Digital Transformations and Data Driving Innovation
Holistically looking at the IoT gives us a clue. By its very nature the IoT will produce data – lots of data – and today businesses crave new ways to stay informed and be relevant. Therefore, it is a must that businesses innovate using data-informed processes to ensure long-term success.
Finding Knowledge in Data Adds Real Value
With the technology market being inherently large, complex and dissimilar it is no surprise that the digital transformation occurring all around us is, in itself, unique to nearly every market it touches. In the IoT environment this means a focus on making smart decisions based on data.
Connecting cities, homes, cars, phones, watches, household appliances and even clothing to the Internet means we are seeing exponential growth in data generation. In 2015, the estimated number of new smart phones that were to be shipped to customers topped 1.4 billion, and by 2020 it is estimated that there will be more than 6.1 billion smartphone users worldwide.[i] As consumers demand more access, so do businesses. Even airplanes are no exception.
Commercial aircraft today can generate close to a terabyte of data per flight.
Thales knows the connected aircraft is here to stay and it is changing the traditional model of the aviation industry because of the data it is generating. However data is not the end state. Using data smartly to gain critical insights is driving the real value behind the connected aircraft. This data is leading to new benefits for pilots, crews, controllers and passengers in the form of data-driven decision making and Thales is helping to identify these insights to drive innovations in operations, maintenance, passenger engagement, pilot awareness and more. It is this type of data-driven decision making that helps support the long-term viability of any industry because it gets to the heart of what every business needs and that is to add real value for customers.
In other very real ways data are helping to predict consumer behaviours and address crime rates in cities around the globe. With endless potential in today’s digital world, the next evolution and challenge to drive innovations through data will be to go beyond predictive information gathering to enable mechanically informed solutions.
AI and Machine Learning: It’s not Skynet
It may be scary to think about machines that can learn; especially with blockbuster sci-fi references to intelligent machines depicted as overlord computer systems such as Skynet from the Terminator franchise. However, artificial intelligence and machine learning do not adhere to a single world order concept but rather compliment the complexities of everyday life – especially as the IoT grants access to more data. Machine learning at its core is about finite insights that machines can be taught based on predefined data channels and these inputs can help to enable more complex artificial intelligence.
Threat detection applications used in IT network security is one example of how these complimentary capabilities get applied. In large networks where every program cannot be scanned for malicious applications, a machine learning solution can help monitor data usage trends in specific categories to identify potential red flags. For example if a photo program that should only access photos is trying to access financial information there is likely a problem that requires human intervention. These are the type of machine learning solutions supported by data that are operating in large networks today. However, in today’s fast moving threat environment complexities grow and networks may require multiple machine learning solutions in order to provide better protection. This complexity adds to human-in-the-loop interface challenges. Leveraging artificial intelligence you can simplify this problem – think Siri for network security. By quickly accessing and interacting with this cyber environment you improve threat response times and can more easily determine if a red flag is actually cause for concern.
For business applications, consider two parallel customers who appear to want the same thing. Even though they may both be after similar results – for example an airline seeking increased ancillary revenues from passengers – how you get there may be different based on the uniqueness of the customer.
This can be a challenge when considering data sets that are so stiflingly enormous they dwarf the rate of growth of the U.S. debt. – an expensive prospect if the work is to be done by humans. This is where machine-based learning and artificial intelligence can help because a program that could monitor these data sets and support quick interaction would enable faster, smarter decisions much like in network security. This is why learnings secured from data have increasing value for businesses because computational reasoning can be unique to each customer. It is this process behind making data meaningful that Thales knows well. As service providers we are innovating by leveraging available data to impact our customers most completely.
With boundless amounts of data becoming accessible because of the IoT environment, the takeaway is clear: reliance on machines that can learn based on data will be necessary if we hope to move at the speed of progress and continue to innovate successfully.
Yet even as digitalization drives new data channels there is still one thing that machines and data require; and that is a business approach which delivers meaningful, long-term and actionable insights.
A New Business Reality
Today, customers’ demands for data-driven technologies are insatiable. For solutions providers, this demand creates the allure to go to market quickly with something new. The question however is does this product add value. Simply knowing technology and data are not enough – faceless data are useless data, and solutions that do not address customer problems are not solutions.
Take your daily commute as an example. If you rely on any type of public transit system you know there are different facets to how you interact with the system, from ticketing to timing to ease of access. Each is being reshaped by today’s digital environment. When timing your commute we know it is inconvenient to wait at the station when there are technologies that could tell you when your mode of transit would arrive. Yet an app without the infrastructure to back it up means nothing.
It is these types of technologies which consumers demand which need to be developed into a strong business case. Take the Thales mobile fare collection system for example. It is a product which allows people to use their phones to manage their trip fares and also acts as an electronic ticket – similar to mobile payment systems. For operators this system generates data. Data that can be used to better manage the system.
For commuters this enabling more ease of access by enabling fare management at their fingertips – a tool especially useful when in a hurry. These are the types of realities to be mindful of in today’s digital environment which will lead to new innovations for operators, consumers and businesses alike.
Given the bullish projection of the IoT market, its clear businesses are determined to latch on in order to seek a business advantage; especially as the world is being reshaped by the digital transformations it brings. From leveraging data-driven decisions, adapting to new learning styles and embracing new approaches to the market, businesses will be able to be successful in the IoT marketplace – a marketplace that will drive one of the largest economic revolutions of our time.
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