Big Data

Finding the money to go smart

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 😀 

Can IoT answer real (estate) questions?

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.