JANUARY 2015 THREE BIG MYTHS ABOUT BIG DATA HOW ANALYTICS CAN OPTIMIZE ENTERPRISE-LEVEL ENERGY MANAGEMENT John Davies VP Senior Analyst, GreenBiz
EXECUTIVE SUMMARY GreenBiz Group partnered with Siemens Building Technologies in an extensive research study seeking to identify the impact of big data and advanced analytics when it comes to managing enterprise-wide building portfolios. The project included a quantitative survey of energy managers from large corporations, hospital systems, governments and educational institutions as well as in-depth interviews with 10 executives responsible for enterprise energy management at their organizations.
MYTH 3: Data Equals Information. No matter how “big,” an incomplete or otherwise flawed set of data cannot provide actionable information. The proper analytics can be developed only with a clear understanding of the quality and quantity of available data. These and other myths are similar to those that occurred during previous technology revolutions, such as the rise of the personal computer and later the Internet.
INTRODUCTION The IT research firm Gartner defines big data as high-volume, highvelocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization. Many large factories and office buildings generate vast amounts of data.
What goals does your organization hope to meet through the management of your enterprise real estate portfolio? Reduce operating costs 99% Increase energy efficiency 94% Meet corporate sustainability targets 80% 72% Improve occupant comfort 63% Improve equipment reliability and useful-life 41% Achieve compliance mandates as weather, occupancy and production schedules management at their organizations.
While that certainly appears to be a positive trend, 60 percent of those using software for data analysis and visualization are not confident or are only somewhat confident that their solutions will help them realize their portfolio goals. These are early days and there are many claims being made about how big data and data analytics will allow those managing enterprise real estate portfolios to achieve breakthroughs in their operations.
THREE MYTHS ABOUT BIG DATA FOR ENTERPRISE ENERGY MANAGEMENT There are three widely held misconceptions that were identified in GreenBiz Group’s quantitative research and in-depth executive interviews regarding big-data analytics for enterprise energy management. Each of these “myths” represent a disconnect between the promise of big-data analytics and the current state of the market. While these myths are true today, they can also serve as guideposts for improvements in the future.
MYTH 1: ONE SIZE FITS ALL When it comes to choosing big-data analytics software to manage energy and sustainability for an enterprise real estate portfolio, not all solutions are created equal, or have equal application. In our interviews, executives estimated that there are somewhere between 50 and 150 companies that have some kind of product offering, whether it’s a building automation system, analytics, fault detection or some other solution.
MYTH 2: IT’S ALL ABOUT TECHNOLOGY While many of the components and software packages to maximize the value of that data, whether it’s that support big-data analytics and data visualization somebody telling the system what to do under can be deployed today, the missing ingredient for many certain scenarios or somebody that’s directly acting companies is the people necessary to do the work. In because of the data they’re getting.
to focus on resource use and waste and recycling issues using the same model they used to analyze energy. According to Grainger’s Senior Manager, Corporate Facilities and Global Sustainability Jeff Rehm, “We’ve had positive results in terms of measuring, tracking, and changing behaviors around how we manage waste in the facilities and that is directly related to how we’ve staffed up.
the depth of datasets in a consistent enough manner with a high degree of confidence about accuracy such that analysis at the portfolio level is useful.” MGM’s Magee notes, “The biggest challenge is really logistical in the sense that we’ve got a lot of equipment that has no sensors, has no control ability, and it’s extremely difficult to get the data out of. So sometimes we have a very incomplete picture.” Only 33 percent of owners report capturing equipment-level data.
THE FUTURE OF BIG DATA AND ANALYTICS HOLDS GREAT PROMISE There are certainly more than three myths when it granular level that becomes cost effective for us. comes to the current state of big data and analytics Typically that’s at a department level, sometimes for enterprise energy management. The market is a business unit level. If a facility is fairly small functioning in a similar manner to past technology then we might just do it at the facility level.
FIVE ACTIONS TO TAKE TODAY Most companies are in the early stages of the journey toward a sophisticated use of big-data and analytics for enterprise energy management. Through our interviews and quantitative data collection, we’ve unearthed advice that should make those efforts more productive. 1. Learn fromYour Peers. It’s important to not only spend time familiarizing yourself with the providers, but also with what others have implemented and how they went about procuring systems.
What were the top factors that were considered when buying a big data/analytics solution? Functionality that meets current needs 61% Price 53% Ease of use 53% Integration with existing equipment and software packages 42% 39% Functionality that meets future plans Assurance of data quality 28% Existing relationship with supplier 8% Brand reputation 8% Other 8% 5. Don’t Boil the Ocean.
ABOUT GREENBIZ GreenBiz Group’s mission is to define and accelerate the business of sustainability. It does this through a wide range of products and services, including its acclaimed website GreenBiz.
