The Impact of the Industrial Internet on Data

Impact of the Industrial Internet

At the recent Bosch Connected World conference in Berlin, Stefan Bungart, Software Leader Europe at GE, presented a very interesting keynote, “How Data Eats the World”—which I assume refers to Marc Andreesen’s statement that “Software eats the world”.  One of the key points he addressed in his keynote was the importance of generating actionable insight from Big Data, securely and in real-time at every level, from local to global and at an industrial scale will be the key to survival. Companies that do not invest in DATA now, will eventually end up like consumer companies which missed the Internet: It will be too late.

As software and the value of data are  becoming a larger part of the business value chain, the lines between different industries become more vague, or as GE’s Chairman and CEO Jeff Immelt once stated: “If you went to bed last night as an industrial company, you’re going to wake up today as a software and analytics company.” This is not only true for an industrial company, but for many companies that produce “things”: cars, jet-engines, boats, trains, lawn-mowers, tooth-brushes, nut-runners, computers, network-equipment, etc. GE, Bosch, Technicolor and Cisco are just a few of the industrial companies that offer an Internet of Things (IoT) platform. By offering the IoT platform, they enter domains of companies such as Amazon (AWS), Google, etc.  As Google and Apple are moving into new areas such as manufacturing cars and watches and offering insurance,  the industry-lines are becoming blurred and service becomes the key differentiator. The best service offerings will be contingent upon the best analytics and the best analytics require a complete and reliable data-platform. Only companies that can leverage data will be able to compete and thrive in the future.

The idea of this “servitization” is that instead of selling assets, companies offer service that utilizes those assets. For example, Siemens offers a service for body-scans to hospitals instead of selling the MRI scanner, Philips sells lightning services to cities and large companies, not the light bulbs.  These business  models enable suppliers  to minimize disruption and repairs as this will cost them money. Also, it is more attractive to have as much functionality of devices in software so that upgrades or adjustments can be done without replacing physical components. This is made possible by the fact that all devices are connected, generate data and can be monitored and managed from another location. The data is used to analyse functionality, power consumption, usage , but also can be utilised to predict  malfunction, proactive maintenance planning, etc.

So what impact does this have on data and on IT? First of all, the volumes are immense. Whereas the total global volume of for example Twitter messages is around 150GB, ONE gas-turbine with around 200 sensors generates close to 600GB per day! But according to IDC only 3% of potentially useful data is tagged and less than 1% is currently analysed. Secondly, the structure of the data is now always straightforward and even a similar device is producing different content (messages) as it can be on a different software level. This has impact on the backend processing and reliability of the analysis of the data.

Also the data often needs to put into context with other master data from thea, locations or customers for real-time decision making. This is a non-trivial task. Next, Governance is an aspect that needs top-level support. Questions like: Who owns the data? Who may see/use the data? What data needs to be kept or archived and for how long? What needs to be answered  and governed in IoT projects with the same priorities as the data in the more traditional applications.

To summarize, managing data and mastering data governance is becoming one of the most important pillars of companies that lead the digital age. Companies that fail to do so will be at risk for becoming a new Blockbuster or Kodak: companies that didn’t adopt quickly enough.  In order to avoid this, companies need to evaluate a data platform can support a comprehensive data strategy which encapsulates scalability, quality, governance, security, ease of use and flexibility, and that enables them to choose the most appropriate data processing infrastructure, whether that is on premise or in the cloud, or most likely a hybrid combination of these.