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A new Dodge-Viewpoint report gauges how well contractors gather and use jobsite data

AEC Tech

A new Dodge-Viewpoint report gauges how well contractors gather and use jobsite data

Information is power, but are contractors collecting what they need to make better decisions?


By John Caulfield, Senior Editor | July 12, 2019

A new report on how contractors collect and use jobsite data is based on responses from 187 companies. Image: Dodge Data & Analytics

More than half of contractors say their methods of collecting and analyzing data from their jobsites need to get better over the next three years, in part for their companies to use these data to predict employee and project performance.

That’s a key finding in a new 40-page SmartMarket report that Viewpoint and Dodge Data & Analytics have released, entitled “Improving Performance with Project Data.” 

The report is based on an online survey conducted from August 14, 2017 through September 12, 2018. The survey used Dodge’s Contractor Panel and contacts provided by Viewpoint. Of its 187 respondents, 98 were GCs, 47 specialty trade contractors, 28 construction managers, nine design-build firms, and four design-contracting firms. Thirty seven percent of respondent companies generate revenues between $50 million and $250 million.

The research focused on five key types of data: project progress, man hours, productivity, safety, and equipment management.

Respondents were asked how they gather, store, manage, secure, analyze and report on each of these types of data. Key trends include a rapid shift away from paper-based forms, reports, and spreadsheets to digital tools and platforms, some that leverage cloud technology; increasing use of apps on mobile phones and digital cameras in the field; the desire for more accurate field data that enable trend analysis across projects; and a focus on data security.

Mobile devices are providing contractors with better ways to gather, input, and store information. Drones and sensors are adding to these data cocktails. Image: Dodge Data & Analytics

 

Data can improve a project’s productivity

More than three fifths of the contractors surveyed say they’ve seen improvements in their data gathering and analysis capabilities over the past three years. That’s especially true among larger companies. By far, the most important project data being gathered are performance information such as scheduling and costs, followed by payroll and man hour data.

The top three benefits from data analysis, say contractors polled, are improving their ability to complete projects at or under budget (53%), greater productivity (47%) and greater profitability (46%).

However, more than half of the respondents (54%) admitted that they need to improve how they gather jobsite info over the next three years. And 45% think their ability to analyze trends across projects must to get sharper, too, as well as their ability to gather current data from the field.

Relying on physical records, like paper forms or spreadsheets, for data collection is steadily giving way among contractors to custom-designed or commercial software, a trend that is likely to be more prevalent over the next three years. Nearly three-quarters of respondents say they were satisfied with using software as a way to collect data.

Most pros (65% of the ones polled) still store their digital information in on-premise servers. But 37% have turned to third-party cloud providers, such as Amazon, to host their field information. The benefits of using the cloud are manifold, the most prominent being the ability to access data from the field while working in the office or remote locations, and vice versa.

Well over half of the contractors surveyed deploy mobile phone apps and cameras to collect and send data from the field. Expect that wave of data to rise, as drones, sensors, and wearables gain more traction as jobsite tools. However, contractors are also acutely aware of how their use of devices presents security risks. That explains why 86% of contractors say they are using anti-malware software, 78% are using enterprise-grade firewalls, and 56% have imposed policies for managing the use of mobile devices.

Contractors acknowledge that they need to be doing a much better job at collecting information from the field, and analyzing that information for purposes of predictive decision making. Image: Dodge Data & Analytics

 

Proffering a plan to gather data

More than one-fifth of contractors surveyed have had experience with predictive analysis of data and business intelligence. That compares with just 7% for artificial intelligence and 6% for machine learning. The largest group of respondents noted they are aware of and understand the concepts of each of these emerging technologies but have not implemented them in their own organizations — 39% for predictive analysis; 47% for artificial intelligence; and 33% for machine learning.

But it is clear that technology is advancing faster than the AEC industry’s ability to assimilate it. The report’s authors offer a four-step process to structure a company’s data collection, analysis, and reporting.

•Determine what aspects of project delivery would benefit most from better information to guide your data strategy

•Identify which types of field data will best-enable the proper analysis to generate critical decision-support information. And think about the minimum level of completeness, accuracy, and timeliness required for each type.

•Develop a focused technology and plan for collection and analysis that incorporates the specific data needed and types of analysis required, as well as financial and human capital investments, the time frames for implementation, clear roles of responsibility, and measurable goals for success.

The report includes two case studies—how Leander Construction is using the cloud to enhance productivity; and how W. Soule & Co. is using data to build better—as well as a Q&A with Jit Kee Chin, Suffolk Construction’s high-profile Chief Data Officer. Chin laments that what’s keeping AI from becoming a more efficient construction tool is still the lack of useful data. Her viewpoint may explain why she sees “great potential” in automated monitoring.

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