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How big data in architecture will transform design

Article-How big data in architecture will transform design

Piyush Prajapati, architect at GAJ takes on Big Data, looking at how it has transformed the way architects are rethinking space.

“The best architecture will derive from both rational and ineffable decisions,” — Phillip G. Bernstein, Architecture Design Data.

At its core, architecture exists to create a physical realm where people live, interact and socialise. It is a beautiful expression of how we see the world and ourselves.  

Picture an architect toiling over a blueprint on a drafting table using set squares and a clay model. These were the creative tools that architects relied on to envision the physical realities decades ago. Over time, these tools have been upgraded many times and architects today are equipped with new tools such as CAD, BIM and powerful rendering platforms. In the current world, it is easier to make mistakes, correct them and learn from them to create more efficient designs and schemes.

Over the past two decades, architectural design has been heavily influenced by digital tools and digitisation processes. With the integration of new technologies and processed theories, these tools have also endorsed the concept of generative design methods and Big Data. Today, our world is highly connected through Big Data, Artificial Intelligence (AI) and the Internet of Things (IoT) and it has significantly changed how we design. It has helped us better understand the spaces we inhabit.

Aside from smartphones and smart applications, modern-day architecture is redesigned and reprogrammed for better productivity, where we make confident design assessments, better real estate investments and even informed decision on urban designs and policy.

Characterised by volume, velocity and variety Big Data is complex and dynamic. However, architects and designers can extract meaningful insights, such as hidden building performance patterns, unknown correlations, and user preferences from this data. It is even possible to quantify spaces and spatial qualities and create prediction models where we are able to forecast the performance of the building and spaces before the start of the construction at the site. This data-driven design is expected to significantly impact the architecture, engineering and construction (AEC) industry in the near future.

Digital Technology

Data-driven design is a design that is the outcome of data and statistics. It gives clear insight on information collected as the input data and the internal functioning that drive the design motive. Because the vastness of data concerning human cognition is colossal, computational tools and methods are required to analyse and process this amount of information.

Predictive design and generative designs are two examples of data-driven design methods. Both of these methods have gained a lot of recognition in the world of architectural design. Predictive design methodologies use a statistical approach to predict future behaviour. By analysing historical precedents we are able to create a model to predict future outcomes.

Predictive analysis is harmonious throughout the process allowing designers to determine the final outcome. Generative design, on the other hand, helps designers to discover unexpected, unexplored innovative designs. It is a process where data is used to produce thousands of design iterations and solutions as the result of automated analysis.  


Predictive and generative design methods are both data-dependent mathematical operations. It is easier to monitor the quantity of, for example, building performance, areas and material quantity whereas, measuring the quality such as the feeling of a space and user experience is difficult but not impossible.

Quantitative measurement is highly structured and number-driven. It quantifies results allowing for a detailed understanding of trends. Qualitative data provides the why. It measures emotions, reasoning and behaviour with the emphasis on understanding rather than just measurement. 

The two can, and should, work together.  

Imagine a scenario where an urban planner needs to make an informed decision on identifying the parts of cities that are functioning well and parts of the city that have potential for improvement. Using Big-Data it is possible to quantify this. Let’s assume we want to identify socially active places in Dubai. By using and analysing data from social media, it is now possible to map the geographic impression of people taking pictures around the city. The density of this activity can illustrate the urban hotspots potential for design interventions.


Data-Driven Design methods make informed decisions by holistically studying multiple data parameters to predict the performance.

Through machine learning computers are able to perform tasks without actually being programmed to do so and can therefore help generate more informed design solutions. This helps determine the choice of one design over another endorsed by the potential usefulness of the design.

Big Data offers tremendous opportunities for architects and designers. It enables opportunities for improvement on-site and off-site. It provides a better level of surety about predictions and planning and it helps mitigate project risks.


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