Capital Projects Struggle to Implement Digitalization

Greg Ray
Luke Wallace

Companies in capital intensive industries are embracing digitalization and leveraging exciting new technologies to increase personnel and environmental safety, promote production efficiencies, attract fresh talent, and more. The proven benefits of more powerful design engineering tools, less expensive and multifunctional unmanned systems, and advanced plant troubleshooting capabilities are difficult to ignore. Artificial intelligence and machine learning technologies are advancing on a daily basis, and promise to usher in revolutionary change. Lost and fighting to realize the benefits of digitalization are owner organizations and teams responsible for ensuring the competitiveness of capital investments.

We recently reached out to Independent Project Analysis (IPA) clients to understand why digitalization tools are so burdensome for projects organizations to implement. For certain, there is no lack of interest and support for digitalization. Representatives from all project organizations we surveyed and interviewed responded that they were implementing some form of digitalization (or some digital technology to improve business*). Most indicated that they are frustrated with their individual company’s current data capabilities (Figure 1). Indeed, these project professionals generally expressed enthusiasm and excitement about the potential of transferring, exchanging, and storing their company’s capital project data using digital technologies. And why shouldn’t they?

A donut chart showing that 80 percent of IPA clients surveyed answered "yes" to the following question: "Are you frustrated with your company's current data capabilities?"idual company’s current data capabilities of their individual companies.

Figure 1: Asked if they were frustrated with the capital project data capabilities available to them, 80 percent of company representatives responded in the affirmative.


A variety of data-driven new technologies have demonstrated value creation in other phases of a project’s lifecycle. For instance, augmented reality (AR) allows for remote safety and Constructability Reviews, all from the comfort of the office. Aerial and other unmanned robotic systems can perform everything from equipment factory acceptance testing (FAT) to safety auditing and sensoring and surveillance for progress measurement. But the same kinds of value-added data integration successes are uncommon in early phases of capital project planning and development. The sheer volume of information generated on projects and the lack of data-focused information management systems have been major roadblocks. Many project professionals we spoke with said their owner organizations are trying to sort out the types of digitalization tools that fit or should fit into their delivery system.

Most project groups are only now developing a process through which data transfer between internal and external systems can be at least semi-automated. IPA clients explained to us that legacy systems and processes are mostly manual, i.e., the project team records the data by hand (typically using a spreadsheet). By and large, these manual methods of collecting and analyzing project data to produce meaningful metrics and actionable insights have been unsuccessful. As a consequence, many owners had simply (or practically) given up on data integration. Owner companies, though, should not expect their project groups to simply give up on digitalization. Indeed, the volume of data that can be collected is what project teams aim to exploit and gain from through digitalization. Companies that fail to sponsor well-conceived data integration initiatives risk deteriorating capital project competitiveness within the next few years.

Getting Started on a Business Case for Digitalization

Some might argue the projects industry has been slow to adapt and adopt the data-driven mindset we see in other industries. However, that argument assumes transition of data into actionable knowledge is comparable among all industries. As a broker of project data, IPA can attest to the difficulty of capturing information on projects. It takes a lot of people who know what data are important, who know how to organize it, and who know how to extract insights from it. Adding the right people with the skills to do this can be expensive.

Naturally, for a project organization unaccustomed to leveraging project data to feed decision making, a change to get digital is a hard pill to swallow. But the onus is on the project organization to explain to business what the return on investment is going to look like. That is a fair business expectation. Because the answer to this question has been difficult for many organizations to quantify, we asked ourselves what would a value proposition look like for this kind of transformation?

The first step in the value proposition should be to determine what data best support this decision. From IPA’s perspective, those data typically include detailed cost information, detailed schedule information, engineering data, and project team information (as well as basic project information such as location). With this information, we can better understand what a competitive project should look like—what the design should look like, how long it should take, how much it should cost, and who we need to run it. Data needs will vary by company, but these data are empirically associated with the biggest gains in internal rates of return.

These suggestions are not particularly clever, and most project teams already endeavor to get this information. The issue is that these data are hard to get. So, what prevents them from getting the data?

Enabling Digitalization Efforts

Most organizations do not have the people, infrastructure, or work processes to get necessary project data. Let’s take a closer look at each of these needs.

Data Professionals: When it comes to people, half of the organizations we talked with had no data people at all. Some companies are looking for data people, but others are being asked to go digital without a budget for expanding the team. This approach will not work. Data analytics requires a unique set of skills to do right. Much of the benefit of digitalization is being able to move numbers around efficiently (i.e., automatically). This requires people who know data structures, who know how to integrate between systems, who know how to perform the analysis, and who know how to program all of this. Like engineering disciplines, these are unique skills that cannot simply be picked up by project professionals on the job or in their free time.

Data Flow Processes: In addition to people, we need tools and processes to ensure we get the data we want and that it flows efficiently. On the process side, many companies we interviewed have a process, but it requires the project team to gather and record data manually at the end of the project. Most companies we talked with explained the process has had limited success. At closeout, a lot of information is missing and disorganized, and the project team is looking to move on and, thus, disinterested in data entry.

Data Infrastructure: In contrast to the majority of companies we interviewed, there were a few companies using automated systems. Information transfer between accounting, scheduling, and cost management software was completely automated. More importantly, contracting systems, e.g., the code of accounts, were mapped to the owner systems. Though only a handful of companies had progressed this far, the gains were significant. For example, one representative said of the system, “Progress reporting was always a month late and involved a team of people re-entering data into our system; now it is instantaneous.”

There Is No Silver Bullet Approach

The conclusion of our preliminary investigation into digitalization was that each organization needs to approach this transformation on its own because there is no simple, one-size-fits-all solution for Industry. The starting point for individual companies is to establish clear objectives for what they hope to accomplish with project data. Once these objectives have been solidified, companies should perform a detailed examination of the processes being deployed to collect, clean, and store the data, and what can be done with this data, before embarking onto any renovation, expansion, or even greenfield program.

At the end of the day, more data should mean better decision making. The project development work process so many organizations follow is about generating enough information to decide whether or not a project is worth doing. Companies that manage to integrate digitalization into their systems should experience comparative advantage in the delivery of their capital projects, but that all depends on how efficiently they can get their hands on the right data.

So What Can IPA Do to Help?

IPA has developed Best Practices and standardized methodologies for the collection, cleaning, storage, access, and use of extensive databases. This information is used on a daily basis to provide actionable insights to improve project outcomes. Our experience and learning can be used to help your project organization maximize the value of its data. Complete the form below to discuss how we can work together to:

  • Consult with senior stakeholders to determine what the business objectives for owner data are—thus determining what data are necessary to collect and store
  • Perform a detailed investigation into existing database(s) and provide recommendations for using current data
  • Evaluate current data collection system and provide recommendations for improvement based on industry Best Practices
  • Develop tailored databases to capture detailed project information
  • Develop customized tools for all aspects of data integration and visualization


*The definition of digitalization varies but is generally ascribed to the process of using digital technology to transform the way we do business.


Greg Ray is a Senior Project Analyst and Luke Wallace is a Senior Research Analyst. Both work in IPA’s North America office in Ashburn, Virginia. 

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