In our latest conversation, we spoke with Varun Kukreja, Associate Director of Solutions Engineering at Zycus, to explore the critical role vendor data quality plays in driving successful business initiatives. Drawing on years of experience supporting global procurement transformation projects, Varun shares how accurate, well-maintained vendor data can be the catalyst for strategic business projects – enabling better compliance, streamlined operations, and reduced fraud risks.
He also sheds light on the direct link between data accuracy and risk reduction, highlighting how cleaner vendor records lead to greater efficiency, fewer payment errors, and smoother cross-team collaboration. Read on for practical steps, real-world examples, and actionable advice to help your organization turn vendor data quality into a strategic advantage.
1. Could you start by presenting yourself, your role at Zykus and your experience in terms of vendor data management and procurement?
My name is Varun Kukreja. I’m the Associate Director for Solutions Engineering at Zycus, where I’ve worked for 14 years. During this time, I’ve been involved in procurement transformation projects covering supplier data management, supplier onboarding, and supplier risk profiling. Over the years, I’ve worked with several clients at different stages of these challenges.
2. What are the most common risks you’ve seen from unclean, incomplete, or obsolete vendor data?
The biggest risk comes in compliance. If procurement spend isn’t based on proper, clean data, it can quickly lead to non-compliance.
Imagine a situation where a preferred vendor for a certain category appears under two names in the system. If the data isn’t deduplicated, there’s a high chance a purchase goes through the non-preferred channel simply because of duplicate entries. From my perspective, compliance faces the biggest risk from poorly maintained data.
Then there’s the audit. Disparate or duplicated data across systems causes major concerns, often showing up as red flags during audit checks.
Operationally, there are risks around supplier onboarding and payments. If payment or bank details aren’t validated against supplier records, you’re exposing the business to errors and potentially losing money.
So broadly, I see three main categories of risk from unmanaged data: compliance, audit, and operations.
3. Based on your experience, what links do you see between vendor data quality and fraud prevention?
Fraud often hides in the gaps, especially in duplicate records or inconsistent naming conventions for the same supplier. If one person creates a supplier under one taxonomy and another person can’t find it, they might create a duplicate under a different name. That gap in the data is a major link between poor data quality and fraud risk.
The other issue is that vendor data is often treated as static. But it actually evolves, for example, supplier banking details change over time. If those changes aren’t monitored, they create an opening for fraud.
These two are tightly linked. That’s why I always tell clients: clean data isn’t a one-time project. It’s dynamic and needs continuous monitoring to maintain quality.
4. When data is obsolete, someone often needs to contact suppliers to get the correct information. Do many of your clients waste time manually correcting obsolete data before making payments?
If the data isn’t clean initially, it’s indeed going to lead to delays and frictions in the payment chain and the vendor onboarding process. Having your vendor data clean from the get-go is a huge efficiency gain, with real dollar value.
Many clients waste significant time manually correcting obsolete data, and the impact goes far beyond vendor management. When supplier data isn’t clean, every stage of the procurement cycle suffers,from onboarding vendors, to sourcing, to analyzing spend and identifying savings opportunities. Instead of focusing on strategic or tactical procurement, teams get stuck chasing and correcting outdated information. Clean, reliable data therefore creates efficiency gains across the board, with a ripple effect that touches the entire payment and sourcing process.
5. In recent years, have you seen vendor data accuracy become more of a focus for organizations? Is it becoming more important for your clients and prospects?
Absolutely. There was a time when vendor data management and data quality were seen largely as IT responsibilities, a back-office concern. Today, they have become boardroom priorities.
The reason is clear: the last five to six years have brought constant uncertainty. First came the pandemic, followed by geopolitical conflicts and other disruptions that severely impacted supply chains. In this context, data quality is more critical than ever.
The need for supply chain resilience has pushed data quality to the top of the agenda. Only with clean, reliable data can companies pursue automation, generate trustworthy analytics, and gain a competitive edge.
Generative AI is also part of this shift. At Zycus, we’re proud to be pioneers in AI and generative AI. But the insights AI delivers are only as good as the data it learns from. If the underlying data is confusing or inconsistent, it risks producing inaccurate or misleading outputs, even hallucinations.
So yes, in recent years we’ve seen vendor data accuracy rise dramatically in importance, gaining recognition as a true strategic priority.
Learn how you can clean your vendor data in our 7 step roadmap!
6. What challenges do companies face when they decide to clean up their vendor data? Where does it hurt first, and what are the biggest obstacles along the way?
Whenever companies ask me where to start, my first question back is: do you actually know how bad your data is? And the answer is usually no. Visibility into data is the biggest challenge. Without proper visibility, organizations struggle to get control of it.
Then comes the effort itself. What priorities should be set? What kind of resources and tools are needed? And which stakeholders need to be involved? Without a clear picture of these efforts, projects quickly become difficult to manage.
And of course, there’s the human side. People need to adapt to new processes and tools, but they often fall back on old ways of working, the same ones that caused the data issues in the first place. Even when the benefits are clear in terms of efficiency gains, change management remains a consistent challenge. In fact, I’d say that in any project, not just data initiatives, but even broader operational transformations, change management is always one of the toughest hurdles companies face.
7. Which teams are usually involved in or affected by vendor data cleaning? Is it only MDM teams or procurement teams? How important is collaboration on this topic?
Vendor data management and vendor data cleaning is definitely a shared responsibility. Procurement usually comes to the forefront, but IT has equal involvement because we’re talking about the tools used to manage this data. In one way or another, IT still owns those tools, whether from a decision-making, maintenance, or interoperability standpoint.
Even when procurement uses dedicated tools to manage supplier data, those tools almost always connect with company ERPs. From that perspective, IT is an equal stakeholder.
Beyond procurement and IT, extended teams like centers of excellence, compliance, and sustainability are also impacted. Good, well-maintained supplier data touches all these areas. Naturally, the people in these departments get involved. In more mature organizations, you’ll even see subdivisions within these roles. So collaboration is essential, with multiple stakeholders sharing responsibility.
8. Have you seen companies have better onboarding processes, audit readiness, fewer payment errors as a result of vendor data cleaning initiatives? What are the main efficiency gains that you’ve seen ?
I think it’s important to put some tangible numbers around this initiative. From my experience, the biggest gains often come from an operational perspective.
During supplier onboardings, for example, I’ve seen improvements of up to 40% in turnaround time. That translates directly into a 40% optimization in the cost of onboarding and supplier management, since you’re able to optimize the resources involved in the process. From an operational efficiency standpoint, supplier onboarding and supplier management can deliver that significant 40% improvement.
The same applies to audits. When data is already clean and optimized at the point of entry, the benefits ripple into ongoing audits and data management. Similar efficiency gains can be seen there as well.
Fraud prevention is another area where the impact is clear. I worked with a company that saved around $2.5 million simply by eliminating disparate, duplicate data that had been enabling fraudulent bank details to slip through. Phishing attempts are also becoming more frequent, which makes clean, validated data even more critical.
There’s also a lot of automation available today, even free and open-source tools, and fraudsters are getting creative. That’s why real-time validation is so powerful.
I often highlight the Zycus-Trustpair partnership: together we provide real-time bank account validation at the vendor onboarding stage. This enhances security and efficiency.
9. How should companies looking to clean their vendor data approach the technology and automation aspect? How important is it, and what’s your advice to companies evaluating different tools and choosing the right solution to clean vendor data?
Today, it’s no longer a question of whether we want automation, but how we should approach it.
The answer is clear: manual processes are slow, error-prone, and inevitably lead to mistakes. At this point, I think it’s almost criminal to debate manual versus automated. The real question is what kind of technology to choose, and how exactly we want to automate.
That’s why it’s important to ask: does the technology enforce rules on the data? Does it provide validation points and checks? And does it guide you toward the right way of automating?
AI is also a major factor today. The key question is: what use cases does AI support, and how can it replicate human behavior while avoiding human error? AI is already being embedded into automation, and that will be one of the most forward-looking ways of evaluating technology.
The partnership between Zycus and Trustpair is a good example. Together, we enable real-time validation and insights, which is a big takeaway for organizations evaluating tools for data cleansing, vendor data management, and ongoing operational processes.
10. If you were to outline the four or five critical steps in launching a vendor data cleaning project, what would you say are the most important milestones on that roadmap for companies?
This question goes back to the challenges companies face when they take on a project of this nature.
The first and most important step is to understand the state of their data. They shouldn’t rush to adopt a new technology just because it looks flashy or promises efficiency gains. Unless they know what they’re trying to tackle, they won’t be able to solve it. It’s as simple as that.
Take the time to assess what your data is, what state it’s in, and what scale you’re dealing with. Some organizations we work with have 200,000 or 300,000 supplier accounts. Not all of it is bad or unmaintained data, so it’s critical to identify the target and subset of data to optimize efforts. Otherwise, you risk duplicating work, which is often what triggered the project in the first place. That’s why the second point is to focus on a very specific, targeted subset of data, the part most critical to delivering real gains.
The next step is responsibility: who owns the data, the project, or the initiative itself? It isn’t just procurement or IT. You need the right stakeholder involvement and proper collaboration. It makes sense to treat this as a strategic project, with a dedicated team working together to define goals and outcomes. In fact, it’s often smart to tie data quality and efficiency gains directly to people’s responsibility matrices or OKRs. Linking the project to measurable objectives ensures accountability and makes success more tangible.
These are the key steps companies should keep in mind when they take on a vendor data cleaning project.
11. Do you have any last words on this topic or any advice to companies?
Bringing together process transformation and fraud prevention into a unified strategy helps companies realize significant gains. It’s not just about fixing data issues; it’s about showing measurable improvements to the bottom line right away.
In the uncertain environment we’re living in, it’s more important than ever to take on these projects sooner rather than later. Otherwise, you risk seeing leakages and wondering why they weren’t addressed earlier.
This isn’t rocket science: with proper stakeholder collaboration, the right insights, and consistent monitoring, it can be done effectively and efficiently. Better today than tomorrow: now is the most opportune moment to take on this kind of project.
Cleaning your vendor data: key takeaways
- Poor vendor data fuels risk: Incomplete, duplicate, or inconsistent records create compliance, audit, and operational risks, opening doors to fraud and payment errors.
- Fraud hides in data gaps: Duplicate or outdated records are prime entry points for fraudsters. Continuous monitoring is essential, as vendor data evolves over time.
- Efficiency gains ripple across procurement: Clean vendor data accelerates onboarding, reduces manual corrections, and improves sourcing and spend analysis, saving time and money.
- Vendor data quality is now a strategic priority: Global uncertainty, supply chain disruptions, and AI adoption have pushed data accuracy from IT’s back office to the boardroom agenda.
- Automation and collaboration are key: Manual processes can’t keep up. Real-time validation, cross-team ownership, and cultural adoption of data governance are critical for long-term success.