Applying a Data-centric Approach to the Collaborative Execution of Capital Projects
RT-372 conducted a thorough literature review to identify barriers to the data-centric approach. This resulted in the identification of 35 barriers. Next, the team modified these barriers to better suit data-centric integration, categorizing them into the five categories show in the table (FR-372, in press).
Following its identification of the barriers to a data-centric approach, the team developed and distributed an online survey to determine the importance of each barrier and how difficult it was to overcome. Figure 1 shows the importance of each barrier on a scale from 1 to 4. Figure 2 shows the difficulty to overcome each barrier, also on a scale from 1 to 4.
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Most barriers had a mean score above 2.5, indicating that the respondents found them to be important. The top four most important barriers were interoperability between software, cost to maintain an operational model of a facility, integration of multiple sources of data, and organizational cultural resistance to change. Out of these barriers, interoperability between software and integration of multiple sources of data were directly related to the data-centric approach, further highlighting the importance of a need to develop a data-centric approach in the industry.
It is important to note that the four most difficult barriers to overcome were interoperability between software, financial investment from small business partners or project stakeholders, organizational culture resistance to change, and integration of multiple sources of data. Surprisingly, organizational cultural resistance to change, interoperability between software, and integration of multiple sources of data had already been identified as important barriers. Financial investment from small business partners or project stakeholders was related to the identified importance of each barrier and the cost to maintain an operational model of a facility. Therefore, the most important barriers closely aligned with those that are difficult to overcome (FR-372, in press).
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. The following tables show the opening pages of both matrices (FR-372, pp. 16-19).
Click on an image below to download that spreadsheet tool.
RT-372 developed a continuous improvement process (shown in the figure below) 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, pp. 20).
