In the rapidly-changing and evolving tech landscape, data is not just a byproduct of day-to-day operations, it's a strategic asset. Machine builders saw this early, and the digital factory took a major step forward when it integrated the industrial internet of things (IIoT).
As machine builders 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 address data challenges in the next year—showing that data readiness is top-of-mind.
Data quality issues have a vast range from inconsistency, accuracy, fragmentation, and collaboration barriers, and it doesn’t stop there. What’s more, some machine builders are piloting AI and ML but haven’t yet fully modernized their digital thread.
On top of this, data silos 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.
Overcoming data silos is crucial for fostering a more integrated environment where data can be shared and used effectively across all levels of an organization for utmost cohesiveness.
Practical Steps to Fix the Data Bottleneck
The first step of fixing the data bottleneck is about connecting all the existing data, not collecting more of it.
Integrated systems like product lifecycle management (PLM) can help machine builders connect data across systems (like ERP, MES), internal and external processes and departments, including engineering, production, and suppliers. This promotes collaboration, enhances communication, and ensures all stakeholders have access to accurate, real-time data and information throughout product development.
Second, consolidating legacy systems and removing manual workarounds (like spreadsheets or on-prem CAD files and BOMs) is imperative as it streamlines processes and enhances data accessibility. This integration reduces errors and inefficiencies, and also facilitates better collaboration and decision-making across teams.
Lastly, creating a unified data model ensures that AI systems are trained on consistent, complete, and up-to-date information. This approach enhances the accuracy and reliability of AI models, leading to better insights and more impactful decision-making across any given organization.
Building a Future-Proof Data Strategy
The AI journey doesn’t end with implementation. In fact, it’s only one step on the intelligent data management journey.
Now, how do we manage data once we have all these systems down? Emphasizing the importance of governance means clearly defining data ownership to establish accountability and ensuring that individuals or teams are responsible for the integrity, accuracy, and reliability of the data.
Encouraging the modernization of a digital thread that spans the entire product lifecycle, from design to service, ensures seamless data flow and integration at every stage. Advocating for modular, flexible systems allows organizations to adapt and evolve with emerging AI capabilities, enabling them to continuously innovate and respond to the ever-changing market demands impactfully.
Looking Ahead
The future of smart machines extends beyond the integration of artificial intelligence. It centers on ensuring that the right data is readily accessible at any given crucial moment. Availability of data is key to enable new, service-oriented business models that help customers to run their machines with more productivity and less downtime.
By prioritizing data accessibility and relevance, organizations can empower their smart machines to make more informed decisions, optimize operations and enhance performance overall.
Dr. Florian Harzenetter is senior director and global advisor for industrial, electronics and high-tech customers at PTC.
Dr. Florian Harzenetter is senior director and global advisor for industrial, electronics and high-tech customers at PTC. In this role, Harzenetter identifies the specific needs of EHT customers and helps align their roadmaps and strategies to ensure successful adoption of PTC technologies. From this perspective, he also helps to align PTC's offerings with customers' needs.
