{"id":2081,"date":"2017-07-05T09:35:25","date_gmt":"2017-07-05T13:35:25","guid":{"rendered":"https:\/\/www.sage.com\/en-us\/blog\/?p=2081"},"modified":"2026-02-16T05:27:29","modified_gmt":"2026-02-16T10:27:29","slug":"big-data-leverage-estimatings-data-explosion","status":"publish","type":"post","link":"https:\/\/www.sage.com\/en-us\/blog\/big-data-leverage-estimatings-data-explosion\/","title":{"rendered":"Big Data: How to leverage estimating\u2019s data explosion"},"content":{"rendered":"
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\n\t\t\t\t\t\tBig Data: How to leverage estimating\u2019s data explosion\t\t\t\t\t<\/h1>\n\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t

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\n\t\t\t\n\t\t\t\t\"\"\t\t\t\tNick Papadopoulos<\/span>\n\t\t\t<\/a>\n\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n

Construction estimating is on the verge of a data explosion. As virtual design and construction methods increase in use, preconstruction teams are dealing with tens to hundreds of estimate iterations depending on a project\u2019s size. Estimators need to find an effective way to organize, manage, and control that information. And they need to leverage it to make better decisions and forecasts.<\/p>\n\n\n\n

Even more traditional estimating teams are dealing with an excessive number of project changes that require quick feedback on the cost impact of each design modification. With local, network, and cloud data storage now readily available and cost effective, there\u2019s no longer any reason not to capture data that can help provide faster, more reliable cost feedback.<\/p>\n\n\n\n

Massive amounts of information are also coming from mobile devices, the \u201cinternet of things,\u201d and building information modeling (BIM) work processes. BIM, alone, will generate a data explosion as more information and details are added to models. Estimators will be overwhelmed with both data and iterations as the need for \u201creal-time\u201d estimating pressures the preconstruction team to respond to design changes.<\/p>\n\n\n\n

Sophisticated imagery\u2014whether it\u2019s from drones, robots, or rovers canvasing the job site\u2014is also now available to measure and feed production rates and progress completion numbers to those that need them. Capturing true production rates would be a tremendous resource to both the building and preconstruction teams.<\/p>\n\n\n\n

If you haven\u2019t already been hit with this data explosion, you will be soon. That will pose some real challenges if your construction company and your estimators are not equipped to deal with, and take advantage of, these new data sources.<\/p>\n\n\n\n

The importance of Big Data in estimating<\/strong><\/p>\n\n\n\n

Aggregation of estimate history coupled with external data is something that needs a Big Data strategy. While many other industries have welcomed Big Data for some time, the construction industry is just now waking up to how they can use it to help understand trends. Estimators can use Big Data\u2014especially estimate and actual cost history\u2014to create conceptual estimates, validate assumptions and third-party estimates, collaborate with designers, and sell work by supporting business development and marketing pursuits. Trying to remember all those unit prices and production rates, and what impacts them, is overwhelming. Big Data allows estimators to respond to cost-related questions with credible, defensible information in seconds or minutes, rather than days or weeks.<\/p>\n\n\n\n

Improving conceptual estimates <\/strong><\/p>\n\n\n\n

Long before design and modeling starts, a project\u2019s feasibility must be determined. At this important go or no-go stage, estimators often have only general project requirements, perhaps a high-level program, and a project description to work with. Sometimes they only get a three-sentence description. Or a sketch on a napkin. Yet potential customers want to know if they can build a school at $30,000 per student, a parking deck for $4,000 per stall, or a hospital for $500,000 per bed. And they expect estimators to know the answer on the spot, because estimators are, in fact, the \u201ccost experts.\u201d Many clients now also feel empowered to challenge estimators with data they mined online. This can result in an awkward silence if the estimator is not prepared.<\/p>\n\n\n\n

Access to historical project information gives estimators the confidence to answer conceptual estimating questions with qualified responses based on similar historical projects. It\u2019s the qualifications that will keep the estimator and his team out of hot water. It also helps convince project owners that your company is the best contractor for the job. Showing an owner that you have built similar projects and have proven metrics to support your conceptual estimate is a huge advantage. You are \u201cwalking the walk\u201d while others are just \u201ctalking the talk.\u201d<\/p>\n\n\n\n

And finally, a facts-based conceptual estimate or benchmarking study with well-documented assumptions, clarifications, and exclusions is also vital to establishing appropriate cost expectations with the owner. As we all know, owners never forget the first number.<\/p>\n\n\n\n

Informed design<\/strong><\/p>\n\n\n\n

Design review meetings are no longer limited to cost. A contractor\u2019s experience on past projects can inform the design team of constraints, efficiencies, and other considerations. What type of exterior glass should be used? How much lobby space is best for the plan? What is the ideal ratio of common area to lab space?<\/p>\n\n\n\n

Big Data is especially useful for supplying cost input into design. By analyzing historical project data, estimators can provide real-world insights to designers to keep costs within budget parameters and avoid expensive redesign costs.<\/p>\n\n\n\n

Taking Big Data a step further<\/strong><\/p>\n\n\n\n

In addition to historical project information, external data sources can also impact an estimator\u2019s decisions and should be considered. These sources include economic impacts affecting cost indexes, time escalations and forecasts, material types, skilled labor availability, and different locations.<\/p>\n\n\n\n

For example, if China is buying massive amounts of copper and a project has huge copper exposure, an estimator may consider potential cost escalations or contingencies that wouldn\u2019t be needed if the copper supply is stable.<\/p>\n\n\n\n

Getting started with Big Data<\/strong><\/p>\n\n\n\n

Big Data can be a challenge to get your arms around. But it\u2019s now possible to harness Big Data with today\u2019s cloud computing, scalable databases, and analytical tools. Look for technology that can centralize your estimate-related information and includes capabilities to easily gather, extract, sort, benchmark, compare, visualize, and present the data.<\/p>\n\n\n\n

Estimating as we know it is changing. Big Data is a major part of that change and can lead to more accurate, proactive estimating.<\/p>\n\n\n

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