Which data collection software is the best? The one that comes with humans.
By: Danny McGee
You can’t manage what you don’t measure.
It’s one of our favorite sayings around Foresight. I’m sure you’ve heard it in every webinar, MicroVlogCast, or if you’ve spent even five minutes with Mike Troupos. It grounds our approach to all projects, across all disciplines, in every area of impact for our clients.
Sustainability is a journey without a clear, singular destination. Instead of measuring completion, we must measure progress. The only way to measure progress is by accurately understanding where you started and repeatedly gathering metrics along the way.
Everyone must start their sustainability journey with data collection – something software companies left and right claim to do. It may seem like the perfect monotonous task to hand to AI, but I assure you, this is one part of your sustainability journey you need expert humans to manage.
Here’s why.
The Data Hierarchy of Needs
Data science is built on a hierarchy, with the efforts at the top reliant on the bottom. You can’t diagnose, optimize, or prescribe on data you don’t have. Often, companies want to race to the top of the pyramid, sacrificing data quality and completeness in the rush toward insights.
At Foresight, we love it when data helps us spot a compressed air or water leakage, confidently predict energy usage so a client can hedge their energy purchase at an amazing price, or accurately understand an engineering project’s Scope 1 decarbonization impact.
It’s a lot of fun to talk about what you can do with data. It’s a lot less fun to talk about how to get good data — or even to understand what good looks like.
But when you over-index on data intelligence and neglect data foundation, your insights will have no value; the quality of your data will always limit the quality of your analysis. This is the problem with fully automated data collection or inexperienced personnel; they may not know what good looks like.
Simply put: if you put bad data in, you’ll only ever get bad analysis out.
What is a “good” data foundation?
A solid data foundation is built on the following four pillars:
DATA COLLECTION:
In the data collection phase, you simply get all the data. But it’s often easier said than done. Do you know who to ask? Do you know the type of data you need? Do you know when it’s complete? How about which nuanced questions to ask? What buildings are in scope or out of scope? What if the data is in different languages? Would you know if the data was wrong?
Data collection is — quite appropriately — the largest portion of the pyramid. It’s a considerable time and energy investment, and one you need to be assured is done with precision. Software solutions may wow with fancy outputs, but there is no assistance with the inputs, which is the part that really matters (and where Foresight shines! We will do it for you.)
RAW DATA STORAGE:
Primary sources need to be saved so your data is defensible and audit-worthy. Storing primary data, like a copy of a utility bill, underpins the entire effort with an accessible, verifiable source. Typing numbers in a spreadsheet without the ability to easily access the raw data associated with that number won’t cut it.
DATA CLEANING:
Now that you have the data, you need to ensure it is good data. Occasionally, meter numbers change, buildings are added, or utility companies change billing cycles. It takes scrutiny to notice such discrepancies and curiosity to dig into the cause. When the data changes or is not aligned with our expectations, our experts must interpret what it means and seek clarity. Data hygiene is not a one-time exercise; it requires continuous vigilance to ensure the foundation is accurate. Nearly every company’s basic inventory of emissions sources will change annually.
STRUCTURED DATA STORAGE:
Once your data is collected, stored, and cleaned, it must be structured in a way that can be analyzed. Not all utility bills are created equal — some are billed biannually, some quarterly, and some monthly. Depending on the utility, region, or country, utility bills can be broken out into different line items or measured in different units. To compare one facility to another, you need an apples-to-apples comparison. Data structuring prepares your data to be usable.
Knowing What Your Data Means
As we move up the pyramid, the data begins to tell a story. AI and software are great at reporting what is happening, but it still requires humans to create a perspective about whether that’s good or bad and how it aligns with your goals. Baselines, benchmarks, checkpoints, and goals act as handholds for your sustainability journey, but it takes people to help you understand if the progress is in the right direction.
At Foresight, our data team works together with our sustainability experts, providing full support for our clients up and down the pyramid. They are making incredible progress on their sustainability journeys, progress we can measure, track, verify, and trust — not because we have the best software, but because we have the best people.
Ready to build a proactive, profitable approach? We can help with all the above and more.
