September 29th, 2026 • Harvey Brice • Reading Time: 4 minutes

At a time when every Third-Party Risk Management program is looking for ways to be more efficient and operate at a lower cost to the enterprise, it’s worth acknowledging that poor data governance can significantly increase your cost of oversight and may be leading you towards a major financial loss event. Cost savings may be at your fingertips simply by making some bold decisions about data.  

AI also presents new opportunities to identify and help resolve common data governance problems. In this blog, we’ll explore some common TPRM pain points and how applying sound data governance principles can reduce duplication of effort, improve data quality, and lower the cost of operating your program. 

Poor adherence to data governance principles can have significant impacts: 

• Creates duplication of effort across your program, as questionnaires are applied at the wrong level of the program (third party, engagement or risk domain), duplicate supplier or engagement records result in duplicated assessments, and different assessments ask equivalent or overlapping questions of the same third party. This places unnecessary burden on third parties, risk assessors and third-party owners. 
• Drives repeated and often urgent clean-up ahead of critical executive and regulatory reporting, or during a real-time crisis, as stale records, manual data-entry errors and conflicting versions of the same information reduce confidence in reporting, require ongoing reconciliation, delay critical analysis, and make it difficult to demonstrate complete, accurate and auditable records to regulators. 
• Makes cost-saving system consolidation and integration harder, more expensive, and sometimes impossible, because Procurement, Information Security, Business Continuity, KYC, GRC, and TPRM teams often maintain the same reference data in different models and relationships. 
• Can mean the difference between identifying and mitigating a risk or experiencing a major financial loss event, because screening may be performed against the wrong legal entity, while inaccurate, incomplete or inconsistent data prevents the correct assessments from being triggered and undermines the identification of genuine risk. 

These may appear to be separate operational problems, but they often share a common root: the way the program sources, structures and governs its data. 

The first step is to think about TPRM data from an enterprise perspective. 

Before asking TPRM to collect or maintain information, establish whether an authoritative version already exists elsewhere in the organization or can be sourced externally. 

Identify the gold copy, define which system owns each data element, and integrate with those sources rather than creating another version. Where appropriate, connect dedicated external sources for entity reference data and corporate actions so that legal entity identities, ownership structures, mergers, acquisitions, name changes and other structural changes are maintained from authoritative sources rather than through manual updates. 

Also assess whether multiple platforms are still needed to support third-party oversight. Each additional platform increases cost, duplicates effort, and complicates data governance. 

The principle is simple: use trusted data that already exists rather than recreating it, and don’t underestimate the effort required to maintain accurate entity-reference data in a changing supplier landscape. Where practical, consolidate third-party governance workflows into a shared platform with a common data model, reducing the mapping and reconciliation required across systems. 

Next, be extremely disciplined about designing the data structure. 

There are many related concepts in defining a third-party relationship: third party, legal entity, service, site, contract, internal business. The data model needs to represent each entity clearly and define how the objects relate to one another. Particular care should be taken with one-to-many and many-to-many relationships, unique identifiers, parent-child structures, and the reuse of shared objects. A design that unnecessarily duplicates objects or forces a complex relationship into an overly simple structure may work initially, but it can create significant problems over time, especially when the program begins integrating with other enterprise systems or scaling its reporting. 

Duplication of objects within an entity is a critical tell that something may be wrong with the underlying data model. 

Now, control the quality of the data that enters and remains within the TPRM application. 

How data enters the application, and how it’s validated at the point of entry, can prevent obvious errors, while clear ownership and stewardship ensure that someone remains accountable for maintaining key data elements. Records should be refreshed when appropriate, stale data identified, and material changes reflected consistently across the program.  

Good data governance should reduce the need for periodic clean-up exercises by making quality part of the operating model rather than something addressed immediately before an audit, regulatory submission, senior management report, or real-time crisis. This aligns with ISO 8000-61 principles for embedding data-quality management into organizational processes, helping ensure that information remains accurate, complete, consistent and fit for purpose. 

AI now offers an opportunity to make data governance more proactive.

AI agents can now be embedded in applications to review both data and the structure in which it’s held. 

These agents can examine connected applications to identify duplicate suppliers or services, conflicting information, stale records, and relationships that don’t conform to the intended data model, then route the findings for human review. They can also identify recurring patterns—such as duplicate objects or inconsistent one-to-many relationships—that signal a structural rather than isolated data-quality problem. 

In this way, AI can support system consolidation and serve as a long-term governance control, identifying drift in the data model, data-entry processes, and integrations, and directing owners to areas that need attention. 

Ultimately, TPRM leadership needs to recognize the symptoms and tackle the root causes. 

The invitation is to be bold about your data. We can suffer the discomfort of change, or we can suffer the discomfort of staying the same. 

If you know you have a problem, measure what it’s costing you: the resources required to maintain multiple platforms, sustain workarounds, repeat duplicated activities, and perform recurring clean-up. Compare that with the cost of fixing the underlying structure. 

• Challenge where your data comes from and who is best placed to own it. 
• Challenge whether the cost of maintaining multiple platforms is justified. 
• Question why you’re collecting each data element, and examine how it’s structured, stored and maintained. 
• Where the underlying model is wrong, redesign it now rather than continuing to build workarounds around it. 

Good data governance can remove significant unnecessary administration from TPRM, freeing teams to spend less time collecting, reconciling and cleaning data — and more time doing the work that actually matters: identifying, understanding and mitigating third-party risk. 

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