We know we want to invest in AI, but we need better data first.
I hear this objection too often. CPOs want to leverage AI and advanced analytics, but they're convinced their data isn't "ready" yet for AI. So they put advanced analytics initiatives on hold while they wait for IT to clean up their mess.
Here's the uncomfortable truth: your procurement data will never be perfect. And waiting for perfection means missing out on insights and savings opportunities right now.
Data quality isn't IT's problem. It's everyone's problem. More importantly, you don't need perfect data to start getting value from modern procurement analytics.
The best approach is to improve your data while simultaneously extracting value from what you already have. Suplari improves your data from day one. Let me show you how based on a decade of experience improving procurement data quality at Suplari.
Why procurement data quality is important
Poor data quality isn't just an IT headache. It's bleeding money from your procurement organization every day.
- When your vendor names aren't standardized, you can't identify spend consolidation opportunities.
- When your category classifications are inconsistent, you miss maverick spending patterns.
- When your contract terms aren't properly captured, you can't track supplier performance against agreements.
I've seen procurement teams struggle with basic questions like "How much are we spending with this supplier?" because the same vendor appears in their system under twelve different name variations. The finance team reports one number, the ERP shows another, and the actual contracts tell a third story. This isn't just frustrating. It's expensive.
Cost of poor procurement data quality
The compound effect is even worse. One missing data point creates a cascade of problems.
- A purchase order without proper category coding skews your spend analysis,
- An incorrect supplier classification affects your diversity reporting,
- A missing contract reference means you can't connect realized spend to negotiated terms.
Each error multiplies across your organization, creating blind spots that cost you negotiating leverage and savings opportunities. The last thing you want is to flood your suppliers with a bunch of RFIs.
But here's what really frustrates me: procurement teams often have the information they need. It's just trapped in unstructured formats or buried in systems they're not fully leveraging.
How procurement data goes bad
Let's stop blaming the usual procurement data management suspects for a moment. Yes, manual data entry creates errors. Yes, disparate systems cause inconsistencies. Yes, lack of standardization creates chaos. But these are symptoms, not root causes.
Used dirty data is better than ignored perfect data
The real problem isn't that your data is incomplete. It's that you're not leveraging the data you already have. Procurement teams have been conditioned to think they need perfect master data and pristine supplier information to do meaningful analysis. This mindset keeps them from extracting value from the wealth of information they're already generating.
Every day, your organization processes invoices with detailed line items, approves purchase orders with rich descriptions, and manages contracts with specific terms and conditions. Your accounts payable team sees delivery confirmations, payment schedules, and service details. Your procurement team handles supplier communications, change orders, and performance feedback.
This information exists. It's sitting in your transactional systems right now. But most procurement teams ignore it because it doesn't fit neatly into their data model.
There is no such thing as perfect master data
Here's the uncomfortable truth: while you're waiting to build perfect supplier master data, your competitors are analyzing the imperfect data they already have and finding savings opportunities you're missing. They're not waiting for perfect category hierarchies or standardized vendor names. They're working with real-world data and getting real-world results.
The data quality problem isn't technical. It's philosophical. We've convinced ourselves that we need perfect data as a foundation to our transformation journey, when what we actually need is to begin improving our data while extracting valuable insights.
Real world example: the hidden goldmine in your purchase orders
Here's something that might surprise you: your purchase orders contain far more valuable information than you're currently extracting. Most procurement teams only capture the basic transaction details: line items, quantities, prices. But purchase orders often read like mini-contracts, containing pricing schedules, payment terms, delivery conditions, and performance requirements.
I recently worked with a prospect who discovered that their purchase orders contained detailed pricing sheets that weren't being captured in their structured data. Another client found 20% down payment terms buried in PO attachments that their accounts payable team was missing, causing payment delays and supplier relationship issues.
Leveraging our natively integrated Agentic layer, we were able to extract this "hidden" unstructured data, add it to our UDM (unified data model) and analyze it to provide incredible visibility into supplier performance, contract compliance, and cost optimization opportunities. Modern AI can extract and structure this information, connecting it to your existing spend data to create a much richer picture of your procurement activities.
The question isn't whether this data exists. It's whether your spend analytics solution is sophisticated enough to leverage it. Don't settle for tools that only look at your ERP line items. Demand solutions that can work with the full spectrum of procurement data that already exists in your organization.
Four practical steps to improve your data quality today
1. Make suppliers partners in data quality
Stop treating data quality as an internal problem. Your suppliers generate most of your procurement data—make them partners in maintaining its accuracy.
Build data quality requirements into supplier contracts and scorecards. When suppliers submit invoices with missing information, send them back. Provide templates and training to help suppliers understand what you need. Most vendors want to comply—they just don't know your requirements.
Show suppliers the business value of good data: faster payments, smoother processes, stronger partnerships. When they see the benefits, they become advocates instead of obstacles.
2. Rethink what counts as procurement data
Look beyond traditional ERP fields. Your contracts contain pricing schedules, performance penalties, and delivery terms. Your purchase orders include delivery locations, project codes, and approval hierarchies. Your invoices have detailed line items and payment terms.
This unstructured data reveals spending patterns, process inefficiencies, and contract compliance issues that most teams ignore. Modern AI can extract and structure this information without manual data entry.
Don't limit yourself to data that fits neatly into your ERP system. Leverage everything your procurement organization generates.
3. Establish procurement-owned data standards
Don't wait for enterprise-wide data governance. Create procurement-specific standards you can implement immediately.
Focus on the 20% of data that drives 80% of your decisions: supplier names, spend categories, contract references, and delivery locations. Implement basic validation rules for duplicate vendors, miscategorized spend, and missing contract references.
Assign data quality ownership to specific team members. Make it part of their job description, not an additional task.
4. Implement continuous data improvement
Build data quality reviews into existing procurement processes. Include data completeness metrics in supplier performance reviews. Fix data quality issues you discover during spend analysis.
Start with manual processes that can be automated later. The important thing is establishing a culture of continuous improvement.
Measure and report on progress. Track supplier master data completeness, category classification accuracy, and contract field population rates. Create feedback loops between your analytics initiatives and data quality efforts.
Why AI makes data quality more urgent (not harder)
Some procurement teams think AI requires perfect data, so they postpone analytics initiatives until their data cleansing projects are complete. This is backwards thinking.
But it's not as simple as feeding your spend data to ChatGPT. To extract repeatable value and avoid creating more bad data, you need a solution that combines a flexible, unified data model with natively integrated AI capabilities to extract and structure your data.
AI actually amplifies both good and bad data. If you have accurate supplier information, AI can identify patterns and opportunities you'd never spot manually. But if you have poor data quality, AI will compound those errors and potentially mislead your decision-making.
The solution isn't to avoid AI until your data is perfect. It’s better to use AI as part of your data quality improvement process. Modern platforms can identify data inconsistencies, suggest corrections, and flag potential errors as part of the analysis process.More importantly, agentic AI can work with imperfect data in ways that traditional analytics tools cannot. It can match vendor variations, infer missing categories from transaction details, and extract structured information from unstructured sources. Instead of requiring perfect input data, AI procurement agents can help create better data as part of the analytical process.
The key is choosing analytics platforms that are designed to work with real-world procurement data, not academic datasets. Look for solutions that can handle vendor name variations, missing category codes, and unstructured information sources. Don't settle for tools that require months of data preparation before they can provide insights.
You don't need perfect data to start getting value
This is the key insight that changes everything: you can start improving your procurement analytics while simultaneously fixing your data quality issues. The two initiatives should run in parallel, not in sequence.
Modern AI platforms like Suplari are built for this reality. They don't require pristine master data as a starting point. Instead, they work with your existing messy data landscape and improve it incrementally.
Here's how this works in practice: Connect your spend data, even with all its flaws, and the AI immediately begins identifying vendor name variations, flagging unusual patterns, and highlighting missing category codes. These insights don't just improve your analysis. They give you a roadmap for data quality improvements while delivering value from day one.
With Suplari, AI actively improves your data quality as it works. It standardizes vendor names across systems, enriches categories using transaction details, and connects disparate data points to build a complete picture. Each invoice processed and contract analyzed adds to your data quality while delivering insights you can act on now.
Bottom line
Data quality is a business problem, not a technical one. The sooner procurement teams accept responsibility for their data, the sooner they can start improving their analytics capabilities and business outcomes.
Stop waiting for perfect data. Start extracting value from what you have while simultaneously building better data practices. Use modern analytics platforms that can work with imperfect data and help you identify improvement opportunities.
Make your suppliers partners in data quality. Build data requirements into contracts, provide training and templates, and hold vendors accountable for the information they submit.
Most importantly, recognize that small, consistent improvements compound over time. You don't need a massive data transformation project to start getting better insights. You need a commitment to continuous improvement and the right tools to support that process.
What data quality improvement will you implement this week? Request a meeting today, and we’ll tell you how we can get started.
About Suplari
Suplari is a procurement intelligence solution that helps businesses modernize procurement operations using AI. Suplari provides actionable intelligence to manage suppliers, deliver savings and manage compliance beyond the limits of traditional spend analytics. Suplari’s unique AI data management foundation empowers enterprise businesses to modernize procurement operating models with reliable, AI-ready data.
