Barcelona, January 8, 2025.-
According to an analysis published by Supply Chain Management Review magazine, the challenges to collecting good data are not a secret: rapidly increasing data volumes, data accuracy and reliability, poor data governance, legacy systems and processes, and IT talent attrition are among the greatest challenges.
Let’s start with the last one: IT talent attrition. In a 2022 report, data company Hakkoda reported that only 3% of companies across all industries had no problem finding talent. Companies are particularly struggling to find machine learning-experienced data scientists (48%) and data architects (34%).
“It’s interesting to note that when asked which roles provided the most business value, data analysts (41%) and data architects (35%) topped the list. Data scientists were a close third,” the company reports.
Finding talent is likely to remain a challenge for the foreseeable future. The other challenges might actually be easier to overcome.
“Robust analytics capabilities help assess compliance rates and identify cost-saving opportunities by quickly identifying unnecessary [procurement] payments,” says Chatwal. “Additionally, addressing new and emerging ESG requirements and integrating AI-driven audit solutions ensure regulatory adherence. Prioritizing data management empowers procurement leaders to navigate challenges and elevate their impact within the supply chain.”
Data volumes are going to continue to increase—estimates are that there are 402.74 million terabytes of new data being generated each day. That won’t change, so companies simply need to have the systems in place to accommodate and analyze all the data they will generate.
Putting in place good data governance is a key to good data management.
“Traditional data governance tools built for data warehousing prove more of an obstacle than an assist in today’s environment,” Hakkoda wrote in a blog posting on the topic. “Data can be sourced from anywhere—and as such, siloing this information creates more problems than it solves. Companies need data governance tools that work with these new frameworks. To combat these problems, organizations must establish a coherent, limber data governance framework that centers on ever-updating compliance standards and guidelines.”
Notably, Hakkoda said that a good data governance framework is about the people, not the processes.
“All the technical wizardry in the world can’t implement a data governance program if the political will isn’t there for the organization to put in place a framework and process for governance,” it said.
Assuming companies can get the data governance part down, are able to work around an IT talent shortage, and are able to upgrade legacy systems, the real work comes in how to address these challenges.
Chatwal offers three suggestions to procurement managers to manage data. It includes ensuring data accuracy and reliability, focus on business functions and processes, and enable better analytics with clean data.
“Consistently validate and refine your vendor master data,” Chatwal says. “Accurate data minimizes the risk of errors, such as duplicate payments. Clearly defined data elements empower your team, delineate roles, and strengthen overall accuracy.”
Chatwal goes on to say that by identifying key business functions and processes, businesses can “gain insights into departmental needs, existing challenges, and pain points [and] understand the technology landscape—what tools are in place to manage data—and align efforts with overall business objectives.”
Finally, as mentioned in the opening, good data is the key. A quality data management program is not possible without clean data.
“High-quality vendor data serves as the foundation for robust analytics,” Chatwal says. “Spend analytics, for instance, relies on accurate and comprehensive data to uncover patterns, identify cost-saving opportunities, and optimize procurement strategies. Clean data ensures your spend analysis is both reliable and actionable.”
Transportation & Logistics Software 