Building Foundations for Industrial Data Science Success

Industrial IoT deployments frequently evolve into full‑blown data science initiatives. Connected sensors on machinery, tools, pallets and finished goods generate vast streams of data that, when leveraged properly, can unlock operational excellence, resilience, and flexibility.

Yet the real value lies not in data collection alone but in the organization’s ability to translate those insights into transformative action. As PwC Consulting principal Steve Pillsbury explains, the goal is to “achieve new operational benchmarks or, in today’s world, create resilience and flexibility.”

[IoT World is North America’s largest IoT event where strategists, technologists and implementers connect, putting IoT, AI, 5G and edge into action across industry verticals. Book your ticket now. ]

Even before COVID‑19, many industrial firms found the transition to data‑driven operations daunting. Data science talent is scarce, and competition for it is intense. While numerous companies have launched digital innovation programs, the return on investment remains limited. Accenture research found that from 2016 to 2018, nearly 80% of digital projects in industrial firms failed to meet expected financial returns.

Preparing the Organization

A key barrier is inadequate planning. Pillsbury notes that most industrial players are stuck in pilot or test phases, with many still struggling to move beyond experimentation. The missing piece is “know‑how” – not merely the right technical skills, but the ability to embed those skills throughout the organization. Employees need to understand the possibilities and how to apply them, which demands a cultural shift toward continuous learning.

PwC’s Digital IQ research identified “transcender” firms—those that build digital programs around people and culture—as the most successful. These organizations prioritize employee education and foster a resilient mindset.

Within a company, data maturity can vary widely. Murali Raj, CIO at HIL, a building‑supply manufacturer, emphasizes the importance of accounting for this variability when designing a transformation roadmap. HIL’s predictive‑maintenance rollout began by building a digital backbone for the entire shop floor, rather than targeting a single line or machine.

Human‑Centered Design Applied to Processes

Securing buy‑in from all levels is essential. Many firms create a “Center of Excellence” to drive digital leadership and best practices. While these experts grasp technology and value drivers, they often discover that end users resist new tools because of poor design or unclear workflows.

To win employee support, leaders should solicit feedback during the design phase and apply human‑centered principles to internal processes. By studying the context of each problem and the pain points of staff, teams can craft digital tools that fit naturally into daily work.

Identifying Individuals to Lead Transformation

Beyond cultural change, the right data‑science leadership is critical. According to Umesh Ramakrishnan of Kingsley Gate Partners, only about 25% of candidates claiming expertise in AI, machine learning or data science are truly qualified. A brief 20–30 minute interview can help uncover those with a strong foundation in science or engineering and hands‑on experience with neural networks or deep learning.

Effective leaders must also translate technical insight into organizational impact. They should excel in communication, persuasion, and the ability to tie individual goals to the company’s mission. As Ramakrishnan notes, the title “champion” or “evangelist” oversimplifies the sophisticated leadership required for successful transformation.

Industrial firms that align talent, technology, and business strategy—supported by leaders who can both articulate vision and drive execution—are the ones that achieve lasting digital success.