As efficient building technologies and certi cations are nourishing, there is a growing disparity between the promises of these buildings and their performance in use. In fact, most buildings are using 1.5 to 5 times their predicted energy usage. While many believe this is primarily due to inaccurate modelling and higher utilisation intensity, a growing body of case studies shows that implementation issues are underlying a substantial portion of this disparity.
Unfortunately, there are no feedback loops within the delivery cycle to allow designers, contractors and project teams to learn from these mistakes.
While there a several techniques to provide feedback, these strategies have been met with limited success due to resource constraints. However, these approaches are overlooking an extensive resource on the client side of user complaints and maintenance tickets, which could provide a data driven, internal feedback cycle.
To capitalise on this data stream, I created a process to analyse and visualise existing maintenance data from a case study in my previous role. Each of the 3,000 helpdesk tickets in the 18 months post handover were classifed according to system, symptom and severity. The issues were then linked with known technical root causes. Through personal experience and project team surveys, each root cause was the attributed to the stages of the process that
produced the issue. A range of visualisations was then tailored for the primary stakeholder groups based on their technical pro ciency and primary drivers.
Extensive research on knowledge management shows that this data alone will not create an e ective feedback loop. To transform this data into useful knowledge, a management process was developed to support social learning within the standard project procedures. While the resources required to manage this process may still be a barrier, it could also present an opportunity for third parties to deliver "feedback as a service" or provide insights using data mining.
Even with these challenges, this extensive resource of client maintenance data can provide an innovative way to capitalise on the existing knowledge of maintenance teams and users to create a robust feedback loop for project teams and key decision makers.