It’s no secret how important it is for organizations to have access to accurate, up-to-date data. Analytics play a crucial role in all aspects of sourcing and procurement management—especially risk mitigation, new product introduction, and cost optimization. Unfortunately, inconsistent, incomplete, or inaccurate data—otherwise known as dirty data—can mar all these efforts entirely. 

The solution? Data cleansing via artificial intelligence. This tried-and-true practice allows organizations to weed out the vulnerabilities in their data sets to ensure more accurate insights.

It’s fair to say that this process is a must-have component for any supply chain to not just survive, but also thrive. But is data cleansing the destination? Or is it simply one part of a more involved journey all organizations need to take to ensure true success and risk avoidance?

The answer might surprise you.

Why Is Dirty Data Such an Issue?

Simply put, when you rely on data that hasn’t been through a rigorous, competent cleansing process, your organization becomes vulnerable. These data sets are often then laden with inaccuracies and outdated figures. The so-called “dirtiness” of it all may be the result of any number of complications. Sometimes, it’s as simple as human error. Other times, it’s due to contract manufacturers making concerted efforts to disguise their data and muddle the market comparison process. Most of the time, though, data is “dirty” simply because it originates from various back-end systems that, by default, don’t allow for cross-platform comparisons. 

In any case, when you base your sourcing, procurement, and spend strategies on dirty data, you’re far more likely to make improperly informed, ineffective decisions. And while it’s difficult to know just how severe the results of these decisions may be, one thing is certain: they’re almost always costly. According to a 2018 study from Gartner, organizations, on average, lose $15 million each year due to dirty data. A separate study conducted by IBM also discovered that in the United States alone, businesses spent $3.1 trillion each year rectifying issues caused by poor-quality data. And with the market as competitive as it is, more organizations simply don’t have that kind of money to lose; especially on problems that could otherwise be avoided. 

Where Does AI Come In?

As previously mentioned, data cleansing is an effective solution to this issue. The problem with utilizing this method, though, has been that although the idea is simple, the execution is often far from it.

Manually cleaning data is a complex and time-consuming process; it’s also incredibly expensive. And, unfortunately, traditional means of data cleansing have proven to be ineffective, with the accuracy of the results difficult to gauge. While the resulting insights are typically far less egregious than those provided by data that hasn’t been cleaned at all, it begs the question: if the process doesn’t guarantee reliable results, what is the point of all that time, effort, and money being spent? 

AI technology sidesteps all of these problems. Once the system is introduced to your organization’s existing data sets, it uses a combination of supervised and unsupervised learning models, fuzzy search, web-crawlers, and robotic processes. It also conducts daily ingestions of millions of entities across several data dimensions.

The AI approach frees data from its silo, allowing organizations to move toward a comprehensive perspective of the entire supply chain network. This doesn’t just ensure more accurate results—it also shortens the insights-to-action process, cutting the time spent working toward reliable data sets from months to weeks or even days.

Why Stop There?

It’s easy to see the value in accessing clean, accurate data, as well as the value of an AI-driven solution. However, it’s important to note that these artifacts won’t make any difference if they don’t provide a more holistic view of the competitive market—and if you don’t know what to do with them.

For this reason, LevaData encourages organizations to view data cleansing as one stop on a more involved journey toward true supply chain management success, rather than the destination. 

Once a third-party solution has cleaned your data and turned over the improved sets, the rest is on you. At this point, your organization will be responsible for harmonizing and cross-referencing this information against the market to (hopefully) garner a more accurate representation of where your opportunities, risks, successes, and failures lie.

Then there’s the matter of knowing what to do with this data. Interpreting this information and turning it into an actionable plan is a complex task, and there’s no way of knowing that your organization’s conclusions will render the best results.

Unfortunately, third-party solutions that focus solely on cleaning dirty data tend to render a disjointed path. To mitigate the complexities of this siloed approach, it’s ideal to invest in a solution that carries you through all the way to the end.

Rely on LevaData to Get You There

With the LevaData platform, you can rest assured you’ll not only receive clean, harmonized data; you’ll also be given prescriptive, predictive insights and a clear call to action. Our technology handles the entire journey to streamline the spend analysis process for supply chain managers and ensure complete oversight and management of the data handling process. As a result, your organization will mitigate the risks otherwise brought on by misinterpreting data or missing out on opportunities provided through a holistic market comparison.

To discover what LevaData’s platform can do to strengthen and streamline your journey toward complete supply management, contact us today