The Foundation of Industrial AI: Addressing the Data Quality Imperative

By Colin Masson

This blog series offers a preview of insights from ARC Advisory Group's upcoming comprehensive reports on the Industrial Data Fabric market landscape, sizing, growth projections, and the key archetypes emerging as organizations assemble their data foundations for the AI era. As I immerse myself in the research and insights for the Industrial Data Fabric Landscape and Archetypes Reports, I'll be sharing some preliminary observations along my voyage of discovery.

The Achilles' Heel of Industrial AI: Why Data Quality Matters More Than Ever

The potential of an Industrial AI (R)Evolution is captivating industries around the world, offering a powerful means to transform operations, boost efficiency, and foster innovation—while also addressing critical skill shortages in manufacturing and supply chain sectors. From fine-tuning predictive maintenance to enforcing rigorous quality standards and driving unmatched operational performance, AI’s impact on the industrial landscape is profound. This momentum is further reflected in growing financial commitments, with US AI-related investments projected to reach as much as 2.5 to 4 percent of GDP—highlighting the urgency of adopting AI to stay competitive in a rapidly digitizing world.

However, as industrial organizations move past the initial hype of Generative AI and begin to strategically choose the right AI and machine learning tools for targeted use cases, one crucial foundational factor is often overlooked or underestimated: the quality of the underlying industrial data.

In my initial report on the Industrial AI (R)Evolution, I placed the Industrial Data Fabric at the very center of ARC Advisory Group's Industrial AI Impact Assessment Model, recognizing its fundamental role. We've since gone on to describe the essential attributes of such a Fabric. Now, as we delve deeper into the practicalities, our recent research highlights the persistent challenges and the emerging solutions.

Our end-of-Q4 2024 survey results—many of which I’ve highlighted in previous blogs—clearly underscored the need for a renewed and urgent focus on data quality. It ranked among the top three challenges industrial organizations face when deploying AI. The findings also revealed the wide variety of components respondents are using to build their own Industrial-grade Data Fabrics (IDFs), signaling that many are actively addressing this critical foundation. You can explore the full data in my blog on IT, OT, and BDM Perspectives on Assembling Industrial-Grade Data Fabrics for AI.

We also delved into Industrial-grade Data Fabrics during several end-user-led sessions at the ARC Advisory Group Industry Leadership Forum 2025 in Orlando. I’ve shared key takeaways from the sessions I hosted in my blog, Industrial AI in Action: Key Takeaways from ARC Forum 2025 on Data, Agents, and End User Success. For deeper insights, I recommend tuning into my interviews with John Dyck, CEO of CESMII—The Smart Manufacturing Institute—on Unlocking Smart Manufacturing: Demystifying Industrial Data Fabrics, and with John Harrington, Chief Product Officer and Co-Founder of HighByte on Bridging the IT/OT Divide with Industrial Data Ops. 

The Unique Data Challenges of the Industrial Sector

The industrial environment presents a distinct set of data challenges that make the need for high-quality data especially critical:

• Volume and Diversity: The volume of data produced by operational technology (OT) and information technology (IT) systems is staggering, driven by a complex network of sensors, machines, and smart factories. Beyond its scale, this data is highly diverse—spanning multiple types, formats, architectures, and deployment environments.

• Data Silos: Valuable information often remains trapped within individual systems and departments, hindering a unified view of operations.

• Lack of Standardization: Inconsistent data formats and the absence of standardized context further compound these challenges, making it difficult to fully leverage this vast stream of information for actionable insights.

• Real-Time Requirements: The dynamic, real-time nature of industrial processes means data must be both accurate and timely. Industrial-grade Data Fabrics are expected to support high-fidelity, real-time processing to meet the demands of mission-critical safety and operational reliability.

The widespread presence of data silos and the lack of standardization underscore the urgent need for solutions that can bridge these divides and deliver a cohesive, end-to-end view of industrial data. 

The High Cost of Poor Data Quality in Industrial AI

The consequences of neglecting data quality can be severe, directly undermining the success of even the most ambitious Industrial AI initiatives.

• "Garbage In, Garbage Out": AI models trained on flawed or biased data inevitably yield unreliable results, increasing the risk of misguided business decisions and undermining trust in AI-driven outcomes.

• High Failure Rates: Studies show that as many as 87 percent of AI projects never make it to production, with poor data quality cited as the leading cause behind these failures.

• Financial Losses: Inaccurate or inconsistent data can result in faulty predictions, operational inefficiencies, and costly mistakes. On average, organizations lose $12.9 million annually due to poor data quality, while the US economy faces an estimated $3.1 trillion in losses each year.

• Erosion of Trust: The use of biased or low-quality data in AI systems can erode stakeholder confidence and damage customer trust.

Without a foundation of trusted, reliable data, achieving a strong return on investment from AI becomes a major hurdle—slowing both adoption and long-term success of these transformative technologies. ARC Advisory Group’s research has consistently highlighted this issue. In addition to our recent Q4 findings, our January 2024 study showed that improving data quality could significantly boost the value of Industrial AI initiatives. Even back in 2021, our analysis on AI-IoT convergence identified poor industrial data quality and management as core challenges. The recurring emphasis on this issue across multiple surveys underscores its persistent and far-reaching impact.

The Foundational Role of Industrial-Grade Data Fabrics

Recognizing the significant impact of data quality challenges, ARC Advisory Group strongly emphasizes the strategic value of modern data architectures—particularly Industrial-grade Data Fabrics. We advocate prioritizing investments in robust IDF foundations to enable a broad spectrum of AI use cases. Many leading industrial organizations are already taking this approach, actively working to develop IDFs while addressing the complex task of establishing effective data governance to ensure both data quality and security.

Industrial-grade Data Fabrics offer a unified, seamless layer for managing and integrating data across diverse sources. They are purpose-built to break down silos and establish the critical “single version of the truth” needed for effective and scalable Industrial AI deployments.

While the promise of Industrial AI is clear, its success depends heavily on the quality of industrial data. Poor data quality continues to be a major barrier, as reinforced by our recent survey findings, which highlight both the challenges and the diverse components organizations are employing. To move forward effectively, organizations must prioritize modern data architectures and implement comprehensive data quality management strategies.

As demonstrated by ARC Advisory Group’s end user clients at the ARC Advisory Group Industry Leadership Forum 2025 in Orlando, Industrial-grade Data Fabrics are no longer just a concept—they are actively being built as a foundational layer. These architectures provide the critical framework to integrate, manage, and govern complex industrial data, paving the way for impactful Industrial AI adoption and reinforcing the foundational role we identified in the Industrial AI (R)Evolution.

Stay tuned for the next posts in this series, where I’ll share early insights from my ongoing research into the Industrial Data Fabric landscape and the key archetypes taking shape in this vital space.

Engage with ARC Advisory Group

For ARC Advisory Group recommendations for Navigating the AI Wars, Closing the Digital Divide by Embracing Industrial AI, assembling your Industrial-Grade Data Fabric, and governing and guiding major decisions about enterprise, cloud, industrial edge, and AI software, please contact Colin Masson at cmasson@arcweb.com or set up a meeting with me, or my fellow Analysts at ARC Advisory Group.