How to Set Environmental Targets That Matter?

Reg Coker

Data Analyst at Fabriq

January 28, 2021


While I generally approach decision making with a slant towards the use of logic and hard numbers, my experience tells me that cold logic is not always the best path to follow. To get a brighter picture of an issue or the impact of one’s actions, one must also look at life from a different perspective - one which panders more towards sentimentality and emotion. My views below are by no means the be-all-and-end-all of how to approach target setting; I have simply drawn from my experience in big corporate finance, where trading floors operations are driven by numbers and algorithms, and from general life experience and observations in the changing environment. With that said, let’s look at how real estate organisations can use some of the principles that I have learned to set environmental targets that matter.

Net Zero Carbon Buildings - How Energy & Sustainability Professionals Can Overcome Data Headaches

Jon Thompson

Head of Product Innovation

October 09, 2020

The last blog in our Net Zero Carbon Buildings series covered five key energy analysis techniques — the ones we think you should all be using to start your journeys to net zero carbon. This time we’re moving on to talk about some of the data problems that often hamper progress, as well as ways you can overcome them.

Frankly, it always amazes us that in the 21st century, energy and sustainability professionals are still having to struggle with inconsistent and poor quality data. But sadly, this is the reality.

The technology has never been better: we live in an age of smart meters, sensors, IoT devices and Building Management Systems. Yet shockingly, in our experience, over 90% of building sensor data is never used. Why is that? Here are our thoughts.

Net Zero Carbon Buildings: Where to begin?

Jon Thompson

Head of Product Innovation

September 17, 2020

Before COVID-19 struck, it felt like everyone was talking about net zero carbon. The UK Government became one of the first major economies to commit to a legally binding target of net zero emissions by 2050, and a host of big businesses followed suit: Apple, Microsoft, Nestlé, British Airways, BP, Shell, Ikea and more.

Meanwhile, the World Green Building Council’s Net Zero Carbon Buildings Commitment was also gaining traction, securing more signatories. This commitment challenges companies, cities, states and regions to ensure that all buildings they directly operate achieve net zero carbon by 2030, and for them to advocate that remaining buildings do so by 2050.

In short, there was a real momentum gathering around net zero carbon.

A Beginners’ Guide to Profile Analysis: The Cornerstone of Energy Analytics for Building Performance

Jon Thompson

Head of Product Innovation

September 01, 2020

Profile analysis was the first thing I learned as an energy analyst. It is the foundation of all other forms of energy analysis. Without an understanding of how buildings use energy on a half-hourly basis, everything else is just noise.

The mass adoption of smart metering and AMRs has meant that ‘energy profiles’ are widely available. Back in the day, they were recorded by an energy profiler that was temporarily installed for a couple of weeks. This meant analysts were dealing with a very limited time period for a limited number of data points. Thankfully, this is no longer the case.

5 Key Steps Towards Data-driven Energy Efficiency

Jon Thompson

Head of Product Innovation

August 25, 2020

In the world of energy efficiency and sustainability, we very rarely make decisions based on data. In a sector filled with experts and specialists, most decisions are made on gut feel and intuition. You may think because you use data in your decision-making process that you’re data-driven. But that’s not the same thing.

Being truly data-driven means putting data at the heart of the decision-making process. It really comes down to a ‘data first’ approach instead of going by gut feel. It means constantly questioning your beliefs and assumptions to form new mental models. Referring back to the data time and time again and continually asking “Why?”.


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