Three Critical Questions to Ask When Evaluating Technology for Your Portfolio

As we talked about in our last blog post, portfolio data (i.e. data about how individual portfolio assets are operating and consuming energy) is the foundation for building a successful performance optimization program. But now that you have it, what do you do with all the building data you’ve collected, centralized and verified?

Figuring out what to do with and how to operationalize such a massive amount of data can be a daunting task. On top of that, the sheer amount of data available makes manual analysis prohibitively time-consuming and prone to error.

At Aquicore, we consider the tool you choose to use the sixth man on your team — and no performance optimization program can exist without a solution that provides analysis and actionable insights from your data.

With so many options available, how do you know which ones provide true value? Here are three critical questions to ask when evaluating technology for your portfolio:

Is your solution specifically tailored for real estate?

Your solution should be built to handle portfolios that include buildings of all sizes in any region, without compromising the value provided to each property.

Does your solution provide machine learning with human-centric design?

To save time and avoid getting overwhelmed, teams should take advantage of machine learning technologies to analyze the large quantity of data collected from a portfolio.

Machine learning solutions that are human-centric and take a design thinking approach to user engagement are more likely to drive real behavior change for an organization.

Is your solution easy to scale across a portfolio?

Your solution for collecting data should be cloud-based and easy to install; this will impact whether the solution can be deployed and adopted across an entire organization.

Learn more about the importance of picking the right tool, as well as the other pillars of a good performance optimization program, in The Definitive Guide to Performance Optimization.