[Editor’s Note: Today’s article is a guest publication from Matthew Holzapfel, Product Marketer at Tamr Inc. Ardent Partners is happy to review and feature guest publications from authors across the procurement and supply management industry. If you or someone you know would like to become a guest contributor, please contact us at editor at cporising dot com. Thanks!]

A search for “Chief Procurement Officer” on LinkedIn shows thousands of companies have put procurement at the heart of their business. Any senior procurement executive has reason to believe their current employer will do the same. If that is the brass ring for procurement leaders, it is time to look at spend management as a more strategic endeavor. It is not just about saving another seven percent next year.

Procurement organizations have secured IT and staff investments that help them do even more to keep costs and vendors in line. These tools and people have been irreplaceable in the struggle to keep wringing water out of an increasingly dry towel. Now these organizations and their tools are maturing and it is time to look beyond the immediate challenge of lowering costs and managing suppliers. Data from supplier relationships is an intelligence gold mine. What can procurement tell a business that the business has no other way to know?

Data is Letters, Not Language

When you fluently speak a language, it is easy to look at letters as words. You do not think of the letters on this page, you more or less immediately understand them as English and move on to the meaning. When you speak the language of procurement, and look at the data around you, you immediately see opportunities to save money, reduce risk, improve compliance and increase efficiency.

That same data, and related data sources within reach, can be immensely valuable to the organization in other strategic ways. It takes understanding the same letters, and recognizing new letters, as part of a different language. Consider the scenarios below:

• Vendors with copper parts are hard lining their price increases, even though the price of copper futures has not moved.
• Suppliers are reluctant to promise increased inventory levels, or are rolling back previous promises regarding stock on hand
• Compliance with using preferred vendors is steadily declining as employee turnover increases

What if these are all happening at the same time? How do you know what actions to take? What needs to change in the organization that might be entirely separate from procurement? You cannot afford data silos that make you think of these as distinct events when they could be correlated. These are the calls that go beyond transactional interactions within departments. These are calls for the C-suite.

At this point, many readers are likely thinking of the limits of the data and tools at their disposal. The tools built for spend analysis were not built for this kind of insight. Even if they cover all of the company’s purchasing, they do not consider contextual information like commodity markets, currency markets, inventory levels, sales volumes, supply chain performance, etc. Further, most data analysis systems for procurement follow division or department lines. These silos and limitations limit the available data and lead to missed signals.

As procurement executives look beyond their own horizons, they need to look at improving their internal data gathering process.

Data unification is a recent development in data analysis that brings data from various sources together. And it is particularly suited to this challenge of spend management, which needs access to ERP systems, PDFs of contracts, inventory tracking systems, sales spread sheets, and more. All of the data regarding spend, vendors, and inventory can be treated as a common resource, and any analysis is easily repeatable or can even be automated as new data sources are folded into the mix.

Further, the ability to see all relevant data as part of the analysis is important not only to catch outliers that are particularly indicative of coming trends, but to understand the global exposure to these trends, and how they manifest themselves so corrective or precautionary measures can take effect in time.

A Two-Step Process, With Lasting Impacts

The first step to unifying data is cataloging data sources. Given the variety and dispersed nature of these sources, it is important to keep a good inventory of available data. Most people in any organization only have immediate access to about ten percent of the company’s data. There are free data catalog tools available right now that can help analysts discover, organize, and understand procurement data in the organization. It is important to get the tools distributed throughout the company so data owners can easily plug their sources into this catalog.

From there, analysts can evaluate fields in each source relevant to their research, to establish where data from each source relates to data in other sources. This data connection process is no small feat. It requires intelligence that is spread around the company, and a global analysis like this could require dozens or hundreds of sources.

To make the road easier, companies are relying on a combination of machine learning and expert sourcing. Machine learning tools can identify data sets that have strong correlation based on data similarities, past matches, and other factors to help analysts quickly combine multiple fields. Where machine learning and the analyst’s own judgment fall short, a trusted network of experts in the data are called upon to clear up issues. This, too, can be aided by machine learning, having software help to identify the best expert for each question.

The Long-lasting Benefits of Data Unification

Once the data is clean and ready for analysis, a handful of tests can uncover risks and opportunities. For example, companies might identify the suppliers most likely to increase prices when certain commodity prices rise. They might run regular reports on the key drivers of non-compliant spend. They could look for changes in terms, like delivery schedules or inventory requirements, which can predict supplier distress. This is all in addition to more tactical analysis, which has likely never been done on the long tail of smaller suppliers that represent a substantial portion of overall spend.

It is a small matter to bring new or changing data into the existing views, especially if machine learning and expert resources are used. This reusability becomes a permanent part of spend management, which has several implications. For example, testing suppliers for fiscal health might reveal that the vendor base is not as diversified as expected because a holding company owns many suppliers. Or it might reveal that one division has far better payment terms from a supplier than many others. A unified view of spend data can be analyzed several ways that bring far more of a company’s spend under control.

The road forward for a procurement executive is paved with a broader mindset and a matching broader view of the company’s spend. In both cases, it is less a matter of reaching out with the intelligence that naturally comes from spend analytics, but applying that intelligence in new ways. It iswhat makes the CPO more than a brass ring for procurement executives – it makes the role a strategic asset to the company as a whole.

About the Author

Matthew Holzapfel, Product Marketer, Tamr

Matt Holzapfel is a Product Marketer at Tamr where he focuses on procurement solutions. Prior to joining Tamr, Matt held positions in Strategy at Sears Holdings and Strategic Sourcing at Dell, where he led the development and implementation of new analytical sourcing tools to significantly lower procurement costs. Matt has a BS in Mechanical Engineering from the University of Illinois at Urbana-Champaign and an MBA from Harvard Business School.

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