The Hidden Costs of Data Chaos

This article is Part 2, and the conclusion of our “Garbage in, Chaos out” series on AI and data integrity. Read Part 1 here.

Flawed data quietly drains the average large organization of $389,780 every year. According to Iron Mountain and FT Longitude's global study of 500 senior executives, that's the measurable price of poor data integrity. Across those companies, that adds up to nearly $14 billion in annual losses.

In Part 1, we explored a fictional bank’s AI lending platform. Leaders expected it to deliver faster approvals, reduce bias, and improve customer experience. Instead, it created confusion, errors, and growing mistrust. The AI hadn't failed on its own. It had been fed flawed data, and the cracks in that foundation quickly spread throughout the organization.

In this final piece, we look at what those cracks actually cost, and how disciplined organizations are flipping the equation from loss to growth.

How losses materialize

At the bank, the costs showed up in subtle but corrosive ways. Staff were pulled into rework, double-checking AI-generated lending decisions. Compliance teams found themselves in constant firefighting mode. Executives second-guessed dashboards they no longer trusted. Customers walked away frustrated when legitimate loans were denied.

The Iron Mountain-FT Longitude study shows these experiences aren't unique. Across industries, organizations report that:

• 76% say technical debt within legacy systems has blocked some of their AI initiatives in the past year
• 70% cannot integrate data sources quickly enough to support real-time analytics
• 69% acknowledge that data integrity and sourcing are significant weaknesses
• Nearly two-thirds (64%) say their AI readiness activities do not generate consistent value

These aren't dramatic failures that make headlines. They're the everyday inefficiencies, stalled projects, and missed opportunities that quietly drain competitiveness, adding up to hundreds of thousands in losses each year.

The upside: a good data dividend

But the story doesn’t end there. The study identifies a disciplined minority, fewer than 10% of organizations, that achieve dramatically better outcomes. On average, these leaders capture a “good data dividend” worth $1.9 billion per company, contributing to an estimated $72 trillion in global revenue gains.

Their advantage doesn’t come from bigger budgets or more advanced AI. It comes from getting the fundamentals right. Data integrity isn’t a back-office concern for these organizations. It’s the foundation for every decision and AI initiative. And while nine in ten organizations say they have seen revenue and profitability gains from information management systems, leaders are turning those gains into consistent, large-scale results.

Practices that pay off

What exactly are these leaders doing differently? The study points to a set of practices that distinguish them from the rest:

• Systematic ROT elimination:100% of leaders actively remove redundant, obsolete, and trivial data, compared with 90% of others
• Clear lineage tracking: 96% of leaders can trace how data flows and transforms across systems, versus 82% of others
• AI nutrition labels: Leaders are 16 percentage points more likely to disclose how data is sourced and used in AI models
• Designated data stewards: 98% of leaders appoint stewards responsible for accountability, compared with 87% of others
• AI-powered cleansing of unstructured data: Leaders bring structure to archives, documents, and legacy files, transforming hidden data into usable assets

Together, these practices create trust: executives can rely on their insights, regulators see accountability, and customers know decisions are based on solid ground. Tools such as Iron Mountain InSight® Digital Experience Platform (DXP) help organizations automate these governance processes, making them sustainable at scale.

From chaos to clarity

For our fictional bank, the turning point came when leaders stopped trying to tweak the AI model and instead tackled the data beneath it. They launched systematic audits of customer records, brought consistency to scattered repositories, introduced governance dashboards, and addressed decades of messy legacy files.

The work wasn't glamorous, but it mattered. Errors fell, compliance stabilized, and confidence began to return among both regulators and customers. The data chaos that once cost hundreds of thousands every year was transformed into a platform for long-term growth.

Investing in data health

The study makes the divide clear. Most organizations are quietly bleeding value. Hundreds of thousands are lost each year to flawed data. A small group is capturing billions in additional revenue. The difference isn't better algorithms. It's disciplined data integrity.

For executives, the choice is stark. Continue to absorb the hidden costs of unreliable information, or adopt proven practices that turn data quality into a competitive advantage.

The journey from chaos to clarity starts with how you manage your data. Discover how with Iron Mountain InSight DXP.