Tag Archives: big data

Where to From Here: Embracing technological change

la libertad tiene un precio. by ... marta ... maduixaaaa, on FlickrIs architecture on the verge of the greatest change in centuries? Ceilidh Higgins looks to the future and predicts disruption of epic proportions. This is part of the ACA’s Where to From Here series, which invites reflections on the recent ACA – SA State of the Profession research.

The architectural profession could be sitting on the brink of the largest shift in how we practice since the Middle Ages and the time of the master builder. Alternatively, we could become totally irrelevant to anything except the boutique house. The scary thing is that much of our profession seems totally unaware this seismic shift could soon occur.

I really enjoyed writing this article for the ACA, it brings together a number of topics I have written about over the last few years.  To read the full article go to the ACA website here.  If you are interested in the ACA-SA State of the Profession research you can find a summary here.  I also recommend checking out the other articles in the series.

Ceilidh Higgins

Image credits:la libertad tiene un precio.” (CC BY-NC-ND 2.0) by  … marta … maduixaaaa 

Is Disruptive Innovation possible in the construction industry?

fishbowl jump by Kay Kim(김기웅), on Flickr
Lately I have been finding the term “disruptive innovation” everywhere.  From events about green buildings and BIM, to blogs and even the Australian Prime Minister – everyone is talking about disruptive innovation, what it means and how it is changing business and our lives.  Along with robots (see my post on robots here), the concept of disruptive innovation seems to have become one of the mainstream technology trends to talk about in 2015 –  replacing big data as the hot topic (and see my post on big data here).  But has disruptive innovation yet impacted on the construction industry? And if it hasn’t yet, will it? I worry that sadly the answer might be no.

The construction industry is one of the least efficient industries – and this is a worldwide issue. This year I heard someone describe the construction industry as ‘the last craft industry’ and this is certainly true.  Whilst so much of production and manufacturing has become rigidly process oriented and quality controlled, prototyped and tested – even in developed countries, almost every building that we build is still a one off design, constructed piece by piece on site.  The inefficiencies of all phases of building – from procurement through to design and construction are outstanding.  Even when a building is not designed by an architect, if it’s larger than a house, it’s almost certainly a one off design.  Even in Australia, where site labour is a significant expense, prefabrication is the exception and not the norm.  We actually have less standardisation than in the larger American and European markets! As architects and designers in Australia we expect to be able to customise almost any product, and often at no extra cost because so much is custom manufactured for each and every project. All of this results in additional costs, both to those supplying services and products related to buildings which are then passed onto those purchasing buildings.  I have seen estimates suggest that the construction industry wastes a mind boggling 20-30% of building costs  – possibly equating to somewhere around $1.7 trillion (USD) worldwide each year! I found one estimate that 50-68% of time on site is wasted!!! Just google construction industry waste and you will find heaps of articles from around the world in relation to both time and materials.

All this would suggest, that buildings and construction should therefore be ripe for disruptive innovations – there is clearly a massive problem here.  BIM, prefabrication and robots have been seen as possible saviours of the industry, that would increase efficiencies but are they effective and are they disruptive innovation?  In the UK, the government determined in 2011 that BIM would generate savings and efficiency for government projects, and they have mandated its use on all government projects over 5 million pounds.  There is plenty of evidence from the UK and also from around the world that is demonstrating that BIM is reducing construction costs (for example refer to this series of articles by David Mitchell on ROI of BIM) – and one assumes without reducing quality of outcomes.  The UK mandate targets that by 2016 all projects will be what is defined as “Level 2 BIM”, but there is no date yet set for “Level 3 BIM”.  So BIM has already been around for easily 10 years already now, and still with no end date for this higher level uptake by industry – 15 years of change seems to slow to me to be defined as disruptive innovation. I’m not so sure that BIM is “our Facebook revolution” (see this article on Digital Built Britain)

Perhaps before we go much further, we need to define – what is disruptive innovation anyway?If I ask google the answer (via wikipedia) is ” A disruptive innovation is an innovation that creates a new market and value network and eventually disrupts an existing market and value network, displacing established market leaders and alliances. The term was defined and phenomenon analysed by Clayton M. Christensen beginning in 1995.”  The frequent examples we are all familiar with include Airbnb, Uber, iTunes and Facebook.  To me, I’m not quite sure that all of these actually meet the requirement for a ‘new market’ – how is the Uber market different from the taxi market? But the key point is that they create a new way of service or product delivery that is completely different from what has come before rather than just being a little bit different – cheaper, easier or more competitive.  For example Amazon is not usually viewed as disruptive innovation, its just a slightly different way of providing goods, at a conceptual level it’s basically the same as the very old fashioned mail order catalouge.

So is BIM a disruptive innovation? I think not. When I first attended RTC back in 2009, and really started to see the possibilities of BIM beyond just 3D modelling and how we could move towards buildings being built from models not documents, and I was seeing all the resultant changes this would bring to our contractual and teaming arrangements, I think I would have considered that BIM would be a disruption to our industry.  But now 6 years later, how much has really changed?  Buildings built from models are still very much the exception rather than the rule, as are alternative procurement and contracting arrangements.  In the same time, Airbnb (started 2008) and Uber (started 2009) have taken over and are serious dominators in their respective markets.  I think there are a few reasons for this slow uptake of change in the construction industry.  One is that with BIM, we still have the option to do things the old way.  We can combine a bit of BIM with traditional paper documents and contracts.  It’s not an all or nothing alternative.  The other is the scale and structure of the market purchasers.

I have been thinking a lot lately about what these kinds of disruptive innovations have in common and how they differ from architecture and construction.  The key issue to me, is that almost all of these commonly discussed disruptive innovators rely on the power of individual consumers and not government and big business.  Can you think of any disruptive innovations that have been driven by or even embraced by Government? Or even big business? (A related question to ponder another day – is activity based working a disruptive innovation?)  If anyone has any suggestions, I’d love to hear them – I can’t think of even one.  So recently when I came across on article on crowd funding for the property industry, I wondered – could this be the driver for disruptive innovation in construction?  Crowdfunding brings in the individual consumer, could this be the missing link?

However upon reading the article, I don’t think so.  Whilst the project funding might be obtained from smaller individual consumers, the project is still run by a larger developer –  it’s just a new way for them to get their start-up capital, like the idea of off the plan apartments really.  Whilst the smaller investors may start putting the pressure on for greater efficiency this is more likely to push incremental improvements rather than disruptive innovation.  The article concludes with the suggestion that within 6 years these crowd funding ventures might be owned by banks, so disruptive innovation seems highly unlikely!

What about other aspects of technology?  Could robots and prefabrication cause disruptive innovation in construction?  Again these are technologies that have been developing for some time – prefabrication for probably over 100 years now! Whilst both offer opportunities for efficiency gains in design and construction, like BIM, they also offer us the opportunity to take small parts and combine prefabricated or robot built items alongside traditional methods.  Robots might be laying bricks, but did they pour the concrete slab yet?

So far, the best opportunity I have seen for disruptive innovation in design and construction is going to come from algorithms rather than robots, through the form of software like Google Flux.  Flux automates the building design based upon site conditions.  (You can find out more about Flux in this video from my presentation and blog on Will a Robot take my job or here on Randy Deutsch’s blog ) There is no reason why either much of the model or the documentation would not be largely automated out of this software as well.  Whilst I believe humans (as architects) will always be involved in designing high quality buildings, much of the work we do as architects could be automated.  I have recently heard said “the computers don’t have to be perfect, they just have to be better than us”.  Why should a human spend time drawing up all the details and layout of a toilet when a computer could do it faster and make sure it meet the building code? The parts could then be prefabricated or assembled on site by robots increasing construction efficiencies.  Developed outside the traditional markets, could Google displace Autodesk as the primary software provider for building design and the disruptive innovator that changes the traditional delivery of architecture? I think it’s possible.

I think it’s also possible, that architects won’t see the potential of these tools, they will see the admittedly ugly buildings that the beta version of the software produces, and believe it’s just a tool for developers to quickly design and build boxy buildings.  If architects don’t engage with these technologies, that is probably what they will become.  But what proportion of our clients are coming to us for high end design? If developers, governments and big business don’t need architects any more what happens to our industry? What happens if construction innovates but architecture doesn’t?  If construction innovates and becomes more efficient, will that leave architecture behind? Does architecture become even more of a boutique industry catering to rich people’s houses?

What about disruptive innovation in construction itself? If not robots or prefab, what could it be?  Is disrupting design sufficient to disrupt construction? Or are there other disruptive innovations out there on the horizon?

Ceilidh Higgins

Image Credits:
Creative Commons Creative Commons Attribution 2.0 Generic License   by  Kay Kim(김기웅) 

Will a Robot take my job?

If I am an architect, a designer, an engineer or even BIM manager – Will a Robot take my job? This is the big question I recently presented in my talk at RTC Australia as part of the session BIMx: Big Ideas around Big Data.  Open up my slideshare presentation above that accompanies this blog post.

NESTA, a UK innovation charity has a quiz you can take to see if a robot is likely to take your job.  The quiz asks a series of 6 questions around skills and ongoing learning, if you manage complex real world tasks, work with, teach and manage people, or design and manage technology, machines and systems. It uses your answers to determine how likely it is a robot would take your job.

The answer is that an architect is “Robot Proof” with a low probability of a robot taking our job.  BUT does this match with our experience? Are architects, engineers, or designers really likely to be robot proof?

Whilst we think a robot won’t take our job – what about a computer?

Many of us would agree that BIM has already resulted in smaller project teams. Computers have long been a part of the design process.  Whilst we often forget CAD standards for ‘computer aided design’, computers can now aid the design process in much more significant ways than back when AutoCAD was released. Its interesting though that today a google search of computer generated architecture still mostly generates links related to rendering and imagery, rather than designs produced by computers.

If you think that BIM won’t take your job – what about Big Data?  We are already using data to check, verify and evaluate options within our designs. As the scale of the data available gets ever bigger these processes become more complex and more powerful. Right now google searching for data generated architecture won’t get you many hits related to buildings, but this is sure to soon change.

Rules based checking might not yet be big data. But it is about using data sources to validate designs or documentation. Examples include checking codes or standards using software such as solibri.

Again data analysis doesn’t necessarily mean big data yet.  Analysis began as something that architects did using pen and paper, a site analysis diagram for example. Data analysis is starting to become more computer driven which allows for much more significant analysis to take place.  Examples include environmental or performance analysis of buildings, or analysis on a larger city scale looking at land use and traffic patterns. This kind of analysis is very much in the realm of current uses for big data.

Data is also the basis of simulations. For example fire or traffic simulation modelling is based upon creating algorithms from data. Currently the simulations used within the AEC industry are relatively simple algorithims.
Big data gives the potential for developing significantly more complex simulations. Last year at RTC in Chicago I discussed the potential for big data to allow us to simulate human behaviour in complex building types such as workspaces with the potential of increasing a companies productivity. (see blog post here)

So, data can evaluate design – but could big data actually drive design? Is it already happening?  As with data based checking, its certainly true that data driven design exists already – and has for some time, although generally not yet into the possibilities of big data. Computational and generative design is data based upon algorithms and therefore data based design. Algorithms are already being used for design in many different ways.

The use of formulas to create design is an example of data driven design.
An example is the façade of the Auckland Savings Bank by BVN and Jasmax which was designed using Microsoft Excel and the Chaos formula.

The structure of the Watercube by PTW and Arup was designed using an algorithm to determine structural steel member sizes.

A simulation is just another kind of algorithm. Rather than just using simulations to test current design proposals, the simulation algorithims can be part of the design software and the design options can be based upon the outcomes of the simulations.  This bandstand by UK architects Flanagan Lawrence was designed using Dynamo and an acoustic simulation algorithm called acoustamo.

Algorithms can be used to optimise an existing design. At the Barclays Centre by ShoP – detailed design of the steel panels was undertaken using CATIA to generate options which allowed a reduction from 230,000 sqm of steel to 150,000sqm. No two of the 12,000 panels are the same.

This exhibition hall building was designed by the University of Stuttgart’s Institute for computational design.

The question – How can you create a resilient timber structure with as little material as possible? This is a simple example of applying one rule to a simple building type. Using an algorithm inspired by a sand dollar one of natures most efficient structures, this building was designed by computer. The human input to the design is the initial idea and the design of the algorithms. (Read more)
As a side note, it was built by robots too.

What about more complexity? The complexity of trees growing in nature? There is actually already an algorithm for that.  The programming to create suburban housing exists too (its initial use is for generating realistic houses for 3d gaming environments). Using rules based criteria such as number of rooms, adjacencies and architectural style, a suburb of varied housing can be produced.

With big data the questions and the building programs can get more complex. And these kinds of design tools are not as far away as you might think.  Autodesk has a lab project in development called Dreamcatcher. “Dreamcatcher is a goal-directed design system that enables designers to input specific design objectives, including functional requirements, material type, manufacturability, performance criteria, and cost restrictions. The infinite computing power of the cloud then takes over.” The publicity for Autodesk’s Project Dreamcatcher suggests it is for industrial design – the same could potential apply to create rules based design solutions for buildings.

Autodesk are not the only company investing in this technology. Google has setup a spinoff called Flux to explore how data will shape our future. Right now Flux software and much of the media is focused on the metro scale data analysis but the future of Flux is about buildings.

Flux asks “What would happen if we stopped designing individual buildings and started designing building seeds” It is based upon the idea that the data will form seeds.

The information would include the codes, standards, weather conditions, occupant data, building product data and other information available about a building, its site, its occupants and client requirements as well as industry data such as materials, systems and construction methods and costs.

Just as each seed grows up to be a different tree, the building data seeds will grow to be different buildings depending upon the site and its constraints, the client requirements and other project specific inputs.
This kind of design will have a significant impact upon the way our industry operates.  (See post by Randy Deutsch)

This is a clip from a talk by Jen Carlilse co-founder of Flux. (Embeded in slide share or at youtube)

We probably all agree that the building examples in the Flux video are somewhat lacking in the architectural beauty department.  If nature could be an algorithm – could beauty also be an algorithm? Is there the possibility that in the future we could use data analysis to design beauty into our buildings, to use data to design buildings like the Sydney Opera House?

So what will my job be? It won’t be drafting disabled toilets anymore that’s for sure.

I’d like to think that the data will allow us to get rid of the drudgery. It will allow us to focus on the best parts of our jobs. It will allow us to realise the true value of design.

We will still evaluate the computer options and talk to the clients. Whilst data can assist us to make decisions, the human race is not about to let everything be decided purely on the basis of data – if we did we would be doing it already. Human nature is that we still want humans involved in decision making. We still need to tell the computers what to do at some level. Does it mean we all become programmers rather than architects and engineers? Could this process can bring out the best in both humans and computers?

What do you think your job could be?

Ceilidh Higgins

 Imaged Credits:
See slideshare presentation for full image credits.

 

Big data at the intersection of building analytics and people analytics

buildings with peopleImagine if you could simulate your building or workplace environment before you built it – not just simulating energy usage or daylighting – but creating a simulation of how the people would behave and work inside your space. And not just a generic sample population, but your actual workforce in a simulation that knows and understands their actual behaviours. Before investing in bricks and mortar (or tables and chairs) – you could test numerous design scenarios and their impact upon not only how the building itself operates, but also how the occupants respond, their use of space, their interactions with one another and more. How would this change the way we design, the way we build and possibly the way we all work?

Many people would think this sounds pretty far fetched, futuristic and certainly a little bit big brother. The reality is that we actually have both the information and the technology available to do this – right now in 2014. Today I’m going to talk about why we would want to look at simulating human behaviour in the built environment and what this could mean for design, as well as discuss the types of data analysis and technologies from different fields that I believe could be brought together to make this kind of simulation of the built environment possible.

My background is in workplace and educational design. A large workplace is probably one of the most complex environments in which to try to predict and understand human behaviour. Unlike a restaurant, a shopping centre or a train station, it is designed to have a large number of diverse activities taking place. Whilst at the same time – and I know this sounds a little strange – there is actually less of a clearly defined purpose in being in a workplace than in many other kinds of enivonrment. Different individuals have different purposes in being there, because they enjoy their work, to socialise or to earn money are just a few. An opposite example of a much more simplified purpose of space would be in a cinema – where almost everyone is in the space for one purpose, which is to see a movie (although they may have different motivations for seeing the movie). In the workplace, because there are so many different activities and behaviours, finding patterns to predict how people work – and even understanding what improves their work is more complex.

The holy grail of workplace design is to be able to prove that certain design elements increase productivity. Most researchers agree that it has historically been almost impossible to measure productivity in knowledge or service oriented workplaces, which today make up the bulk of first world workplaces. We can however measure a lot of approximations of productivity – or things that we expect to have a close correlation with productivity – things such as staff retention, absenteeism or self reported satisfaction and comfort levels. This kind of data is readily available.

Another key issue in workplace design centres around the actual useage of space. Real estate is a significant business cost (though much less significant than the people cost) During the design phase of any project there is great debate over wdifferent kinds of spaces and how and if they will get used. Do we allocate individual offices to sit empty, will staff actually use that large breakout space, will that training room sit empty for half the year? From the workplace designer through to the facilities manager and the CEO, the ability to simulate occupant behaviour in the workplace has a huge potential to impact upon what and how we design our workplaces. To me this could be the next significant game changer in workplace design and productivity.

It’s being made possible by big data. In the past, we have not had access to enough information about either building systems or occupant behaviours to be able to simulate these kinds of complex environments. There is software that can simulate human behaviour – and it has been around for more than 20 years. Commonly used software that simulates vehicular and pedestrian behaviour or fire engineering modelling is all simulation software which is based upon predicting human behaviour. However, the difference between these previous software models, and predicting behaviour of occupants of a workplace or other complex building type is the complexity of the human interactions. Human behaviour in a fire situation or within a train station environment is much simpler than in a workplace. There are less possibilities because of the limited scenario, and also we are essentially only tracking one variable – movement. Workplace design has made very limited to use of this kind of simulation, for example Google campus at Mountainview has been designed to ensure that all staff are within 2 and a half minutes walk of each other. Movement within the workplace, or other building types, is a pretty simple and limiting factor to use to test and simulate our designs. Big data, and in particular, combining information from the fields known as Building Analytics and People Analytics, could give us the opportunity to feed a huge range of different kinds of building and human behaviour data into a simulated building model.

Building Analytics is currently seen as the next big thing in building and asset management as well as an important factor for environmentally sustainable buildings. Probably most people in this room have at least some familiarity with this field. In the past, data gathered from buidling tuning or the BMS was more limited and unlikely to be in real time. However this has been changing. Building managers can now have real time access to a range of data – from factors such as which lights or appliances are in use, to the temperature, CO2 and VOC levels, heat or movement maps of actual occupation coming through motion or heat sensors, lifts that track occupant destinations or individuals movement through security systems via access cards or CCTV. Many of these systems are already commonly available in any new large commercial development. Facilities managers and building owners are using them to understand and predict occupant behaviours in relation to building systems. Historical data from the same systems can then be combined with real time data to predict or simulate things like building energy usage in a given period or what the impact of certain weather conditions might be on occupant comfort.

This type of building analytics does take into occupant behaviours, but only at a very simple level, things like is the space occupied or not occupied – because this is key information for the running of building services such as lighting and air conditioning. Whilst this data is firstly being used to control the systems and secondly to predict building performance it also provides us with real time reliable data on occupied versus unoccupied space. The ability to use web based booking systems for rooms or desks was the first step that created some kind of data around anticipated space usage, but it wasn’t real usage data, only a prediction of usage. Today BMS data can be combined with this kind of booking system, and it is possible to not only track advance bookings but real time actual usage – if someone doesn’t turn up to use the booked space it can be reallocated to somebody else. Whilst this kind of information can help manage a building it doesn’t predict behaviours or improve occupant performance.

This is where People Analytics can start to work with building analytics to create a fuller picture of how space is actually being used, and what this means for the occupants.

So what is people analytics? People analytics looks at data generated by people and companies rather than data generated by building systems. It is a growing field of social science with implications in particular for human resources and recruitment – and in my view for designers. People anayltics starts to look at and analyse any available kind of data in order to find patterns and understand human behaviours – its anthropology using computer generated information. Today, data generated by people can include anything from emails, to social media usage, to bluetooth and movement tracking, voice recordings, computer data logs, organisational plans, charts and documents or google searches. If you think about your electronic footprint, even without anyone planning on tracking what you do, there is a lot out there. The more we use the web or cloud based services, the more data exists about our habits, our performance or our personalities. In the past the quantities of data have been so much smaller, that there was not sufficient amounts of data to generate patterns or the computing power to crunch it. Today there is.

By analysing huge amounts of historical data it is possible to identify patterns or characteristics of certain groups of people, or how to predict or promote certain behaviours. Once the historic data set has been created, it is then possible to analyse data of new people to identify who fits the patterns. We still don’t always know what the data can mean on its own though. One of my personal favourite odd ball data correlations is that super guru computer programmers apparently have a tendency to like a certain Japanese manga website! You can see the applications to recruitment and HR immediately.

Another fun example of the use of large samples of aggregate data is the Twitter happiness index. This website analyses the use of certain words on Twitter every day since 2008. Words are assigned values from 1 to 9 to signify sad to happy. The overall happiness score for each day is then calculated and graphed. There are also Twitter election indexes, oscar indexes and many more aimed at trying to predict outcomes based upon twitter traffic. Elections polling has been a high profile example demonstrating people analytics to the public. In the 2012 US presidential election, big data was used by a number of forecasters to accurately predict the results in all 51 states. These are all examples of different uses of people analytics.

But how does all of this relate to buildings, and workplaces in particular?

Lets start with a really simply example of using other kinds of data in combination with building systems data which was undertaken by Immersive, a big data company based in Melbourne, Australia. By taking the historic heat sensor data from a workplace BMS and analysing it against the organisation’s project planning data for the same time period, it was possible to determine what the actual space usage and occupancy loads had been over the period compared to the predicted project staffing levels. Using the same forward project planning data, it was then possible to predict the organisations actual future space needs. Whilst this takes into account some level of occupant behaviour – space occupancy – again its a single variable, where we are still looking at physical space more than actual occupant behaviours.

But what if we could take multiple kinds of data – data that is more specifically tracking behaviours in the specific context of the workplace? And not just data about electronic interactions – what if we can gather the same types and quantities of data about our face to face interactions as our electronic interactions? In analyising workplace productivity, this tracking of real time physical interactions is important – because in most companies, much of the informal collaboration still happens face to face. The theory is that in organisations where knowledge work is undertaken, social networks define how information is transferred informally across the organisation, and that this informal sharing is creating a transfer of knowledge. This new knowledge then has a significant influence on how the work gets done and therefore on productivity – kind of like how you learn just as much by going for drinks at RTC as you do in the presentations – people are sharing what they already know.

If organisations can find ways to firstly understand these social collaborative networks and then secondly promote them, social scientists believe that the organisations productivity can be enhanced. The office space itself then becomes one means of modifying social behaviours in order to promote certain kinds of interactions. But how to collect information on face to face interactions – we are not all going to suddenly start skyping the person sitting next to us.

Enter the sociometric badge. Developed by a team at MIT, this device contains a number of sensors. An IR transreciver allows the devices to sense one another, bluetooth records their physical location in space, an accelerometer can figure out if you are sitting or standing and a microphone detects audio. Right now this device is approximately the size of your building access card although slightly thicker and can be worn around your neck. In the future your smart phone will probably be able to track all of this anyway – its actually already got almost all of the same sensors. The sociometric badges have been used to track and record the behaviour of building occupants in a number of studies investigating the way we work. The outcomes have been published in a great book called “People Analytics: How Social Sensing Technology Will Transform Business and What It Tells Us about the Future of Work” by Ben Waber.

One of the interesting things is that the microphone doesn’t even record what you actually say. It records things like tone, change in volume and speaking speed – which are considered social signals, and which are in fact more important in our face to face interactions than the words we actually speak. Early tests in laboratory environments included speed dating and salary negotiation simulations. Computers were able to predict outcomes with over 85% accuracy based upon 5 minutes or less of these recorded social signals.

These devices have since been utilised in a variety of actual real workplace studies. So far sociometric badging has found that call centre productivity is enhanced when team members take breaks together and that the amount of time spent interacting and the amount of physical movement are god predictors of creative days.

These studies, and most in the book, are based around understanding and modifying behaviours rather than modifying environments, but as any architect or designer knows, if you modify the environment, you have the opportunity to modify the behaviours. One of the studies of most interest to us, looking at how changing physical space impacts on occupant behaviours, was a study which investigated the size of lunch tables in a workplaces cafe spaces. Using data from the sociometric badges within an online travel company, it was found that staff that sat at larger lunch tables were more productive. Within the existing office environment there were 2 different spaces staff could choose to eat lunch – one had small tables for 4 people, and the other larger tables seating up to 12 (or they could chose to eat by themselves at their desk). The data quickly showed that the people who ate lunch together would then tend to communicate further that day. The staff that sat at the larger tables were more likely to speak with others outside of the group they had arranged to lunch with, and formed larger conversational groups at the lunch tables. These wider lunch time conversations led to links and collaborations in the organisation that were not otherwise being formed. These links were part of the knowledge sharing that led to greater productivity.

In another MIT project, the cubicles themselves were equipped with sensors so both the office environment and the people within it were being analysed. The cubicles were fitted with blinds instead of typical workstation cubicle screens, in order to provide privacy or allow collaboration. Based upon the collaborations that the data had identified as being most beneficial, the automated blinds would open or close overnight. In this way the building itself can even automatically respond to data analysis.

Often, the data coming out of these studies is not surprising the social scientists or the building designers. What is is doing though, is proving things we know instinctively, the things we have seen work before.

When you think about this information about your day, what you do, where you go and who you talk to is then combined with your electronic footprint, the information about your colleagues and then possibly also the building data – its a pretty full story of what happens inside a given workplace or building in a day. The possibilities for analysis and experimentation will be endless. Why is this so important to design and construction though? So far this is all about modifying existing environments. Being able to test and prove what works is the next step.

In an example that initially does not seem to be related to physical space, but to health, the sociometric badge data is combined with data about how disease spreads. The impact of sickness on the work environment, the interactions and the productivity can then be simulated across a range of scenarios with different people being the disease originator and different simulated responses such as stay home versus solider on being tested. One suggested solution to minimise spread of disease was to change the regular seating layout, which has the effect of reducing the level of interactions between people who already knew each other.

Moving into the not so distant future – there is no reason why the possibilities of physical environments could not be tested inside a BIM, with the algorithms behind the behaviours of the sims being developed from these kinds of behavioral data sets. We have the technology available to us already.

While this isn’t about BIM as we know it today, the link between the the building model and the simulations is obvious. But will architectural practices embrace these technologies or will this lead to another new kind of consultant in our team?

Imagine the value of the design and simulation team who can prove to the client organisation that workplace productivity could be enhanced simply by working with them? Translate that to all kinds of building typologies – and the whole definition if what architecture is or could be may change. Perhaps big data is going to have an even more significant impact on change in our industry than BIM, in ways we haven’t even imagined yet.

Ceilidh Higgins

This blog is the text from my presentation at RTC North America last month, as part of the session BIMx: Big ideas around big data.  I had a great time over there and attended some excellent classes.  If you are in Europe, RTC will be in Dublin later in the year.

Image Credits: Via Flickr Creative Commons
Big Buildings https://www.flickr.com/photos/neilarmstrong2/5480543083/
The New York Times on the New Art of Flikr https://www.flickr.com/photos/thomashawk/2442371176/

Worktech Melbourne: Jellybean Working

Yummy Jelly Beans by Ruth L, on FlickrLast week I headed down to Melbourne for Worktech 2014 . It is the first time I have attended a Worktech event , going based upon a few great recommendations – I had been told it was the best workplace event in Australia. I wasn’t disappointed. As well as getting a peak inside of NAB’s new 700 Bourke St at Docklands (which was the event venue) the presentations were generally of a very high standard – great presenters with relevant and focussed material. I also found it interesting to note that most of the presenters were not designers – but representing the client organsiations such as NAB, Bupa, Google, MLC and RMIT to mention a few – which sets the apart from many other workplace seminars. The day was jam packed – there were approximately 15 sessions, as well as the presentations these included a site tour and an interactive session moderated by Rosemary Kirkby. I thought rather than give you a sentence or 2 about all of them I’m going to share my thoughts on the 2 sessions I found most interesting , over this post and the next. If you see other sessions in the program and wonder what they were about – feel free to comment, tweet or email me and I am happy to share further thoughts.

Philip Ross is a workplace futurologist at Ungroup, and so not surprisingly, he was talking about the future of the work in a presentation which he described as being about Jellybean Working. At first I wondered what the reference to jellybeans might mean, surely not the fad for jellybean shaped desks, but as I got interested in his talk the reference to jellybeans faded into the background. Although he did explain towards the end, and so will I. The main focus of Philip’s presentation was the impact that technology would have on the workplace of the future – and not a far range future, but within the next few years, technology that already exists. Whilst we are all now familiar with the idea that we work using technology tools – and that they change and update all the time, I would say less of the audience were aware of the technologies that makes up what Philip described as the next break through in the workplace – big data and real time activity tracking.

Philip spoke about a range of technologies and methods for tracking activity both in the workplace and elsewhere including sociometric badging, social media and short range wireless as well as other technologies such as driverless cars which would change the way we work.

Sociometric badging is an area I have been interested in since I first came across it about 12 months ago. MIT have invented a device, which they call the sociometric badge which allows you to record data about the movement and behaviour of the person wearing the badge as they move around the workspace during the day. The device is about the size of security swipe card (and can have this function embedded into it I recollect) and it contains a whole bunch of bluetooth and/or wireless sensors, motion detectors and recording devices. So not only will it track where you walk, but if you are standing or sitting and if you are talking to someone and how loudly for how long. Put very simply and crudely, through a whole lot of measures, the data then indicates who you talk to a lot, who you say hello to in passing and where and how you spend your day. The data can then be used to create social maps of an organsiation, which are now being considered important signifiers of innovation and collaboration. I could probably write a whole blog post on this topic…If you are interested in reading more, I highly recommend “People Analytics: How Social Sensing Technology Will Transform Business and What It Tells Us about the Future of Work” by Ben Waber.

As Philip pointed out, all of this leads to the question of privacy. However studies are showing that privacy is very much a generational idea, and that generally younger generations have less concerns with sharing information than older generations. Many people seem to no longer care about privacy at all – Philip referred to a website ijustmadelove (which I am not linking to my blog – I don’t want to think about how much spam it might set off!) where people are choosing to share very private information. Whilst some information we are sharing by choice, other information is shared without most of us realising it. We are already sharing significant amounts of information with search engines and retailers, and not just online but a link between instore and online.

Using short range wifi to allow checking into a locations via a social media application means that the next time you enter that space the retailer will know every time you are inside their store. They will also have your purchase history and your browsing history while in store. This will lead to live time direct advertising. Apple are also already using a low energy bluetooth system called iBeacon which doesn’t even require you to check in. I already have something like this happening on my phone which I think is operating via gps tracking – every time I pass the local burrito place after using an evoucher there – at this point it doesn’t offer me any deals but it does pop up to tell me I am near the restaurant.

All of this eventually leads us eventually back to the jellybean. Philip uses the term jellybean to refer to the social media dot or blob that indicates if you are online or offline. He suggests this will become more important in the workplace, signaling to others where you are and what you are working on. I know in an office I worked in with an instant message network people did stop calling the phones if your greet dot wasn’t on. However in the fairly near future, not only will this social media icon just signify that you are online, the technology to concurrently edit documents with other users, will also allow it to signify who is working on a document, whilst tracking technology will identify where in space they are physically located. Philip defines “Jellybean working” as the intersection of technology, people and physical space.

All of this freaks people out a little sometimes, but if you think about it, Apple and Google probably know everything about you already. Which is a nice segway into my next weeks blog post, which will be on Hayden Perkins presentation about NYC Google and Let the User Decide.

Would you sign up for a sociometric badge in your workplace? What new technologies or social media platforms do you think will transform the way we work? Do you use social media in your workplace?

P.S. I’m excited to announce that I’ll be presenting at RTC NA in Chicago in June in a session on a similar topic to this one – entitled BIMx:Big Ideas around Big Data. Registration for the conference hasn’t opened yet, but the RTC AUS conference registration has, it will be on in May in Melbourne and I am also presenting there – a session called Get your Groupon. Check out the RTC Events Site.

Image Credits:

Creative Commons Attribution-No Derivative Works 2.0 Generic License  by  Ruth L