Hypotheses
Over the course of 2 one-day workshops, all 12 foresight files were presented to the members of the group, who were able to comment on them and add to them. Working from the material in the files, hypotheses on how the macro-variables might evolve were built collectively, distinguishing between:
- Trend-based hypotheses: which relate to dynamics already under way.
- Contrasting hypotheses: which take up uncertainties, controversies and the seeds of change.
- Disruptive hypotheses: which relate to disruptions.
Trend-based hypothesis: Platform economy
The volume of data generated continues to grow exponentially, supported by the expansion of access to very high-speed broadband (5G). The majority of companies find themselves unable to cope with this explosion of data and to transform their business model. They are thus forced to outsource the exploitation of their data to platforms, whose power increases significantly. The latter act as intermediaries between the company and the customer, which notably leads to a reduction in the sales function.
In this context, the big data and IoT markets will experience strong growth overall (35 billion IoT units in 2030, growth rate above 5%/year). This development is fostered by improved energy efficiency of infrastructures (LPWA, NB-IoT, 5G, etc.) and by the emergence of standards and norms that enable the interoperability of systems. Players do indeed manage to cooperate effectively. The massive development of IoT requires a strengthening of security technologies across all players in the value chain and is accompanied by significant investment in cybersecurity.
As regards industry, the massive development of IoT affects the entire value chain. It also makes it possible to develop "test and learn", co-conception and co-design through customer usage and, more generally, to take the customer experience into account more directly. Big data still comes up against the difficulty of establishing a genuine "data driven" culture and of recruiting the skill profiles related to data management (data scientists). There is a greater shift towards mastering "fast" rather than "big data". Overall, technological developments enable manufacturers to develop mass customisation, to integrate all functions into the workshop, including marketing and sales, and to simulate the entire factory through the use of "digital twins".
Contrasting hypothesis: Development limited by a lack of cooperation and by the appearance of security flaws. A balanced relationship between companies and platforms
The volume of data generated continues to grow exponentially, supported by the expansion of access to very high-speed broadband (5G). Companies generally seek to regain partial control over their data and tend to reject platforms and the cloud in favour of developing their own exploitation tools. Companies thus manage to reclaim a degree of sovereignty over their data at the expense of platforms; some traditional players even manage to acquire a key position in the digital sector. The position of platforms thus becomes more fragile overall. This fragility is reinforced by the significant development of open data and openness. Nevertheless, platforms are still active but coexist more with traditional players who have found their place in the digital landscape.
The big data and IoT markets will experience growth that is not overall but sector-specific. Scandals relating to data use, notably in the HR, food and health sectors, are holding back the development of IoT. This leads to the implementation of policies aimed at strengthening ethical practices regarding the collection and use of data.
The lack of cooperation between players (notably between manufacturers and platforms) is holding back the emergence of standards and norms, which results in poor interoperability of systems. This lack of cooperation between players also leads to slower development of cybersecurity even though there is a demand for stronger ethical practices regarding the collection and use of data
In this context of technological uncertainty, manufacturers limit IoT essentially to internal use (maintenance, stock management, etc.) in order to control risks as far as possible. The use of big data, meanwhile, relies primarily on "machine learning" algorithms and focuses on restricted data.
Disruptive hypothesis: The rebellion of companies and consumers
The volume of data generated continues to grow exponentially, supported by the expansion of access to very high-speed broadband (5G). Large-scale technological disasters and the proliferation of cyber-attacks notably in the health and nuclear sectors and in IoT generally lead to strong distrust among consumers, citizens and companies towards platforms and data sharing. In this context, companies decide to close off access to their data as far as possible and to manage it through their own clouds and network-based organisation. The balance of power shifts largely towards companies at the expense of platforms and pure players, in so far as they hold the data that has become a rarer and monetisable resource, essential to feeding AI processes.
The loss of confidence among consumers and citizens in the reliability of certain technologies leads to fairly weak development of the IoT and big data markets, focused on applications not sensitive to security issues. The growth of these technologies is also held back by the open conflicts simmering between manufacturers and platforms. These conflicts prevent the emergence of standards and result in a lack of interoperability of systems. Furthermore, the rollout of the 5G network is delayed by resistance from citizens who fear health problems.
In this context of widespread distrust, manufacturers make marginal use of IoT technologies and favour the management of internal data over big data.