Data is widely recognised as being crucial to practically every business in today’s technology-driven society. Within the construction industry this is particularly true; with even a small miscalculation having the potential to result in significant delays and increased costs of millions of pounds where key milestones are missed.
Data analytics is a method for identifying patterns, trends, and linkages in existing data. Its goal is to solve a problem by using construction data to gain a better knowledge of the situation and anticipate future behaviour based on previous actions.
Data analytics in the construction industry is now being utilised to tackle a wide range of issues, some of which can have a staggering number of variables; not all of which are necessarily immediately apparent.
To produce “informed” predictions, data analytics analyses these variables to identify the relevant factors within a data collection.
Every day, construction project teams deal with a slew of moving pieces on the job site; subcontractors, materials, equipment as well as additional factors such as inevitable changes to scope and contract adjustments. The more complicated construction projects get, especially in an era of greater remote workers, the more tools you’ll need to consider for greater communication and data gathering to make informed decisions.
All of this generates a mass of data, which needs to be filtered and analysed to be of use to the project decision-makers. This can take significant time and resources to achieve manually; enter machine learning and artificial intelligence. These are data analytics approaches that greatly increase the speed and accuracy of this analysis and filtering; organising data and identifying patterns with much greater efficiency than a human could achieve.
Data construction analytics enable preconstruction teams to build budgets that account for all potential project aspects, such as regional labour and material prices, among other things. Solutions using data analytics, machine learning, and artificial intelligence will likely bring about major changes to how engineering and construction firms bid on, and execute, projects; for example, by combining tender information, current business status and past performance data analytics could support go/no go decision making.
The same methods could also be used to support the assessment of bids and predict issues a project may face up to years before starting on site.
In summary, data analytics aids businesses in completing projects more effectively by identifying trends in data and then supporting project decision-makers in identifying possible issues before they happen and taking mitigation to limit their impact.