The Future of Preconstruction is Capturing and Leveraging Data

The construction industry is rapidly changing. Driving this change is technology and the recognition that embracing change and innovative ideas is what is needed to grow.

In 2022, IBM found that 80% of valuable data had been created in only 1 year. CEO Today writes, “In order to keep up with this rapid increase in data volume, organizations must develop strategies to not only store data efficiently but use it more effectively and efficiently.” 

Yet, Autodesk and FMI Consulting 2021 found that only 12% of responding GCs report “they always incorporate project data into their decision-making strategies.” 

Capturing, and more importantly, leveraging estimate data is enabling preconstruction teams to make better, more informed project decisions quickly and efficiently.

Gareth McGlynn, host of The Preconstruction Podcast visited with Stewart Carroll, President of preconstruction software company, Beck Technology, to discuss how precon teams can capture and leverage data.

Listen below.

What do we mean by data in preconstruction? Data is any item within an estimate and attributes of that item. There is also descriptive information about the project. Historically, preconstruction hasn’t been good at capturing it. One of precon’s greatest weaknesses is that, as Stewart says, “as an industry, we have become very deliverable-centric” and it’s the deliverable that gets memorialized, not the data behind it. So, all the work that went behind creating the deliverable essentially gets lost.

The work that went into the deliverable has value, capturing it allows precon teams to be able to go back and look at what you did in the past and analyze it to current projects.

With the onslaught of new technologies that automate most estimating processes and therefore drastically reduce the number of man-made errors, now is the time for preconstruction to begin looking toward the next step: data analysis. How do we take the data we currently have, make sure it’s accessible, and leverage it to make our estimating efforts more robust than ever?

Because estimators are typically focused on results, there is no process in place to track all the steps and moves they’re making along the way to the end goal. This is critical for data analysis: if you don’t know how you got to the end result, how are you going to be able to improve your processes and use them to calculate better predictions for the next project?

 

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