Setting Up Industrial Data Scientists for Success

In this special guest feature, Heiko Claussen, SVP of Artificial Intelligence & Industrial Data Scientist, AspenTech, highlights that while the concept of an Industrial Data Scientist is a relatively new workforce phenomenon, it speaks to an important and irreversible shift toward digital transformation across nearly every industrial space. Heiko is responsible for the company’s Industry 4.0 Strategy, Industrial AI Research, and Data Science. Prior to AspenTech, he was Head of Autonomous Machines and Principal Key Expert of AI at Siemens and led initiatives to enable autonomous machine applications for factory automation. Heiko is a recipient of numerous technical awards and recognitions – having been named Inventor of the Year twice at Siemens, and also authored 99 inventions and 47 individual patents. Heiko holds a Ph.D. degree in Electrical Engineering from the University of Southampton, UK; a master’s degree in Electrical Engineering from the University of Ulster, UK.

Gone are the days where data scientists and industrial domain experts stay in their own lanes; that approach simply can’t and won’t work anymore.

Enter the “Industrial Data Scientist,” a new, increasingly essential pillar of industrial work-forces.

With the dual skill sets of a citizen data scientist and industrial domain expert, the Industrial Data Scientist represents a best-of-both-worlds approach to a new kind of tech-driven, data-empowered domain expert. This shift away from the old, siloed way of working represents a sea change in the way that organizations think about how best to tap into and leverage the well of unused, unoptimized industrial data at their fingertips.

Traditional data scientists tended to arrive at industrial organizations from more typical backgrounds like computer science or software engineering. Operating with some, if not significant, distance from on-the-ground realities, these data scientists were largely focused on contributing academic insights or research expertise to longer term initiatives. Today, however, this model of doing deep data work without domain expertise is becoming unsustainable.

The concept of an Industrial Data Scientist is a relatively new workforce phenomenon that speaks to an important and irreversible shift toward digital transformation across nearly every industrial space. Industrial Data Scientists incorporate domain knowledge into data science projects, helping to demystify the value of data science in industrial settings by approaching their work with a close-up understanding of the business needs they’re working to improve. The domain-specific fluency that Industrial Data Scientists bring to their work empowers them to build comprehensive, performant, and sustainable Industrial AI and machine learning (ML) models that are fit for purpose in addressing real-world use cases.

Providing a clear roadmap

With any new and evolving role, growing pains are normal. While an adjustment period is to be expected, there are proactive ways in which leadership can mitigate the learning curve both for Industrial Data Scientists themselves and for colleagues across the organization who will eventually collaborate with them. As these tech-driven, data-empowered domain experts begin to establish their ability to transform and leverage new Industrial AI applications to continue pushing the envelope, it’s important that organizations identify core ways in which to support their success — especially since the role’s very existence represents a generational shift in expertise, expectations, and, ultimately, industry norms.

Most Industrial Data Scientists want to focus on understanding the real-world business case, and then analyzing industrial data and building ML models that can be embedded into fit-for-purpose industrial applications targeted at those use cases. Understanding this foundational work process will help leadership and colleagues collaborate more effectively with Industrial Data Scientists as they look to coordinate domain knowledge resources for new or ongoing projects (e.g., piloting a new Industrial AI initiative).  

Additional steps for supporting your Industrial Data Scientists’ success can include:

Simplifying infrastructure: Industrial Data Scientists dedicate a substantial part of their time to infrastructure. From environment creation to deployment, the tasks can be complex and variable, slowing down output and undercutting ROI. Few data scientists want to upskill and work with the infrastructure or DevOps aspects of a given project’s lifecycle, but in such a dynamic field, it is difficult for them to stay up to date with the latest technology. Investing in a more simplified Industrial AI infrastructure can make a significant impact on ROI by sharpening the focus of the project and yielding new time, cost, and labor savings.

Simplifying deployment: Deploying and productizing an ML model is very time-consuming. Given its complexity, it is usually handled by a team of dedicated Data Engineers. To reduce the dependency on external teams, Industrial Data Scientists need systems that can provide more robust CI/CD (continuous integration and continuous delivery) data pipelines. Leveling up this capability helps teams quickly move their projects from experimentation to productization, generate value, and increase the probability of success.

Leaders at industrial facilities can help their Industrial Data Scientists focus on their core mission by investing in a robust, scalable, and secure Industrial AI infrastructure. That means a full-stack approach to:

• Abstracting complexity in data science projects
• Democratizing the ability to fetch critical datasets
• Facilitating AI and ML project deployments into production
• Streamlining collaboration across domain experts and engineers

Today’s up and coming Industrial Data Scientists have profound potential to deliver new, innovative value for their organizations. To live up to this potential, however, they need leadership to be aware of the common roadblocks to their success and to make meaningful commitments toward creating environments where they can deliver on their goals for accelerating time to market, increasing productivity, driving innovations, and delivering new value.

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