Garbage In, Garbage Out: Why Data Quality Is the Foundation of AI Success in Asset-Intensive Industries
- 7 hours ago
- 3 min read
By Trudi Hable, Chief Revenue Officer, Radix
Charles Babbage, widely regarded as the father of the computer, was once asked whether his calculating machine could produce the correct answer even if incorrect data was entered. His response was blunt: he could not comprehend the kind of confused thinking that would even prompt such a question.
To Babbage, the answer was obvious — bad data produces bad results. That exchange, centuries old, gave birth to what we now call the "garbage in, garbage out" principle, and it has never been more relevant than it is today.
As artificial intelligence continues to reshape asset-intensive industries like energy, manufacturing, transportation, and utilities, leaders are discovering that the greatest barrier to AI success is not the technology itself. It's the data underneath it.
For industrial companies racing to integrate AI into their operations, the temptation is to focus on the sophistication of the model or the elegance of the interface. But without a strong data foundation, built on clean, accurate, timely, and contextually rich information, even the most advanced AI system will underperform, mislead, or fail entirely.
"Without ingredients like context and interoperability, the challenges of successfully integrating AI increase," says Justin Conroy, Vice President of Digital Product Portfolio and Strategic Partnerships at Radix. "What makes AI challenging is not just the infrastructure — it's the uncertainty and the data layer that often is not properly built on a strong data foundation."
This is not just a technical issue. It's a strategic one. When AI is fed incomplete or decontextualized data, the insights it generates can lead operators and engineers in the wrong direction — creating costly errors, eroding trust in the system, and shelving investments that could have delivered genuine value.
So, what does high-quality data look like in an industrial context? It goes beyond accuracy and timeliness. It requires contextualization that ensures that data elements carry the operational meaning needed to make them useful. A pressure reading from a pump, for example, is only valuable if it's understood in the context of that pump's age, location, maintenance history, and its role in the broader system.
This is why forward-thinking industrial organizations are investing in AI models and the data management frameworks that feed them. Solutions like Cognite's CDF (Cognite Data Fusion) are designed to remove operational silos and deliver the contextual layer that transforms raw data into actionable insight.
Together with Cognite, Radix delivers the bottom line for operational leaders by ensuring their data is ready to support AI – even before those leaders determine what AI can do for their operations. The companies that get this right will not just avoid costly failures — they will build an AI foundation capable of scaling with every innovation that follows.
If Babbage were alive today, he would likely find the modern AI hype cycle just as baffling as that original question; not because AI isn't powerful, but because so few organizations are taking the time to build the data foundation it needs to thrive.
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About Radix
Founded in 2010, Radix is a privately held technology solutions and services company operating globally, empowering customers with consulting, engineering, operations technology, and digital solutions. Radix combines key capabilities and practices to enable our worldwide customers to thrive in their technology journey. With North American headquarters in Houston, TX, and headquarters in Rio de Janeiro, Brazil, including offices in São Paulo and Belo Horizonte, Radix provides technology-based, data-driven solutions to asset-intensive industries. Radix's robust capabilities extend to more than 30 countries worldwide. Learn more: www.radixeng.com
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