What can we expect in the next decade?

Technological innovations are developing at an unprecedented speed. At the turn of the century, mobile telephony was a luxury and the features of a typical mobile were limited to calls, texts and basic games.

But since then the use and ubiquity of mobile telephones has increased exponentially, accelerated by the game-changing launch of Apple’s iPhone in 2007. Now nearly all individuals of all social classes possess a smartphone, and it is perceived not as a luxury, but a necessity.

Smartphones have become an integral part of our lives and act as our digital personal assistants, which accompany us in every situation. The incorporation of other functions into smartphones have transformed them into a multifunctional device, effectively replacing cameras, walkmans, and navigation systems.

Given the success and the increased use of smartphones, social media networks as well as platform economy companies such as Facebook, AirBnB, Amazon, Spotify, etc have developed concurrently and became some of the most valuable companies of our times.

The nature of DLT means that the data is 100% tamper proof and has an extra layer of security from the fact that it is decentralised.

But what makes these companies so valuable? The answer is quite simple: their user data. These platforms collect user data to varying but often large extents: On the one hand to enable their own services, on the other for targeted advertising. From the user’s point of view, this involves both opportunities and risks. This vast data gathering exercise has also resulted in wider debates about the protection of personal data and data sovereignty.

Machine learning has developed to try to collect and analyse these enormous amounts of data. Its goal to link data intelligently, to recognise connections, to draw conclusions and to make predictions.

Examples of use for machine learning can be found everywhere in everyday life, including speech recognition in digital assistants, mail spam filters, or face recognition in smartphone photo galleries. It also can provide many other benefits to consumers, such as in the prevention and detection of fraud.

The larger the data amount is which those algorithms can access, the more they learn. But this data is analysed by machines only, not by humans. Theoretically, this should lead to greater fairness, but this is a false assumption. Machines use algorithms which are unregulated and uncontestable, even if they’re wrong. Therefore, machine learning can result in bias and can actively reinforce discrimination.

Due to this bias, the need for unbiased governance methods, such as Distributed Ledger Technology (DLT) and its characteristic immutability, is increasing. The nature of DLT means that the data is 100% tamper proof and has an extra layer of security from the fact that it is decentralised. Interestingly, DLT is simulataneously secure and transparent as it allows all information to be freely viewable by all relevant parties.

The applications of DLT are wide ranging, but we see a particular opportunity for its application to trade finance. Even in today’s more digitised world, trade finance transactions are still largely paper-based, and as such there is a huge opportunity to increase efficiency and transparency through use of automisation and DLT.  We have already taken steps towards the automation of trade finance and are investing in technology that will allow us to digitise paper-based data, compliance checks and manual data analysis. We expect automation to move quickly and expect that this will be applied to 80% of our trade finance transactions in the next few years.

DLT and blockchain are still emerging technologies, but large companies and institutions are gradually recognising its potential. As such over the next decade we would expect to see the application of this technology grow, not only within trade finance but also in other areas such as capital markets infrastructure.

So, what other technologies will shape our lives in the next ten years? The debate around data is likely to be taken to the next level as we start to examine where data is actually being stored. Cloud technology is used across all kinds of platforms, including smartphones and smart TVs via streaming platforms. But the use of the term “cloud” to describe the storage location is a misnomer. Data is not left hanging in the air, but instead has to be stored somewhere. And that somewhere is on space satellites.

Which brings us to the question of what happens when we run out of room on the space satellites to store data. Potentially, the need and demand for cloud data storage among consumers and businesses could drive a race in the development of space technology, similar to the boom in telecommunications which we have seen in the past decade.

This new focus on space technology, for the first time fuelled by private commercial enterprises rather than governments, is likely to spur further debate over the ownership of data and the putting into place of laws governing data sovereignity. This, accompanied by greater application of technologies such as DLT, will help society to overcome the negative implications of big data and mark a new era in how it is shared and stored.

In addition to the new way of sharing and hosting big data, emerging technologies such as quantum computing will enable us to analyse and draw more precise conclusions from big data. It’s one of the areas of focus for our research and development unit and we’re already seeing how it could help various industries such as logistics, chemical and financial services to enhance and develop more efficient business processes and decisions.

By Michael F. Spitz, CEO of the Main Incubator, research and development unit of Commerzbank.