How Bad Data Undermines ROI, AI, And CX

Artificial Intelligence

Fotograzia – Getty Images

For years, organizations have treated data quality as a technical problem, something to be handled quietly by IT teams in the background. Today, that assumption is no longer sustainable, and poor data quality has evolved into a full-scale business risk, one that directly impacts revenue, customer relationships, and the ability to deploy transformative technologies like AI.

Despite record investments in digital transformation, many companies are discovering a hard truth: their most ambitious initiatives are built on unstable foundations. When customer data is fragmented, outdated, or inaccurate, every system that depends on it, from analytics dashboards to AI-driven personalization, becomes less effective.

The result is a growing data trust crisis inside modern organizations.

The Data Trust Gap Is Now a Revenue Problem

Recent research from Stibo Systems highlights just how widespread this issue has become. While 91% of executives say customer data management is “very” or “extremely” important to their organization’s future success, only 31% say they fully trust the data they rely on.

“That disconnect has real financial consequences,” said Neda Nia, Chief Product and Growth Officer at Stibo Systems. “According to our recent data, more than half of business leaders report losing revenue due to poor data quality, and nearly 82% say inaccuracies cost their organizations thousands if not millions a year through misdirected marketing spend, flawed forecasts, failed product launches, and missed cross-sell opportunities. But the cost of poor data trust isn’t just financial. It acts as a negative multiplier eroding confidence in the decisions leaders make internally and degrading the experiences customers receive externally. When you can’t trust your data, that doubt ripples outward in both directions, and the damage compounds.”

What was once seen as an operational inefficiency is now a growth problem….

Source