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High-quality data enables procurement teams to manage risk, build stronger supplier relationships, identify cost-saving opportunities, and streamline operations. It is also the foundation for leveraging AI. Yet, procurement data is often incomplete, inconsistent, and scattered across different systems. And nobody seems willing to invest resources to cleaning it up.
All procurement teams claim poor data quality prevents them from running more efficient processes or making better decisions. But bad data is not a valid excuse for inaction anymore. If data quality is holding you back, it’s time to take control, it’s time to take action. AI can help you improve your data quality faster and more effectively than ever before.
Here are six steps to help you improve your procurement data quality using AI.
Not all data is equally valuable. Start by identifying which properties are critical to your business objectives. This could include transactional data, supplier information, payment terms, contract expiry dates, or category-specific attributes.
Your aim is to build a data model that maps the relationships between critical elements like suppliers, transactions, categories, and contracts. This model acts as your blueprint for what “good data” looks like within your organization and which connections between data points drive insights. By defining what matters, you help AI tools focus on the right areas, enabling more accurate analysis, better predictions, and smarter decision-making.
Before you can improve your data, you need to understand its current state. A data quality audit assesses how complete, consistent, and accurate your data is. Look for missing values, duplicate records, inconsistent formats, or outdated information.
The results give you a baseline for tracking progress and pinpointing where AI can add value, like filling in gaps, deduplicating entries, or correcting anomalies using pattern recognition and contextual logic. As part of this step, tag records that are correct and incorrect ones for later cleansing or enrichment. This will help train the model and streamline future AI-driven interventions.
Procurement often focuses on 100% accuracy before accepting solutions. This mindset can be in the way of improvements, especially when coming from a starting point of 70-80% accuracy. Once you’ve audited your data, define what data quality success will look like. Set SMART (specific, measurable, achievable, relevant, time-bound) goals and distinct Key Performance Indicators (KPIs) for your data quality improvement efforts and consider how AI could support these goals using data cleansing, harmonization, and enrichment.
For example, your goals might include reducing the number of incomplete supplier records, standardizing item descriptions across all categories, or correctly assigning transaction data to categories in a defined timeframe. When paired with your audit baseline, these objectives make it easier to measure progress, guide AI model development, and keep your team aligned on outcomes.
Look for a solution that helps you build a centralized procurement data lake, serving as a single source of truth, that offers AI-driven recommendations for cleansing, harmonizing, and enriching your data and acts on these recommendations. The right platform won’t just surface issues; it will help you resolve them in ways that are scalable, repeatable, and tailored to the procurement function - without throwing bodies at the problem.
Even the most advanced AI tools are only as effective as the people using them. Ensure cross-functional collaboration between procurement, finance, operations, and IT teams to ensure all stakeholders are aligned on the objectives and approach. Then invest in hands-on training to equip your team to work with data and AI. This includes understanding your data structure, validating AI outputs, interpreting insights, and knowing when to challenge AI-generated recommendations. The Human in the Loop only makes sense, if he knows what he’s supposed to do.
To measure the impact of your efforts, track the defined KPIs align with your goals. Regularly review these KPIs to fine-tune your strategy, adjust AI models, and ensure your data quality initiatives stay on course. This will also help you demonstrate the ROI of your AI investments and enable scaling to adjacent use cases using your AI platform.
Procure Ai supports various data analytics and spend analysis use cases through our Unified Analytics capability, from data cleansing, harmonization, enrichment, to spend classification, spend analytics, and opportunity detection. We can also help you establish a centralized data lake for procurement and build your team's data literacy.
Improving procurement data quality doesn’t require a massive investment of time and people. Following a smart, step-by-step approach powered by the right tools will do the trick. Start with these six steps and use AI to do the heavy lifting.
Procure Ai’s Procurement Automation Platform overcomes data silos and empowers you with information, insights, and actions across all systems. It enables a deeper analysis and understanding of categories, suppliers, processes, and operational risks.
Want a deeper understanding of how AI enables data cleansing, harmonization, and enrichment? Read our full overview here.
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