Scenario 1: The open-source revolution

Over the years leading up to 2025, the number of devices with embedded sensors has grown by the billions, creating the IoT for practically all environments. This could happen, because businesses across industries acknowledged the enormous benefits of collecting data at various points and heavily invested in installing such sensors. There were also less investment-heavy initiatives supporting the development of the IoT, such as the Phonvert project which triggered people all over the world to recycle their old smartphones to an IoT device which can now collect data22. Moreover, businesses realised that by letting their customers (both private consumers and businesses) reap bottom-line value from the IoT devices and the data these devices collect, customers are much more accepting of the new products and services that ensued. Customers now trust businesses with their data and are more willing to disclose it. They also invest in new IoT products and services that are on offer.

Because data is collected in many ways in the IoT, it is very multi-faceted: Data can originate directly from humans and their behaviour (e.g. sound recordings, video material, written text), as well as from the environment (e.g. weather data, data on air pollution) and from machines (e.g. a smart factory robot which acts autonomously while producing goods). In consequence, different big data analysis systems are necessary to treat these different types of data. There are separate solutions for structured and unstructured data, but also more and more combinatory solutions that integrate systems.

In order to actually gain value from the IoT, businesses need to analyse the data they collect. Real- time response has become the state of the art, and the demand for systems supporting real-time data analysis is strong. Service delivery was accelerated to a real-time standard as well. Those businesses who until now failed to install real-time systems for analysing large data volumes are struggling and starting to fall behind their competition: Not only can those businesses implementing real-time systems make strategic decisions much faster and react directly to (or even predict) customer needs, but real-time systems also make completely new business models possible. For example, car insurances can now personalise their pricing and coverage schemes to the driving behaviour and environmental conditions in real-time by collecting the necessary data via sensors in the car and its environment. Those driving safely and in less accident-prone areas benefit from lower insurance prices. When transporting passengers, the premium increases slightly so that additional costs in the case of an accident are also covered. This dynamic pricing and coverage system offers many benefits for drivers, and the new real-time insurances are able to gain competitive advantage over those that still use the old-fashioned static schemes. Both sides profit: The driver paying car insurance profits from fairer pricing and is rewarded for safe driving. Moreover, he can be certain that all potential damage is covered, e.g. when transporting a number of other people. The car insurance can estimate risks better and thus reduce its losses. Many other examples for real-time products, services and

business models exist. Especially the first movers are able to capture market share from those that adopt too slowly, because they can establish themselves as price leaders for a certain time.

Generally, the notion of “data is good” has become accepted due to the immense benefits for all sides, and previous fears of “big brother is watching you” have been set aside.

Governments and regulatory institutions have become aware of the large socio-economic benefits these developments can evoke. Openness for the collection, use and sharing of data is supported strongly, and restrictions previously blocking this trend have been limited. For example, the EU’s strategy for a Digital Single Market in the EU, including the proposition of a “European free flow of data initiative” (EC, 2015a), was implemented. The EU-US Privacy Shield agreed upon in February 2016 (EC, 2016a; EC, 2016b) was trimmed down so that data can now move more freely between the US and the EU than ever before. However, governmental institutions also want their piece of the data pie. For example, building on first pilots (Heaton, 2015), more projects were developed in the field of predictive policing to help crime prevention (e.g. by tracking known perpetrators and using algorithms to identify likely areas and times for specific crimes) or help fire fighters with identifying potential hot spots. Having recognized the huge potential of data use and its possibilities, governments exploit data for various other purposes too. For instance, they use data from the industrial sector in order to try to improve the international competitiveness of their own regions in comparison to other regions. So not only is the attitude towards exploitation of data generally open, but there is also competition between various entities for who can reap the most value from the available data. This further highlights the relevance big data analysis systems play for governments in the scenario. In addition, data analyses are used by governments to identify and track down individuals, e.g. who reside in a place without a right of residence or who have a high potential for committing crimes such as tax fraud. Considering these intentions, it is clear that governments are one major customer group for real-time big data analysis systems.

All this would not be possible without sufficient connectivity to transfer the data from the IoT devices to where it is stored and analysed. Therefore, the mobile network and overall connectivity has been boosted over the last years: Large-scale investments by new network and connectivity providers increased competition and gave an incentive for incumbent network providers to invest heavily. For example, the 5G network was developed as planned. Technologies in which newcomer network providers invested include BTLE and the LiFi technology, which further support ubiquitous connectivity.

Thanks to the well-developed connectivity, as well as government and regulatory support, the IoT can generate immense data volumes. That is why the hardware for systems that store and analyse this data had to become more efficient. Despite the fact that hardware became cheaper over the years in general, because of the necessary increase in efficiency, hardware component costs stagnated, which

is why the hardware cost aspect for big data analysis systems (i.e. also RIMDB) stays the same for users.

However, a certain cost benefit for users comes from the fact that the most common RIMDB nowadays are not commercially distributed proprietary RIMDB, but open-source RIMDB: Now, in 2025, they have taken over the market. A few years earlier, in cooperation with Oracle and open calls to the developer community, Google further developed its DB Cloud SQL (a cloud-version of the relational open-source DB MySQL), so that it now runs completely in memory. With that, Google created a well-working open-source RIMDB available through the cloud and to the developer community. The latter continues development, so that it set a very high standard for all RIMDB. Google profits from this move by offering highly targeted additional services to their users. These services are based on further analyses of the data that their users upload to the Cloud SQL DB, and comparing data sets and queries from different users. Naturally, the data of one user is not shared with other users without permission, as this would nullify the value of the data. However, through these add-on services Google can for example suggest new types of analyses to the DB users, or suggest other users as potential partners for sharing data. Considering the incredibly large amounts of data at Google’s fingertips, the additional suggestions and offers they can make to their users are widely unmatched. In the wake of these events, a few other open-source RIMDB have emerged and attained a solid user base. To retain some market share, the previously commercial RIMDB vendors felt pressured to open the code of their RIMDB as well, and moved away from business models where RIMDB licenses are a central source of income. However, the new open-source in-memory Cloud SQL DB, and other emerging open-source RIMDB, have cost them a significant part of market share. The previously proprietary RIMDB vendors are struggling to retain some market share with their open- source RIMDB due to the strong position of other open-source RIMDB, most of all the in-memory Cloud SQL.