The Data Trust Drought: Why Confidence Is Drying Up and How to Help Restore It

• The trust gap is the disconnect between expected and actual data quality and reliability.
• Inconsistent or fragmented data generally slows decisions and can weaken AI performance.
• AI often exposes data issues — it doesn’t tend to create them.
• Closing the gap can start with shared definitions, ownership, and practical improvements.

Even as organizations today create and consume an enormous volume of data, they’re often starved for confidence in it. Teams, managers, and even executive leaders increasingly find themselves asking the same question: “Can we trust what we’re seeing?”

When a sales dashboard doesn’t match a finance report or when two departments maintain conflicting records for the same customers, everyday decision‑making can feel more uncertain than it should.

Businesses can struggle to fully trust their data when making decisions, and data quality remains one of the most persistent barriers to becoming more data‑driven. And as organizations work toward more automation and intelligent tools, teams are being reminded that trustworthiness (rather than volume alone) is what helps make data useful.

This erosion of confidence is what many refer to as the trust gap, defined as the divide between the reliability people expect from their data and the experience they actually have when using it.

For many companies, there are three core elements at the heart of data trust, according to Dun & Bradstreet Chief Ethics & Compliance Officer Hilary Wandall.

“First, there’s competence,” she explains. “Do you as a company, or whoever you’re wanting to engage with, demonstrate competence with data and data management? Secondly, intent is vital. Is the intent benevolent? Is the company striving to achieve a degree of beneficence in terms of the actions that are undertaken with the data?”

The third key element is reliability. “Reliability goes hand-in-hand with whether or not an enterprise comes across as having integrity and being competent,” Wandall says. “If you are competent but you’re not reliable in terms of data quality and data management, if you are benevolent or aim to be beneficent but lack competence, or if you aren’t reliable, you are unlikely to earn or build real trust in your data.”

The trust gap rarely emerges from a single failure. It tends to be the cumulative effect of small inconsistencies that stack up over time.

“The issue often starts with the number of disparate systems an organization uses,” explains Jake Sullivan, Dun & Bradstreet data management solutions expert. “When customer or supplier records live in different applications with no consistent way to reconcile them, it becomes difficult for people to know which version to rely on.”

This fragmentation is widespread. A Forrester Consulting study indicates that organizations now manage an average of more than 30 internal and 32 external data sources, making ongoing and timely reconciliation even more challenging.

This fragmentation can affect organizations at all levels of data management maturity. Some companies have strong processes but struggle to consolidate information efficiently. Others are still wrestling with the basics, like duplicate entries, outdated records, or differences in how departments define the same information.

These inconsistencies introduce a subtle but powerful friction: People can begin to hesitate, doubt, and re-examine numbers, or avoid using certain information or reports altogether.

Researchers are helping to explore this challenge. Studies of data issues show that strengthening data quality and finding trustworthy sources remain among the biggest hurdles to adopting more advanced analytics or automation. Even organizations excited about AI adoption may cite foundational issues — data accuracy, completeness, and consistency — as key challenges that must be addressed before they can move forward confidently with a digital transformation strategy.

It’s hard to ignore how AI is making conversations about trust in data more urgent. Some analysts suggest that an increasing number of business decisions will soon be supported or automated by AI‑driven systems — but only if the underlying information is reliable.

Sullivan sees this firsthand. “If your data is inconsistent or incomplete,” he notes, “traditional AI, generative AI, and agentic AI systems are likely to struggle. You’ll probably see issues like contradictory answers or confusion about which information to use and how to use it. It’s because the system only has what you give it. And if that input is messy, the output will be too.” 

That doesn’t mean AI is the problem. In fact, AI often reveals issues that were already there. Frequently, the trust gap becomes visible not because AI created it, but because AI makes inconsistencies harder to ignore.

The consequences extend beyond AI performance. More than half of organizations have experienced privacy or security lapses tied to poor or insufficient data management, according to the Forrester Consulting study.

AI Readiness and Its Connection to Data Trust

AI readiness refers to an organization’s ability to successfully deploy, scale, and rely on AI systems — something that can depend more on the quality and trustworthiness of its data than on the algorithms themselves. When data is accurate, consistent, and well‑governed, AI models can produce more reliable, explainable outputs that support confident decision‑making. But when a data trust gap exists, AI readiness can quickly erode.

Incomplete records, mismatched definitions, and fragmented systems introduce noise that AI may not be able to compensate for, which can result in contradictory or low‑value insights. For most enterprises, being AI‑ready isn't realistic if data isn’t trustworthy. Closing the data trust gap is crucial for realizing meaningful value from most AI initiatives.

The trust gap affects different teams in different ways. Finance may lose time reconciling conflicting numbers before closing the books. Sales and marketing might see mismatched customer lists that disrupt campaigns or territory planning.

Operations may struggle with supplier records that are incomplete or inconsistent. Leadership may hesitate to rely on dashboards that deliver different answers depending on the source.

Across all these scenarios, the impact is the same: Decisions can take longer, misalignment may increase, and the organization can miss opportunities to act with clarity.

Sullivan sees communication as one of the biggest levers for improvement. “People sometimes assume data quality is the responsibility of a data analyst or a data steward," he says, “but almost anyone who enters or uses information contributes to its accuracy. When teams talk to each other about what’s working, and what isn’t, they can identify gaps much faster.”

That cultural alignment — encouraging everyone to play a part — is often just as important as technology or processes.

Organizations looking to better understand or reduce a data trust gap might consider tracking data quality indicators such as accuracy, completeness, consistency, or timeliness. Observing trends in missing values, error rates, or conflicting records could offer helpful signals about where trust issues tend to arise and where improvements may have the greatest effect.

Some enterprises also keep an eye on match‑rate and reconciliation metrics, including duplicate rates or cross-system alignment. Shifts in these areas may provide insight into how well customer, supplier, or other data aligns across platforms, which can influence how reliably teams can use downstream analytics and reporting.

In addition, usage- and behavior‑based indicators (like dashboard adoption, reliance on approved data sources, or reductions in manual workarounds) may serve as informal markers of increasing confidence. When these patterns trend in a positive direction, they can suggest that teams feel more comfortable using shared data resources, even as underlying data work continues.

Closing the trust gap doesn’t require an overhaul on day one. In fact, Sullivan emphasizes the importance of starting with a baseline. “It’s hard to fix what you haven’t measured,” he explains. “Even documenting the refresh cycles, rules, and ownership you think you’re following can reveal where inconsistencies already exist.”

From there, organizations typically create a checklist that focuses on foundational steps such as:

1. Clarify ownership and definitions.

Agree on how key terms are defined — like what counts as a customer, an opportunity, or an active supplier — and assign clear responsibility for maintaining those definitions across systems.

2. Strengthen communication between teams.

When CRM and ERP teams, for example, communicate regularly, differences can be addressed proactively rather than discovered during quarterly reporting crunches.

3. Reduce duplicates and improve match rates.

Organizations often find value in cleaning up and consolidating systems where possible. Even small reductions in duplication can improve confidence across departments.

4. Focus on practical improvements, not perfection.

Some industry research suggests that the highest‑performing organizations don’t aim for perfect data — they aim for reliable, explainable data that supports real business decisions.

And according to Sullivan, the journey doesn’t have to be long or overwhelming. “Some companies imagine that improving data foundations will take years,” he says, “but with the right focus, we’ve seen meaningful improvements in a matter of months.” 

As companies continue to adopt more automation and more sophisticated analytical tools, the importance of data trust will only grow. But this is also an opportunity. The organizations that invest in clarity, consistency, and communication today are more likely to construct a foundation that supports faster decisions, stronger customer experiences, and more value from future technologies — whatever they may be.

Recent Forrester Consulting analysis of Dun & Bradstreet customers found that strengthening foundational data practices can deliver a 208% ROI with a 14‑month payback period — evidence that building a trusted data foundation can help improve confidence and measurable business value.

The path forward starts with a simple idea: Make sure people can rely on the data they use every day. The more trustworthy it becomes, the more likely it will be for the entire business to execute strategy with confidence.