在任何主要的建筑项目中介绍主计划，您可能会发现大量大数据示例。它们可能包括与七个不同时间表相关的建筑信息模型数据的Terabytes，每个数据都在整个项目的测序中都链接了自己的逻辑。

If the logic that ties some 23,000 activities—about the average in a two-year project—is a bust, the time it takes the project manager to track down the problems manually while the schedule is moving forward can push a project seriously off its calendar. When that happens, everyone's margins are at risk with each cascading delay and threat of litigation.

这就是为什么在当今的流行语《信息技术术语》中的流行语宾果游戏中，大数据已成为一个热门数字。

Gartner Research研究副总裁Mark Beyer在最近的一份报告中，summed up the trend。他写道：“大数据的规模如此之大，以至于超出了传统数据管理技术的能力。”“它需要仅仅管理新技术才能单独管理新技术。”

Beyer补充说，这并不是大数据的大小。他补充说：“复杂的统计模型可以使300吉数文件'看起来比110架数据库更大，即使这两者都在多核，分布式并行处理平台上运行。”“这就是为什么大数据迅速成为企业的重大挑战的原因。”

在一个工程和建筑行业中，3D BIM技术的覆盖范围在添加时间安排以制造4D BIM时会增长更加复杂，每天大数据随着俗气的轰动。

Yet few firms are investing in data-taming tools. According to Gartner, firms in the construction and materials industries with annual revenue of about $250 million invest about 1.6% of that in IT. For firms with annual revenue of about $10 billion, the average is 1.1%—dead last compared to industries such as banking, health care, retail and transportation (seerelated story here). And that's for all IT operations. Within those percentages, firms are making even smaller investments in software tools to tame big data.

Breaking New Ground

But for every firm whose tech strategy is still to work off spreadsheets and re-enter data into different silos, other engineering and construction firms are breaking new ground.MWH Global这是一家“湿”基础架构公司，该公司正在为客户构建独特的应用程序，这要归功于该公司从其数据系统中获取知识的能力。Turner Construction等其他公司正在与Virginia Tech的研究人员加入，以寻找使用Jobsite视频和图像来查看生产力数据的新方法，即使用结构化和非结构化数据的混搭。还有一些人正在寻求对复杂计划项目数据的更好看法。

“毫无疑问，时间表是驯服的熊，”建筑管理公司首席信息官兼高级副总裁Shawn Pressley说。山国际。

“这是当你钻到原理图edule's work breakdown structure or enterprise breakdown structure—that's where problems can get nasty," Pressley adds. When project managers start to associate costs to a three-day schedule of a crane, the five-day schedule of its crew, the seven-day schedule of the materials and the cost of the crew's labor—then factoring that into the schedule—the sheer size of the data can get out of control.

A prime example is the delivery of steel to a site, Pressley notes. "The schedule says when the truck carrying the steel needs to be there and [a beam] needs to be bolted to column 8B, for example," he says. "You can overcomplicate a schedule so much that logic ties don't work. That's when you get logic [busts] that say faucets on the 23rd floor will delay the concrete on the fifth."

Faulty logic woven throughout a firm's files is a common problem among the multinational crews that make up today's massive project schedules.

他补充说：“我们在中东的一个项目中有一个男人给我发送时间表来分析。这显示了三个星期内通常在三个星期内做的事情的周转时间。”

It was examples such as this that sent Hill in search of a new breed of business intelligence and analysis tools. The company found a solution withAcumen, an Austin, Texas, startup whose services include risk analysis of project schedules as well as benchmarking tools to measure schedule quality against industry data.