Data and the Internet of Things - just the beginning?

Carlos Palminha, Nomad Digital’s Head of Development (Porto), looks at the world of data and IoT after speaking at the 2nd Annual R&D Innovation Strategies & Process Excellence summit earlier this year. With data threatening to drown us, how do we see beneath the tip of the iceberg?  He explains:

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, therefore fuelling greater demands.

Data and the Internet of Things - just the beginning? image

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% of global CEOs believe the next three years will be more critical for their industry than the last 50 yearsi, but only 5% of organisations feel that they have mastered digital to a point of differentiation from their competitorsii.  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 some 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, such as the ones used in industrial factory plants: “Which components will be subject to service; with what probability, or how will the energy consumption of the site be used …?”

In the seminar sessions at the 2nd Annual R&D Innovation Strategies & Process Excellence summit which I presented at recently, we heard about how the food and chemical industries are gearing up to use these factors (IoT, sensors, big data, machine learning and predictive analytics) to advance their productivity.  Examples included reducing energy and water usage in dairy processing or using in-line sensor technology for level of nutritional components.  The benefits are clearly cost savings, but also improved time management and 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 to the shore, 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; things such as power traction, energy management, air conditioning, exit doors, sensors … the list goes on.  It is through the WiFi connectivity on-board trains that information becomes available about passenger connectivity, bandwidth usage and smart ticketing; all providing a rich stream of information.

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.

In the summit it was common between different industries that some of the patterns around digital transformation bring the users into part of the innovation processes, such as SAP Design Servicesiii.  Things like 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 digitisation, 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 digitisation and users by having breakthrough ideas and seamless execution are the ones who will successfully drive innovation and thrive in the future.

i  Source: Forbes Insights, 2016 Global CEO Outlook
ii Source: Accenture, Digital Transformation in the Age of the Customer