The speculation around Big Data, and the power it wields to change the foundations of industry as we know it, is rife.
We are collectively gathering ever increasing amounts of data from an ever increasing number of ‘things, our homes, our cars, our kitchen appliances, our industrial machinery. Big Data is an idea that has captured the attention of the world’s largest – and most forward thinking businesses—and it is spreading like wildfire.
Big Data is getting to be Very Big. Now it needs to make sure it’s also smart.
Anyone who’s made a perfect cake knows it didn’t happen simply by getting the ingredients together. To deliver a culinary masterpiece, each one of the individual ingredients needs to be combined and manipulated in a very specific and methodical way.
Data’s no different. With the use of sensors, machines are able to provide almost unlimited ingredients; a constant, unending river of rich, raw intelligence. The issue with this raw data, is that, alone, it’s just flour, eggs and sugar waiting for a recipe. It is information that, unless manipulated in its own specific and methodical way, isn’t able to deliver on what Big Data promises to be.
The Big Data Aspiration
The aspiration is not to make the perfect cake – although that might come later with connected ovens – it’s to deliver rich, intelligence-based insights about a machine’s performance which are actionable, instantly and autonomously.
Making this leap from raw information to implemented action is, however, highly complex and in many current IoT use cases, especially Industrial IoT, it may involve the coordination of several different specialists which might be individually responsible for integrating machine sensors, collecting and handing off raw data, analyzing data, supplying findings and then carrying out physical changes to machines or procedures based on the intelligence.
If the same process was used in baking, no one would eat cake.
Keeping the Promise
The Internet of Things and Big Data intelligence promises to have a profound effect on global business and industry.
To see ubiquitous adoption however, the solutions developed for this market must be seamless, cost effective and robust. They must be comprehensive in their ability to, not only retrieve rich raw data from machines in the field, but to analyze it with intelligence and to autonomously manipulate a machine’s performance based on ever-increasing insight in real time.
Until that’s the case, the Big Data promise, in all but a few cases, will continue to be just a promise but one that, most of those watching, sincerely hope is kept.