While Vic Damone and Jane Powell wanted their eggs with a kiss in the 1950s musical Rich, Young and Pretty, in the near future, your kitchen might well know exactly how you want them thanks to the Internet of Things (IoT).
If it ever becomes a reality, you probably will not have your kitchen supplier to thank. Rather it will be manufacturers that are currently investing in using IoT in advanced scenarios like predictive maintenance for machine equipment. In order to make such cases a reality, a precise understanding of the context of the connected assets is mandatory.
This is where the digital twin comes in, which is a computerized companion mirroring the state of a physical asset. A digital twin has to determine three things about its physical twin in order to remain relevant:
- How were you doing?
- How are you doing?
- How will you be doing?
Although that might sound simple, the challenge is immense because your digital twin has to cover hindsight, insight, and foresight—something that no single technology can do at the moment.
So here is where innovative companies are pushing the boundaries. In order to create a digital twin that can answer those three questions in real time, a number of technologies will probably have to be forged. I think it will probably be by combining graph database technology with time-series database technology and complex event processing capabilities—all fueled by in-memory technologies.
The graph will basically mimic the asset by representing it in the form of nodes and relationships, as graphs are really good in quickly adapting to unstructured data. Then new insights (read nodes and relations) can be added on-the-fly, extending the context continuously.
CEP technology filters, analyzes, and enriches the data that needs to be stored in the graph. The graph houses the most recent values on the relationship and offloads the previous values into the time-series database. This way, a digital twin is created that can not only continuously adopt itself to the changing behavior of a physical thing, but can also serve anyone with information about the past, the present, and the future.
Because the digital twin was needed in the space of IoT, common thinking tends to link digital twins to physical assets, or “things.” That is a misperception that will quickly dissolve as companies in other industries adopt twins as an ideal way to administer not just the behavior of things but also of their customers. Where it was the fashion to talk about customer 360 views a few years ago, the real-time implementation could quickly be replaced by the “customer digital twin.”
Once these technologies are mastered by the industry, it is inevitable that prices go down and that these technologies (in a downsized) version enter the smart homes, allowing you to reapply these three fundamental questions to any kind of mundane task. In our case in point, how did you cook your eggs before, how do you want them now, and how might you want them in the future?
While this might be a little over the top, there are more serious applications to be found. For example, understanding if your electric home equipment is properly functioning or might malfunction in the near future could prevent your fridge from catching fire. Or, in another more innovative approach, if the home understands when you normally switch lights on and off during the evening, it could continue doing this when you are on holiday, giving the burglars the impression that you are still at home.
To conclude, the role of the digital twin is first and foremost intended to enrich decision systems. It then can become an ideal interrogation candidate if it can collect as much data as possible on any asset, whether a thing or a human. It can then answer the three questions and a stream of new and innovative ideas, services, and products will emerge. How d’ya like that?