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Manual Data Entry: The Dirty Little Secrets of Energy Management

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Shira Weintraub's picture
Marketing Program Manager Urjanet

Shira Weintraub is the Marketing Programs Manager for Urjanet, the world's first provider of automated Big Energy Data that enables organizations across all industries to make smarter, more...

  • Member since 2014
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  • Oct 24, 2014

Most companies engage in manual data entry in some form. One area manual data entry is prevalent is energy management. Although it is essentially ubiquitous, the process consists of an abundance of glaring inefficiencies. The reason manual data entry has infiltrated most organizations is that, up until a few years ago, there was no other option for collecting and cataloguing energy data, which could only be received via paper or pdf documents. Manual data entry's most prominent shortcomings are explicitly unveiled here in the context of energy management and an alternative is presented: automated utility billing data.


With a manual data entry process, 30 to 45 days can pass from the time a utility provider reads a meter to the time a large company’s corporate headquarters has enough energy data in its system to begin analysis and reporting. Typically, each of the locations -- which could be in the thousands -- a large company operates has its own utility account. Once a utility company reads the meter associated with one store location, two-and-a-half days may pass before the bill is even printed at the utility's site. After printing, a store manager receives the bill in the mail about 17 to 28 days later.

Upon receipt of the utility bill, the manager generally scans it and reviews and enters the most critical data points into a field system, which is independent of the corporate office's database, within a 48-hour window. An accountant in the corporate office is then tasked with gathering the information from the field system and transferring it into the corporate headquarter's database system, which may take between two and four days from the time the data is entered into the field system. With delays in the utility billing process, a company may have only 70% of its total energy data a month or more after the initial meter read. Oftentimes, historical data must be used as a supplement for the missing utility information in order to conduct analysis within a reasonable time frame.

Losing over a month of time can be detrimental to a company's bottom line, as it prevents executives from spotting anomalies in energy usage and increases the difficulty of managing energy consumption and cost in individual locations. Automated utility data feeds directly into corporate systems on average three days from the time the utility provider reads the meter. The bill information is pulled a maximum of one day after the read and is delivered to the customer in the next two days, which is over ten times faster than manual data entry. With a quicker process, accounting professionals can avoid paying late fees, which saves thousands of dollars on high ticket invoices.


The full manual data entry process can cost up to $11.63 per bill due to expenses such as printing, scanning, writing checks, and coding. This collection process quickly becomes a top line item in a company's expenditures. An organization with 3,000 different utility bills coming in each month may be spending $400,000 annually just to gather and organize energy data. According to Groom Energy, data acquisition can use 50% of energy management project budgets. Automating this procedure costs $1 - $2 per bill, which holds the potential to dramatically decrease a company's expenses.

Labor Productivity and Employee Satisfaction

Companies that include manual data entry in their operations may hire data entry specialists or portion off some of these responsibilities onto analysts. Data entry specialists, burdened with entering information for hundreds of bills per day, often become less productive and burnout as time goes on due to the repetitive nature of the work. Additionally, analysts who are required to take on manual data entry must divert time away from forecasting, reporting, and other higher level work. Automating the data collection and standardization process allows a company to use its employees in ways that add more value. Analysts can focus on identifying opportunities for improvement rather than on data collection. Additionally, employees who have opportunities to use their skills and abilities tend to be the most satisfied at their workplace and motivated to work harder.


The most common source of data inaccuracy is human error, resulting from manually keying in data incorrectly. Entering data into a system manually can lead to a 3% error rate, which proves costly when handling a plethora of utility accounts and transactions. These initial missteps carry adverse impacts further down the line on energy budgeting, future consumption estimations, and procurement. With a host of new tariffs and billing structures entering the market, the likelihood of errors will increase.

Furthermore, it is difficult to track the source of an error in a manual data entry process. Reasons for inaccuracies include human error due to incorrect value entry, human error due to incorrect mapping, and utility billing mistakes. When an error is detected, sifting back through each previous procedure is an arduous task, especially when the only source of original documentation is a physical paper copy of the bill in a distant location.

Automating the data collection and standardization process removes the potential for human error and data value errors. It also reduces confusion resulting from the variety of bill formats. Plus, an automated system has an appetite for data. The more data it can collect, the easier it is to ensure accuracy because more checks and balances can be created. For example, the "X" piece of information can validate that the "Y" data point was correct. With more accurate data, models of energy usage and costs are more reliable and indicative of a company's performance.


Employees involved in manual data entry often only gather the information from the most critical fields to success due to time pressures. Additionally, data entry specialists are incentivized to collect fewer data points because having fewer data targets per statement keeps the error rate lower. They generally capture about 10 - 25 out of the total of about 125 distinct data points.

Contrastingly, an automated system parcels out all of the information available on the bill and puts it into a standard format. With all of this information at hand, companies can be sure to take advantage of any opportunities to seek out better utility tariffs, which can save thousands of dollars per year. They also have more clarity into which locations would be best suited for new factories and plants based on energy costs.

Carbon Emissions

Carbon dioxide emissions are associated with a number of steps in the manual data entry process including paper production, printing, and transportation. Reducing the frequency and size of utility bill shipments can help lower the greenhouse gases generated from this process. As a matter of fact, the pulp and paper manufacturing industry is one of the world's largest users of energy and emitters of greenhouse gases and transportation represents a whopping 32% of carbon emissions in the U.S. The transition to an electronic data feed can help mitigate these negative environmental impacts.

Record Keeping

Surprisingly, some companies actually pay monthly fees to keep boxes filled with paper copies of utility invoices in public storage units. This archaic form of record keeping is not only expensive but also location specific, making access for employees out of the area difficult. Storing data in a cloud-based system eliminates this problem and improves efficiencies, as human interaction is not required for data extraction.


With an array of drawbacks, manual data entry is outdated and prevents timely, top-notch energy management at scale. Missing chances to boost revenue and cut costs is common when actions are based on old, incomplete information. Will your company continue to drown in energy data with a manual process, or will it begin to swim in a sea of new opportunities with automated utility data? The decision should be clear.

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