FMJ Facilities Management magazine providing industry-specific intelligence to FM and property professionals
UNLOCKING THE POWER OF DATA
With this bewildering array of systems and consumers, how do you know if the building services are operating appropriately? Surely, BMS is telling us this? If it’s anything like the majority of building management systems, once you find it, often in the basement, you have to go through an array of pages. You may have various BMS screens to get through, each page telling you what is happening to a specific piece of the plant right now, but not about last night or further back in time.
To see that you have to click back further, assuming logs have been set up, and then use your experience and expertise to judge whether the operation was optimal or not. With many BMS, a lot of the information is logged behind a huge menu. This means you need to know where you’re going before you start because there is nothing [on the system] guiding you to issues.
So, how can you prioritise your time? How can you make better sense of your data? That’s what we set out to do. Each building has an array of plant hidden from view. This includes chillers, terminal units, boilers, pumps and air handlers – all massive energy consumers – and all controlled by a complex network of embedded devices.
The first challenge was to access the data, the tens, if not hundreds of thousands of points of reference generally unstored and unanalysed. To do this we developed a small device that we put in the building called a data acquisition device or DAD. Once set up the device polls all the data within the network which are securely uploaded to the cloud where storage is unlimited. Our ISO27001 system then models the data automatically, enabling our team of technical account managers and building analysts to analyse it and provide insight on a web based interactive, collaborative platform.
By unravelling this bewildering array of data streams and providing data-led insight, this system enables a reduction in operational and energy waste reducing carbon emissions whilst increasing efficiency in maintenance activities.
It allows FM teams to focus on internal environmental quality to improve occupant wellbeing and productivity and facilitates proactive maintenance, which reduces unanticipated breakdowns and operational expenditure. This approach is redefining the KPIs of property performance, saving our customers up to 30 per cent in energy costs, reducing temperature complaints by over 50 per cent and improving maintenance efficiency and first-time fixes.
HOW DO YOUR BUILDINGS RANK?
Using the myriad of data feeds, the Building Operational Performance (BOP) Score can model and score each building against the performance of Demand Logic’s pool of properties and provide a proxy for industry level of performance. Millions of data points are analysed and aggregated to provide a single BOP Score. This score can rank buildings against their peers, highlights improved or deteriorating property and facilitates drill down to poorly performing spaces to help building managers and FMs to rectify problems.
We’re opening this process up to industry parties and professionals to galvanise the best brains in the industry and strive for continuous improvement of the scoring metrics so that all buildings can be compared fairly and effectively.
The Building Operational Performance Score does the data analysis for you, with a rating system that allows users to instantly see which buildings are performing well from the perspectives of energy efficiency, occupier comfort and mechanical performance. Property professionals are already using the system to improve property performance and drive engagement.
How do you think your buildings rank? And if you can answer that one, how do you know? Is it through subjective KPIs or maintenance reports? Both of these metrics are relying on someone’s opinion, often self-auditing. Is it based on actual data? And if so against what benchmark and how do you know how productive the space is? Is it through complaints? Or simply gut feeling?
Whatever the answer it’s time to look at how effectively you’re optimising your BMS data and in turn the properties’ performance.
