Data is no longer a byproduct of day-to-day operations. It has become a strategic asset. Machine builders grasped this early on, and in parallel, the digital factory took a major step forward with the rise of the industrial internet of things (IIoT). As machine manufacturers address the next wave of data, brought on by a push to modernize with artificial intelligence (AI) and machine learning (ML), a research study from Deloitte found 91% of companies expect to face data-related challenges in the coming year—showing that data readiness is top-of-mind.
Data Quality: The Achilles’ Heel
Inconsistencies, accuracy, fragmentation, and organizational silos, and it doesn’t stop there. Some machine builders are even testing AI and ML but haven’t yet fully modernized their digital thread.
Data silos, in particular, can severely undermine performance. They are withholding builders from sourcing parts on time, tracking process performance, and training AI models. This occurs when data is isolated within a specific department or system, preventing it from being easily accessible to all parts of an organization. This can lead to problems like inefficient decision-making and a lack of collaboration across working teams.
Breaking down these silos is therefore essential to building an integrated environment where data can flow freely and support collective performance.
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Three practical steps to fix the Data Bottleneck
1/ Connect existing data before creating more.
The first priority is to integrate what already exists. Systems like PLM play a central role by connecting ERP, MES, engineering, production, and suppliers into a single unified flow. The result: consolidated information, smoother exchanges, and real‑time visibility across product development.
2/ Consolidate legacy systems and eliminate manual tools.
It’s time to move beyond Excel spreadsheets, on-prem CAD files, and local BOMs—sources of inefficiencies and errors. Integrated systems streamline processes and enhance data accessibility. Teams collaborate more effectively, and teams’ decisions are built on more accurate and reliable information.
3/ Create a unified data model
Training AI requires datasets that are consistent, complete, and up-to-date. A unified data model directly enhances the accuracy and reliability of AI models, leading to more impactful decision‑making across the entire organization.
Building a Sustainable Data Strategy
The AI journey does not end with implementation. In fact, it’s only one step on the intelligent data management journey.
So how should data be managed once systems are in place? First, clear governance must define data ownership to establish accountability and ensure that individuals or teams are responsible for the integrity, accuracy, and reliability of the data.
Next, organizations must ensure full digital continuity throughout the entire product lifecycle—from design to service—to maintain a seamless flow of data and integration at every stage. Finally, modular and flexible systems will allow companies to integrate future AI capabilities, innovate continuously, and remain agile as markets evolve.
Looking Ahead
The future of smart machines extends beyond the integration of artificial intelligence. The real differentiator is the quality and availability of data. Without accurate, timely data, service‑oriented business models simply cannot function. And yet, these models are precisely what reduce machine downtime, support intelligent decision‑making, and ultimately boost productivity for end‑users.
Success, however, requires close collaboration between IT, engineering, and operations. When these functions work in a unified way, companies can effectively leverage their collective expertise to streamline data management processes and create an ecosystem where smart machines thrive to facilitate innovation and deliver that genuine competitive advantage in the marketplace.
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