Data-Driven Design: Construction’s Big Data Problem
Reposted with permission on February 7, 2014 from DataDrivenDesignBlog by Randy Deutsch, Associate Professor at the School of Architecture at the University of Illinois at Urbana-Champaign:
It is clear from reading the McKinsey Global Institute (MGI) report, Big data: The next frontier for innovation, competition, and productivity, that big data is now recognized as an important factor of production, alongside labor and capital.
But what about productivity?
Most by now recognize that leaders in every sector will have to grapple with the implications of big data.
What does this mean for design and construction?
MGI studied big data in five domains—healthcare in the United States, the public sector in Europe, retail in the United States, and manufacturing and personal-location data globally. Big data can generate value in each domain.
Some sectors are positioned for greater gains from the use of big data than others.
And, as one might expect, the Construction sector has some work to do if it is to see comparable gains brought about by capturing, analyzing and applying big data.
On MGI’s Big data value potential index, the construction sector falls somewhere in the middle of the horizontal continuum, between low and high potential value.
Adding value while reducing waste
On the vertical portion of the chart, measuring productivity growth in the US between 2000-2008, it will come as no surprise that the AEC is not productive – registering the lowest growth of any industry.
In fact, as stated in the report, several sectors, including construction, educational services, and arts and entertainment, have posted negative productivity growth, which probably indicates that these sectors face strong systemic barriers to increasing productivity.
These systemic barriers as clearly spelled-out by Paul Teicholz,? Professor (Research) Emeritus, Department of Civil and Environmental Engineering ?Stanford University, in Labor-Productivity Declines in the Construction Industry: Causes and Remedies (Another Look).
One of the goals for using big data in the AEC industry is to help – along with the use of BIM, collaborative workflows and integrated delivery methods – improve productivity for owners. Adding value while reducing waste.
Construction appears on the chart as a good – not a service – and one that is not particularly easy to capture the value potential of big data.
AEC talent, according to the chart, is hard to come by – not a lot of data scientists working in the architecture, engineering and construction space.
What the chart calls a “data-driven mindset” also registers a medium rating for the AEC industry.
And lastly, data availability is also neither easy nor hard to capture.
According to the MGI report, Construction has the least stored data of any industry: 51 Petabytes (as of 2009) compared with 966 Petabytes for Manufacturing and 848 Petabytes for Government. The stored data per firm is among the lowest as well.
In terms of the type of data generated and stored (it varies by sector) Construction – as one would expect – has most of its data in the form of images; next, in text and numbers; and the least amount of data stored in video and audio… read more.
Continue reading on the DataDrivenDesignBlog to learn more about the potential benefits for utilizing big data in construction and to learn more about the data challenges in the industry today.
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