One of the crucial aspects of BIM is the data rich environment connecting project information from different sub-sectors. (Mesároš et al., 2020). Therefore, developing models with consistent and trustworthy building data has gained significant importance in the industry. In contrast, incorrect or incomplete building data in a model could result in chained mistakes across disciplines, rework, or inadequate models for other stages of the building lifecycle.
Most of the improvements in BIM data in organisations take place in data quality reviews by BIM specialists. The lack of integration and complexity of existing data checking tools raised the expertise leading to assessment tools used mostly by BIM specialists. After a specialist reviews a project, corrections are communicated to the designers to solve the data issues in their models. This process is repeated until the desired quality is reached by the design team. Furthermore, missing, or wrong basic data structure can often lead to incomplete or inaccurate data checking processes.
The higher goal of this research is to produce perceivable benefits in the organisational data checking process. This is approached by facilitating the implementation of BIM standards and increasing the compliance of objects during design periods before entering the organisational review. Previous research showed that professionals would prefer to use simple dedicated quality checkers that can minimise manual tasks precisely and reliably instead of advanced software solutions. Thus, the goal is not to replace current workflows and practices, but instead to enhance basic data structures in models before entering the data reviews, by developing and implementing a new design-integrated checking and reporting tool.
The new checking process was verified and validated with specialists and modelers in three ongoing projects. This research showed that the developed design-integrated tool can produce the perceivable benefits in the organisational data checking process explained below:
•Enhancements in data quality before and after regular organisational checking reviews.
•Decrease in the duration and iterations in the organisational reviews.
•Increase of effectiveness and efficiency in detection and correction of data quality issues.
•Decrease of personnel frustration in the organisational process.
Thus, the research fulfilled the main objective to produce perceivable benefits in the organisational data checking process by developing and implementing a dedicated solution that engages designers in the process. The role-specific approach was essential to achieve a solution that meets the specific needs and system requirements of the target group, the designers. The purpose was to add a new prechecking layer to support and enhance existing data quality practices and processes. The result was a steering instrument for modelers working on the detailed design phase to involve them in identifying and correcting data quality issues.
Although the perceived benefits may vary in different contexts and organisations, the new data checking and reporting solution would raise awareness and promote designers’ engagement in the organisational data checking process, who are in a dominant position to identify and correct data quality issues.