IT professionals used to say “content is everything,” meaning that data is all-powerful. Across the industry, “context is everything” rings true, meaning that data isn’t worth anything without putting it into context. For example, when politicians or CEOs are trained for media interviews, they know that if something they say is taken out of context, it can be interpreted to mean something completely different. This also applies to data analytics; if data is collected and saved in the cloud or stored locally and then analysed in a vacuum, there is a chance that it may not reveal anything useful by itself.
In the digital era, data remains one of our most important assets and weapons, . However, the one thing that prevents data from having an impact on business performance is context – that’s why “context is everything”.
In this day and age, we increasingly rely on context to reveal the truth. This is especially relevant to the way news is reported on social media versus traditional media channels. When things are taken out of context, this encourages misinformation. If we relate this to the language of data analytics, it simply translates to giving importance to context as the key to avoid gathering unusable and irrelevant data.
The importance of context
Today, workers are inundated with large volumes of complex information, and it is up to them to decide what is relevant in each situation. This results in workers spending long periods of time analysing this data and trying to make sense of it all – time which could be used much more productively. At the same time, the safety of workers is of paramount importance in industry – so how can businesses ensure safety standards are met while maintaining productivity?
Context-aware IoT helps to notify workers of potentially dangerous situations and address any accidents. This IoT system is easily scalable to accommodate a large number of users and different sensors, processes data in a timely manner and makes smart decisions to enhance workers’ safety and efficiency. 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 analyse 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.
Making self-service analytics simple
IoT has the power to revolutionise the way people work, the way they live, and the way they interact with day-to-day objects. But it’s important to remember that context is the key thread that binds everything together.
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.
Although the vision of IoT has developed significantly in the last few years, there is still much room for improvement and integration in many sectors of society. By employing ‘context hubs’, organisations can not only ensure a more productive workforce, but can analyse their data much more effectively. Time and time again, we see how companies are successfully implementing technology to increase uptake, whilst solving business problems and having a direct impact on business performance.
Bart Schouw, Chief Evangelist in the Office of the CTO, Software AG