Spend Data Analysis: It's Not One-Size-Fits-All

Over the past decade, procurement teams have increasingly recognized the importance of digital spend analysis. By identifying, categorizing, and analyzing spend data, purchasing departments are  better informed. With that information, they can use fact-based knowledge to make strategic decisions for the business.  This power has made spend data analysis a key element for many organizations. However, there continue to be challenges along the way. One of the largest is the struggle to customize a data analysis platform for your business. 

For a full guide on getting maximum value from your spend data analysis tool, download our white paper on spend analysis best practices. 

Having a spend data analysis tool that can be tailored to your specific business case is crucial for several reasons. Most important among them are the flexibility, reduced exception handling, and increased data transparency that custom-fit solutions bring to your team members. Together, these allow the full procurement function to make more accurate decisions faster.

Spend data analysis is all about being able to leverage facts to make faster, better decisions. Learn the 10 steps to better spend analysis.#spendanalysis #digitalprocurement https://t.co/Mm92MY6yp1 pic.twitter.com/M5aoiXpbAT

– JAGGAER (@JaggaerPro) June 3, 2020

It can’t be a black box

Many organizations fall into the trap of adopting templates, rules, and processes that would be better used as guidelines. These one–size–fits–all solutions often create more problems further down the road. Every organization has different needs from its procurement system. Because no two organizations are identical, neither is their data, so they need the flexibility to take different approaches in analyzing it. At JAGGAER, we refer to these strict, limited solutions as “black boxes”. Strict technology algorithms are often inconsistent with the format of your organization’s data, the way you manage it, or the results you need from the analysis.  Data is locked in the black box, and if it doesn’t fit the strict rules of the system, it’s very difficult to get in or out. 

In some cases, teams adopt a black box to save time and costs on implementation. By using an “out-of-the-box” solution, IT teams can speed up the process and get the procurement function up and running quickly. But this proves to be costly in the long run.  

The time cost of exception handling

When an algorithm tries to automate the data management in one particular way, but it doesn’t fit the needs of your organization, the system treats that data as an exception. To be blunter, it’s an error. When an exception arises, the software simply can’t deal with it, and a  user needs to deal with manually. In many cases,  a separate team of resources, who may or may not have any context for the error or understanding of your business, handles exceptions. The manual work piles up and may not be handled accurately, leading to errors in data. If exceptions aren’t resolved correctly, they can’t be integrated back in with the rest of your data, leaving a fractured database and increased exceptions further down the line. 

This effectively erases the very benefit of automating the spend data analysis process by removing the automation itself.

Build transparency 

The full data analysis process, from input to automation to exception handling, needs to be completely transparent to make sure that those working with it have a complete understanding. Contrary to popular belief, data analysis isn’t a completely objective science and requires context. Business details provide the background for classification, and in the case of spend analysis, this means categorizing data in alignment with the company’s sourcing strategy. 

A solution provider with a flexible application helps bring transparency to each individual step of the data analysis process. As the solution maps to the specific business case, so can the data. And when the data is readily available for all who need it, they can analyze it more quickly and accurately. 

Ultimately, you need to have a data platform that can cater to your specific needs, rather than the other way around. The idea of ​​cost savings with a simpler, out of the box implementation might seem tempting, but having a more robust, specialized solution will bring savings over time. By choosing a customized platform, you’ll increase data accuracy and transparency while reducing time spent on exception handling.