Analytics and Jobs
The domain of analytics covers basically six questions:
- What happened?
- Why did it happen?
- What will happen?
- Why/when will it happen?
- How do we respond to what happens?
- What’s the worst that can happen?
If an army of data scientists is not practical or there is a lack of talent in the developer community, organizations will have to adopt self-service analytics tools. This will help reduce the need for AI and machine learning experts and empower the knowledge workers in the company.
I think this adoption is going to be massive and rapid. Let me explain.
A Simpler Life
The contextualist isn’t a technical data scientist, and, as such, the role will only really thrive if the technology used can be simplified to a point that the analyst’s and developer’s roles are reduced to a minimum. This is going to happen soon, most likely.
Software companies are putting their efforts and resources toward making existing technology more easily accessible in order to increase mainstream adoption. Currently machine learning, AI, and other technologies are so difficult to implement that only the really big companies can afford the very expensive workforce.
For more articles like this one, go to the 2020 Data Sourcebook
Simplification will come through self-?service interfaces, making it easier to apply the technology in a horizontal (generic) way, or through solution accelerators, where domain-relevant knowledge and decisions can be applied in a vertical way.
These will harness the power of AI and machine learning so that knowledge workers who understand a certain process in detail can avoid issues and even make predictions, as long as the context can be given by its master—the contextualist.
What’s Ahead
Every industrial revolution has had a profound effect on how society defined work. With the first revolution, people left the field and moved into factories. And, with advances, they moved from factories to offices, and from villages into cities.
The questions often asked now are: With the next wave of digitalization and AI, where will people go next? Will they still have a job at all, or will we face massive unemployment?
In the long run, I tend to be an optimist. Surely, companies will restructure to suit the new reality. The banking and financial world provides a good litmus test. Digitalization has been going on for 2 or 3 decades, and we have seen the first signs of automation and algorithms replacing traders.
However, we shouldn’t forget that we are still at the dawn of digitalization, and, as such, many of the AI and machine learning analytical models are still at a stage that they can be considered infants. As with any infants, they need teachers. Teachers give them the context for what they need to learn.
Although some people believe that in a digital world you can work anywhere, in reality, many production processes take place in physical, centralized locations. If you are a contextualist, your highest contribution is probably going to happen where the action is, such as at the factory floor, as you have to monitor the things happening and decide on the contextual aspects to report.
There will probably be an element of virtual reality and augmented reality, allowing people to be in a different location while doing contextual assessments. But in practice, the contextualist will be expected to be at the heart of the operational business processes.
We used to say, “Content is king,” meaning that data is all-powerful. Now we can say, “Context is king.” Data means nothing without context.
Are you the next contextualist?