The power of data to unlock risk insight

The construction sector needs to be more effective in using multiple data sources to inform and enhance sector risk insights. By doing so, firms can enhance risk oversight and drive down insurance costs.

Throughout the economy data is becoming a life blood to more effectively managing risk. We’ve all heard of big data, predictive analytics, algorithms, artificial intelligence, and machine learning. These are the major data science driven buzzwords from an emerging sector of the economy that is growing at a rapid pace (and is being fueled by technologies ability to gather more and more data). Make no mistake about it, there truly is great power in harnessing the multitude of data within each industry sector to reduce risk, improve productivity, improve profitability, improve sustainability and keep your everyone safe. It is becoming a primary imperative within every sector of the economy to gather data, organize data, and utilize data to make better decisions.

The construction sector is more challenged that other sectors of the economy to organize its data to better manage risk. Every new project is often a new design, at a new location, involving new stakeholders (design, subcontractors, prime contractor staff) and dealing with an entirely new set of outdoor conditions. 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. Construction deals in unique projects exposed to the outdoor elements and because of this they are challenged to capture data on a consistent basis. As a result, construction cannot yet harness the power data as effectively as the manufacturing sector.

For construction to become more effective at harnessing the power of data to make better decisions on their projects the industry must rely upon the power of co-mingling multiple sources of data available within their organization and from outside their organization. Because the industry doesn’t yet have the luxury of the manufacturing sector, in that manufacturing will primarily draw data from within their specific manufacturing facilities, construction must rely upon many data sources and then co-mingle that data to derive the decision-making value the manufacturing sector is already beginning to harness quite effectively. 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.)
2. Design Data – All data captured within the design process in both paper and digital formats.
3. Pre-Construction Data - Data captured in between the design and construction phases including procurement process data and subcontractor/supplier data.
4. Construction Data – Data primarily captured utilizing project management technology and point solution technology (RFI data, defect data, safety data, etc.).
5. IoT Data – Tied to project management technology, this would be the data provided in real-time from the project site (or throughout the supply chain) utilizing IoT technology (sensor technology that monitors everything from worker movement to temperature to vibration to computer vision sources).
6. Accounting Data – Data related to the financials of the jobsite.
7. 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.
8. Weather Data – Weather plays a significant role within the construction sector and its addition to the co-mingled data set is necessary.
9. Insurance Data – The data of 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.
10. Other Project Data – Capturing data from other projects allows for the ability to benchmark the above data sets.
11. Data of Peers – Data alliances are forming within the construction sector all over the world. Through such alliances construction stakeholders can 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.

 

There are several other data sources the construction sector could utilize to formulate a highly effective decision-making platform. The above are but a few key data sets that your firm should consider harnessing and co-mingling to fuel better decisions. There are a few standout data sets from the above that you should pay particular attention to like: i) Insurance Data as it provides a functional inventory of what is going wrong on your projects, ii) Construction Data as it provides information from your staff at all levels of what is going wrong, iii) IoT data provides unbiased source of data around the jobsite environment, and iv) Data of Peers which represents an emerging trend within construction that allows you to compare your firm to your peers. 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.