In terms of economic value tied to data and knowledge a perfect storm is on the horizon created by the convergence of:
- explosion of data availability, both in the general context and in the private one. The former relates to academia and open research, news, social media, product/service information (data sheets and advertisement) and so on, the latter to data generated by industry processes and by individual activity (including biometrics and physiological data). In 2020 every person on the planet (on average) generates 1.7 MB per second. The overall data space is estimated in 44 ZB and some 2.5 EB are exchanged on Internet every day;
- the change rate of data, meaning that there is both a continuous update of data values making previous one obsolete and that there is something to learn about the change (i.e. the derivative of data may be even more important than the data itself, like the change in customers’ interest and the reasons of this change…). Actually, there is a growing interest shift from Big Data to Fast Data;
- the acceleration of the Digital Transformation increasing the creation of data along the whole value chain and the shift to services (and “servitization” of products) in turns generating and using more data;
- the value shift from raw data to metadata and the use of AI for the creation of metadata
- ambient and object awareness through embedded machine learning
All of the above points to massive use of machines (AI software) to deal with data, to extract knowledge from data and metadata and to make knowledge executable (i.e. to contextualise knowledge).
It is most likely that this decade will be remembered as a transition period from human knowledge assisted by machine to a shared knowledge formed by human and machine knowledge working together and reinforcing with one another. The economic value is likely to shift to the interaction of these two broad areas of knowledge as in the last twenty years the value shifted from the ownership of knowledge to the capability to access the required knowledge.
Augmented Reality in this decade will become a tool to seamlessly bridge the data/knowledge space with production and more generally with business. Together with Virtual Reality it will become a crucial technology for “using” distributed knowledge, in particular the one owned by machines.
An important aspect is the growth of products and services “knowledge”. As more of these will become equipped, either directly or through connectivity with the cyberspace, with artificial intelligence they will become context and user aware. Hence they will adapt their operation and the interactions with the users according to this growing “inner” knowledge. Already today the iPhone “knows” the recharging habits of its owner and plans for recharging speed accordingly (IOS 13 and later). This might seem a trivial feature but what is important is the concept of embedding machine learning in a device so that it becomes aware of its use. This is an absolute change of paradigm!