Applying a Data-centric Approach to the Collaborative Execution of Capital Projects

Data is the lifeblood of projects and organizations. Having a data-centric approach to delivering and managing facilities enables collaboration, informs decision-making, and supports advanced computational approaches such as automation and artificial intelligence. However, compared to other industries, the construction industry is one of the least digitalized industries and this shortcoming has a significant impact on its life cycle management of facilities.

To overcome this data deficit, construction organizations and project teams should expand their data-centric approaches throughout the planning, design, construction, and operations phases of a facility. According to Dell’s Digital Transformation Index, 80 percent of organizations fast-tracked at least some digital transformation programs during 2020. These higher levels of digitalization can have significant impacts on the overall success of organizations. According to a survey by McKinsey Global Institute, data-driven organizations are more likely to be profitable and have more success at acquiring and retaining customers. Another study by Franz and Messner found a significant positive relationship between digitalization practices such as BIM use adoption and the speed of delivery, perceived facility quality, and group cohesion within the project team. An essential step to the digitalization transformation of an organization is understanding and continuously improving the organization’s data-centric approaches.

CII chartered Research Team 372 (RT-372), Smart Data-centric Life Cycle Approach to Collaborative Execution of Capital Projects, to develop resources that could inform project teams and organizations about their past, current, and future levels of data-centric integration. By applying the rigorous, metric-driven approach described in this report, project teams and organizations can plan and adopt data-centric workflows and apply practices to realize the benefits of a data-centric approach. RT-372 focused on how organizations and projects teams can overcome current barriers and enable collaborative and informed approaches to embracing a data-centric integration approach to developing, executing, turning over, operating, and maintaining capital projects.

To meet its research goal, the team set out to achieve the following objectives:

• Identify the barriers to data-centric collaboration.
• Create two data-centric maturity assessment matrices – one at a project level and the other at an organizational level – along with implementation guidelines to support continuous improvement.
• Validate these data-centric maturity tools by applying them to case studies.

The team’s initial research efforts identified 22 important barriers that can limit implementing more data-driven practices. In the team’s industry survey, practicing experts singled out the following barriers as most important:

1. The challenges of integrating multiple data sources
2. The cost to maintain an operational model of a facility
3. The organizational culture’s resistance to change

In addition, the experts named the most challenging barriers to overcome:

1. The financial investments needed from small business partners or project stakeholders
2. The organizational culture’s resistance to change
3. The challenges of integrating multiple data sources

To help teams overcome these barriers at the organizational and project levels, RT-372 developed and tested an assessment system for measuring the maturity of the data-centric approaches of an organization or a project. The team started by reviewing existing quantitative metric systems for related technology-specific maturity tools in the industry, which required it to evaluate more than 20 such instruments. Through a content analysis of these instruments along with lessons learned to overcome important barriers, the team developed two maturity matrices that could measure the data-centric on a project or within an organization (provided as two Excel spreadsheets and included in this report as Appendices C and D).