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.