As in many dominant fields, Artificial Intelligence has also made its way into manufacturing. With the power that industrial AI brings, manufacturing teams can leverage real-time data to optimize production processes and reduce downtime of critical assets.

Process manufacturers face rising demands for production capacity, and continually face production losses that have a direct effect on quality and yield. 

Manufacturers are increasingly turning to Industrial IoT solutions – that employ Industrial AI technologies – to quickly investigate and solve the root causes of such production losses.

When implementing a big data analytics solution on a production line, the solution will collect and analyze the data at hand. 

Naturally, as this task will entail statistical and machine learning expertise, companies must hire data scientists for the job. 

This is easier said than done.

The shortage of 250,000 data scientists in the US alone, with a staggering 29% increase in year-over-year demand for these professionals, have led to a situation where data scientists can be picky in choosing the projects and companies they’re interested in working for.

Moreover, data scientist salaries are very high, with their median base salary being $130K. It’s no surprise therefore, that LinkedIn ranks data scientists as the number 1 job in America for 2019.

The difficulty and costs in attracting and retaining data scientists has led to a new emerging role – the Citizen Data Scientist. A role often given to existing employees in an organization trained to use data analytics tools and technologies to extract insights from big data. 

What it means to be a Citizen Data Scientist in manufacturing

Gartner defines a Citizen Data Scientist as a person that creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.

Citizen Data ScientistOne of the main advantages of this role in manufacturing is that it leverages the engineering skills that already exist within the company. With manufacturing teams consisting of multiple types of engineers – such as quality engineers, process engineers, maintenance engineers, and chemical engineers – the skillset and potential are there for companies to cross-train and certify specific staff members to become their Citizen Data Scientists.

Production engineers can fill the role of the Citizen Data Scientist, bringing to bear their background in production processes and assets, math, statistics, and modeling. 

Production engineers use these skills to bring more value from analytics because they can:

  • Better understand the data and its integrity
  • Quickly assess the effect of machine learning models to a business problem
  • Identify false-positives with confidence

Simply put – the manufacturing Citizen Data Scientist can get to meaningful, accurate and actionable insights – faster than data scientists that lack a deep understanding of the production line processes and assets. 

By incorporating a  Citizen Data Scientist into your team, your engineers can now act as “power users” who may not be experts in data science, but are capable of using the tools to provide strategic and operational insights for the manufacturing business.

Citizen data scientists can essentially perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise, as well as more budget.

We don’t see the Citizen Data Scientist replacing the need for data scientists. There will always be some need for “heavy lifting” AI algorithm work. 

But by filling the Citizen Data Scientist role, and deploying an Industrial AI solution, organizations can adapt their analytics models as their business needs change – ensuring their business agility – without reliance on the availability of data scientist professionals.

As the manufacturing industry’s readiness to adopt AI-powered solutions grows, we expect to see more positions of Citizen Data Scientists created and filled in most production teams.