A key trend in the new future of emerging skillsets is the ‘industrial data scientist’. These scientists will be a new breed of tech-driven, data-empowered domain experts with access to more industrial data (than ever before) and AI/ML and analytics tools needed to translate that information into actionable intelligence across the enterprise.
“Industrial data scientists will represent a new kind of crossroads between our traditional understanding of citizen data scientists and industrial domain experts: workers who possess the domain expertise of the latter but are increasingly shifting over to the data realm occupied by the former,” Adi Pendyala Senior Director Market Strategy, AspenTech, tells Abdulaziz Khattak for OGN.
CHALLENGES OF REMOTE DATA SCIENCE
When talking about industrial data scientists specifically, their mission is often impeded by a number of organisational, technical, and process challenges that prevent them from being able to do what they do best – build more comprehensive, performant AI/ML models that address real-world use cases.
These challenges have only been exacerbated by the shift to remote work. Some of these challenges include:
● Coordinating domain knowledge resources across subject matter experts and managing pertinent data across scattered files and disparate tools.
● Collaboration with other domain experts to tune, test, train, and improve models on an ongoing basis models, to deliver on business goals.
● Deciphering where to put their ML code and how to version and collaborate on that code, efficiently.
● Handling and scaling additional resources, in near real-time, due to increased computational complexity or data volume.
● Connecting to diverse data sources and overcoming data integration and mobility hurdles associated with accessing real-time historian data.
● Sharing results and deploying models in live production environments.
● Pulling together the IT, data engineer, DevOps, and integration efforts needed to productise proofs of concept.
EMPOWERING DATA SCIENTISTS
As digitalisation initiatives and workforce changes shift more domain experts from traditional engineering positions toward data-driven work, the industrial data scientist is poised to become an increasingly integral part of how industrial organisations digitally transform and leverage AI applications to drive new value.
That being said, the best way to empower them is by providing the tools that enhance the work and free them up to do what they do best.
Enabling this group to accelerate time-to-market, increase productivity and drive stronger innovation will be a major catalyst to industrial digital transformation initiatives.
One specific technology paradigm that underpins their success is Industrial AI. When implemented successfully, Industrial AI unlocks new opportunities for capital-intensive organisations to drive higher levels of profitability and sustainability in industrial operations.
Giving industrial data scientists the ability to provide drive such impactful business value will become pivotal in realising the vision of the self-optimising plant of the future. –Tradearabia News Service