Best Practices for Leveraging Data Analytics in Procurement
Benjamin B. M. Shao, Robert D. St. Louis, Karen Corral, Ziru Li
This study examines the procurement practices of 15 Fortune 500 companies to understand why most are not fully utilizing data analytics. Through surveys and in-depth interviews, the researchers investigated the primary challenges organizations face in advancing their analytics capabilities. Based on the findings, the paper proposes five best practices executives can follow to derive more value from data analytics in procurement.
Problem
Many large organizations are investing in data analytics to improve their procurement functions, but struggle to move beyond basic descriptive reports. This prevents them from achieving significant cost reductions, operational efficiencies, and strategic advantages. The study addresses the gap between the potential of advanced analytics and its current limited application in corporate procurement.
Outcome
- Most companies studied had not progressed beyond descriptive analytics (dashboards and visualizations). - Key challenges include inappropriate data granularity, data cleansing difficulties, reluctance to adopt advanced analytics, and difficulty demonstrating ROI. - Best Practice 1: Define clear taxonomies and processes for capturing high-quality procurement data. - Best Practice 2: Hire people with the right mix of technical and business skills and provide them with proper analytics tools. - Best Practice 3: Establish a clear vision for how data analytics will add value and create a competitive advantage. - Best Practice 4: Frame requests to analytics teams as business problems to be solved, not just data to be pulled. - Best Practice 5: Foster close collaboration between the procurement analytics team, the IT department, and the enterprise analytics team.
Host: Welcome to A.I.S. Insights — powered by Living Knowledge. I’m your host, Anna Ivy Summers. Host: Today, we’re diving into a study called "Best Practices for Leveraging Data Analytics in Procurement." Host: It examines the practices of 15 Fortune 500 companies to understand why most are not fully utilizing data analytics, and it proposes five best practices executives can follow to derive more value. Host: With me is our expert analyst, Alex Ian Sutherland. Alex, welcome. Expert: Great to be here, Anna. Host: So, let's start with the big picture. Companies are investing heavily in data analytics. What's the problem this study is trying to solve? Expert: The problem is a significant gap between potential and reality. Many large organizations are stuck in first gear. They're investing in these powerful analytics engines but are only using them to generate basic descriptive reports, like dashboards showing past spending. Host: Like looking in the rearview mirror instead of at the road ahead? Expert: Precisely. The study found that nine of the fifteen companies hadn't progressed beyond this descriptive stage. They're missing out on the real strategic advantages—like predicting supply chain disruptions or optimizing costs in real-time. This prevents them from achieving significant savings and efficiencies. Host: So how did the researchers get this inside look at what's happening in these massive companies? Expert: It was a very direct approach. They conducted surveys with Chief Procurement Officers, or CPOs, from 15 different Fortune 500 companies—we’re talking major players in industries from auto manufacturing to financial services. They then followed up with in-depth interviews to really understand the day-to-day challenges. Host: And what did they find? What are these key challenges that are keeping companies stuck in that rearview-mirror mode? Expert: The challenges were surprisingly universal. The first big one was poor data quality—what the study calls inappropriate data granularity. Basically, the data being collected wasn't detailed enough to answer complex questions. Another was the sheer difficulty of cleaning and integrating data from different systems. Host: I can imagine that's a huge task. Any other roadblocks? Expert: Yes, two more that are less about technology and more about people. First, a reluctance from managers to adopt advanced analytics. They weren't comfortable with the complexity. And second, it was difficult to demonstrate a clear return on investment, or ROI, for moving to more advanced predictive or prescriptive analytics. Host: So if those are the problems, what does the study say about the solution? What are the key findings for best practices? Expert: The research laid out five clear best practices. The first two are foundational: Define clear rules, or taxonomies, for how data is captured to ensure it’s high quality from the start. And second, hire people with a blend of technical and business skills and give them the right tools. Host: That makes sense. Get your house in order first. What comes next? Expert: Next is about strategy and communication. The third practice is to establish a clear vision for how analytics will create a competitive advantage. The fourth is a game-changer: Frame requests to your analytics team as business problems to solve, not just data to pull. Host: Can you give me an example of that? That sounds crucial. Expert: Absolutely. Instead of asking your team to "pull a report on our top 20 suppliers," you ask, "how can we reduce supply chain risk from our top 20 suppliers by 15%?" It changes the entire dynamic. It turns your data analysts from report-generators into strategic problem-solvers. Host: That’s a powerful shift in perspective. And the final best practice? Expert: The fifth one is fostering close collaboration between the procurement analytics team, the central IT department, and any enterprise-wide analytics groups. You can't operate in a silo. Success requires shared knowledge, tools, and infrastructure. Host: So, Alex, this is the most important question for our listeners. Why does this matter for a business leader who might not even be in procurement? Expert: Because these principles are universal. That mindset shift from asking for data to asking for solutions applies to marketing, to sales, to HR, to any part of the business. It’s about leveraging your expert teams to solve core business challenges, not just track metrics. Expert: The study also highlights that without a clear vision and buy-in from the top, even the best data strategy will fail. It shows that driving value from data is as much about culture and communication as it is about technology. Host: So to summarize: get your data foundations right, build a team with both business and tech skills, create a clear vision, and—most importantly—empower your teams to solve business problems, not just pull reports. Host: It’s a clear roadmap for moving from simply looking at the past to actively shaping the future. Host: Alex, this has been incredibly insightful. Thank you for breaking it down for us. Expert: My pleasure, Anna. Host: And a big thank you to our listeners for tuning into A.I.S. Insights. We'll see you next time.
data analytics, procurement, best practices, supply chain management, analytics hierarchy, business intelligence, strategic sourcing
