Opportunity is missed by most people, because it is dressed in overalls and looks like work. I believe this quote by Thomas A. Edison captures the last few months very well. Over the summer, the EMEA team has explored the maturity of IoT amongst major European companies. Here in this blog post, I will try to extract the essence of our findings and elaborate on why you should embrace the potential in IoT Analytics, too. Feedback is always highly appreciated.
Many of the early use cases of IoT in hospitals relate to using resources efficiently, whether tracking particular pieces of equipment to improve utilisation, or informing family about a patient’s location. Even small improvements can greatly reduce costs, which are always under scrutiny.
Analytics will increase automation even where it has not traditionally been possible. An example of such an area is healthcare. Computers can analyse significantly more data than any healthcare professional, providing vital information and potentially making treatment more effective. Is it ethical to rely on computers? Or is it unethical not to?
IoT technology is also helping to improve demand planning and inventory management in retail. From Costa Coffee to Walmart, real-time data streaming helps identify stock shortages to reorder efficiently.
Not all IoT deployments are big, but even the most simple can make a huge difference. For example, sensor-driven replenishment of towels or emptying bins can make for a much cleaner, pleasant washroom.
Making quantum leaps: disruptive innovation
IoT technology is often used for incremental changes to efficiency and effectiveness. But perhaps the real value of the IoT may lie in its potential to turn the world upside-down and make disruptive innovation?
One area expecting quantum leaps is the automotive insurance ecosystem. Technology can provide advanced maintenance capabilities, service provision on the go, and education about crash risks.
The manufacturing industry has been quick to see the potential to provide a service and a solution, rather than a product (for example, selling holes, not drills). When utilising the industrial IoT, ‘think broad’ and use more data rather than less for analysis, is the recommendation.
Personalisation, self-service and experience
IoT, paired with real-time analytics, is a game-changer. By improving customer intelligence, businesses from shops to theme parks and telecoms can tailor unique customer experiences.
Predictive modelling will play a key part, with benefits ranging far beyond marketing. For example, to identify potential fraud and credit risk, employees more likely to leave, and even patients most likely to respond to treatment.
Early applications of health IoT largely focused on wearables for the ‘worried well’. But our study showed an encouraging trend: IoT technology to support self-care for the chronically ill, providing effective care management while improving the patient experience.
Interestingly, the importance of face-to-face meetings and experiences is increasing in the age of digitalisation. Events have become a key part of marketing in an IoT-linked world, and IoT helps us share them with others.
The respondents in our IoT study clearly stated that it is a challenge to become IoT ready and develop the necessary skills, including data science skills. They build, borrow or buy skills, often in combination.
At the same time, we see the rise of citizen data scientists whose job role is not data science, but who analyse as part of their job. To be effective, they need good quality data. For some companies, investment are best made in data operations, to ensure ready-to-use, quality data.
IoT and analytics deployments also affect systems administrators, who are critical to their success and whose jobs are rendered harder by the sheer volume of data, as well as real-time streaming – and, who must support multiple functions, including the rise of citizen data scientists.
Organisations need to overcome three main challenges to become IoT-ready. They must manage data, ensure its quality, and develop use cases by supporting innovation and experimentation, while also encouraging improvement.
Data management and privacy. The IoT threatens to make previous data handling and privacy concerns look like a walk in the park. How to exploit the potential but remain on the right side of privacy concerns? The key is to put customers in control of their own data.
Learning from experience – your own and others
Deploying IoT technology necessitates learning from experience - fast: To be agile and adapt to circumstances.
While experimentation is essential, ‘learning quickly’ is better than ´failing fast´. The key is to learn from problems and overcome them. According to Gartner’s Emerging Technology Hype Cycle technology reaches a ‘peak of inflated expectations’, followed by a trough of disillusionment, before (hopefully) climbing gently up to a plateau of productivity. IoT is right at the top of the peak of inflated expectations. Does this mean that companies should stop investing? No. It merely means that they need to be prepared to be agile, and learn fast from deployments.
IoT will be useful. It is already useful, as our study has shown. It is a matter of identifying the right uses, through trying them out, and adapting them for success. IoT adoption is an ongoing process and there are already many IoT examples and areas to learn from.
For example, the development of radio-frequency identification (RFID) technology may hold lessons for IoT deployments, including the importance to let technology spread at its own pace as well as selling solutions to customers’ problems, rather than technology.
The world of sport, and particularly sports analytics, also holds plenty of lessons for IoT deployments. In sports, small changes may eventually add up to really big improvements. It is vital to focus on what really matters and not get distracted.
Design thinking and the widening skills gap
With so much data available, how do you know what to use to get the best insights? The answer may lie in design thinking, which places the user at the heart of the design process, supporting a focus on what really matters: the customer’s wants and needs.
Design thinking is particularly important in analytics prototyping and experimentation. While a tolerance of failure is essential for any experimentation, design thinking lets you ask more questions and perhaps avoid failure entirely.
I do not know everything about IoT, but this summer has shown me what an interesting topic it is. I encourage you to read the full study and please let me or any of the authors referred to in this blog post hear your thoughts.