Why Your Procurement Data is Bad – and How to Fix It
In a world where spend visibility is a basic requirement for even average procurement performance, why is procurement data so terrible?
After spending millions of dollars on technology, why do many procurement organizations struggle to achieve spend visibility to identify savings opportunities?
Poor procurement data causes major operational and management issues for the procurement department and is a huge roadblock to any kind of transformation project. But there is hope. With the right processes, tools, and assistance from procurement professionals, organizations can move from data chaos to clarity, providing a solid foundation for capturing and realizing value from procurement initiatives.
Understanding the Problem
After working with more than 600 companies that were struggling with data, we’ve concluded that bad data is a systemic problem. Simply put, companies tend to have much better data on what they sell rather than on what they buy. But why? Three basic reasons: poor data discipline; general ledger codes that are designed for accountants, not procurement; and too many systems overall.
- Poor data discipline. ERP and P2P systems work perfectly if you use them perfectly. However, most organizations suffer from data input errors, miscoding, lack of controls, poor guesses, and other issues that compound to create untrustworthy or unusable data. Consider this: An AP clerk who does not report to procurement receives an invoice from a supplier he’s never heard of. The clerk searches the system for the supplier name, but doesn’t find it because the person who created the vendor record used an unofficial version of the same vendor name. The clerk doesn’t try too hard to find the vendor because his job is to process paperwork, and he is measured on throughput. Due to a lack of controls, he is able to set up a new vendor on the fly, and suddenly the company has two entries for the same vendor. The business just lost visibility into that vendor. And most systems that impact downstream data quality for procurement are similarly error-prone, compounding the problem.
- General ledger codes that are designed for accountants. GL systems are designed and implemented for the people who write the checks. While they may be effective for accounting or finance, procurement category data doesn’t fit into such software, and spending is hard to identify. Normalizing redundant vendor names and organizing a company’s spend into procurement categories that reflect the way the supply market is structured is critical. Most company’s spend breaks down into 90 to 130 different categories of expenditures, with a different set of vendors in each area. Vendors in any given category should be competitors and viable candidates to fill the supply needs of the category.
- Too many systems. Whether due to acquisitions, decentralized divisions, or purchasing card strategies, many companies have data scattered across multiple systems. Even if data can be consolidated, vendor naming conventions and GL codes usually aren’t harmonized, creating a visibility nightmare. In one case, a procurement leader attempted to consolidate and clean up data manually; the project took more than nine months, and by that time the data was already out of date.
The Importance of Cleaning Up Data
You acknowledge your procurement data is in poor shape. Why do you need to fix it?
Sourcing in the dark is no way for professionals to operate. Spend visibility is an absolute critical path element, and no procurement organization can make a claim to world-class status or even average performance if it lacks this entry-level capability. It’s embarrassing to ask vendors what you spend with them, especially as you seek to lay the groundwork for competitive sourcing. And it’s embarrassing when your CEO, CFO, or private equity owner asks you for spend information, and it takes you a week to pull it. And even then, you lack confidence in its accuracy.
You might be surprised at what you discover. We regularly find entire spend categories that no one owns and no one thought about as a category. Those discoveries typically lead to significant savings and quality improvements.
You can’t manage what you can’t see. If you have no way to track adherence to contracts you source and put into place, you have no way to be sure you have achieved savings. Chest-thumping over savings that never make it to the bottom line is a sure way to undermine procurement’s credibility and limit career success for procurement professionals. Savings measurement and sustainability are critical for credibility, and the first step is to ensure that people are using the proper vendors.
Artificial intelligence-driven analytic processes can consolidate financial data from disparate systems into one procurement database and organize it the way the supply market works. That means 1) taking the data in whatever shape it is currently in, 2) cleaning it up, 3) categorizing it the way you source products and services, and 4) refreshing it every month or quarter. In real life, this can take as little as five weeks.
Here’s one example of how AI-driven data consolidation and analysis works. Most organizations use shipping services – some, constantly and at great cost. That was true of one of our clients, a $1.5 billion company wanting to get a handle on its small parcel shipping expenses. Here’s what the company’s data showed once analyzed:
- One of two major vendors was listed under 19 variants of its name.
- The shipping spend with one major vendor was charged to 26 different GL codes.
- One of the two major vendors accounted for 85% of the shipping volume, but no one owned the vendor relationship. Consequently, the vendor’s pricing was different for different parts of the organization and pricing did not reflect the combined volumes.
The entire organizational spend was pulled across all categories and GL codes. In just a few weeks, redundant vendor names were normalized and the spend was categorized the way the supply market is organized. The project resulted in close to $1 million in savings on the original spend of more than $5 million. The company also established a consolidated supplier management program to better handle this and other vendor relationships.
Ongoing Spend Visibility is Table Stakes for Demonstrating Value
Data capture, organization, and analysis is the foundation of any procurement transformation. You can’t perform advanced analytics to determine the success of your procurement initiatives if you don’t have granular, line-item usage data for key spend categories. Without good data, choosing impactful savings initiatives and showing value from them is impossible. Ongoing spend visibility is table stakes for any organization hoping to capture the greatest return its procurement spend can generate.