Your business is probably in the process of finishing a project they have spent years on. The building information models and drawings have served their production purpose and the new building will soon be opening its doors. The IT depatment has been given the greenlight to backup the project files to an archive disk. And then it’s off to the next project… then the next… then the next. As projects are completed, your company will continue to amass an ever growing archive of past project files destined to collect digital dust.
Can new value be extracted from production files to improve business processes? What if you had a scalable, searchable database for leveraging data from your projects?
We believe there is an immense amount of business insight that can be gained from these digital assets beyond the typical production cycle. An architect’s models contain a history of design decisions related to space and content. Models from the building trades contain information such as costs, quantities, and schedules. With a data-driven workflow, these attributes can serve as ways to benchmark a portfolio, evaluate quality, and improve upon historical trends.
Our database is designed to represent building information as a scalable structure for connecting data across multiple projects, documents, elements, and parameters.
As a digital design agency, we are working on data-driven workflows and services for helping businesses leverage their building information assets. We have recently created a database framework called Minecart which harvests data from building information sources such as Revit and IFC formats. Our database is designed to represent building information as a scalable structure for connecting data across multiple projects, documents, elements, and parameters.
Here is how we are using this concept in our work today…
Project Quality and Health Analysis
Evaluating the quality and fitness of project models can be a time consuming and tedious task for project BIM managers. The process usually involves a lot of manual inspection of model assets such as families, views, naming conventions and graphic standards. This can be time consuming and is a task that many teams will often neglect as they work towards their next deadline.
This process can accelerate a team’s ability to stay on top of model production issues and reduce the overhead associated with maintaining a project model.
Our approach involves exporting the contents of each project model to the Minecart database for auditing purposes. We then employ templated queries and visuals which report on important quality-related metrics. Some of these queries include the ability to assess file size, unused families, views not on sheets, model groups, and model warnings. Ultimately this process can accelerate a team’s ability to stay on top of model production issues and reduce the overhead associated with maintaining a project model.
Standards and Content Evaluation
The development of model content and standards can be a costly overhead item for many businesses. Often we have seen this activity occur in a vacuum wherein decisions occur outside of real-world project constraints. As many business leaders will lament, this results in standards and content libraries that don’t achieve the level of adoption desired by an organization.
The aim becomes adapting an organization’s standards to meet real-world requirements based on observable data.
A data-driven approach to standards and content involves the analysis of real-world production to evaluate what leads to the best outcomes for a team. For example, using our Minecart database we can quickly assess the most common types of family content used in buildings organized by market and client. Furthermore, we can compare the content to what is available in the firm’s standard library. This type of analysis has shown that the types of content provided to teams is not always the same content that a team needs to produce the work. The aim then becomes adapting an organization’s standards to meet real-world requirements based on observable data.
Portfolio Benchmarking and Comparison
The database can serve as a resource for understanding trends within a body of work and to inform future projects.
By transforming a file-based approach into a data-based approach for BIM, we can establish centralized repositories of project information. This has profound effects on how an organization can start to use their building information as a component of their portfolio. For example, using a query of Minecart can return the space area breakdowns of commercial projects completed in a specified timeframe. The database can also query the quantities related to material finishes or what interior furniture was specified in those same projects.
In summary, the database can serve as a resource for understanding trends within a body of work and to inform future projects. When combined with other data sources, such as enterprise resource planning systems, project data help to draw correlations between design, budgets, and human resources.
What do you think is hiding in the mounds of unused data in the building industry?