Using Data to Test Sustainable and Ethical Impacts in Real Estate
Covering an estimated 13 billion square feet of commercial real estate space across 90 countries, Measurabl is in a unique position. Its clients include tenants, lenders, owners, and any stakeholders with a controlling portion of a commercial building. But it’s not their PII that Measurabl needs to be so concerned about. The sensitive information filtering through Measurabl’s programming relates to ESG data, which investors use to determine the risk of a commercial building. Ultimately, it determines the risk of an unethical investment based on the building’s environmental impact.
Measurabl’s acquisition of WegoWise, a third-party utility tracker, expanded its services to residential real estate. This came with another need for data to produce a functional ESG-focused platform that would be intuitive and automated to simplify the convenience of utility tracking and energy benchmarking.
Behind the scenes, Will Luongo, a Measurabl staff engineer, worked with a subset of engineers and local developers, using scripted test datasets in a singular staging environment. Production data proved to be tricky to use as it needed to be heavily sanitized and manipulated. Then, as Measurabl expanded, GDPR forced the need for fake data.
Simplifying Data Synthesis in Local Environments
One key to Measurabl’s continued success was finding that “round peg for a round hole,” as Luongo notes. Tonic rectified several pain points for Measurabl’s engineers, and the constant need to scrub data clean was effectively taken off the table. As Measurabl scaled, this proved critical to the ability of the engineers to do their job efficiently.
In its infancy, Measurabl employed several dozen engineers, but as the company grew and hired more than 100 engineers, the hard-coded staging environments proved unsuitable. Per Luongo,
“That doesn’t scale well, having one environment that everyone’s using for their testing, that has the test data, you can’t run it locally, everything's hardcoded… Part of the solution needed those schemas to be accessible locally.”
An initial workaround involved Luongo co-writing a script that “took the shape or actual definition of the database and made it so that would at least be there, and you could run the app but it wouldn’t have anything in it.” Tonic’s test data allows engineers and developers to work more efficiently in a local environment. With the concerns about data security addressed, Measurabl was able to work with much larger sets of data right in local environments.
By switching to Tonic, Measurabl has seen a remarkable increase in the number of datasets engineers are working with. According to Luongo, the development teams went from working with a mere 10 datasets to being able to produce over 100,000 in no time. Maybe most importantly, Tonic mimics the production data so engineers are working with the closest thing they’ll get to the real information.
With its less efficient system in place, Measurabl engineers would spend hours working in core components of the primary applications importing and sorting data to have the set needed for their test in staging. That time has since been cut down, and what once took hours to complete now takes no more than two minutes.
These unique datasets from Tonic allow developers to work on special features and unique functions. The more hands-on they can get with a finished set of data that more accurately depicts a final product, the more likely they are to avoid developing in a staging environment that may not be suited for the tests they're running.
The most important impact Tonic has had on Measurabl and its engineers is it has given the company the ability to ramp up utilization without the original hurdles. As more engineers are hired, they’ll jump into local environments and work with the clean, fake data provided by Tonic to achieve the best results. Per Luongo, the hope is to empower developers with individual sandboxes to promote growth and implement Tonic on a more one-on-one, personal level.