they see more data, refining accuracy and relevance
Why Data Quality Comes First
Machine learning is powerful but cannot fix messy data. My focus is to ensure every transaction:
- Is correctly categorised
- Matches actual goods or services
- Reflects accurate spend values
This guarantees that insights are reliable and actionable.
Practical Tips for Better Spend Analysis and Machine Learning Integration
Here are some practical steps I’ve found really helpful across the whole spend analysis journey:
Data Preparation Tips
- Normalise supplier names early and often. Inconsistent names cause classification headaches
- Define and stick to a spend taxonomy. A clear hierarchy makes both manual and machine learning analysis more reliable
- Automate routine validations. Flag duplicates or incorrect supplier codes without wasting time on manual checks
Machine Learning Tips
- Train models on a small set of well-labelled examples. This boosts accuracy quickly
- Combine machine learning with simple statistics. It keeps anomaly detection precise and easy to explain
- Use interpretable models. When decision-makers understand why a forecast or alert happens, they trust the insights more
- Iteratively train your models. Feed corrections regularly to help models learn and improve over time
Analysis and Visualisation Tips
- Visualise your data before drawing conclusions. Charts reveal patterns and anomalies that tables often hide
- Blend internal and external data. Adding market rates alongside your spend data gives you more power in negotiations
- Build explainability into your machine learning. Trust in predictions comes when people understand the logic behind them
Spend Analytics & Machine Learning – Bringing It All Together
Turning messy, unreliable spend data into clear and actionable intelligence is truly transformative. My role is to make sure the data is accurate, structured and ready. Machine learning then spots patterns, detects risks and predicts future trends at scale.
Data analytics and machine learning aren’t just technical tools; they are strategic enablers. They help businesses make smarter decisions, strengthen supplier relationships and optimise spend long term.
The real question isn’t whether you can afford to analyse your spend data. It’s whether you can afford not to.
Curious to learn more?
Check out our other posts for more practical tips on spend visibility, clean data and smarter decision-making.