Manufacturing is not typically the first industry that comes to mind when discussing data-driven transformation. For years, it has been considered a traditional sector, slow to adopt new technologies. However, that perception is rapidly changing.
According to Meghan Cipperley, Senior Vice President of Learning at Data Society:
Manufacturers are increasingly recognizing that without high-quality, structured data, they cannot optimize their operations, train AI models, or improve decision-making processes. Companies that embrace data literacy position themselves at the forefront of the industry’s digital transformation.
Why Manufacturing is Investing in Data Training
Historically, manufacturing has been slower to adopt data-driven processes compared to finance, healthcare, and technology sectors. However, that is changing due to:
- The Rise of Industrial AI & Automation: Manufacturers are integrating AI into their supply chains, but these systems require accurate and structured data to function properly (World Economic Forum).
- Demand for Real-Time Analytics: Companies are leveraging predictive analytics to optimize maintenance schedules, reduce downtime, and improve efficiency (McKinsey & Company).
- Workforce Digital Upskilling: As frontline workers interact with more technology-driven systems, they need foundational data literacy to make informed decisions and ensure operations run smoothly.
Meghan Cipperley explains:
“If you don’t have the data, you cannot train your large language model.”
She continues:
“Frontline workers are asking how they can contribute to a data-driven culture. Leaders are recognizing that data literacy isn’t just for analysts. It must be embedded across the workforce.”
The Biggest Challenge: Data Readiness
Many manufacturing companies recognize data’s importance but often struggle with data quality, accessibility, and workforce training. Without proper data governance and literacy programs, AI adoption remains a challenge. As recommended by dataPARC: “Manufacturers need to assess their current data ecosystems to identify gaps and areas for improvement before diving into artificial intelligence. […] It includes fostering a culture that embraces data-driven decision-making and encourages continuous data utilization and integration.”
To bridge this gap, companies must:
Invest in Data Literacy Training Across All Levels
- Employees should understand how to collect, analyze, and apply data to make data-driven decisions.
- Training should not be limited to analysts and engineers but should include production managers, technicians, and frontline staff.
Ensure AI Investments Have a Clear ROI
- AI should be used to solve real operational challenges, not just as a tech trend.
- Companies should measure how AI adoption improves efficiency, reduces costs, and enhances productivity.
Empower Employees to Work with AI & Analytics Tools
- Workers need training that helps them interpret and use AI-powered insights, ensuring that new technology aligns with day-to-day operations.
Final Thoughts
Manufacturing is on track to undergo a quiet data revolution, and companies that invest in data literacy and AI adoption will have a competitive advantage in the Fourth Industrial Revolution.
If your organization is looking to build a data-driven workforce and maximize AI adoption, Data Society can help.
Contact Data Society today to learn how we can develop a customized data training program for your team.
