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At a time when organizations are dedicating vast resources and staff hours to exploring the applications of artificial intelligence in the workplace, data integrity has become a make-or-break factor for business success. AI has the potential to transform the workplace experience and drive significant improvements in day-to-day business operations. However, the true value of these AI tools can only be realized if the data feeding these systems is accurate, reliable and responsibly sourced. Yet research from Iron Mountain and FT Longitude, as well as insights from Prosper Insights & Analytics, reveals most organizations are falling short, resulting in major costs.

The Data Integrity Gap in the U.S.

With the increased focus on AI, leaders around the world are beginning to recognize the impact of poor data integrity on achieving their AI goals. The United States, in particular, faces unique challenges due to a lack of robust data. According to Iron Mountain’s report, “Responsibly Sourced Data: AI’s Crucial Ingredient,” Forty percent of U.S. respondents say they experienced misguided strategic decisions due to data integrity flaws over the past 12 months – making this the most cited impact in the country. This considerably surpasses the global average of 27%, indicating U.S. organizations are at greater risk of poor decision-making from data flaws.

This issue is not just a technical problem; it is a strategic one as well. Poor data integrity leads to flawed decisions that can reverberate throughout an organization, undermining business outcomes and eroding trust internally and among customers, partners and regulators. According to a recent Prosper Insights & Analytics survey, 40% of U.S. adults and 43% of executives worry that AI systems can produce incorrect or misleading information. These so-called “hallucinations” can dramatically affect decision-making, leading to far-reaching business, compliance and reputational losses.

Prosper Insights & Analytics

The High Cost of Bad Data

These gaps in data integrity can also significantly impact earnings by creating inefficiencies, compliance failures, and poor AI outputs. According to the research from Iron Mountain and FT Longitude, data integrity flaws cost U.S. organizations, on average, $443,550 over the last year.

However, many organizations are already addressing these challenges and implementing strategies to mitigate the risk and monetary losses associated with data integrity gaps. According to McKinsey & Co.’s report, “The State of AI: How Organizations Are Rewiring to Capture Value,” companies are actively managing risks related to inaccuracy, cybersecurity and intellectual property infringement. These three generative AI-related vulnerabilities are the most cited reasons for negative consequences within respondents’ organizations.

The Good Data Dividend Is Real

On the other side of these challenges is what Iron Mountain has identified as the “good data dividend.” Organizations and business leaders investing in robust information management systems are realizing extraordinary results. The research from Iron Mountain and FT Longitude found that U.S. organizations reported a 10.8% revenue increase over the last 12 months, equating to $2.2 billion — a direct result of their information management systems and strategies. This figure surpasses the global average of 10.5%.

“With the rise of open-source and specialized AI models, data integrity, transparency and trust are more critical than ever,” said Narasimha Goli, chief technology officer at Iron Mountain. “At Iron Mountain, we are investing in solutions such as our InSight Digital Experience Platform (DXP) to help our customers get their information ready for use in generative AI and other AI-powered applications. By ensuring their data is being sourced responsibly, organizations can harness the full potential of their information to drive intelligent decision-making and unlock new growth opportunities.”

The ‘good data dividend’ equated to a total global average revenue gain of $72 trillion, or average revenue growth of $1.9 billion per organization. These benefits are being realized across sectors such as finance, retail and manufacturing. Through these AI tools, organizations are achieving higher productivity, improving customer experience and creating new revenue streams as a result of trustworthy AI.

What sets successful leaders apart is their ability to actively manage the quality, security and traceability of the data they collect. They regularly audit data streams, eliminate redundant or obsolete data, set up automated validation checkpoints and embed compliance and security mechanisms at the core of every workflow.

Turning Information Management Into a Competitive Advantage

How can organizations close the data integrity gap and make the most of the good data dividend? Iron Mountain has identified several key information management practices that every organization should implement to drive superior AI outcomes. These include:

Eliminate ROT data. Regularly scan for and remove redundant, obsolete and trivial data. These audits help organizations reduce attack surfaces and ensure that AI models are fueled by relevant and up-to-date data.

Regularly scan for and remove redundant, obsolete and trivial data. These audits help organizations reduce attack surfaces and ensure that AI models are fueled by relevant and up-to-date data. Automate data quality checks. Implement automated quality checkpoints to validate and extract data. Automating these processes allows organizations to scale faster and frees staff for higher-value tasks.

Implement automated quality checkpoints to validate and extract data. Automating these processes allows organizations to scale faster and frees staff for higher-value tasks. Embed security, compliance, and keep humans in the loop. As AI draws on more data streams, it is important to maintain strict security and compliance practices at the core of AI models, with regular human oversight to ensure that automation does not lead to unchecked errors or blind spots.

As AI draws on more data streams, it is important to maintain strict security and compliance practices at the core of AI models, with regular human oversight to ensure that automation does not lead to unchecked errors or blind spots. Applying AI nutrition labels and tracking data lineage. AI nutrition labels provide clear insight into the datasets and models behind AI outputs. These labels are a critical component of maintaining strong data lineage, which is crucial for understanding where data originates, aiding in compliance checks and boosting overall confidence in AI outputs.

AI nutrition labels provide clear insight into the datasets and models behind AI outputs. These labels are a critical component of maintaining strong data lineage, which is crucial for understanding where data originates, aiding in compliance checks and boosting overall confidence in AI outputs. Education and Upskill. The AI learning curve is steep and requires robust training and investment to ensure these tools are being used efficiently and effectively. Organizations that focus on early investment with their employees are reducing implementations risks, creating value across the organization and driving genuine adoption.

The Bottom Line

Organizations looking to drive successful AI integration must focus on data integrity. In the United States, businesses must ensure they are bridging the data integrity gap or risk falling behind their global peers. By practicing best-in-class information management strategies, companies can avoid the monetary costs associated with bad data and potentially unlock billions in new value.

Invest in your data, and the good data dividend will follow. In an era of immense data creation, the real winners will be those who manage it best.