Part 1: Taking a data driven approach to selecting the best technologies for buildings

Welcome to the first in this series of short posts where we help you navigate the myriad of new and existing digital technologies that can help buildings improve energy efficiency and drive sustainability. Importantly we will also discuss how you can make more informed, data driven decisions when selecting these technologies. As always we appreciate your questions and feedback. We hope you enjoy and find the series helpful.

Often when selecting a new digital solution for a building, we will focus on the features, benefits, pricing, client references and many other factors related to the product. These are of course important considerations in the selection process however we often tend to neglect two of the most important aspects when evaluating digital solutions for a building. That is the data ecosystem and data lifecycle for the building itself.

It would seem logical that we should take a more data driven approach when selecting a digital solution right? It is after all, all about the data. i.e. the ability to be able to gather live and historical usage data for a sustainability reporting solution, the ability to be able to monitor occupancy data to control the HVAC systems and optimise space utilisation, the ability to monitor valve position data, temperature data and flow sensor data to detect faulty equipment, etc.

The term data ecosystem refers to these equipment and systems that a building uses to collect, store, analyse and leverage this data. Typically, no two buildings leverage the same data in the same way. As such, each building could be considered to have a unique data ecosystem.

While the data ecosystem covers the equipment and systems, the data life cycle describes the data journey from when the data is first generated to when it delivers actionable value for the business. i.e. energy usage data might be collected by a smart submeter, that data may then be analysed by an energy management system to identify patterns, finally the energy management system might deliver actionable insights in the form of a demand response strategy for the building.

Evaluating the building data ecosystem and building data life cycle is a critical first step in the selection of any digital solution. The exercise enables us to clearly identify the existing digital capabilities of a building and importantly identify the gaps to achieve a particular business outcome. Any proposed new solution can then be evaluated based on its ability to address those gaps and improve that business outcome. i.e. if the desired business outcome is to improve energy efficiency then any proposed solution show be evaluated based on its ability to integrate with the existing building equipment and systems and to improve the buildings ability to collect, analyse and deliver actionable insights that improve energy efficiency.

The good news is that today much of this building evaluation and solution selection process can be automated. In the coming series of posts we will look at how we can quickly evaluate a buildings data ecosystem either by answering a number of questions or by ingesting BIM (Building Information Models). We will also examine how artificial intelligence can be used to map the data lifecycle for a building to identify the best combination of digital solutions to help deliver improved business outcomes.