Building Trust: Poor Quality Data a Silent Enterprise Risk

For all the talk of innovation and analytics, most business decisions still come down to trust. Can we trust what the numbers are telling us? Can we trust that our systems are secure? Can we trust the data?

In many companies, the honest answer is: not always.

Behind boardroom dashboards and AI pilots lies a quieter problem: data that’s inaccurate, incomplete or exposed to risk. From misreported KPIs to security breaches, weak data quality and oversight cost companies time, money and credibility. And as more decisions become automated, the risk only grows.

When Bad Data Looks Good
Consider what happened at a major retailer when a single data quality issue cascaded through its pricing system. A duplicate supplier record in the master database led to incorrect cost calculations across multiple product lines. The error went undetected for three months, resulting in margin erosion worth $2.3 million before analysts identified the discrepancy.

This wasn’t a cyberattack or system failure. It was a data quality problem that looked like a business performance issue until forensic analysis revealed the truth.

According to a Gartner report, organizations lose an average of $12.9 million each year due to poor data quality. That number doesn’t include the cost of regulatory fines or the erosion of executive trust when dashboards deliver conflicting information.

Recent MIT research reveals an even more troubling dimension: companies with poor data quality practices are 23% more likely to experience significant strategic missteps during periods of rapid change. In an era where agility determines survival, bad data becomes a competitive liability.

AI systems only amplify the problem. They don’t magically clean dirty data. They learn from it, perpetuate it, and scale it across the organization. The challenge goes beyond accuracy to trust itself. When executives lose confidence in data-driven insights, innovation stalls, and organizations revert to intuition-based decision making.

What Trustworthy Data Really Looks Like
Trustworthy data is the result of structured engineering and engineering reliability into every step of the data lifecycle.

FPT LATAM’s Data Engineering team has observed a consistent pattern across enterprise implementations: organizations that successfully build data trust do more than deploy new tools. They adopt structured, scalable practices that prioritize integrity as their data volumes grow.

The foundation often includes validation rules that catch errors at the point of ingestion, automated cleansing routines to remove duplicates and standardize formats, and ongoing monitoring systems that track quality across every data stream. But technology alone isn’t enough.

Reliable data environments also require clear accountability. This means assigning ownership to specific data domains, maintaining detailed lineage to trace how data evolves from source to use and enforcing access controls that protect sensitive information without blocking business needs.

Cloud-native platforms now make it easier to embed these practices into the architecture itself, ensuring that quality and security are not afterthoughts, but core principles by design.

The Questions That Matter
If data is driving your strategy, it’s time to challenge the assumptions behind it. For executives evaluating their organization’s data maturity, the critical questions focus on strategic rather than technical concerns:

Are our decisions based on verified, consistent data?
How are we measuring and monitoring data quality?
Do our teams have a shared, consistent view of the truth?
Is our data infrastructure designed to scale without introducing risk?

These questions reveal whether data serves as a strategic asset or remains a hidden liability.

Where Trust Begins
Data quality is not the final steps in a digital transformation journey. They’re the foundation that makes everything else possible.

Trust doesn’t come from intuition or vendor promises. It comes from systematic engineering that makes data reliability a core business capability. It comes from knowing your information is clean, consistent, and protected.

When data is trustworthy, the business can move faster and take bigger risks. Not recklessly, but with real confidence.

Because in a world that depends on digital decisions, trust is everything.