Key Takeaways
- Data-restricted procurement means pulling prices from free or unreliable sources or AI, taking suppliers at their word, and stitching it all together by hand. No independent benchmark and little to no forward view.
- Moving from data-restricted to data-informed doesn’t take a data analyst, a new department, or a bigger budget. It takes three things: someone to own the data, one exposure tracked consistently, and one early win that builds trust in it.
- Once you’re data-informed, you stop defending pricing decisions after the fact. You walk into supplier and leadership conversations with the numbers already in hand.
What is the data maturity curve?
Expana’s data maturity curve describes how a procurement team’s use of commodity data evolves over time, from ad hoc and reactive at one end to fully embedded and predictive at the other. It’s the framework Expana’s Chief Product Officer, Vinay Kapoor, and I introduced in a C-suite briefing on commodity data in the AI era, and it runs through four stages: Data-Restricted, Data-Informed, Data-Driven, and Data-Optimised.
You can’t jump from one end of that curve to the other in a single move. Read the full data maturity curve for the complete picture across all four stages. This piece is about the first, most achievable step: getting from data-restricted to data-informed.
What ‘data-restricted’ and ‘data-informed’ mean
If you’re running procurement for a lean team, you may already know what data-restricted procurement looks like. Data-restricted procurement means pulling prices from public or unreliable sources, using AI to pull insights without being able to check its work, taking suppliers at their word on market context, and stitching it all together in a spreadsheet before every significant decision. There’s no independent benchmark to check anything against and little to no forward view. Just whatever happened up to today.
Data-informed procurement starts the moment that changes for even one commodity. It isn’t a wholesale transformation project. It’s the point where you have one independent, trusted data source, and you use it before decisions get made, not after. Maybe that means pulling category cost data before a negotiation instead of walking in with only what the supplier has told you.
The gap between the two stages isn’t sophistication. It’s whether you have one number you trust and how you use it.
Being data-informed is about having the real context behind a price: understanding not just what a supplier is charging, but whether that number holds up against the wider market.
Whether you’re pricing beef, packaging, or a specialty ingredient sourced from three time zones away, the pattern is the same: procurement data maturity climbs in steps, not leaps.
The real cost of staying data-restricted
Manually piecing together data from five different places
When you’re data-restricted, every pricing decision means checking multiple sources: a free data site for one commodity, a supplier email for another, a quick search for a third, and whatever you remember from the last negotiation for everything else. A wide portfolio makes this worse, not better. Checking beef, chicken, and packaging each means a different source, a different format, a different level of trust. None of it lives in one place, and building a clear picture before a purchasing decision takes time a team of one or two doesn’t have to spare.
Data you can’t fully trust, but have no alternative to
Public sources aren’t necessarily wrong, but they’re rarely granular enough to be statistically reliable, and there’s no way to verify them independently. If you’ve ever gone back to a supplier with a number pulled from the open internet and had it picked apart, you know what that costs. Not just the argument, but also your credibility the next time you’re in that room. The same risk shows up internally: if leadership challenges a cost projection and the only backup is a free site, an export from AI or a supplier’s own framing of the market, the conversation shifts from “here’s the data” to “here’s my best guess,” and that’s a hard position to negotiate from.
No Forward View: Reacting to Price Moves After They’ve Already Happened
Free and supplier-provided data is historical by nature. It tells you what happened, not what’s coming. Without a forecast, every price move is something you respond to after the fact, rather than something you saw coming and planned around. That’s the gap that turns procurement into a defensive position instead of a proactive one. You find out a key input has spiked at the same time as everyone else buying it, instead of having had the window to lock in pricing before the move happened.
How to actually move from data-restricted to data-informed
You don’t need a new hire or a new department to make this shift, and you don’t need to make it in one leap. Three things, in order.
- Assign a Data Champion. This doesn’t need to be a new hire. On a lean team of one or two, it can be you, or whichever procurement manager already owns your highest-spend category. The point isn’t the title. It’s having one person responsible for the data question, so it doesn’t fall through the cracks between five other priorities.
- Start basic exposure tracking on your highest-spend commodity. Before you can track anything meaningfully, list every source you’re currently using per commodity: free sites, supplier updates, informal contacts. Most lean teams find they’re patching together three or four sources per category, and none of them are independent. Pick the commodity where a bad price call costs you the most, and get one credible, third-party number for it. This is where independent benchmark pricing does the heaviest lifting. A simple, consistent view of one exposure is worth more at this stage than a complicated system covering everything.
- Show a quick, tangible win to build trust in the data. The first time that benchmark changes the outcome of a negotiation, or catches a supplier price that’s drifted from the market, say so. Bring it into your next leadership update, not just the negotiation itself. This is what actually moves you to data-informed: not just having the number, but using it visibly enough that the rest of the business starts trusting it too. It’s also the step that changes how you defend pricing decisions to leadership when a forecast doesn’t land exactly as expected. You can point to the reasoning behind the number, not just the number itself.
None of this requires a data science background. It requires one person, one number, and one visible win. You can see how Wegman’s have matured in real-life using credible data here.
What changes once you’re data-informed
|
|
Data-Restricted |
Data-Informed |
|
Data sources |
Free sites, supplier-provided pricing, informal contacts |
One independent, verified benchmark for core commodities |
|
Decision style |
Reactive, respond to price moves after they happen |
Proactive, pull data ahead of negotiations and planning |
|
Forecasting |
None, historical data only |
Basic forward view on core commodities |
|
Credibility with leadership |
Numbers get questioned; hard to defend on the spot |
One trusted source leadership recognizes and accepts |
|
Time to insight |
Hours, pulling from multiple places each time |
Minutes, from a single source |
This is a deliberately small shift, and that’s the point. You’re not trying to reach Data-Driven (the stage after this one) in one move. That’s a longer conversation about embedding data across strategic planning, and it usually involves a bigger team than yours. Being data-informed is about getting one number you trust, using it consistently category by category until the whole portfolio is covered, not just the one commodity you started with.
For a lean procurement team, that difference shows up in the moments that matter most: the supplier renegotiation, the board question about why costs are up, the decision on whether to lock in a price now or wait. Data-restricted teams walk into those moments with a best guess. Data-informed teams walk in with a number they can defend.
If your team is ready to begin their data maturity journey, speak to one of our specialists to find out how Expana can help. To find out how to move from Data-Informed to Data-Driven, subscribe to our newsletter to ensure you don’t miss our next guide.
FAQs
What is the procurement data maturity curve?
The procurement data maturity curve describes how a team’s use of commodity data evolves over time: from Stage 1 (Data-Restricted), through Stage 2 (Data-Informed) and Stage 3 (Data-Driven), to Stage 4 (Data-Optimised). Each stage reflects not just what data a team has access to, but whether that data is used proactively and consistently in decision-making. A team can technically have access to good data and still be data-restricted in practice, if that data only gets pulled out after something has already gone wrong.
What’s the difference between data-restricted and data-informed procurement?
Data-restricted teams rely on free, unverified sources and supplier-provided pricing with no independent benchmark. Data-informed teams have introduced at least one trusted, independent data source and use it proactively: checking benchmark prices before a negotiation rather than relying only on what a supplier shares. The difference isn’t the amount of data available. It’s whether the team reaches for it before a decision or only after one has gone wrong.
How can a small procurement team move up the data maturity curve without hiring a data analyst?
A lean team can move from data-restricted to data-informed by adopting a single independent commodity benchmark rather than building internal data capability. The highest-leverage first step is replacing free, unverified data with one credible source for your core commodities, so every decision starts from the same reference point. No dashboard build. No new headcount. Just one number the whole team agrees to trust and use.
How do I validate supplier pricing without an internal benchmark?
Without an internal benchmark, the most reliable option is an independent, third-party commodity price source that isn’t tied to any single supplier. Expana’s benchmark pricing gives procurement teams a reference point to check supplier quotes against, rather than relying on the supplier’s own framing of the market. That makes it possible to spot a pricing request that’s drifted away from where the broader market actually sits.
How much can better commodity data actually save a procurement team?
The savings depend on commodity spend and category, but procurement teams using independent benchmark data and forecasting to inform purchasing timing and negotiations have reduced input costs by up to 3%, based on Expana client outcomes. On a lean team covering a wide portfolio, that saving compounds across every category it’s applied to, not just the one you start with. Estimate your own potential savings based on your specific commodity spend, or see how procurement and sourcing teams at a similar stage have made the move.
Written by Luana Clapis
