Data and IoT: is it just the beginning?

Today the pace of innovation continues to accelerate, not only due to technological advancements and the speed of translating ideas into products and services, but also because the adoption of technology is so fast that the market creates an instant need; fuelling greater demands.

Undoubtedly, one of today’s major drivers of innovation is digital transformation and digitisation. Digital transformation is omnipresent in our daily work, private lives, education, public health, industry, mobility, government and society in general.  According to recent studies, 72 per cent of global CEOs believe the next three years will be more critical for their industry than the last 50 years[i], but only 5 per cent of organisations feel that they have mastered digitalisation to a point of differentiation from their competitors[ii]. This aspect is even more critical in industries or market sectors which are more conservative in terms of technology adoption, and more process driven, such as the rail industry.

Although digital transformation may vary between different businesses and industries, there are a set of patterns that we can commonly identify between them. By looking at other industries and making a parallel to the rail industry, where Nomad is a key stakeholder, we can draw some interesting references, with the following providing consistent factors across digital transformation: IoT, connectivity, big data, machine learning and predictive analytics. But briefly their definitions:

• IoT is the process of technological evolution which connects everything through the power of sensors
• Connectivity is about capturing data from those sensors and big data is the storage of that information (ready for future analysis)
• Machine learning is about computers using the data through experience to create patterns of learning
• Predictive analytics is about making predictions about future or unknown events and patterns.

There has been a lot of discussion recently concerning how the food and chemical industries are gearing up to use the above in an attempt to advance their productivity and to take advantage of the clear cost saving benefits, but to also assist them in improving time management, better processes and use of resources.

Different industries have evaluated patterns that weren’t necessarily obvious to them through the interpretation of data and analysis of trends. They’ve achieved this not only through the notification of any deviations from planned activity, but also by automatically detecting upcoming anomalies.

It’s a reality today that huge swathes of data can be collected from train on-board information sensors, which is then delivered, stored and can also be manually analysed using systems like ND Fleetview manual analytics. This allows teams to plot data and over time the available network bandwidth becomes visible as a heatmap. The rail industry can learn a great deal from information sources; such as power traction, energy management, air conditioning, exit doors, sensors…the list goes on.  It is through the Wi-Fi connectivity on-board trains that information becomes available about passenger connectivity, bandwidth usage and smart ticketing; all providing a rich stream of information.

The set of patterns identified in most digital transformations.

But are we as an industry taking advantages of this information collection and how do we embrace machine learning?  

If we look at what the industry is doing today, we are capable of containerising train data collection, storing it as big data and applying manual analytics, but that is just the first step. By using this data and bringing relevance and meaning to it, we can further assess how machine learning and predictive analytics will help us spot trends not visible to the human eye and evaluate patterns that weren’t necessarily obvious. This is where the real value comes in and where the future opportunities are.

It is known that between different industries some of the patterns around digital transformation bring the users into part of the innovation processes, such as SAP Design Services[i]. Things such as design thinking and UX Design make the user the focus of the analysis, encouraging engagement with end-users and consumers, helping organisations on their digital transformation by creating business value with human-centred design.

In the rail industry where we have a variety of users: passengers, train drivers, ticket offices, maintenance staff etc., they can become ‘behavioural sensors’, helping us to understand their interaction with technology and the train.

If we cross connect the two disciplines, technology and people, i.e. learning from data and how humans behave as sensors of information, it puts us in a hugely powerful position: men and machine combined to understand behaviour and needs. Just one example is how new technological innovations can make informed choices about a passenger’s next station, following a service disruption, if there is an important connection to make. This could be delivered through intelligent assessment of a passenger’s smart/e-ticket journey information, passenger counting and web usage, which could all combine to provide information about their on-going journey and destination details.

In bringing innovation to the rail industry via digital transformation and digitalisation, the focus should be both the technology and the continued processes of gaining insights from the data it creates. This is increasingly becoming a challenge for companies and business leaders who need to balance creativity and execution. The ones that can master and connect the dots between digitalisation and users by having breakthrough ideas and seamless execution are the ones who will successfully drive innovation and thrive in the future.

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