Big data revolution in construction is here to stay

October 31, 2021 | 4 minute read
Karthik Venkatasubramanian
VP, Data Science & Analytics
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Last year, a large Tier-1 contractor invited us to speak about digital disruption and how leveraging data could dramatically improve how organizations operate. Participants in the meeting were a mix of excited believers and reserved skeptics.

The managing director asked, “How can we focus on this data thing when our hands are full trying to deliver projects on a daily basis?”

This question isn’t unusual. Our industry is facing tightening margins, cost and schedule blow-outs, stringent new regulations, and workforce productivity challenges.

We also heard frustrated claims of: “That’s how we’ve always done it here,” “[o]ur systems aren’t integrated,” and “[i]s anyone else really doing any of this?”

Amongst all the noise surrounding data, digitisation, and innovation, the “signal” can sometimes be hard to decipher.

Datafication is real

Yes, “datafication” is a word. Kenneth Neil Cukier and Viktor Mayer-Schoenberger coined the phrase in 2013. Datafication describes the process of taking aspects of the world that have never been quantified and rendering them into data.

Datafication of the E&C industry is a good case in point. A $500M engineering project running for 3-4 years creates more than 6.3M documents and 8.5M correspondence items with 3,000+ users from more than 13 countries.*

The adoption of digital tools and growing technologies is also rapidly producing data as a “byproduct.” In fact, over 40 zettabytes of data around the world will be created within the next 2 years.

Converting this data “byproduct” into an “asset” requires strategic intent, a data culture (including the right systems), and supportive senior management.

Designing a data strategy will help unlock the potential of data already stored in E&C and owner organizations.

Understanding the stories data is trying to tell

Project sites are increasingly becoming digital and connected. For example, LIDARS sensors are used for surveying, drones for aerial mapping, IoT sensors for assets and equipment, NFC and RFID to track materials and equipment, AR, VR, etc.

The list seems to grow every few months. Oracle Construction and Engineering recently launched an Innovation Lab in Illinois to help organizations determine how to leverage such technologies, both for improved project outcomes as well as insights than can help support continuous improvement.

Indeed, this array of emerging technologies generates massive amounts of data waiting to be integrated with everything else that generates data to understand correlations, patterns, and signals. This “network” isn’t just limited to connected devices; people are also connected.

In the $500M project referenced earlier, more than 55% of the data generated is cross-organizational, with people exchanging information for project delivery.** Project networks can be the topic of full-fledged social network analysis – much like LinkedIn and Facebook.

More companies are becoming interested in understanding the stories that data is trying to tell.

Practice makes perfect

Companies are beginning to recognize the value of leveraging data. An adaptive and iterative approach to data helps by: getting some early runs on the board, learning from mistakes, experimenting, getting buy-in, and demonstrating value.

Owners and contractors that have previously delivered projects and own data can ask these questions before embarking on a new project:

  • What do we already know about projects of this type?
  • How long do processes take?
  • What is our baseline for different processes?
  • Who are our best and worst performing partners?
  • How much delay should we expect in this schedule?
  • How do we factor for cost overruns based on past experience?
  • How do we bid based on what we already know about our processes, baseline, and efficiency?
  • How do we optimize project team selection based on the supply chain partners?
  • How do we compare to industry benchmarks in general?

Where’s the data hiding?

Most organizations who own data can answer many of these questions. Data that’s “hiding” requires extraction, analysis, translation, and interpretation. E&C professionals can launch their digital journey by uncovering the value of data on just one project.

The true power of data becomes game-changing when data can help analyze predictive questions such as:

  • Are there potential safety risks on site that could become issues in the future?
  • Is the schedule at risk of an overrun?
  • Will the change requests and variations cause a cost blow-out?
  • Are there signs of potential disputes and litigations?
  • Does the schedule need to be changed based on the weather forecast?
  • Will equipment and materials arrive on time based on the schedule?
  • How can workflows and work processes be optimized?

Machine learning, anomaly detection, sentiment analysis, and many other artificial intelligence (AI) algorithms – all of which need data for making predictions – can help answer these questions. More organizations are beginning to use parts of their historical data to build their AI capabilities.

The data revolution is here to stay

The Tier-1 contractor we met with has realized it can’t avoid working with data. Data is progressively shaping their strategy, including looking at their systems landscape and the data they’ve been creating to-date.

Large screen dashboards reflecting project performance are facilitating the conversation and increasing transparency. The contractor is setting short and medium-term goals to build on their data capabilities.

One senior executive quipped, “It’s a start, but we’re jumping on the data bandwagon.”

Read Construction Dive's Playbook, in association with Oracle's Aconex, to learn more about how to leverage big data across your project portfolio.

Discover how Oracle Construction and Engineering is helping power project success for asset-intensive industries.

[*] [**]Data analysis from projects delivered on Oracle Aconex


Karthik Venkatasubramanian

VP, Data Science & Analytics

Sr. Director of Data Strategy and Operations

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