The Problems of Manually Entering Utility Bill Data

Editor’s note: This is the second post in a three-parts series investigating the challenges, pitfalls and alternatives to manually collecting utility bill data.

In part one of this series, Urjanet’s CMO, Gary Brooks, said how shocked he was when he heard some of the largest organizations in the world make million-dollar energy decisions based on manually entered utility data that is prone to human error and often incomplete and inaccurate. In this post, he’ll cover the frustrations and pitfalls of doing manual utility bill data entry.

Q: What are the biggest issues organizations have with utility data?

A: Most companies are frustrated by four challenges that seem to have plagued the energy management industry for decades:

1. Collection — a lack of utility data standards has made it very difficult for multi-facility organizations to automate collecting data from hundreds of utilities each using different data formats, tariffs, taxes, etc.

2. Normalization — converting disparate data from all those different utility providers requires a process to turn the data into a consistent, standardized and useful format.

3. Delivery — delivering data in the various formats required by energy management, accounting, facilities and procurement systems often requires expensive customization.

4. Quality — ensuring data quality, consistency and reliability requires hundreds of automated audits across the collection, normalization and delivery process.

Q: Do organizations assume the utility data is available when they begin energy management projects?

A: Yes! This is another major source of frustration. Many organizations assume “we can just get our energy use and cost data from our financial system.” Unfortunately, this is rarely the case. Just because a company has invested millions of dollars in accounting software, or has sophisticated supply chain software, doesn’t mean the energy data will be magically available. During a recent webinar: Don’t Underestimate the Cost of Manually Obtaining Energy Data, FirstFuel software executive Paul Baier said, “Data acquisition can represent up to 50% of the overall [energy or sustainability management] project costs and causes risk to the project success.”

• Human Error—manual data entry is prone to human errors that lead to inaccurate data
• Old Data—manual data entry is time consuming which leads to delays in the availability of data.
• Incomplete Data—only the “must have” data is manually entered leaving a significant amount of data behind.
• Lack of Domain Expertise—often, data entry clerks not have the domain expertise needed to understand hundreds of different utility tariffs, semantics, tariffs, etc. resulting in unnoticed errors

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