It is said that form follows function in architecture, and we are seeing something similar in IT, where job titles follow current trends. So move over data scientist. The “contextualist” is here.
We have a lot of shifts in IT, and today we are at the dawn of Industrial Revolution Number Four. This revolution started in the communications industry about a decade ago, with advances in mobile technology that helped disrupt the traditional media world by offering online news and entertainment.
Now digitalization is finding its way into other industries—in particular, manufacturing, where, through the use of IoT, it is beginning to radically alter and optimize production processes.
Recently, I came across an interesting initiative of the IADSS (Initiative for Analytics and Data Science Standards), which organized a workshop to start developing standards on definitions of analytics roles, skill sets, and career paths in data science.
Some of the titles discussed? Data scientist, data analyst, business analyst, machine learning engineer, data engineer, BI analyst, BI professional, database engineer, machine learning expert, statistical analyst—and they were missing the more exotic ones such as AI wizard!
As you can see from this list, the tide has turned dramatically in IT. In the past decade, packaged solutions were all the fashion, and the focus was cost reduction—through the implementation of standard products and replacement of enterprise resource planning and CRM systems. Accordingly, IT job titles were related to implementation and adoption of those solutions—such as black belt implementation consultants.
Because new topics such as cloud, AI, machine learning, and IoT are largely uncharted territories—and there are no ready-made solutions in the market—the mood has shifted from buying packaged applications to bespoke development. And so, the job roles and titles are changing.
But will this last? Will we stay in the developer era forever? With salaries going through the roof, the developer community probably wouldn’t mind. Most likely, this tide will turn as well—but how and when?
Signs of the Times
The first signs can be found in the market. Take, for example, Covestro, which makes high-tech polymer materials that are used in the automotive and construction industries, among others.
In its quest to improve its production process, Covestro took a different tack than most companies. Covestro knew that hiring an army of data scientists was a challenge; the right ones are hard to find—and if they leave, the company will lose that knowledge. It opted instead to pair its factory engineers with advanced self-service analytics.
These knowledge workers are now, on a daily basis, analyzing the production process with the help of the embedded AI and machine learning models. They improve effectiveness by feeding in events that they deem relevant. Those events act as context for those analytical AI and machine learning systems under the hood. This vastly enhances effectiveness in detecting and predicting faults and failures.
Here is how it works: If something happens in the factory that engineers consider important, it will be logged into what they call the context hub. The engineers can also automate the logging through performance monitors, once they figure out that those events are significant for the analytics and happening regularly. That includes occurrences such as maintenance events or production situations—for example, the heat of an engine remaining above a certain level longer than 10 minutes.
In my eyes, those engineers are the frontrunners of the next generation of workers.
For more articles like this one, go to the 2020 Data Sourceboook
Introducing the Contextualist
Contextualism traditionally has been considered a sort of philosophy, which focuses on the context around something that happens, rather than the event itself. The idea is that an action, event, or expression can only be understood relative to its context.
If the trend toward enabling business and field operations with data science capabilities continues, more and more companies will find out that they need employees such as those at Covestro. Just as Covestro’s field engineers are using events for AI-based systems to improve decision making, many people will find that their future occupation will have a similar task.
When your task becomes augmented by AI, and your job depends on the effectiveness of the AI making decisions, then it becomes an important task in your daily life to feed in the AI-relevant contextual information. That way, the suggestions, or even decisions, that AI makes can improve over time.