IoT 3 mins read

In IoT Analytics, Context is Key

We used to say “content is king,” meaning data is all-powerful. Today we can say “context is king.” Data means nothing without context.

Bart Schouw Bart Schouw

We used to say “content is king,” meaning data is all-powerful. Today we can say “context is king.” Data means nothing without context.

Context is a truth known to all politicians – if something they say is taken out of context it can interpreted as something completely different from their original meaning. The same can be said for IoT analytics; if data is captured and stored and then analyzed in a vacuum, it may not mean enough to reveal anything useful.

In other words, if on a factory floor an error event occurs every day at the same time, and there is nothing apparently wrong with the machinery, how does a data scientist get to the root of the problem? He/she adds context.

Take, for example, Covestro, which makes high tech polymers materials used in the automotive and construction industries. In its quest to improve its production process, Covestro took a different tack. It knew that hiring an army of data scientists was a challenge – the right ones are hard to find – and if they leave, the company would lose that knowledge. 

Instead it decided to arm its factory engineers with advanced self-service analytics. Coverstro’s knowledge workers analyze the production process daily with the help of the embedded AI and ML models. They improve the effectiveness of those models by feeding in events that they deem relevant. Those events act as context for the models. This vastly enhances the models’ effectiveness in detecting and predicting faults and failures.

Here is how it works: If something happens in the factory that the engineers deem important, it will be logged into what they call the context hub. The engineers can even automate some of the logging, once they figure out that those events are significant for the analytics and happen regularly. That includes things like maintenance events or production situations – like the heat of the engine remaining above a certain level longer than 10 minutes.

In my eyes, those engineers are the front-runners of the next generation of workers. Introducing the Contextualist!

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, I believe.

Self-service analytics are the answer

Software companies are putting their efforts and resources toward making existing tech more easily accessible in order to increase mainstream adoption. Currently ML, AI and other technologies are so difficult to implement that only the really big companies can afford the very expensive workforce.

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.

When your task becomes augmented by AI, and 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 context-related information – so the decisions that AI makes can improve over time.

Are you the next Contextualist?