Artificial intelligence (AI) is poised to fuel economies, deliver unprecedented efficiency, and drive unprecedented innovation. Yet, delivering measurable value from AI demands more than vast datasets — it requires a foundation of data trust. 

As organizations strive to harness their growing data reserves to meet AI’s insatiable appetite, many find themselves falling short due to a lack of understanding and confidence in their data. This reality is reflected in the fact that only a minority of organizations report meaningful progress in AI adoption, underscoring the challenges ahead.

Scaling AI demands actionable and trusted data. However, most Chief Data Officers (CDOs) cite data quality as their top challenge, highlighting the foundational barriers that must be addressed. Until these challenges are overcome, enterprise data will remain a liability rather than an asset, and the full potential of AI will remain out of reach.

The high stakes of data trust in a digital world

Data trust is the confidence that data is accurate, reliable, and fit for its intended purpose. It ensures that data is properly managed, high in quality, and free from errors or inconsistencies that could undermine business processes or decisions. Without trust in their data, organizations face inefficiencies, poor decision-making, compliance failures, and an inability to achieve strategic objectives.

Yet, many organizations continue to experience difficulties maintaining consistent data quality across systems, directly limiting AI outcomes. Without resolving these issues, even the simplest AI initiatives risk failure, resulting in wasted resources, stalled innovation, and diminished return on investment (ROI).

Why data trust matters to business success

AI models are only as effective as the data they rely on. Poor data quality leads to inaccurate insights, slowed operations, and compliance risks, ultimately eroding the ROI of AI initiatives. For organizations to succeed, they need to establish trust in their data, enabling accurate insights for better decision-making and efficient operations, free from bottlenecks and delays.

Trust is also crucial for compliance with regulatory requirements and scalable, impactful AI initiatives; in fact, it impacts every aspect of the data journey throughout an organization. Achieving data trust requires a structured approach. Data must be organized by cataloging and classifying data to understand its source, ownership, and context. This foundational step provides a clear picture of the data landscape.

Once this is complete, users can begin to understand the data, assess its quality, and establish lineage. Understanding how data transforms over time and how it impacts business outcomes is crucial for reliability. The final step is to improve the data by cleansing, standardizing, enriching, and consolidating it to create a single source of truth. This ensures consistency and accuracy across systems.

These steps lay the groundwork for effectively managing data at every touchpoint. With the integration of AI, this framework becomes even more powerful, enabling real-time quality checks, automated workflows, and continuous improvements.

Addressing data quality challenges in the modern enterprise

One of the most significant hurdles organizations face is the lack of trust in their data and how this impacts the potential of AI. Many companies have invested in new platforms and popular AI initiatives but neglected the foundational step of organizing their data.

The consequences of poor data quality are far-reaching. Inaccurate insights undermine decision-making and strategic planning and lead to operational inefficiencies where delays and errors slow down processes and waste resources. It exposes organizations to financial penalties from regulatory non-compliance arising from inconsistent data management practices. It also halts future growth as poor-quality data constrains the scalability and impact of AI initiatives.

Unifying data ecosystems is critical for a solid data trust foundation

Fragmented systems are another significant barrier to data trust. Without unified standards for data formats, definitions, and validations, organizations struggle to establish centralized control. Legacy systems, often ill-equipped to handle modern data volumes, further exacerbate the problem. These systems were designed for periodic updates rather than the continuous, real-time streams demanded by AI, leading to inefficiencies and scalability limitations.

To address these challenges, organizations must implement centralized governance, quality, and observability within a single framework. This enables them to leverage data lineage and track their data as it moves through systems to ensure transparency and identify issues in real-time. It also ensures they can regularly validate data integrity to support consistent, reliable AI models by conducting real-time quality checks. 

The benefits of such a unified approach include improved collaboration, real-time visibility, and enhanced scalability, enabling businesses to align their AI efforts with strategic goals.

The role of data quality in AI-driven growth

Organizations increasingly recognize the importance of data quality in driving AI success. Investing in data quality saves time and money by reducing delays and preventing costly mistakes. High-quality data enables accelerated decision-making through accurate, timely insights that drive better outcomes.

Reliable data fuels advanced, personalized AI solutions that accelerate innovation in the enterprise, while streamlined workflows automated processes to reduce inefficiencies and redundancies. Tangible business value is calculated through improved compliance, cost savings, and revenue growth resulting from consistent, high-quality data.

Turning data trust into business value

For organizations to maximize the potential of AI, they must embed data trust into their daily operations. This involves using automated systems like data observability to validate data integrity throughout its lifecycle, integrated governance to maintain reliability, and assuring continuous validation within evolving data ecosystems.

By addressing data quality challenges and investing in unified platforms, organizations can transform data trust into a strategic advantage. This enables them to scale AI initiatives, achieve measurable ROI, and thrive in a data-driven economy.

Organizations can get ahead of this to position themselves for AI success by building the foundations today that will enable data trust needed to turn AI into a growth engine. Aligning investments will ensure tools procured make actionable and reliable, and unifying ecosystems helps to break down silos by delivering centralized governance and observability. Results will be delivered through leveraging the high-quality data and will drive efficiency, growth, and competitive advantage.

As the next wave of AI innovation approaches, the organizations that prioritize data trust will lead the way, transforming their data into a powerful catalyst for business transformation.

About the author:

Jay Limburn is Chief Product Officer at Ataccama, and has a deep background in data governance, data quality, MDM and AI, and has spent over two decades helping companies align their technology vision, strategy, and execution to deliver business outcomes.

Limburn holds 17 patents in areas including machine learning, data governance and data generation. His passion is building market-leading, customer-focused products that enable enterprises to embrace AI innovation to augment human intelligence.

Before joining Ataccama, Limburn was Vice President of Product Management at IBM, where he led the team responsible for the Watsonx AI platform and IBM’s Data Fabric portfolio. In his spare time, he is an ardent follower of Formula 1 and spends his weekends golfing, playing guitar and enjoying time with his family.