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Since almost a decade now, with Industry 4.0 and Internet of Things (IoT) two major buzzwords are propelling a new hype in industrial and civic environments. IoT covers the networking and digital communication of all relevant entities in a considered domain, being it devices, systems, applications or people, simply all involved “Things”. Industry 4.0 approaches then use the IoT respectively IIoT components to communicate data and to provide as well as optimize cross-functional applications (Henning, 2017).

Among others, the aim of Industry 4.0 is a flexible, market oriented and optimized production area with integrated processes. The major focus is on Just-in-Time logistics and an operational lot size of one, depending on market demand. By that, Industry 4.0 also emphasizes agile, self-learning enterprises (Niederhaus, 2016).

The new approaches with communication-ready elements in combination with a comprehensive network provide masses of data, even in real-time for further processing where required. According to projections, 20 billion connected devices are expected until 2020, gathering data from any sources (Schleede, 2014). Powerful data collection and data storage in combination with analysis tools and transparent user representations are a tremendous playground for new start-up firms. Innovative use of fast developing new technologies, mostly linked to a solid IT-background and flexible working structures are the push in today’s markets for many trendsetting applications (Syska, 2016).

Some industries are more advanced applying digital transformation processes and Industry 4.0 concepts, while others have plausible reasons to progress more carefully. Faster adopting sectors with reasonable business success are obvious in public communication areas using dedicated and mostly mobile Apps. Also, private home sectors, medical applications or public traffic optimizations are examples with fast adopting environments. Instead, processing or manufacturing industries are naturally slower, since their installations are safety relevant, capital intensive and run for decades. Required significant investment for new intelligent devices and for optimization applications are hard to justify when plants yet run stable, especially when results of changes are not guaranteed. Apart from economic aspects, dominantly safety reasons would allow installations of new technologies only when these are bullet-proof and manageable. Another essential hurdle with growing significance is the security issue on processing data and its communication networks (NI/trend-watch, 2015).

No doubt, IoT networks and Industry 4.0 installations will progress with a tremendous potential to improve all industry performances. In processing industries this will only happen when economically justified and stable in terms of safety and security. Another challenge for upcoming digitized environments is the availability of sufficiently educated people to install and maintain these kinds of applications. Eventually, the overall approach to the situation could be to: Think big  –  Start small  –  Learn fast (Mui, 2014).

 

 

 

Henning, C. (2017), Six Steps to IIoT. Retrieved from http://profinews.com/2017/05/six-steps-to-iiot/

Niederhaus, E. (2016), Machtbeziehungen und Digitalisierung der Industrie 4.0. Retrieved from https://blog.vdi.de/2016/08/machtbeziehungen-und-digitalisierung-der-industrie-4-0/

Schlede, D. (2014), Finding security in the cloud. Retrieved from https://www.controleng.com/single-article/internet-of-things-finding-security-in-the-cloud

Mui, C. (2014), Think Big, Start Small and Learn Fast. Retrieved from https://www.forbes.com/sites/chunkamui/2016/01/03/6-words/#2f2c400b1a3b

Syska, A. (2016), Industrie 4.0 Interview. Retrieved from https://www.process.vogel.de

NI/trend-watch (2015), The Industrial Internet of Things. Retrieved from http://www.ni.com/pdf/company/en/Trend_Watch_IIOT.pdf

 

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With the significant rise of digitalization and the presence of a digital economy, we are able communicate worldwide within social and professional communities using large networks build on different distributed platforms. This creates tremendous opportunities for individuals, but moreover also for almost any company on the market and especially also for governmental bodies.

Rising digitalization gave companies and governments the change to snoop on individuals. Moreover, the utilization of personal data grew as a new revenue stream for many companies (Morozov, 2018). As a consequence, mass surveillance increasingly became a part of our social, economic and political lives. Companies as Google or Facebook store every communication interaction on their networks from the first day of use with whatever device. For example, Google stores user locations, search history, the frequency of device utilization and the interaction partners. Accordingly, Google e.g. is able to create an advertisement profile based on collected information as gender, age, interests, income etc. (Curran, 2018).

Even worse, Facebook shared user data and some of its analysis with third parties, as it become public beginning of 2018 (The Economist, 2018). These data are incredible useful for marketers and analysts to develop advertising campaigns. Moreover, as in the case with the Facebook data, they were used by Cambridge Analytica, a British data mining and consulting firm, to create psychological profiles of US voters who were then targeted online with dedicated Trump influencing material (Curran, 2018; James, 2018). This affair exemplary brought to light the concerns about the way third-party content platforms handle the data their users provide. The danger of abusing these sources by steering public opinions became quite apparent (MEDIA Protocol, 2018). As a consequence, claims to better protect personal data against political or economic misuse have been raised. The new European Union General Data Protection Regulation (GDPR), valid for any market company, can be considered as a related governmental protection step, although critics complain already about it as a business hurdle and blocking role for easy, fast marketing actions (Morozov, 2018).

New technologies as e.g. Blockchains may contribute to improve the situation. Data control can be moved away from third-party intermediaries, with data exchanges as a direct relationship between publisher and consumer without a centralized data “hoarder”. As result, the marketing and advertising industry would be decentralized and consumer transparency in the digital content ecosystem would increase (MEDIA Protocol, 2018).

 

 

Curran, D. (2018). Are you ready? Here is all the data Facebook and Google have on you. Retrieved from https://www.theguardian.com/commentisfree/2018/mar/28/all-the-data-facebook-google-has-on-you-privacy.

James, N. (2018). The Warnings and Opportunities of the Facebook Data Scandal. Retrieved from http://digitalmarketingmagazine.co.uk/digital-marketing-data/the-warnings-and-opportunities-of-the-facebook-data-scandal/4843. Digital Marketing Magazine.

MEDIA Protocol. (2018). What The Facebook/Cambridge Analytica Scandal Means To The Digital Content Ecosystem. Retrieved from https://medium.com/@mediaprotocolsm/what-the-facebook-cambridge-analytica-scandal-means-to-the-digital-content-ecosystem-7ddcba19761.

Morozov, E. (2018). After the Facebook scandal it’s time to base the digital economy on public v private ownership of data. Retrieved from https://www.theguardian.com/technology/2018/mar/31/big-data-lie-exposed-simply-blaming-facebook-wont-fix-reclaim-private-information.

The Economist. (2018). The Facebook scandal could change politics as well as the internet. Retrieved from https://www.economist.com/united-states/2018/03/22/the-facebook-scandal-could-change-politics-as-well-as-the-internet.