Counting your steps with a tracker; checking into your favourite restaurant on Facebook; and swiping your security pass to get into work.
What do all these things have in common?
Data. And more specifically, its generation.
How do we characterise big data?
The concept is most commonly defined by the three Vs model:
- Volume, there’s lots of it
- Velocity, high speed creation or analysis
- Variety, drawn from a multitude of sources: documents, databases, sensors, wearable tech, social media
But it’s in the analysis of that data where the magic happens.
Why is big data important for property?
It’s possible to collect and analyse huge amounts of information about the built environment around us — opening a world of possibilities.
Big data can help identify patterns, market trends, customer preferences and other insights that we can use to improve building efficiency, reduce costs, analyse risk, predict behaviours and outcomes, as well as optimise business models and inform strategies.
It can ultimately help us design, build and adapt cities and infrastructure: fine-tuned to the way we live and need to get around.
In Arup’s A2 magazine, Francine Bennett, CEO and co-founder of big data specialists Mastodon C says: “Buildings, vehicles and mobile phones all have sensors in them that produce lots of data about what’s going on in the physical environment.
“Where you’ve got lots of data, you can find ways to make things work better — whether that’s personalising environments or finding efficiencies in the way you use resources.”
So, let’s take a look at just a small sample of organisations working in this area now.
Who’s working in this area?
Mastodon C | Helped the Energy Saving Trust make sense of information from trials looking at which energy saving approaches work best in the UK’s building stock. The London-based company created and now operates a system using open source technology to gather data from more than 2,000 buildings and 18,500 sensors — 850 gigabytes to be precise — so EST can explore, chart and calculate performance across a range of indicators.
BuildingIQ | Developed an energy management system, together with the Commonwealth Scientific + Industrial Research Organisation to predict energy demand and directly adjust a building’s heating and ventilation system parameters in real time, making best use of energy. The US firm’s system continuously processes gigabytes of information from various sources including power meters, thermometers, pressure sensors as well as weather and cost data, in order to automatically reduce energy consumption, it says, by around 10% to 25%.
Arup | The National Assembly for Wales commissioned the property professional services firm to create mobile phone matrices to support two projects in Swansea and Newport — a scheme appraisal and local development plan. This work tracked footfall and journeys through these areas, using big data analysis to give deeper insights into how people use spaces.
SmartZip | The Californian firm says it helps estate agents target prospects by predicting home buying and selling behaviour using raw data from national sources, amounting to 1.3million gigabytes on more than 95 million homes and their owners across the US. Its predictive analytics engine examines this information, including variable such as consumer habits, equity, price trends and hobbies and interests, to find local seller signals and makes listing predictions for particular areas.