Leaving Liability in the Lurch: The Case for Clean, AI-Powered Supplier Data

Leaving Liability in the Lurch: The Case for Clean, AI-Powered Supplier Data

by Joshua Skeens — 3 months ago in Supply Chain Management 2 min. read
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Pardon the cliché, but in these uncertain times, out-of-date supplier data isn’t only useless – it’s a liability that can take your business from humming along to grinding to a halt in a moment.

Back in the day, you could hire consultants for data clean-up projects, run spend analysis cleansing or go through a recovery audit analysis. But business moves too quickly for these one-time efforts.

And besides,, who has the time? It’s far better to implement long-term solutions that can ensure the sustainability of your data now and forever.


Supplier data can become stale within minutes of generation, which can leave all manner of cross-functional departments in the lurch.

Your organization’s procurement arm, in particular, has the most to lose if you lack access to clean and correct supplier data that autonomously updates and grows stronger over time.
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Got Bad Data?

It can be tough to evaluate massive data sets and determine at a glance if it’s accurate or inaccurate, business-endangering or procurement-supporting. Here’s a quick-hit checklist that outlines the most common symptoms of bad data:

  • Delays in finding the right set of new or alternative suppliers. If your data causes you to be unable to shift quickly away from suppliers or find new ones, there’s something wrong with your data.
  • Significant costs of manual record search, data entry, and rework. Your data should save you time, not waste it.
  • Delayed ROI, lost payments, high costs, and onboarding delays. Forget time delays – no one wants to lose money, and your supplier data should be an ally in saving money, not yet another drain on your capital!
  • Default sources are “the big guys” or suppliers you have no business working with. Your data should be uniquely tailored to your business’s needs, and if it’s not, you’ve got a huge problem.
  • Mistrust of data. Your data should be your bible, your business’s bread and butter. If you can’t trust it, what can you trust?
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Which Way to Data Cleanup?

Let’s say you’ve identified your supplier data as being symptomatic of bad data. There are two ways to tackle that problem to avoid doing damage to your procurement team and your supply chain: the old way and the new way.

The old method of cleaning up data involves health checks, need assessments and data record cleaning, which sounds great…until you realize that the moment someone manually enters something into your supplier data records, you have to do the whole process over again. It’s not worth paying six figures to do this!

The solution is simple: identify an automated way of managing, cleaning, and updating supplier data regularly to ensure that your supplier information stays updated and agile without spending time and money to keep it that way. Sounds so simple, right?

Machine learning is the future of supplier data fixes, maintenance and storage. Artificial intelligence tools make it undeniably easy to fix supplier data and ensure it stays fixed.


These programs can automatically extract keywords for supplier websites to create structured, updated profiles, automatically cleanse data and remove duplicates, and search for and capture diversity insights to ensure your supply chain remains as agile as possible.

A clean and correct supplier record that updates independently and grows stronger over time benefits not only the procurement arm of your business but every other department.

Reducing liability and establishing a supplier record that captures and maintains clean, self-updating data isn’t just about transforming your operations – it’s about kicking liability concerns to the curb and freeing up your time and money to focus on consistently growing your business over time.

Joshua Skeens

Joshua Skeens, Chief Technology Officer, Cerdant, a Logically Company.

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