Flavio Briatore reveals how he approaches business, how he became successful and what motivates him. What lessons are there for a budding PropTech entrepreneur.
When we started Beringar we spent a lot of time with clients in the health sector understanding their space utilisation needs. We discovered that to use IoT in the National Health Service in the UK we would have to find a way to get the data out without touching the buildings data infrastructure. So, no wired or Wi-fi connections were possible, partly due to data security concerns and partly due to spotty Wi-Fi that sometimes was there and worked and sometimes did not. A constraint was introduced to our way of working that we had to overcome.
As we have discovered, constraints are our friend. They force us to think of inventive ways to overcome them and usually lead to a better product in the end. This constraint led us to focus on LPWAN technologies as a potential data transmission solution (in turn leading to new constraints around data packet size) and to prototyping using LoRaWAN.
Why LoRaWAN? Well, it was a technology that we could find willing experts to help us with and had the benefit of flexibility compared to SigFox. We wanted to send data every minute and SigFox would not allow us to do that. LPWAN technology also had the benefit of great range and the ability to work from deep in the bowels of a building. Something that we knew that 3G or 4G would not be able to cope with from our initial tests. This choice meant we could get the data out of the building securely and reliably.
Making the choice to work with LoRaWAN meant that we needed to really focus on the data that was important and package it in a way that could let us fulfil the demand to count and position people in a building, report the environment and understand space utilisation. All in a few bytes transmitted once per minute. As I mentioned, constraints force creativity, so we found a way to package this information in a few different message types and stayed within the duty cycle limits for LoRaWAN. This choice reduced our data transmission, storage and management headache a lot.
The other benefit of LoRaWAN, as we have discovered, is the ability for us to be in control of the deployment. We choose how many gateways and when they will be installed. We manage the network and ensure that we deliver a high quality service. This is important in the overall end to end service we deliver to the customer. No waiting for a telco to roll out in that area and no service provider help desk to worry about if something is not right. This choice helped with speed to market and quality of service.
So LoRaWAN has challenges - its radio being the principle one. Doing large scale firmware updates is not trivial and we always have the issue of occasional dropped packets - but the pros outweigh the cons. It’s fast to deploy; you’re in control; it’s quite reliable; it has great range with low cost hardware and you can remain independent of the large carriers. There is a lot to like.
What do you use to get data out for your IoT deployment and what are the pros and cons?
Main image credit: Ryan Stefan via Unsplash
What if the drivers of value are change because of technology? What if the grade B office is suddenly in demand because it has scooter charging points, a drone landing pad, a huge space for rechargeable supercapacitors or a convenient booking system that allows it to be occupied and productive 24/7?
While undertaking the R&D work to develop the Beringar Hex, we really wanted to find a way to minimise the management headache that IoT devices can create. Powering the device was very close to the top of that list, so we went and listened to potential customers that had used battery powered devices in the past to gauge the size of the issue. Many had believed the battery life estimates they were given by suppliers, only to discover that, as one put it “we should have invested in Duracell - we seem to be constantly buying batteries!”.
In the end, we opted for a powered device in the end, based on the power-hungry nature of machine vision and the premise that our goal was for our data to become part of the operational model of a building. To do this it needs to become part of the building structure, so power shouldn’t be an issue. That said, we have a number of cases where battery powered devices are in demand. These include trials, proof of concept deployments, short term surveys and unit testing. So, we are still looking for that “perfect battery” that can provide long deployment potential and be easy to manage.
I read an article this morning about the 10 Things the Perfect IoT Battery Should Do and wondered just how far away we are from that perfect battery.
Pack a lot of power into a small space
Efficiently deliver that power quickly, and/or incrementally, as needed for a particular application, without degrading battery capacity
Be easily recharged in a variety of ways, including wirelessly, such as over Wi-Fi networks
Make it simple to remotely monitor battery output, remaining battery life, as well as overall battery health
Avoid self-discharge to hold their charge for extend time periods, even under adverse environmental conditions
Be able to be recharged many times, in a variety of ways, without affecting battery capacity
Avoid emitting waste heat that could cause problems
Last a long time to avoid the need for premature disposal, and be environmentally friendly when finally retired
Be inexpensive enough to allow for widespread deployment in many kinds of IoT devices
Use a flexible design that makes it easy for IoT device makers to incorporate in a wide variety of products
Given we are all experiencing peace dividends from the self-driving car revolution at the moment, perhaps that battery emerge in the next 2-3 years?
Well, with advances in materials such as graphene, we can deal with #1, #2 and #3 - pack a lot of power into a small space, efficiently deliver power, be easily (and quickly) recharged without affecting battery capacity (#6) Indeed, from our discussions with some of the world’s most eminent graphene specialists, we are going to see a huge increase in battery capacity and longevity (#7 and #8) coupled with a reduction in weight and an ability to sculpt units to match the device design more closely (#10). With advances in cloud technology and connectivity, such as LoRaWAN networks, we can easily monitor overall battery health (#4). So, that leaves #5 and #9 to deal with. Perhaps we will see that perfect battery sooner than we think.
At Beringar, we realised from the very start that the benefits of smart technology in real estate were a combination of quick wins, medium term returns and long term transformations. That's why we launched our sensors as a service model last May. Today, it seems Siemens want to go further to offer their clients a way to procure entire smart buildings as a service, either as new builds or by retrofitting existing buildings. This is an exciting development, especially if backed by a major corporation with deep pockets.
It does, however, throw up a number of important questions, some of which can be answered by the experiences of public-private partnership or public finance initiative projects (PPP/PFI). In these projects the private sector make a return for the transfer of risk of developing infrastructure on behalf of (usually) public sector clients. The contracting process is long, expensive and complex, but the results are a risk free high quality building from which to operate for up to 25 years. One of our co-founders, Paul Byrne, has 15 years experience in this market and is one of the UKs foremost experts on modelling this type of deal. At the end of the day, it breaks back to a complicated financial model that spells out the risk and reward for each party to the contract.
We would love to see some of the great tools created to manage PPP/PFI contracts be applied to smart building procurement - this such as clear payment mechanisms and comparators studies. These would allow more transparency and enable all concerned to see you the huge upside to going smart.
We stand ready to help. Waiting for your call Siemens 😀
In a report dating back to June 2016, McKinsey & Company looked at the changes needed to improve productivity in the global construction industry (Imagining construction’s digital future). The industry employs 7% of the global workforce, and with a staggering $10 trillion spent each year on new infrastructure, even tiny changes in productivity will have marked effects on both jobs and sector value. But, according to figures presented by Antony Slumbers in a tweet today, the construction industry spends around 1% on R&D compared to tech companies like Amazon who spend 12%. Clearly, there is scope to spend more to achieve greater productivity returns.
In our view, truly smart buildings are started on the drawing board. So, if the necessary step of getting them out of the ground is “dumb”, how can we possibly create a smart building? How can we know, for example, that it was built according to the plans; with the materials specified; in weather conditions that were optimum; by people that knew what they were doing, and; were safe while doing it? What impact would this data have on the ongoing use, management and maintenance of the asset? Do we have the spec sheet and is it performing according to spec? If it were a software or hardware package we would, so do we need the same for our buildings along with the mechanisms to check performance against spec?
We think we do, with digital platforms and sensors used before, during and after construction will provide that certainty and a whole lot more besides. The five trends highlighted by McKinsey are really all about platform and sensing capabilities for construction that generate data and insights that can be used to accelerate the build and/or increase the durability of the final output.
Think use pattern feedback; materials durability; occupant satisfaction; repair rates; scheduled vs unscheduled maintenance; thermal and acoustic performance; energy consumption; operational costs. Imagine these statistics being available to the designer, construction firm, lender, developer, owner, occupier and user of the building and think about how quickly innovation would happen once the building is smart from inception.
If we assume for a moment that more data is a good thing, then having the ability to create more data must also be good right? "What gets measured gets managed" goes to mantra. But assuming just having measurements will impact on productivity might be a little naïve. Perhaps it's what we do with those measurements that is more important.
Given that real estate is the most valuable asset class on Earth, one would assume we would swimming in data and insight about how buildings perform - how else can we decide to build them, rent them or value them? But sadly, the actual use of buildings is a mystery. Apart from fairly simplistic headcount information not much is known about how a building is used, but this is something that IoT has the potential to change. New devices that allow building occupiers, owners, funders and designers to look inside their buildings and understand the true demand for space of different types in real time can help make better, faster real estate decisions.
For the occupier it might help answer whether they have too much space. Real time demand data.
For the owner it might help answer whether the configuration of space is optimal. Real time use pattern data.
For the funder it might help answer whether their investment is in demand on a daily basis. Real time asset performance data.
For the designer, it might help to answer what there next building should include more or less of. Real time design feedback data.
In each of these cases, the core data is the same, but how it is analysed and interpreted can make it useful in many ways.
So, as always with data analysis, start by working out your role and your important question. Start with the outputs. There is a way to get the data you need to answer it. That's the power IoT provides today.
About 2 years ago we were set the challenge by a client managing a substantial portfolio of property to help him understand what was actually going on inside his estate. He had line of business managers putting pressure on him to increase the volume of business services delivered from the estate, and intuitively he knew there was space and time available within the 300 buildings he managed- but he lacked the data on where and when the space was available. Without it he couldn’t unlock the potential tied up in the portfolio - the potential to be more productive, efficient and flexible. To do more with the investment his business had already made.
This is a very common scenario, because the actual use of buildings by people in the real world is one of the least understood aspects of real estate. Real estate equates to about 10% of business running costs. This investment by businesses is managed using assumptions, rules of thumb, anecdotes and rough estimates. There is very little objective data on how people and buildings interact daily. This results in architects designing new buildings without feedback on the use characteristics of previous designs; in businesses invariably acquiring more space than they truly need; and in highly valuable assets with high running costs being used to a fraction of their design capacity.
In the world of the past, where landlords and long leases ruled, it was only a problem for the occupiers. They ended up paying rent for space they didn’t need or no longer used. However, in the world today, with the emergence of Space-as-a-service driven by real estate service companies like Regus, WeWork and Knotel, occupiers will only pay for what they use. So, as a landlord, it puts you in a very different position. You have valuable, expensive assets and you need to make sure they are delivering a healthy return on the investment. Without the certainty of long leases of the past, how are you going to do that?
Perhaps landlords need to start thinking more like airlines - about use patterns, buying behaviour, load factors, yield management, external factors, demand pricing and resource planning. Space is a perishable resource after all - just like a seat on an aircraft. Each square metre, if not used each moment, is lost potential. The airline industry has spent decades refining data systems to fully understand how assets are used. Data is just an important as pilots, aircraft and airports. If there is no data, there is no way for an airline to operate efficiently and generate profit.
It is into this world that real estate industry is embarking. A world where power has shifted from provider to user. A world where choice is omnipresent. A world where demand and supply of space will meet much more frequently. In short, a world where data will determine the winners and losers.
It is for this world we created Beringar. We have always believed that good data drives good property decisions and have made careers helping our clients to acquire the data they need. Today, that’s more important than ever. With advances in IoT, machine vision, machine learning, artificial intelligence, cloud computing and connectivity, it finally becomes possible to understand how people and buildings relate on a daily basis.
From the data collected, insights will emerge that will help architects improve designs; ensure occupiers waste less and allow landlords generating higher returns. It will be these insights that will allow the assets to increase in value, not because of where they are, but of how productive they are. It will be these insights that will help businesses be more productive, efficient and responsive.
Welcome to the future. Today.
If you were running a factory and not a health facility how much of your capital asset base would you devote to unproductive uses? How much would you devote to the staff canteen? How much to offices? How much to meeting and training rooms?
Sir Robert Naylor’s report on the future of the NHS estate highlights many issues facing the organisation as it tries to meet the demand of delivering more sophisticated and successful health care to a growing and ageing population. It shows that the NHS estate can be a enabler of more productive healthcare.