Is Your Utility Data Collection Process Outdated? 7 Questions to Ask

Does your energy management process bring about thoughts of efficiency, reliability and automation, or are you concerned this process is manual, outdated and a big risk to your company?

Organizations around the world are facing pressure from their customers, regulators, and economics to improve sustainability performance. These pressures are driving them to completely reexamine their energy management processes that include the collection of energy data from hundreds of disparate utilities—the data they rely on to make major energy decisions.

During these examinations, many organizations have realized that their energy data collection processes are manual, inefficient, and outdated which impedes their ability to identify opportunities to reduce energy consumption, costs, and carbon emissions.  Are your energy data collection processes outdated?

Here are seven ways to know if your company’s utility data collection processes are outdated:

1. Manual—do you use the word “manual” when describing you energy data collection process? Manual data collection is prone to human error that results in frequent mistakes that can be very costly.
2. Delay—do you wait 30-60 days to access your utility bill data? Long delays make it impossible for energy and facility managers to react and make real-time decisions.
3. Incomplete—are you only collecting the “must have” data from you utility bills and leaving behind a significant amount valuable data? If so, why?
4. Lack of Expertise—do your data entry specialists lack the domain expertise needed to compare and understand hundreds of disparate tariffs, semantics, and rate schedules?  If so, errors and inaccuracies in your bill data is likely going unnoticed.
5. Exposed—does your lack of data make you feel exposed to risk or missed opportunities?
6. Date Inconsistencies—are the billing dates for your various utilities synchronized on the first or last day of the month? If not, are you using comparable year-over-year frameworks to ensure accurate forecasting?  Failure to account for all the days in an invoice period can leads to almost a 3% reporting error.
7. Lack of Standardization—is the data you’re collecting from your disparate utilities in different formats? Do you manually aggregate and normalize your data. How is this working out for you?

A new energy management trend is quickly emerging where leading multi-facility brands, energy software vendors, and energy service providers are replacing their outdated energy data collection processes with big energy data services. These new services automate the collection and normalization of data from your utilities and deliver high-quality, reliable and timely data directly to your energy management, accounting, procurement and facility management systems.

Gary Brooks is the CMO of Urjanet – the world’s first provider of automated Big Energy Data that enables companies, governments and educational institutions to make smarter, more profitable and eco-friendly decisions for energy management. Gary is a B2B marketing addict, occasional blogger, innovator and change agent with an entrepreneurial spirit and a proven track record of delivering breakthrough revenue performance. He has fueled his passion for innovation by serving in executive leadership roles at Alta Vista, Ariba, Bomgar, Cortera, Fortress Technologies, TRADEX, KnowledgeStorm and Servigistics. He holds a B.S. from Northeastern University and an M.S. from Leslie University. In 2006 the Technology Association of Georgia (TAG) named Brooks as Marketing Executive of the Year.