How to Improve Procurement Data Quality Before Actual Spend Analysis

Written by Anna Pilipiuk, Head of Growth Strategy & Product

Introduction

Procurement teams already know that spend analysis is only as strong as the data behind it.

But in many organisations, the problem is not the analysis itself. The problem is that the underlying data does not contain enough detail to support accurate categorisation, supplier consolidation, meaningful savings analysis or strategic sourcing.

A supplier name and a spend amount are not always enough. To manage spend properly, procurement needs to know what was bought, from whom, for what purpose, and often at a much more detailed level than the raw data provides.

That is why data quality matters before spend analysis begins. If the data is incomplete, inconsistent, inaccurate or too high level, the result will be a spend view that looks tidy on the surface but does not help procurement act on it. This guide explains how to improve procurement data quality so you can get to a spend view you can trust.

Why Data Quality Matters Before Spend Analysis

Procurement leaders are expected to answer questions quickly:

• How much are we spending by category?
• What suppliers are we spending it on? 
• How many suppliers are we using?
• Where are the savings opportunities?
• Where are we exposed to risk?

If the data is weak, those answers become slow, uncertain or misleading.

A dataset that only shows a supplier name and a spend amount does not tell procurement much. A higher-quality dataset should provide clear visibility into what the spend relates to, including the relevant cost centre, budget owner, GL codes applied and sufficiently detailed descriptions of the goods or services purchased. This granularity is critical for enabling procurement teams to compare like with like, identify overlapping spend patterns, inform category management strategies and future sourcing opportunities, and challenge organisational spend more effectively.

The same principle applies across all categories, especially in Indirect categories where the majority of spend is typically service-based. Professional Services, IT, Facilities Management, Travel, and Marketing spend all need enough detail to be interpreted correctly. Without that detail, procurement cannot reliably see where money is going or how to improve it.

Where Procurement Data Usually Falls Short

Most organisations run into the same issues:

• Supplier names are inconsistent across systems. One supplier may appear under several different variations due to differences in finance system setups, the use of multiple legal entities or supplier accounts, inconsistent naming conventions, manual data entry errors. It makes sense from Finance perspective, but not from Procurement.
• Transaction descriptions are too high-level to be useful. “Consulting,” “software,” or “marketing” may be too broad to support the analysis for procurement activities.
• Data is fragmented across ERP, accounts payable, travel platforms, corporate card feeds, spreadsheets and local systems, all collecting different data points which is hard to standardise and can lead to duplications if merged together.
• Important fields are missing. There may be no description, no stakeholder, no project name or no product information.
• Depending on organisational maturity, additional enrichment fields — such as preferred supplier status, contract coverage, supplier segmentation, or risk indicators — may be missing or inconsistently maintained, limiting the depth and effectiveness of spend analysis.

These issues are common, and they are exactly why spend analysis often becomes a manual exercise before it becomes a strategic one.

1. Consolidate All Relevant Data Sources

The first step is to bring the data together.

That usually means combining:

• ERP data
• Accounts payable data
• Supplier master data
• P-card data
• Travel and expense data
• Contract data
• Invoice data
• Spreadsheets and offline records where necessary

The goal is to create one view of spend, even if the source data originally lives in multiple systems.

This matters because the detail needed for procurement analysis is often split across different places. One system may hold the payment information, another may hold the description, and another may hold the supplier details. If those records are not connected, procurement only sees part of the picture.

As part of the consolidation process, it is important to enrich line-level transactions with all relevant supporting data points, leveraging the data from Supplier Management Systems, CMS and others.Applying these attributes consistently at transaction level enables more accurate and meaningful analysis later on.

Particular attention should also be paid to potential duplication issues when integrating multiple data sources. For example, a single expense recorded in an ERP system may appear as several segmented transactions once travel or P-card data is incorporated. Without careful reconciliation and matching logic, this can lead to double counting or distorted spend visibility. Ensuring transactions are correctly matched and aligned across systems is therefore critical to maintaining data accuracy and integrity.

Consolidation creates the foundation. Without it, the rest of the work becomes much harder.

A single, consolidated dataset is essential before any cleaning or analysis can begin, and forms the foundation for effective spend visibility.

Step 2: Standardise the Data Structure

Once the data is consolidated, it needs to be standardised.

That means making sure the data follows a consistent structure across all records. Dates should be formatted the same way. Currency should be converted to a single currency you are reporting in, applying the right conversion rate where applicable. Supplier names should follow a standard format. Fields should be aligned so the same type of information appears in the same place across the dataset. If there are gaps or missing information for certain transactions, it is important to understand whether the data is genuinely not applicable or whether that should be addressed later in the process.

This stage is not about interpretation. It is about making the data usable. Standardisation is what gives procurement a clean working base.

Step 3: Clean Obvious Errors and Duplicates

Once the structure is standardised, the next step is cleansing.

This is where obvious issues are removed or corrected. Common examples include human errors in the descriptions of goods or services purchased, incorrect coding or categorisation selections that clearly do not align with the nature of the spend, transactions which are not related to third party spend such as intercompany transactions and taxes, incomplete or outdated master data.

That kind of error is common when non-procurement teams are entering data or when the data is downloaded from ERP systems, picking up every single transaction.

Cleaning the data does not mean making assumptions. It means fixing what is clearly wrong and removing noise that would distort the analysis.

This is critical to building strong category management strategies.

Step 4: Enrich the Data Where Detail Is Missing

This is the step that makes the biggest difference.

In many cases, the raw transaction data simply does not contain enough detail to support accurate categorisation. That is where enrichment comes in.

Data enrichment means adding the extra information procurement needs to understand the spend properly.

That might include:

• Looking at contracts to identify what was actually purchased
• Checking invoices for a more detailed description
• Reviewing supplier websites or publicly available information
• Speaking with internal stakeholders who own or use the spend
• Adding a new field that explains the real nature of the purchase

For example, if the data says only “consulting,” procurement may still not know whether that is legal consulting, tax consulting, procurement consulting, audit support or management consulting. Those are very different spends, and they should not all be allocated to the same category in your taxonomy.

The same is true for software. A record might show a software supplier and a spend amount, but not tell procurement whether the purchase was for business software, licences, or marketing. To procurement, that distinction matters. Enrichment is what turns a generic transaction into analysable spend.

Manual vs AI-Assisted Enrichment

Parts of this enrichment process can also be partially automated through the use of AI. We are not talking about the spend analysis itself yet, but about the acceleration of collection and interpretation of supporting information. For example, organisations may use lightweight AI agents or large language models such as Claude to analyse supplier websites and identify the types of goods or services a supplier provides. Similarly, AI-powered document extraction tools can be used to review contracts and generate summaries of the products, services, or commercial terms being purchased.

Used appropriately, these tools can significantly reduce the manual effort involved in cleansing data and extracting additional contextual information required for more accurate categorisation and spend analysis.

What AI cannot solve is the lack of underlying data quality and detail. If the source data is too vague or high level, AI will still struggle to accurately determine what was actually purchased or how the spend should be categorised.

This is particularly challenging with large global suppliers that provide thousands of different products or services across multiple business areas. Without sufficient transaction detail, validated descriptions, contract information, or stakeholder context, AI models are often unable to identify the precise nature of the purchase and may instead infer or “guess” the category based on limited signals.

For procurement, that creates significant risk. Incorrect categorisation can distort spend visibility, impact category strategies, and ultimately weaken sourcing and supplier management decisions. While AI can significantly improve speed, scale, and validation during the enrichment process, it cannot reliably replace procurement judgement where the underlying data lacks sufficient context.

That is why manual input still matters.

In practice, the strongest approach is usually a combination of both:

• Use automation where the pattern is clear
• Use manual review where the detail is ambiguous
• Use stakeholder input where the source documents are not enough

The point is not to choose between manual and AI. The point is to use both intelligently.

Step 5: Focus on High-Impact Spend First

Not every record needs to be fixed at the same time.

The fastest route to value is to focus on the spend that matters most. In most organisations, a relatively small proportion of suppliers accounts for a large proportion of spend. That means procurement can make faster progress by prioritising the categories, suppliers and business areas with the biggest value at stake.

This is the 80/20 principle in action.

Instead of spending months trying to clean every low-value line, procurement can concentrate on the spend that will actually drive decisions, savings and supplier rationalisation.

That approach is especially important when teams are under pressure to show quick results.

When Is the Data Ready for Spend Analysis?

Procurement data becomes truly usable when the organisation can trust it enough to act on it confidently. The ultimate objective is not simply clean data, but spend data that is categorised at a granular and meaningful level, enabling effective category management and sourcing strategies.

High-level classifications alone are rarely sufficient. Grouping all spend under broad labels such as “professional services” offers little strategic insight. Procurement teams need visibility into the underlying nature of the spend, distinguishing, for example, between audit services, tax consulting, training or recruitment. It is this level of detail that transforms spend visibility into actionable procurement intelligence.

That does not mean every record is perfect. It means the key spend lines contain enough detail to answer practical business questions and support decision-making.

At that point, procurement should be able to:

• See where spend is concentrated
• Compare suppliers accurately
• Make sure the data supports category planning and future management
• Identify consolidation opportunities
• Build a more reliable category view
• Support sourcing activity with confidence

If the data cannot do those things yet, it still needs more work.

Why This Matters for Procurement Teams

For procurement leaders, better data quality is not just about tidy reporting. It is about control and confidence with reduced rework and failed initiatives.

When the data is strong enough, teams can move faster, work with suppliers more effectively, support business initiatives and make better decisions.

That is especially important for large organisations with significant spend, multiple business units with global exposure and complex supplier bases. In that environment, poor data quality does not just slow down analysis. It hides opportunities. And when the business is asking procurement to save money, reduce supplier risk or prepare for M&A activity, hidden opportunities are exactly what you cannot afford.

Watch How Leading Teams Apply This in Practice

If you’re dealing with limited visibility across your spend data, the full session explores these challenges in more depth, including real examples and practical approaches you can apply immediately.

In the webinar, we cover:

• Real-world examples of poor data impacting savings
• Step-by-step approaches to improving data visibility
• How to build category strategies despite data gaps
• Where AI delivers value, and where it doesn’t

Watch the full webinar here