Home What are the key drivers to successful enterprise IoT development?

What are the key drivers to successful enterprise IoT development?

As IoT moves beyond pure data collection how do companies stay on top of this market and pull the most value from it?

We sat down recently with three IoT authorities from Nokia: Khamis Abulgubein, IoT market development for automotive and transportation; Lee L’Esperance, IoT business modeling and Jacques Vermeulen, Nokia global Smart City business development.

They outlined ten key considerations when looking to extract the maximum value from Enterprise IoT.

RW:  We have discussed why companies aren’t getting value out of the IoT yet because we are in “the connecting things” phase. What are the additional drivers for unleashing the value of the IoT ? 

Lee L’Esperance:  Industrial IoT is really where things started first and we still seem to be in that phase of connecting siloed things and collecting data from them. However, we’re starting to see companies linking the silos horizontally, creating a horizontal ecosystem approach that brings partners, ideas, and new business opportunities together.

Combining information sets in this way will provide additional value.  Next, we should start seeing companies and government entities expand toward what we call Enterprise IoT.  In this phase, you will start to see new business models emerge through this horizontal approach to analyzing the data. Also, enterprises will look more towards collaboration to solve unique problems. Let’s take a city as an example. In order for IoT to take off in a city, a whole ecosystem is needed to help solve unique problems and explore new business models. 

At Nokia we’ve seen — through our ng Connect IoT community —  some unique business models take off, such as connected bus shelters and 4K video streaming applications that would have never been uncovered without an ecosystem approach that includes multiple partners from various industries and disciplines.  I haven’t observed other companies who have implemented such a broad ecosystem program of this nature.

Jacques Vermeulen: From a practical viewpoint, Nokia has seen success through a horizontal, secure, open, real-time and scalable solution for Smart Cities. You need to maximize value and functionality with a horizontal network approach.

These solutions are combined with vertical insertion points addressing specific use cases to support cities. For example, governments worldwide are starting to understand the need for horizontal approaches and are transitioning their approach from brownfield siloed Smart City deployments to horizontal network infrastructures and starting from scratch – more of a greenfield approach. To elaborate, every major city has a large shopping mall.  These malls are loaded with features such as:  security and access control, building management systems to optimize heating, ventilation and air conditioning resources and location based services for the visitors to provide them with coupons and incentives based on their profiles from previous experiences in the mall. 

And that is just a starting point.  Looking at the needs more broadly, you can discover very interesting use cases combining these elements.  Think about an emergency situation at a mall where there may be a need to deal with things like the people traffic within and coming to the mall.   

This would require warning solutions for people commuting, both by private and public means of transport.  These systems should then be interconnected with traffic control to prioritize the situation for the emergency coordinators coming to the site. They also could leverage security systems for real-time video and photographs of the situation at hand, and be proactively alerted to what is going on. A value added solution like this only comes to fruition if you look at a city in a holistic way and match the right IoT infrastructure to that. 

From my perspective, in an emergency situation, I would go even further.  I’d like for them to have the data to know my exact location and a portion of my medical records accessible in case I needed special care.  Once we get into the territory of e-health, this supports the argument for a horizontal approach even more because now we have a more meaningful solution combining data from different verticals versus taking a silo approach. 

Khamis Abulgubein: IoT suppliers need to work within a cohesive system as Lee has described. The only way you will be successful is if you collaborate and build an ecosystem, you have to build for the whole customer experience. This approach lends itself more towards Enterprise IoT where you will see business models offering a whole new customer experience and new services that would not have been possible in the past. 

This is the notion of “servitization” in which manufacturers provide a holistic experience to the customer and by selling usage based services rather than just engaging in a single transaction through a sale of a physical product.  An example would be a washing machine that is sold bundled with usage monitoring and proactive maintenance.     

Another example is the Connected Rental Car experience which is an interesting business model we developed with Hertz, SAP and other partners. On the surface, we are providing a premium service for business travelers. Along with that, we are also using the rental car as a passive payment platform for use cases such as parking garages, fuel pumps, and quick service restaurants.

This approach provides transaction fee revenue share into the service which helps which helps pay for the upgraded rental car personalization and connectivity service and platform. 

RW: We are collecting a lot more data these days, but is that all that is needed for enterprises to become smarter?

LL: It’s not the data it’s the analytics. Simply collecting data doesn’t make you smart.  You can be collecting a lot of data, but if you don’t do the right things with it, there is no value to it. The data needs to become actionable information. 

Analytics is the key to this, especially over time as analytics become better and artificial intelligence gets its entry into enterprises. For example, let’s look at an application where you are using sensors to collect data and measure weather conditions; having the weather data is only one part of the picture. 

Now, if you combine the weather data with other measured data and analytics, you can begin to predict things.  That is how we get smarter. When you take that weather data even further and mix it with data from traffic sensors to predict how the weather will impact traffic for example, the value of the information that may be obtainable will you get closer to that 36 x the value of the internet today as quoted by Bell Labs in the article Enterprise IoT – the best is yet to come.  This number becomes theoretically possible when you collect, analyze and look across all of the data to unlock the value.

KA: I also went through this in my lab. It is all about getting smarter and thinking about different ways to use the data. But you need to read between the data lines for new opportunity. We installed temperature sensors in my data center to see if there were temperature fluctuations in my lab. After a week we didn’t really pay attention to it anymore — that is, until we had an air conditioner issue. The next thing I knew I was getting proper alerts and further the system got smarter and sensed that there was going to be an air conditioner failure soon – in about an hour. I was able to call the technician prior to the failure actually happening and saved my servers from being impacted. 

Another example could be car tracking. Step 1 – you can see where your car is. Step 2 towards a smart solution – I’m alerted when it isn’t at my house when it should be.  Despite the endless possibilities, the challenge is with the fragmentation on getting devices connected to a network.

JV: In fact, Nokia is tackling this. Our IoT can help make sense out of different verticals and the fragmentation. In a smart city complex, there are not always engineers sitting at the buttons to make sense of the data on their dashboard. We are making complex data, data queries and analytics algorithms as simple as possible, so that people do not have to have special knowledge in order to do something logical and useful with their data. The sum of the parts is more than the whole.

More and more native language patterns are understood to translate in more complex technical queries and automate the process of making sense and identifying opportunities from the analytics.

RW: It sounds like this “servitization” would wring a lot of efficiency out of current product-customer relationships, and thus could be seen as a threat to manufacturers. How would you recommend they embrace this trend?

KA: I think there are a number of things contributing to that. As the price of components comes down, more is being put into products.  There are shorter product lifecycles and software is controlling a lot of things which requires continual updating.  Also, enterprises are looking for more flexibility. 

It’s better when we share; one major trend I’ve seen is that of not owning but sharing instead, or paying for use. An example of this is car sharing which is happening in many cities today — look at ZipCar, Turo, Enterprise Car Share, Hertz. 

Customers who only need a car from time to time can sign up for the service and pay for use on a daily/hourly basis – combine this with self-driving technology and this may significantly change the auto manufacturing and auto buying markets. 

LL: You need to think OpEx-ish. Enterprises seem to be heading towards OpEx versus CapEx models in many industries. Servitization enables that trend by providing end to end solutions and new business models like pay per use, goods sharing, or even risk sharing. This can represent an opportunity for manufacturers to expand into new services and develop consistent revenue streams.

RW: When considering data collection and analysis, AI integrations, bots, machine learning capabilities, are companies running into the challenge of whether their own end customers are ready for this?  If you are an enterprise, how do you best prepare clients for this?

KA:  Use data to delight and not deluge — surprise them and delight them at the same time.  For example, with cold temperature logistics – like transporting milk, today the person delivering food might tell the grocery store that the milk made it on time and wasn’t spoiled.  But if they can give more detailed information such as Tuesday the temperature was a steady 40 degrees, and on Wednesday it stayed very close to the requested temperature range – ensuring shelf life of the product, then that would be impressive.    

Another example is from the connected rental car experience that I mentioned previously. Passive payment platforms that can communicate with gas pumps, parking garages, quick service restaurants etc. provides more convenience to customers, and rental car operators can actually attract loyal customers and bring in more revenue from these services over time. 

LL: You need to simplify and secure. Enterprises can show customers the value of their IoT services by keeping the process of using these services and the interaction with them simple and secure. Choosing the right platform that will allow a seamless experience for the end user is absolutely the key to this.

This article as produced in partnership with Nokia. It is part of a series of articles where the team from Nokia will be providing expert advice and delve further into data analytics, security and IoT platforms.  

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