Vision and data analytics both required to unlock business value from IoT


Business analytics and IoT are closely intertwined, perhaps even two sides of the same coin. When used together, they open the door to an extensive range of optimisation options in various sectors, from health care to agriculture and manufacturing. Many companies, however, have found that adding value is harder than it looks. What can we learn from their experience?

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Managing the hype – and the data

Many companies get caught up in the hype around IoT. They are looking for ways to connect devices and to integrate sensor measurements into devices or installations, but without really knowing why. Connecting sensors – and collecting data more generally – however, is only valuable if you can do something with the results of the data streams. Business analytics ensures that the data flows are captured and analysed, and then – this is crucial – that the most relevant results are returned to the business in the form of insights. These insights must be used to drive concrete action to improve processes or start new projects.

The rise of IoT has had a major impact on the management and analysis of data. As more and more sensors are used and more and more devices are connected, the amount of data is rapidly increasing. This means that the computing power needed to capture and process all this data has also increased hugely. This, in turn, increases the importance of business analytics. With more data collected, however, the amount of irrelevant data also increases. Smart data analysis is more essential than ever to pick out the relevant from the unimportant.

Harnessing the possibilities

The rise of the IoT and associated analytics may bring challenges, but it also brings a new range of opportunities. Companies and organisations wanting to actively commit to IoT and analytics need to put some thought into the data management process. Done right, this effort is likely to generate value. Tips for success include:

Thinking carefully before you start about what you want to achieve in business terms.

It is easy to get caught up in what is possible without looking at what is strategically advisable and, in particular, what will add business value.

Look at what you already have before investing in more.

Many companies are not aware of the amount of valuable data that is already available to them. Most companies have a lot of different data sources with historical and new data. By connecting these sources and analysing in new and creative ways, companies can generate new insights without the need for big investments in new technology.

Start small, find value and scale fast.

Start with small cases that generate value quickly. You can then use your experience to scale up. Trying to start with a big bang approach is likely to result in problems, like losing track of the end goal and business value.

Optimising day-to-day operations is often the way to add most value from analytics. For example, using streaming data analytics technology means that it is possible to apply real-time analyses to a continuous flow of data. When you have large numbers of sensors all producing a flow of data, there is far too much to store. By using streaming analytics, it is possible to analyse the data stream and only have to keep track of the relevant data.

This technique is particularly useful to detect anomalies or potential problems. Take Visa, for example. Its transactions involve real-time analysis of the data flow to check whether there is any fraud going on. Streaming technology can also be used to provide feedback on train driving behaviour. This helps drivers adjust their operational handling to limit energy consumption and component wear and tear. The same is true in manufacturing, where analytics is used to optimise processes by providing optimal process setpoints based on complex machine learning algorithms and optimisation techniques.

Identify opportunities

Opportunities for IoT applications span a wide range of sectors. In the manufacturing industry, IoT applications are already widespread. Manufacturing companies have been capturing enormous amounts of data via fixed sensors at their production facilities for some time, but often failing to take full advantage of it. Now companies are beginning to see that they can use the data for process optimisation, predictive maintenance and quality-related applications.

IoT also offers added value in the energy sector. The electricity network is becoming increasingly complex, with new ways of generating energy emerging. IoT and analytics can help in monitoring the network, visualising data, optimising investments and detecting anomalies in consumer behaviour. Analytics can also contribute a lot in the transport sector, where self-driving cars with a multitude of sensors are becoming more and more realistic.

These are just a few of the opportunities, and more are emerging all the time. Companies in all fields would do well to stay alert to the possibilities to avoid falling behind. It is clear that analytics is the engine to drive value from data; otherwise data just remains data, even if it is captured by the most cutting-edge IoT technologies.


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About Author

Adriaan Van Horenbeek

Consultant Analytics

Adriaan holds master degrees in electromechanical engineering (2008) and industrial management (2009) and received a PhD (2013) in mechanical engineering at the University of Leuven for research on predictive maintenance in cooperation with several industrial companies like Bekaert and Atlas Copco. He worked two years as an industry asset management consultant for Stork where he performed reliability engineering projects at for example Umicore, BASF and VPK. Today, at SAS he holds the position of pre-sales manufacturing expert and generates value through analytics within the process and manufacturing industry. His background of engineering skills, management skills and data analytics skills makes him an ideal partner to his clients to develop and embed analytics within their manufacturing processes.

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