Power of trust in analytics

Putting data to work in engineering and construction

 

Turning data into competitive advantage 

Engineering and construction (E&C) firms are under pressure to meet increasing client demands to deliver more complex projects in less time, at lower cost, and with tech-driven efficiency.

Data analytics is a key innovation that can help meet those demands. And certainly, there’s no shortage of valuable E&C project and industry data. However, many firms don’t have an advanced data analytics program capable of turning that data into insights, and it’s hard to know where to start. 

In our three-part series, we reveal an approach E&C leaders should consider to get a data analytics program off the ground, as well as case studies of industry-leading E&C companies that have enjoyed quick wins and demonstrated the value of data analytics for their organizations. 

See below for series highlights and links to information designed to support E&C firms as they explore data analytics for better decision making, with results leaders can trust.

Getting started with data analytics


Video transcript

Construction companies are sitting on a tremendous valuable asset, and that asset is their data. Their data in the form of project field labor, safety reports, contractor bids and schedules.  Engineering and construction executives can look the other industries and realize that expanding their data and analytic capabilities can help fight off new entrants and actually help drive their business in overall performance.Construction companies can get started - and it’s a simple three step approach. The first step is understanding data and data environment. A lot of organizations that we work with do not have a secure data structure such as a cloud environment with which they can set up an actual data analytic programs to run their analytics. Second, it’s really important to understand data capabilities within your organization. The actual capabilities of your team to be able to run these programs can sometimes be very sophisticated organization need to look outside in order to find the right resources. And third, it’s really important to set up a road map. Really trying to balance out quick wins, medium system enhancements, and long-term, what I would consider kind of transformational upgrades to the data and analytic programs.


Series highlights

  • 1. Understanding data
  • 2. Leveraging existing data
  • 3. Building analytics capabilities
  • 4. Global Construction Survey

1. Understanding data

Catch up to tech-driven industries

 

Few E&C firms consider themselves on the cutting edge of technology adoption. In fact, most do not use advanced data analytics, which can provide strategic insights for better project performance, resource efficiency, compliance and risk management.

While some digital innovations can be complex and more difficult to implement, standing up a foundational data analytics capability can be a quick, compelling way for E&C firms to not only improve their business, but to differentiate themselves from the competition.

First, E&C firms need to know what data they have. Tracking down and organizing large, scattered caches of data can be overwhelming. The key in getting started is to clearly define business goals and objectives for data analysis, and make a plan for how the organization will use the results—rather than have the available data drive the process. 

Want to know how to start? Read more.

The power of trust in analytics
Part 1 - Understanding your data

How E&C companies can stand up a data analytics program


Video transcript

There's no one size fits all for EMC organization to set up a data and analytics program. There are a few characteristics that we've seen be very successful. The first is really to involve the business and involve key stakeholders that are going to really drive this, so not just hired people from the outside, but involve everyone that's going to be critical to making this a success. The second is really important to understand your data and understand where the limitations of that data is. The last thing you want to do is set up a program where you're trying to gather all this unstructured data and you don't have the technology or capability to actually analyze it. The third is set up these targeted pilots. You can get quick wins. If you can build momentum right away, the overall program is going to have a much greater chance of overall success.

2. Leveraging existing data

Chart a clear path for developing advanced data analytics

 

While engineering and construction companies understand the valuable business intelligence they can gain through advanced data analytics, less than half of them have a plan in place to build what they need.

To stand up data analytics capabilities, E&C firms need a road map they can easily follow, with elements such as what data to use and where to find it, how to transform the data for analysis software, and which tools and methodologies to use.

Once they’ve defined their approach, E&C firms can begin to tap into program and project data already available in project and enterprise systems and databases. Quick wins will then help drive buy-in to expand data analytics across the organization.

Trying to turn ad hoc data analysis into a coordinated effort? Read more.

The power of trust in analytics
Part 2 - Leveraging existing data in the engineering and construction industry

Identifying quick wins with data analytics


Video transcript

When we look at quick wins, there's really no one size fits all. What we like to do is take a look at it from the standpoint of where is the value within the organization, so you're actually identifying quick wins that are going to provide the most value, whether that's quality, schedule, safety. The other is where is your data? If you don't have good data in area, the last thing you want to do is identify that for a quick win and it turns into a longer term project that really doesn't get off the ground. The key [00:00:30] there is really finding the right value between the actual data and where it's going to add benefit to the organization.


Case study: Global EPC resource assessment

Challenge

A major engineering, procurement and construction company experienced significant variances between how divisions and regions staffed major capital construction projects, impacting cost and schedule performance.

Solution

The company sought insights and opportunities to improve project performance and reduce costs by optimizing global staffing based on data from past projects.

Establish key goals and objectives for the analysis of staffing and resources models of recently completed projects, such as determining if sufficient resources are in the right roles to reduce risk, control costs, manage schedules and ensure quality.

Assess data to gain a clear picture of the relevant and available information. The company looked back at five years of projects in excess of $500 million, gathering key attributes such as peak and average staffing, and staffing by title and group.

Validate the data sources, population and key attributes, and then extract and normalize the data for inconsistencies.

Perform detailed analysis of staffing levels across the population of projects.

Results

Determined key reasons where peak staffing for some projects significantly exceeded peers

Established internal benchmarks and KPIs to be used in assessing staffing levels for future projects

Developed a predictive model for anticipating the number of project personnel required by job category

Created a system allowing for review of staffing levels on a recurring basis with minimal effort

3. Building analytics capabilities

Empower project personnel with data-driven insights

 

Establishing a predictive analytics program can be easier said than done. 

While a majority of engineering and construction executives say their organizations will be “data driven” in the next five years, fewer have actually launched such programs to date.

There’s no one-size-fits-all approach to putting an advanced analytics program into place. However, E&C firms have a variety of techniques and tools from which to choose, and in Part 3 of our series we outline four basic phases to follow.

The development of a predictive analytics program isn’t the end of the journey; models also need to be operationalized and leveraged effectively across the organization. E&C firms can help ensure success by tasking integrated teams, seeking buy-in from the field, starting off with large and easily accessible data sets, and launching targeted pilots.

Interested in developing industry-leading data analytics capabilities? Read more.

Power of trust in analytics
Building robust data analytics capabilities in the engineering and construction industry

Keys to success for implementing a data analytics


Video transcript

There are several keys to success for implementing a data and analytics program. The first is really to get buy in from the business and make sure that the key stakeholders are actively engaged. The second is to set up those targeted pilots that are going to be important to get that quick momentum. And the third is figuring out how do you operationalize this throughout the organization? How do you scale it, and what level of depth that you go? If you can find a way to operationalize this and you can [00:00:30] repeat this in a rapid succession, that's where you're going to provide value to the overall organization and really transform how data and analytics are actually driving the business.


Case Study: Prediction is possible. One company’s path to forecasting safety incidents

Challenge

A top-40 U.S. energy and natural resources contractor was challenged with voluminous and unintegrated data with significant quality issues.

Solution

In order to establish a predictive data analytics program that could anticipate safety issues and create efficiencies, among other goals, the company followed a phased approach.

1) Investigate the current state of the company’s data and analytics relative to leading practices and assess opportunities

2) Design and configure a cloud-based data analytics environment

2) Develop and deploy with quick-win opportunities for safety, financials, schedule, work acquisition and the contractor’s self-perform subsidiary.

4) Conduct operational handoff with a two-year strategy to improve data maturity and leverage insights for business performance.

Results

89% accurate model capable of predicting safety incidents three days in advance

$40 million in near-term margin opportunities

$500 million in overall project value opportunities

One new internal data analytics team

4. Global Construction Survey

Is your organization ready?

 

The Global Construction Survey 2019 benchmarks how prepared companies are for a highly competitive and unpredictable future.  The survey results segment the industry’s players into three percentiles:

  • Top 20 percent: Innovative leaders
  • Middle 60 percent: Followers
  • Bottom 20 percent: Behind the curve

The survey – which has been running annually since 2005 – features the perspectives of senior executives from 223 engineering & construction companies and project owners from a variety of industries. Our index is based upon their responses to questions covering governance and controls, innovation and technology, and people.

Learn more

Future-Ready Index: Leaders and followers in the engineering & construction industry

When structured right, a data analytics effort can begin to produce immediate value and allow E&C firms to make quicker, more informed decisions that keep employees safe, projects on track, and profit margins healthy.
– Clay Gilge, Principal and Major Projects Advisory practice lead for KPMG

Learn more

When it comes to data analytics, the engineering and construction industry has unique challenges and opportunities. We look forward to demonstrating how a robust data analytics capability can work for your company. 

 

Power of trust in analytics
Part 1 - Engineering & construction series: Understanding your data
Power of trust in analytics
Part 2 - Leveraging existing data in the engineering and construction industry
Power of trust in analytics
Part 3: Building robust data analytics capabilities in engineering and construction
 
 
 
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