Smart Data Model-centric Life Cycle Approach to Collaborative Execution of Capital Projects

RT-372 Topic Summary
RT 372


Current practice for capital projects emphasizes the use of diverse software tools that support parts of data-centric integration approaches. However, there is no framework upon which firms or project teams can assess or plan their needs for data management and implementation of smart tools to align their efforts with project goals. The industry lacks a comprehensive understanding of how data-centric approaches can be deployed by project teams and organizations across the project lifecycle. Furthermore, project teams need clear requirements for understanding the data needs of each unique project and then executing smart data-centric approaches.

This situation presents an opportunity for a research team to create coordinated industry guidelines for leveraging data-centric integration in the pursuit of collaborative delivery of capital projects. For this reason, the Research Team 372 focused on how organizations and project teams can overcome remaining barriers to adopting, adapting, and embracing a smart data-centric integration framework so data-sharing can support a more collaborative approach to developing, executing, turning over, operating, and maintaining capital projects.

To achieve the research goal, CII formed RT-372 with the following objectives:

  • Identify the barriers to data-centric collaboration.
  • Create data-centric maturity assessment matrices.
  • Develop a continuous data-centric maturity framework.

The research team started with a literature review to identify barriers to the data-centric approach and capture why this process had not happened naturally. The team analyzed the barriers identified during the literature review and ranked them, refining the aggregated list so it would align with the needs of data-centric approaches to capital projects. This final analysis leveraged a team of experts to structure the list of barriers, removing, modifying, merging, and sometimes adding barriers. The top barriers were interoperability between software tools, integration of multiple sources of data, cost to maintain an operational model of a facility, and organizational cultural resistance to change, and the most difficult barriers to overcome were interoperability between software tools, financial investment from small business partners or project stakeholders, organizational cultural resistance to change, and integration of multiple sources of data.

Once the team had identified the more pertinent data-centric barriers, it developed two Excel-based assessment tools to measure the maturity of data-centric approaches at the organizational and project levels. Finally, RT-372 developed a data-centric maturity framework. The objective was to identify the key steps to implement the data-centric approach at the organizational and project levels and support continuous improvement. This framework highlights how to use the tools and how to measure the data-centric actions both on the organizational and project levels. Furthermore, this framework connects project level data-centric maturity and organizational data-centric maturity to enable the improvements on the project level to help the advancement of the organizational level.

Key Findings and Implementation Tools

1 : Data-centric Maturity Assessment Matrices

RT-372 created two data-centric maturity assessment matrices: one for the organizational level and one for the project level. Team members validated these matrices internally, reviewing and testing to ensure that they could complete the matrices in their own projects and organizations. They further tested the matrices through a series of case studies that demonstrated an alignment between the growth in organizational maturity over time and growing project-level maturity scores in the matrices over corresponding time periods. Figures 3 and 4 offer sample applications of both matrices (FR-372, in press).

Click on an image below to download that spreadsheet tool.

Reference: (FR-372)

2 : The Data-centric Continuous Improvement Process

RT-372 developed a continuous improvement process (shown in Figure 5) to ensure that data-centric maturity continues to grow over time at the organizational and project levels. The framework provides a basis and process for using the matrices to perform organizational strategic planning to adopt data-centric approaches in company data requirements, collaborative processes, technical infrastructure needs, personnel and training needs, as well as identifying use cases for the data. The process for implementing the data-centric approach at the organizational level aligns with its adoption in the organization’s capital projects. In this way, the projects’ adoption of data-centric advances the development and handover of data that advance the organization’s implementation (FR-372, in press).

Reference: (FR-372)

Key Performance Indicators

Presentations from CII Events

Session - Driving Value: Data and the Power of Disciplined Collaboration

Publication Date: 08/2021 Presenter: Number of Slides: 30 Event Code: JC21