Empowering Domain Experts with Data Science for Smarter Manufacturing Insights

Advanced analytics and artificial intelligence are reshaping manufacturing, yet raw data alone offers limited value. Mike Loughran, CTO for the UK and Ireland at Rockwell Automation, emphasizes that context and domain expertise are essential to transform data into actionable business insights.

While consumer markets have seen AI‑driven personalization—from targeted ads to location‑based recommendations—manufacturing can unlock similar benefits. Analytics delivers timely, data‑driven decisions that surface critical insights when they matter most.

Clear Benefits

Digital transformation backed by analytics can elevate revenue by up to 10%, cut operating costs by up to 12%, and boost asset efficiency by as much as 30%. These figures reflect real gains reported by organizations that have embraced data‑centric operations.

Implementing analytics in manufacturing presents unique challenges. The prevailing “centralized‑cloud” model often fails because high network bandwidth costs and latency hinder real‑time decision making. Running models at the edge, close to where data originates, is typically more effective.

Managing High Data Volumes

Manufacturers generate massive streams of real‑time plant data and historian records. Yet, only a small subset may be relevant to a specific use case. Integrating heterogeneous sources—each with distinct protocols and legacy technologies—requires sophisticated data aggregation and a unified data model to clarify relationships.

Insights must reach the right stakeholder or automated system within a short window to remain actionable. Achieving this demands deep knowledge of both the underlying processes and the analytics techniques—a rare combination in one individual.

Successful transformation hinges on partnering with experts who understand both manufacturing operations and analytics. Such partners bring industry heritage, familiarity with process hardware and operation technology, and alignment with your business objectives.

Simplifying Data Science in Practice

Tools that empower control and process engineers to conduct analytics—without constant reliance on data scientists—are essential. Two core requirements surfaced from our digital‑transformation conversations: digital workers and machine learning capabilities.

Companies can streamline data analysis through four steps:

1. Identify critical operational attributes.
2. Define logical data structures.
3. Implement high‑velocity data capture practices.
4. Reuse models across the information layer for speed and efficiency.

This approach accelerates results by equipping engineers with robust data‑science tools.

Our ThingWorx Analytics platform streamlines the data‑science workflow for automation and control engineers. It identifies the most predictive tags, applies auto‑machine‑learning to select optimal algorithms, and evaluates multiple scenarios to determine the best model combination—all without manual intervention.

This simplification empowers domain experts to extract latent value from collected data, heralding the era of the citizen data scientist.

The author is Mike Loughran, CTO for the UK and Ireland at Rockwell Automation.

About the author

Mike Loughran has served Rockwell Automation for over 14 years, progressing from software sales to the C‑suite, where he leads technology strategy for the UK and Ireland.