Solving Procurement’s Data Quality Challenge with AI and Advanced Analytics

Accurate and reliable data is critical for procurement teams to manage risk, optimize supplier relationships, identify cost-saving opportunities, and streamline processes. It is also key for leveraging AI. But having high-quality data is easier said than done. Procurement teams often have to rely on incomplete transaction data or manually captured data points that are scattered across multiple systems. On top of this, a limited understanding of how data points connect across the end-to-end procurement process or why they matter (is anybody else thinking about category codes in Purchase Requisitions?) makes it even harder to create data-based insights and actions when making decisions.

This article examines the importance of data quality in procurement, outlines key concepts for ensuring data integrity, discusses the limitations of traditional methods, and highlights how artificial intelligence (AI) can enhance procurement data management.

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What is data in a procurement context?  

“Data” is a widely used term, but its meaning relies heavily on the context in which it is used. The dictionary definition of the term states that it describes “factual information used as a basis for reasoning, discussion, or calculation” or “information in digital form that can be transmitted or processed”. This definition is very broad, encompassing anything from economic indicators to personal contact details, and everything in between.  

In the context of procurement, the importance of data has long been recognized and has gained new significance over the last decade. Procurement data can be grouped into a few key categories, including transactional, supplier, category, market, risk, ESG, pricing, contract, performance, and process data. Procurement teams have access to hundreds of unique data properties across the source-to-settle process, which goes way beyond the roughly 15 data points required in an ERP to transact with suppliers. While these data points are not all immediately valuable by themselves, the connections, interdependencies, and subsequent patterns are the enablers of automated insights, processes, and decisions.

What is bad data?

Bad data comes in multiple shapes and forms. To illustrate the point and give an example, a company could have the same supplier in multiple ERPs. Multiple supplier numbers, typos or abbreviations in the name, and other variances make it difficult for a system to recognize that it is always the same supplier. SUPPLIER 1, SUPPLEIR 1, SUPPLIER 1 Inc., SUPPLIER 1 Incorporated. A category manager will recognize that these are the same supplier; the same supplier, a system might not.  

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Another example of bad data could be the association between suppliers and categories, which complicates spend analysis and category strategy development.  

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The importance of high-quality data

Data quality is paramount in procurement as it directly impacts operational efficiency, opportunity identification, and decision-making. The consequences of poor data quality are far-reaching and include:

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• Operational inefficiencies: Inaccurate data can cause delays in procurement processes, requiring manual interventions and leading to increased process costs. Think about those Purchase Requisitions again.

• Costly decisions: Decisions based on incorrect data can lead to poor supplier selection, missed opportunities, and increased risk exposure, resulting in additional costs for the organization. Have you ever seen a twisted number in a proposal that impacted your decision?

• Compliance issues: Inaccurate records can lead to compliance problems, affecting regulatory audits and legal obligations related to ESG or sanctions checks.

• Damaged supplier relationships: Bad data can strain supplier relationships due to miscommunication, lengthy and duplicated onboarding and qualification requests, or delayed payments.

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High-quality data is also crucial for enabling digital transformation and the adoption of AI. According to the Deloitte 2023 CPO survey, data quality issues are considered the most significant barrier to digitalization in procurement (and the benefits that come with it). Meanwhile, in the Hackett 2025 CPO Agenda, data quality concerns are cited as the biggest roadblock to procurement AI adoption to support business objectives. According to the report, more than 70% of organizations see data quality issues as a moderate or major concern.  

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Procurement data quality and analytics challenges

Several challenges hinder effective procurement data processing and analytics:

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• Manual processes: Manual data management is boring and time-consuming, and therefore prone to errors. Especially when stakeholders are involved in creating or selecting data relevant to procurement processes, a lack of understanding of the data's nature and relevance leads to careless data entries. For example, the rationale for adding a cost center to a PR is well understood, but selecting the correct commodity code is a constant struggle for procurement teams worldwide. This is one reason why Intake Management is so popular these days.

• Lack of strategic alignment: Often, procurement data analytics strategies are not aligned with the department's functional strategy, resulting in a disconnect between data insights and business objectives. According to the 2023 Gartner Data and Analytics in Procurement Survey, 67% of procurement leaders report that their top challenge in getting the most out of data and analytics (D&A) is not having a proper D&A strategy aligned with their functional strategy.

• Data fragmentation: Procurement data is often scattered across multiple systems, making it difficult to consolidate and analyze.

• Insufficient skills and resources: Procurement teams often lack the necessary skills and resources to effectively manage and analyze large datasets. Many professionals lack a fundamental understanding of structured data, as exemplified by RFI questionnaires, RFP price structures, or spend analytics. Ask around who knows how to build a table that can be turned into a pivot table or regression analysis.

• Inadequate technology infrastructure: Outdated, inadequate, and disconnected technology limits the ability to capture, process, and analyze data efficiently.

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These challenges, coupled with the impact of poor data quality, underscore the need for effective data management strategies in procurement.

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Key Concepts for Ensuring Data Quality

To ensure high-quality data can enable effective analytics, procurement teams must focus on three critical processes:

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Data Cleansing  

Data cleansing involves identifying and correcting errors, inconsistencies, and inaccuracies within procurement data to ensure its reliability and accuracy. This process addresses common issues such as duplicate records, outdated information, and inconsistent formatting, ultimately improving data quality.  

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Data Harmonization  

Data harmonization involves integrating and standardizing data from multiple sources to create a unified, consistent dataset. By centralizing information such as supplier details, product specifications, and pricing, it eliminates data silos and inconsistencies. This is particularly important in environments with multiple ERP or procurement systems, where, for example, a single supplier may be represented by different numbers across systems. Harmonized data enables more effective spend analysis and supplier management.

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Data Enrichment

Data enrichment involves enhancing existing data by adding supplementary information or valuable insights. This may include updating supplier profiles with certifications, capabilities, credit ratings, risk scores, or ESG compliance metrics. For categories, it can involve incorporating market trends, relevant indices, or currency rates. Enriched data offers a deeper understanding of supplier health, risk exposure, and strategic alignment, enabling category managers to gain a comprehensive, 360-degree view for more informed decision-making.

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How AI Can Help Improve Your Data Quality

Artificial intelligence (AI) offers a transformative solution for cleaning, harmonizing, and enriching procurement data. It can help procurement teams with the following tasks:  

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• Data cleansing: AI algorithms can automatically detect and correct errors in real-time, improving accuracy and efficiency.

• Spend classification: AI can automatically assign or reclassify transactional information according to the category taxonomy, creating a better understanding of the true supply base and spend within a category.

• Data harmonization: AI tools can seamlessly integrate disparate datasets. From translating transactional data to aligning supplier records, harmonized data can increase consistency across systems.

• Data enrichment: AI can enrich data with external insights, such as market trends, financial health, ESG, or risk scores, to create a holistic view of suppliers and support decision-making.

• Visibility and transparency: AI can provide a unified view of procurement data, facilitating faster analysis and more informed strategic decision-making.

• Predictive analytics: AI can recognize patterns and factor in probabilities to support scenario analysis, sales and operations planning (S&OP), and negotiation behaviors.  

• Opportunity identification: AI can highlight inconsistencies and patterns, and recommend improvement initiatives such as harmonizing payment terms, consolidating suppliers, or pricing across products and suppliers.

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By leveraging AI, procurement teams can overcome traditional data management challenges, automate operational processes, and make informed decisions based on high-quality data. This supports strategic objectives, such as cost savings and risk management, and enhances supplier relationships.

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Bad data is a bad excuse

Many Procurement teams claim poor data quality is holding them back from running more efficient processes or making better decisions. At the same time, there are very few organizations that have a clear data strategy and invest in improving their data quality. Bad data has become a kill-it-all argument and excuse for inaction.  

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Given the rapid advancement of AI and the outlined proven use cases, bad data shouldn’t be an acceptable excuse anymore in 2025. Nobody needs perfect data to start their AI journey with the data cleansing, harmonization, classification, or enrichment use case. Nobody needs perfect data to start with autonomous sourcing and negotiations or Intake Management. Having a clear data strategy and the willingness and acceptance that data will improve over time, and with it, the results, is all it takes to get ready for AI in Procurement.

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Unified Analytics with Procure Ai

Procure Ai supports various data analytics and management use cases and helps teams build their data literacy. We can help you establish a centralized data lake for procurement and recommend optimal strategies for cleansing, harmonizing, and enriching your data. With Unified Analytics, we support spend analytics, transaction classification, data cleansing, harmonization, enrichment, and opportunity detection.

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The Procure Ai platform architecture enables you to link multiple related AI use cases to drive deeper automation across the end-to-end procurement process, from intake management to autonomous sourcing, negotiation, and seamless operations - built on clean data.  

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Laying the foundation for better procurement decisions

High-quality data is critical for efficient procurement operations and strategic decision-making. While several challenges hinder effective procurement data processing and analytics, including data fragmentation and a reliance on manual processes, AI can address and overcome many of these challenges. By automating data management tasks and aligning data analytics with procurement strategies, AI can increase process efficiencies through automation and provide actionable insights on savings, supplier relationship management, and risk mitigation opportunities.

Are you ready to empower your enterprise with reliable data intelligence for sound decision-making? Procure Ai can help you build a data lake for procurement and leverage Unified Analytics to overcome your data quality challenge, enable process automation, and make data-driven decisions. Contact us to learn more.