The Reasons Why Good Energy Data Goes Bad

Manual data collection processes introduce a variety of problems that can cause good data to go bad. This was the focus of Urjanet’s recent webinar, “What Happens When Good Energy Data Goes Bad?”

In this session with Johnson Controls and Verdantix, we learned more about how poor data quality may limit a company’s ability to leverage data for improved outcomes. Plus, it may also drive erroneous decisions. Looking at utility bill data opens up the possibilities of identifying inaccuracies in charges you’re receiving from utilities. As organizations start producing these larger datasets, it’s important to focus to ensure data validation steps in all parts of the process.

• Human Error — manual data entry is prone to human errors that lead to inaccurate data.
• Old Data — manual date entry is time consuming which lead to delays in the availability of data.
• Partial Data — in an attempt to contain costs, organizations may only capture the “must have” energy data and leave behind other valuable data which leads to missed opportunities.
• Lack of Domain Expertise — data entry clerks typically do not have the domain expertise needed to understand hundreds of different utility tariffs and rate schedules, which causes inaccuracies going unnoticed.

Click here to view the full webinar “What Happens When Good Energy Data Goes Bad?”

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• Beyond The Bill: Refining Your Energy Management Program with Interval Data

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