Scenario 3: Platforms for the win

The IoT is just as far developed in scenario three as it is in the previous two scenarios. Ubiquitous connectivity was achieved as well – although the network and connectivity development required a large-scale government investment effort, as private investors felt it would have been too much of a financial burden for them to boost the network and connectivity development far enough. Because of less private competition, there are not that many innovative solutions for achieving ubiquitous connectivity in the market. Nevertheless, openWifi initiatives and a better network coverage through government investment make the IoT and connection of all its devices with embedded sensors possible. Also in this case, initiatives such as the German Digitale Strategie 2025 -amongst others- pushed the development of the optical fibre network forward.

As the investment in the network and connectivity development shows, governments support the trend towards IoT and big data. Therefore, similar to the previous scenarios, regulatory restrictions previously inhibiting open collection, use and sharing of data -also across borders- are now trimmed down so that maximum socio-economic benefits can be reaped from the IoT and the data it makes available.

Because of all this support for the IoT, the data volumes it creates are enormous. Hardware requires higher efficiency than before for dealing with these data volumes. Although prices for hardware decreased in general, the costs for big data solutions have been staying the same for many years when it comes to hardware costs.

However, the cost-aspect did not stop real-time response from becoming the state of the art. Those companies who were not able to adapt and did not implement a real-time system for analysing data have lost the competitive race and were kicked out of the market. Because of their price-sensitivity and the fact that costs for big data analysis solutions (including RIMDB) continue to be rather high, SMEs were particularly affected. For many companies, their products, service offers and business models were not competitive anymore. In this case, initiatives such as the German efforts for supporting Industry 4.0 and digitisation for SMEs (see e.g. BMWI, 2016: Mittelstand 4.0 – Digitale Produktions- und Arbeitsprozesse, Digitalisierungsoffensive Mittelstand) failed to a large extend. However, the fact that some firms had to cease business also opened up room for new digital businesses. Those companies that are still in the market already have implemented a real-time big data system and offer services and products in real-time (or even with predictive characteristics). Overall, improvements in response speed for data analysis are just marginal, meaning that the currently used systems have none to extremely low latencies, rendering further efforts to become “more real-time” unnecessary. Most companies therefore just need very occasional updates on their implemented systems, but do not need to buy new systems or more extensive upgrades all that frequently. Hence, the demand for new real-time systems is much lower now in comparison to previous years: Now, in 2025, market saturation has been reached. The only firms in need of completely new implementation of real-time systems are start-ups.

Until recently, the large commercial RIMDB vendors had a comfortable positioning in the market for big data analysis. Now, this market saturation poses a serious threat for the commercial vendors of proprietary RIMDB. However, one RIMDB vendor recognized this development early enough, and created a new business model: Their previously commercially distributed RIMDB was made available as an open-source solution, and is now used as a vehicle for a platform business model. The RIMDB vendor provides the framework for the core technology (i.e. their RIMDB) for free, and developers can write their own solutions for problems. The vendor starts making money as soon as the developers sell their solutions by receiving a share of the revenue. In essence, this business model is similar to other platform businesses such as the App-Store, or AirBnB, where the platform provider starts profiting as soon as a transaction is made. Additionally, some vendors sell a selection of use cases with packages of solutions and relevant data as add-ons. This gives the RIMDB vendor an excellent competitive position: Both the developers and their customers are customers of the RIMDB platform and are indirectly charged a small fee for the service of the platform when a transaction is made. The developers benefit as they have a very little up-front investment, and the users can profit from the large offer on the platform they can choose from. Value is being created at all ends – for the developers, their customers and for the RIMDB vendor as the platform provider.

5 Conclusions for RIMDB and Industry 4.0

Based on the results of this thesis, this section presents the conclusions. Because the research question was twofold, the conclusions from the scenarios will also be split: Firstly, conclusions for RIMDB will be discussed, and secondly, the conclusions for what these scenarios can mean for Industry 4.0.

This thesis cannot and does not aim to provide a perfect template of strategic steps to success when it comes to real-time big data analysis and Industry 4.0. However, as mentioned at various points, the main goal of this thesis is to broaden the point of view as well as the scope of considered possibilities of strategic decision-makers by illustrating various possible future developments in a narrative, holistic way through the scenarios. Thus, this thesis can serve as an aid when formulating strategies and deciding on next steps for businesses. Overall, decision-makers are advised to watch the future development of the six identified key drivers closely over the next years, in order to be up to date in which direction they develop and be prepared for possible changes. Every entity will have to decide for him- or herself which of the results and lines of thought presented in this thesis are indeed incorporated into the decision-making processes. After all, as Alan Kay put it, “The best way to predict the future is to invent it.” (TED Conferences, LLC, n.d., paragraph 1).