On-Site Magazine

The power of co-mingling data to more effectively manage risk

August 27, 2020   By David Bowcott

Harnessing data on productivity, profitability, sustainability and safety from a multitude of sources is becoming a primary imperative across the economy. PHOTO: Adobe Stock/Metamorworks

Throughout the economy, data is becoming the lifeblood of more effectively managing risk.

We’ve all heard of predictive analytics, algorithms, artificial intelligence, big data and machine learning. These are the major buzzwords tied to data science, an emerging sector of the economy that is growing at a rapid pace — and being fuelled by technology’s ability to gather more and more data.

There truly is great power in harnessing a multitude of data to reduce risk while improving productivity, profitability, sustainability and safety. In fact, it is becoming a primary imperative across all sectors to gather, organize and utilize data to make better decisions.

The construction sector is more challenged than others when it comes to organizing its data to better manage risk. Nearly every new project is a new design, at a new location, involving new stakeholders (design, subcontractors, prime contractor staff) and deals with an entirely new set of outdoor conditions.

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Unfortunately, the construction sector doesn’t have the data capture stability of the manufacturing sector — yet — whereby manufacturers make the same item over and over, in the same location, with the same stakeholders (supervision, workers, suppliers) and often under the exact same indoor conditions. As a result, construction cannot yet harness the power of data as effectively as the manufacturing sector.

To advance and stabilize its data capture ability, construction must rely upon the power of co-mingling multiple sources of internal and external data. The following are some examples of data sources the construction sector should be tapping into and knitting together to fuel better decision making:

  1. Project data — the various internal and external data on the project itself (nature of project, geotechnical data, environmental data, etc).
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  3. Design data — All data captured within the design process in both paper and digital formats.
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  5. Pre-construction data — Data captured in between the design and construction phases, including procurement process data and subcontractor/supplier data.
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  7. Construction data — Data primarily captured utilizing project management technology and point solution technology (RFI data, defect data, safety data, etc.).
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  9. Internet of Things (IoT) data — Tied to project management technology, this is the data provided in real-time from the project site (or throughout the supply chain) utilizing sensor technology that monitors everything from worker movement to temperature to vibration to computer vision sources.
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  11. Accounting data — Related to the financials of the job site.
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  13. Post-construction data — Data beyond construction provides tremendous insight on the quality of the design and workmanship. In some cases, the contractor has responsibility for the operations phase of the asset and can easily document post-construction data. Otherwise you could rely upon the owner to supply such data and/or will see issues in post-construction via warranty claims.
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  15. Weather data — Weather plays a significant role within the construction sector and its addition to the co-mingled data set is necessary.
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  17. Insurance data — The data from claims made to the insurance sector. Effectively a register of major events that went wrong on the project related to property, liability, worker injury, defective design, defective workmanship, subcontractor failure and environmental issues.
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  19. Other project data — Capturing data from other projects allows for the ability to benchmark the above data sets.
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  21. Data of peers — Data alliances are forming within the construction sector all over the world. Through such alliances construction stakeholders not only compare data from project to project, but can compare data from their firm to the data of their peers. In addition, some advisory partners have data sets which could allow for such comparison.
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The above are but a few key data sets that your firm should consider harnessing and co-mingling to fuel better decisions, though there are a range of other data sources that could be utilized to formulate a highly effective decision-making platform.

Of those above, several stand out as deserving particular attention. Insurance data is one example, as it provides a functional inventory of what is going wrong on your projects. At the same time, construction data provides insight on project issues directly from staff at all levels of your organization, while IoT data is able to keep an unbiased record of your job site environment. Though still an emerging trend, data from fellow construction companies can help benchmark how your firm stacks up against peers around the world.

By developing a co-mingled data platform you will ensure your firm is ideally prepared to handle the risks ahead and be in a much better position to run future jobs productively and profitably.

 


David Bowcott is Global Director — Growth, Innovation & Insight, Global Construction and Infrastructure Group at Aon Risk
Solutions.

This column first appeared in the August 2020 issue of On-Site. Read through the complete issue here.


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