Procurement Data Challenges and Solutions: Strengthening Procurement Data Management and Governance

• The state of procurement data
• Data quality and Procurement’s strategic role
• Overcoming data challenges
• Harnessing technology for data improvement
• Turning procurement data into value
• Harnessing AI technology for data improvement
• Securing strategic success through data quality

Procurement data is nothing less than the function’s life blood. It gives teams an objective look at everything they touch, from the price of a single shipment all the way up to how the organization is contributing to pollution across the globe.

However, most Procurement teams don’t have an ideal view of their data. In fact, a 2023 study by Ardent Partners found that even among best-in-class procurement organizations, only 54% had full enterprise spend visibility.

Data should empower teams to coordinate their assets and spread their impact across the entire business. Instead, it’s often a distraction or even an outright roadblock to execution, trust, and value. This is largely due to weak procurement data management practices, fragmented supplier data, inconsistent vendor master data, and the absence of strong data governance or data cleansing standards.

Why is this the case, especially in a world where digital transformation has become a foundational procurement strategy? Let’s dive into the realities of procurement data and find out.

The State of Procurement Data: Supplier Data, Master Data, and the Rising Complexity of Big Data

Procurement data lives everywhere. Even most medium-sized enterprises have at least one suite, one ERP, and one P-card solution. Each one processes a different set of transactions and none of them format their data the same way. Large, global organizations multiply this complexity, sometimes storing spend data in more than 100 independent solutions.

As a result, a single data source may not even hold 10% of an organization’s spend and procurement data. Technically, teams can consolidate all these sources, but the process would have to be a full-time job performed by dedicated headcount. Worse still, the data would be outdated by the time this produced any meaningful visibility.

These challenges often stem from inconsistent master data management, siloed vendor data repositories, weak supplier information controls, and the absence of scalable data governance frameworks that modern procurement teams need to operate effectively in a big-data environment.

Data Quality and Procurement’s Strategic Role: Why Supplier Master Data and Data Governance Matter

To further complicate matters, even the data that Procurement can see is like the Wild West. With no unifying standards, Procurement data is often riddled with inaccuracies, conflicting information, and missing context.

Of course, these quality issues make enterprise-wide management out of the question, but they also impede category management. Even the most granular sub-category is a guessing game when teams don’t know what information might be missing.

As a result, teams can almost never squeeze the full value out of their opportunities. There will always be some angle or spend structure that even the best team just can’t account for. There’s also no way to ensure that even the best sourcing decisions aren’t disrupting processes or increasing costs elsewhere in the business. Most concerningly, these data quality issues mean that major third-party risk situations may be hidden all over the business just waiting to erupt.

Overall, these issues with Procurement data keep the function from maturing beyond process management and low-level savings delivery. In many ways, operating without comprehensive procurement data visibility can be summed up as doing less in more time.

This is why procurement data analysis, structured supplier master data management, and strong data governance practices are no longer optional for procurement leaders and procurement professionals seeking to elevate procurement strategy and improve procurement efficiency.

Overcoming Data Challenges with Strong Procurement Data Management and Data Cleansing Practices

The good news is that Procurement doesn’t have to stay in this limited impact state. You can begin overcoming your data challenges by reframing how the organization thinks about, treats, and uses data.

The first step is to consolidate data storage as much as possible. Consolidating ERPs and tactical sourcing tools may not be the most exciting work, but you can start by moving Procurement away from spreadsheets and other data storage methods that increase fragmentation.

You can also make progress by building standard operating procedures (SOPs) around data hygiene. By clearly defining how your team should enter and verify data, you can quickly reduce errors and inaccuracies that will create confusion and distrust six months from now. This has been a defining success factor for indirect procurement at our client Thompson Reuters.

Applying structured master data management, consistent supplier data cleansing, standardized vendor master data processes, and strong data governance policy frameworks enables procurement operations to improve procurement efficiency and support better procurement decisions across all procurement activities.

Harnessing Technology for Data Improvement: AI, Data Analytics, and Master Data Automation

However, the real improvements must be radical and comprehensive. The only way to truly unlock Procurement’s strategic potential is to secure on-demand, up-to-date access to 100% of the organization’s spend.

Of course, SOPs and manual effort won’t accomplish this goal, which is why our spend analytics solution relies on AI. Our machine-learning model offloads the process of:

• Normalizing vendor naming conventions across all provided data files
• Categorizing spend into a strategic sourcing taxonomy instead of by vendor names or accounting categories
• Refreshing data monthly or quarterly

The final result is a complete transformation of an organization’s procurement data landscape—100% of spend accounted for with 97% of it organized into actionable sourcing categories. The 3% remainer is typically made up of miscellaneous and one-off purchases that aren’t substantial enough to form their own categories.

This type of automation is essential for effective procurement data management, big-data analytics, supplier data normalization, and scalable vendor master data processes that modern procurement teams need to operate efficiently.

Learn more about the difference between our AI-driven consolidation process and legacy methods.

Turning Procurement Data Into Value: Enhancing Procurement Performance and Decision Making

However, spend is only one side of strategic procurement data. Without a way to turn consolidated insights into manageable opportunities, you risk creating a new mess of project, forecast, and impact data.

This is largely a risk because many teams manage projects and track savings in spreadsheets. This approach isn’t only inefficient and error-prone, it also stunts what leaders can do with a complex, global procurement pipeline.

By contrast, our procurement performance management solution is like a CRM for Procurement. It consolidates the ideas and opportunities that spend intelligence reveals into a single pipeline that you can use to:

• Forecast net financial, ESG, and TPRM impact
• Manage performance at both global and individual levels
• Give buyers a tool for managing their daily work
• Centralize all procurement data, including realized financial results
• Include stakeholders in the project management process

These capabilities strengthen procurement decision making, streamline procurement workflows, improve procurement activity management, and provide procurement professionals with visibility needed for better procurement performance and procurement efficiency.

Harnessing AI for Procurement Data Improvement: Enhancing Data Analysis and Supplier Insights

Over the past three years, artificial intelligence has pushed every other technology buzzword out of its way. Now, there isn’t a business problem that someone isn’t claiming they can help you solve with generative AI.

There’s no question that AI holds incredible value for Procurement. It’s the ultimate way to manage massive amounts of data. It can also automate tasks that once required new headcount investments. AI can even find opportunities and concerning trends ten-times faster than a human.

However, investing in AI is like taking the training wheels off a bike. AI-enabled procurement data analytics is only as strong as the underlying supplier information, vendor master data, and data governance practices supporting it. Feeding poor data into an AI model creates operational risk across supply chain management, contract management, and broader procurement operations.

Download our deep-dive into procurement AI, from SpendHQ’s internal data and computing experts.

Most importantly, you need to build the right procurement data foundation before you turn to AI to make sense of it. Technology isn’t magic. It will only produce impressive results if it has usable data to work with. Feeding inaccurate, incomplete, or fragmented data to an AI is like washing a window with a bucket of mud: the result won’t provide the value you were looking for.

Securing Strategic Success Through Data Quality: Strengthening Supplier Data, Procurement Governance, and Supply Chain Management

Data for procurement analytics can be your organization’s greatest strategic asset, even if it currently looks like a hopeless swamp of random information. Technology can give you a path forward.

But to make these improvements stick, we recommend first understanding your own data landscape. This includes evaluating supplier data management processes, vendor relationship data, procurement workflows, contract management data, and overall master data governance across the supply chain.

To help you dive deeper into the complexities of procurement data, our internal data experts Mitch Couper, Vice President of Delivery and Business Intelligence, and Robert Birch, Director of Data and Analytics, sat down with Art of Procurement to explain:

• What distracts Procurement from making data a top priority and how to refocus
• What Procurement stands to lose by settling for subpar data
• Why AI isn’t a magic solution…but it is an invaluable aid