Big Data coming to construction risk management
By DAVID BOWCOTTSoftware
We often take for granted the impact information technology and the Internet has had on our ability to accurately collect and analyze data. Through technology the world has managed to capture 2.8 zettabytes (ZB) of data. And what’s even more impressive is that by 2020 that amount will increase by 50 times. The goal is to harness this data and turn it into information, which in turn can be transformed into knowledge and from there into wisdom.
Not all of the data captured relates to the construction or asset management industries, however, there is a substantial amount. By interlinking this data, our industry could gain significant insight on how to more efficiently create and operate assets of all types.
I’ve had the opportunity to work on several complex projects over the years, and part of working on these has been identifying and quantifying associated risks—with a keen focus on those risks that could cause delays. What’s interesting to note from these risk identification and quantification sessions, is that all parties brought their own experiences to the table during the quantification phase of the discussion. Often their perception of what was a big risk was driven by anecdotal evidence—for instance, “We just had a pollution event on a job and therefore it is a major risk we could face on this job.” True quantitative analysis is often not used during the quantification phase of risk assessment for projects. By harnessing the data of our industry we truly have the opportunity to make sound decisions on risk knowing these decisions are made from a strong foundation of past experiences.
So where to start? There are several industries that have this data. The trick is gathering it, organizing it into a unified format, and interlinking the data in preparation for analysis. Some industries that house construction and asset management data include:
- The insurance industry—Think about it, every time something goes wrong on a project, the first call made outside the company is to their insurance broker. They’re trying to see if the event is covered by one of the policies securing the project (or by one of the practice policies of the participants involved in the project). The insurance sector is sitting on a potential gold mine of information.
- The technical advisory industry—Often projects are done using technical advisors that help owners and/or their lenders assess, manage and monitor risks. For instance, most major projects use lender technical advisors that provide third-party opinion and monitoring to the project debt in order to give the debt investors comfort that its principal and interest will be paid back. These companies house significant information.
- The contractor and design community—Collectively these players hold the true root cause of loss information. They know what caused a job to slow down as they had front row seats to the project’s execution. The trick is finding ways to release this information without creating a bad branding event for those firms involved. It is possible.
Once you’ve identified the key sources of data and developed the plan to mine and organize the data, you can then move onto the analysis. Imagine the potential outputs from such a data set.
- Which project delivery model has the least issues in construction and operations phases?
- Which asset type has the best track record of success?
- Even specific asks like: what are the root causes of loss issues associated with hospitals built in Ontario over the past 10 years?
Big Data applied to the construction and asset management industries could significantly improve certainty around project delivery and project operations. Over the next 5 to 10 years this data will be synthesized and its output will be made available to the market place. It will take away most of the voodoo currently used to quantify risk. Further, it will ensure the time you spend on treating risk is well spent as you are focusing your risk solution energy on risks that really do have the best chance of disrupting your asset’s performance.