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By Jeff Wing
March 29, 2017
Big Data. Big promises. There is lots of high-flying talk about the big data-driven future of construction. Is there anything to it?
Using any kind of business process software results in steady data-creation and storage. SaaS’s project management software is no different, but for one major (and pretty thrilling) distinction. Can you guess what it is? Hint: The Cloud. Because the SaaS array of Project Management tools is shared in a collaborative cloud environment, the “many users” nature of the cloud means, among other things, the daily creation and storage of a hugely phenomenal amount of data.
Today’s data point is tomorrow’s pixel in the problem-solving big picture. As mentioned earlier, every time an Uber passenger grabs a ride, a data point is being routinely generated. The same can be said of construction teams making day-to-day use of cloud-based PM tools. Reports, punch lists, submittals; over time, the day-to-day massing of this routinely generated data will build for your company an incredible vault of business intelligence. Remember, NextGen SaaS is preparing to make data the new Coin of the Realm.
The earlier example of Uber’s raw data being mined for urban planning intel in Part 3 is the tip of an immense iceberg. On the whole, the construction sector’s plans for using the data are driven by the goal of a more dramatically resource-efficient construction industry. Just incidentally, this will make for less expensive buildings.
As the construction sector lays plans to enter this much-anticipated realm of Big Data possibilities, though, it’s also worth noting that a construction company’s reported and recorded details of the project, or “data points”— drawings, daily logs, RFIs, punch lists, inspections, etc.—will be the exclusive, walled-off property of the originating company. On the other hand, it's a fact that Big Data’s analytical accuracy increases with the size of the dataset being mined. The more data, the more granular the analysis. When whole business sectors agree to pool their collective and anonymized datasets, the resulting intel will be more specifically actionable than ever before.
But even in the Big Data model, this sort of “pooling” of your company data with that of other companies’ data would be strictly voluntary; an Opt-In action. It’s only when a company chooses to opt-in to a collective Big Data pool with other participating entities, that the company’s data will mix and intermingle with the data of other companies contributing to the data pool. In this scenario, ALL contributed company data would be made non-human readable by the process itself. Concern about the pooling of corporate datasets is misplaced. Big Data turns all contributed data into illegible numerical coding that can only be “read” by the Big Data algorithm, and so is rendered useless to human eyes. SaaS clients who do allow their anonymous data to enter into the vast data pool would share in the Big Data returns that flowed from that group participation.
In one example of a group of companies opting in to a Big Data share, data collected from the participants, stripped of identifying code and converted to “non-human readable form,” could be pooled by region and project type; for instance, all residential builders in south Texas. Priceless strategic information will be drawn from the common data and made available to SaaS clients who opted-in by agreeing to add their data to the mix. But per the classic opt-in model, the default will be non-participation.
The competitive advantages to be had in the coming NextGen/Big Data future will be to do with the strategic accuracy and creativity of the tailored algorithms a company is able to send into the data matrix. The old “garbage in-garbage out” axiom that, from the beginning, has accurately described the limits of computation, will never have been truer than in the coming Big Data epoch. The strategic tools available through Big Data are deeply buried. Creating the specialized algorithm that can find and extract those tools from the massed data—this will be the name of the game in the coming era.
Millions upon millions of pooled data points, filtered through increasingly customized and sensitive Big Data “instructions,” will form a nearly limitless resource of game-changing business revelation. All that would be left to do is write strategically tailored algorithms, let them loose in your data, and when they return with the quarry take action based on their findings. The results can be far-seeing. Procore Technologies’ Bassem Hamdy elaborates.
“The Holy Grail of data analysis is what we call predictive analysis. That's the goal. You get lots of anonymized records, put them all together with some smart data scientists, and suddenly you know where to put your next building, and when. That is what we're actually trying to get to––predictive analytics. Predictive and prescriptive. So predictive––‘this is going to happen’. And prescriptive––‘you should do this.’ They go hand-in-hand.”
NextGen SaaS will not only provide from its vast data resources very particular and actionable predictions. NextGen SaaS, in partnership with Big Data, will actually be able to advise on a next move based on those predictions.
“If copper is predicted by the data to hit an all-time low, and I know I have a big project next year, let me buy the copper now and warehouse it,” Hamdy says. “That’s the cheaper way to go about it because my data-mined futures markets say copper is going to go up 100%. Or if I’m building a bridge, I can buy the materials early and make those preparations.
“But if the bridge is of this particular design, and is over a river this wide, in seasonal weather conditions such as this in Alabama, for example––is it prone to safety accidents? If you have that data through thousands of bridge projects, and you know how many workers have suffered falls in that kind of work in that region and in that seasonal weather window, you take predictive extra steps to prevent those statistically likely accidents from happening.”
This is where NextGen SaaS can outdo its Classic predecessor and play a role in those humane calculations that prevent loss of life or limb on construction projects. When the collected data is varied enough and the analysis of it is granular enough, strikingly clear pictures emerge from the fog of numbers and decisions can be made with confidence. Numbers don’t lie. They can’t, rather. The huge data sets associated with NextGen SaaS will offer up previously unseen patterns and trends with amazing clarity. What we’re talking about is a new class of Business Intelligence. NextGen SaaS stands to be a momentous shift in strategic business intel and how it is used, and even in how it allows you to pitch. Bassem Hamdy sets the scene.
“Just imagine the moment that somebody walks into that room to present to an owner. The guy says to the owner, ‘Look, there's going to be three accidents on the project, it's going to run four months late, and the cost of concrete's going up. All these other guys are lying to you. It's a two-year project.’ I mean, it’s like buying from a psychic.”
Next Generation SaaS
The Anatomy of a Request for Information (RFI)
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