IoT, from concept to SMART

There is a lot of talk about SmartCities, SmartBuildings, IoT, data. And it is clear to everyone who deals with the built environment that something is going to change. But it very often lingers on a conceptual level. It turns out to be very difficult to make it concrete and to take steps today that will have effects tomorrow. In this blog we want to share our experiences and insights with you so far. This without pretending that we know exactly where we will all be in 10 years' time. However, talking about does not lead to change and that is necessary. So we are also going to make it concrete.

It starts with data
If we keep in mind the big goal, namely that everything will be connected to the internet and that the insights help us to use buildings in a smarter, more sustainable and efficient way, one thing is immediately clear. We need data. Lots of data, because that data will help us gain those insights. To do that properly, there are a few conditions that the data must meet.

Universal and interchangeable
Data must be universal. This means that if you have different buildings, that data must be mutually comparable. In addition, this data will have to be exchanged between all interested parties. This means that the ownership of that data should not be an obstacle to the collaboration. And that also means that your partners make all relevant data available with open APIs.

The vision of Liftinsight: Let's talk concretely about lifts: There are countless brands and types of lifts. This means that defining data from one elevator system is different from the other system. For example, we measure the number of elevator movements. But do you also count 'readjusting' or 'driving during inspection during maintenance or inspections'? And if a door does not close and makes a second attempt, is that one or two closing movements? And is the data from your lift still available if the maintenance is transferred to another maintenance company? And how do you, as a lift company, deal with data from different lift systems that you have to maintain. That is why we have a universal sensor solution that works on all elevators. And all data that is visible in our software solution can, without restrictions, be permanently used in other systems through an API.

You can solve 80% of the problems with 20% of the data
What is important in this phase is that you should not go too deep, but wide. By that we mean that you must apply the Pareto principle. Do not get 100% of the data to be collected from all systems, but remember that with 20% of the data you can collect 80% of the knowledge. And do that as widely as possible in your building. Not only measure your energy consumption, but also temperature, numbers of visitors, room occupancy, temperature, humidity and so on. But don't let your project fail on too deep an investigation into each individual building part. If you have several buildings and installations, do not limit yourself to one building, but do several (preferably all), then you have comparison material and you can better base your analyzes.

This is always possible if it appears that there is value to be gained from data that can be collected with more effort.

Analysis by AI
After you have collected the data, it is important to combine that data with data from all systems and find correlations. What is the correlation between the number of visitors in your building, the number of elevator movements and the energy consumption? And what is the relationship between the weather, the transport flows and the number of disruptions? Could you make predictions based on different parameters. Machine learning and Artificial Intelligence (AI) can uncover unexpected connections and help you anticipate them.

Business cases for next steps
After gathering insights, you may want to change things. And that's what makes it so difficult now. You do not yet know what the insights are and therefore you do not know what you want to change. At this point you see that projects are stagnating. That is why our call is to work step by step and first collect data and not worry about what you will eventually do with it. If you have made this clear in due course, the business case for further investments can be made.

Example: Every car brand has its own navigation software. Yet everyone knows that Google Maps and Waze have the best traffic information. Why: Exactly, because the data from all smartphones is universal, comparable and available. Therefore, be careful with the vendor lock-in of some manufacturers.

Preliminary conclusion
Don't make it too complex, start collecting data, make sure that data is and remains available and that data is universal and therefore comparable. Analyze the data and then create substantiated business cases for improvements in your buildings.

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