Achieving Next-Level Building Analytics

In an effort to leverage big data and advanced analytics toward the goal of increased energy efficiency with a relatively quick return on the investment (ROI) within the commercial real estate (CRE) space, building owners and operators have turned to a building management system (BMS) to provide real-time actionable information about a building portfolio. While a BMS is taking on an increasingly critical role in CRE, there are some inherent shortcomings.

For example, a BMS is often a legacy system that does not store data and is unable to establish peak performance standards to stay ahead of system drift (what is drift?). There also are limitations with respect to scaling across a portfolio.

Advanced data analytics offered by Entic's cloud-based technology complements a BMS in such a way to deliver insights into the health and function of a building's system, enabling owners and operators to reduce energy waste, increase building efficiency, stay ahead of safety issues and ensure optimal occupant comfort.

The ROI is favorable. Research found that organizations continuing to make investments in analytics have experienced an average of $13 in return for each dollar spent.

Approaches to Next-Level Building Analytics

There are many approaches to consider when choosing a building analytics solution that best suits a property's immediate and long-term needs:

Consultants: Costly, Manual, Not Scalable
Consultants are traditionally considered the most reliable option to solve focused problems because of their niche knowledge base, but can also the most expensive one. Scaling a consultant approach portfolio-wide is time intensive as well as costly when considering the overall health of a portfolio.

Custom Builds: Pricey, Ineffective, Not Scalable
The custom-built solution is costly and time-consuming because it relies on creating customized data analytics instead of relying on an off-the-shelf software package. Once built, the analytics will integrate with a BMS and continuously map to a building's systems. Costs increase as a result of the need to engage in deep domain expertise into an HVAC, BMS, IT equipment and networking. Large-scale projects may be subjected to the risk of the introduction of bugs in the software development process. There may be less than rigorous formal testing, resulting in business-critical errors.  Most custom builds are a one-time event with ongoing maintenance costs and can often can result in failure.

Implementing Analytics into Existing BMS: Limited Functionality, Ineffective, Not Scalable
While embedding analytics into a building's BMS is less time-consuming than a custom build, most systems lack the infrastructure to support the speed of data collection and reporting. Additionally, most portfolios rely on more than one BMS; therefore, this approach can be ineffective in accessing portfolio-wide analytics. An important first step is to install a modern BMS that integrates all the disparate systems to provide a holistic view of a facility. Building analytics software takes that solution to the next level by interpreting the massive amounts of data generated by a BMS. There are several choices available in the emerging analytics sector. Assessing the priorities, budget and staffing levels leads to the optimal choice for a given facility.

Managed Software as a Service (MSaaS): Less Expensive, Easier to Scale, Most Reliable
This approach combines a SaaS analytics model with remote engineering experts who provide each property's owners and operators remote assistance in achieving peak performance through insights and actions on how to improve efficiency and reduce energy costs. This option is less expensive than using consultants and custom builds and can be more reliable and portfolio-friendly than embedding analytics into an existing BMS. Both approaches work alongside legacy systems and existing team members to achieve advanced analytics and energy efficiency.

Software-as-a-Service (SaaS): Lighter Product, More Affordable, Easy to Scale, No Guarantee of Success
This option is a more cost-effective solution, easy to integrate alongside legacy systems and provides teams rapid access to building intelligence accessible from anywhere. However, the software alone does not guarantee success. The building engineering team must be able to regularly utilize the SaaS and understand the analytics to take action and drive efficiency to reduce energy waste, which is not something every team has the native ability to do.

Machine Learning: The Future, Artificial Intelligence
The future of the advanced portfolio-wide analytics space is being driven toward machine learning. In this approach, the technology will be able to utilize AI and statistical techniques to progressively improve performance with advanced data and to learn the true needs of each unique system and property without being explicitly programmed to do so.

Implementing the Best Approach

According to McKinsey & Company, three keys to building a data driven-strategy include choosing the right data, building the right model to predict and optimize business outcomes, and transform internal culture to trust big data-based models. When considering an approach for CRE portfolios, the right solution is both cost-effective and easy to scale, but in many cases not achieved through strictly one approach.

Our friends at Blackstone call these low-cost/no-cost approaches. According to Don Anderson, Chief Sustainability Officer, firms like Entic provide visibility and remote diagnostics associated with BMS deployment. "Fifteen percent enterprise-wide energy cost savings are common as HVAC systems are tuned and properly controlled," says Anderson. "This makes buildings more comfortable and cost competitive and drives portfolio-wide consistency across our building engineers and management companies."

For instance, SaaS solutions are the easiest to scale for the least cost and to speak to McKinsey's three-prong approach, would certainly provide 'the right data' with the need for a team of IT and building experts to address the concerns this data set provides. Most portfolios are unable to provide a large enough team to partner with the SaaS approach in an effort to achieve the highest ROI. In this situation, a frequent approach is to pair a SaaS with MSaaS.

Closing

The use of next-level building analytics provides CRE portfolio managers the opportunity to have a competitive edge over others in the industry through pursuing a proactive approach. So, why should you act now? Early adopters of advanced analytics are twice as likely to be in the top quartile of financial performance within their industries, five times as likely to make decisions much faster than market peers, and three times as likely to execute decisions as intended. Despite the opportunities big data and advanced analytics supply to businesses, research from Bain & Company have shown that only four percent of companies combine the right people, tools, data, and organization focus to enjoy these advantages. In moving a CRE energy efficiency strategy toward implementing advanced analytics, it is important to consider the infrastructure that is in place and its ability to cost-effectively scale across a portfolio for a maximized ROI. Learn more about how some large companies are achieving success with Entic's next-level, portfolio-wide, building analytics solution.