Creating the Big Energy Data Strategy for Your Business: Part 2

In my first blog in this this series, I gave an introduction to Big Energy Data and its potential for big rewards. Now we’ll dive into the key strategic and technological challenges in the collection of energy data.

No energy management strategy will reach its full potential without a solid plan to automate the collection and delivery of energy data across the enterprise. Great data fuels great decision-making, and an energy management team starved of data will always be making decisions on partial guesswork.

According to Paul Baier of Groom Energy: “Data acquisition can represent up to 50% of the overall (energy or sustainability management) project costs and causes risk to the project success. This problem is particularly acute for companies with operations in the U.S. It simply doesn’t matter how good the energy or sustainability software is if data are missing.”

It’s no wonder why data collection should be a key aspect of your Energy Data Strategy. Interval meter data gives insight into current energy consumption, usually in 15-minute intervals. If an organization can collect, deliver and analyze this information anywhere close to real-time, quick action can be taken to better manage energy consumption. But few companies are equipped to collect and deliver this volume of near real-time data.

Monthly utility billing data, which gives great insight into what happened during the billing period, is also challenging to collect. There can be hundreds of utilities, thousands of accounts, and a wide variety of rate plans to contend with. Processing the data through manual data entry is very time-intensive and prone to errors.

This is where energy data-as-a-service vendors fill a huge void. All of the heavy lifting to collect and deliver the energy data is an automated service running behind the scenes. These vendors connect directly to any number of utilities and automate the collection and delivery process to a customer’s front-end analysis tools. A robust system like this:

• Captures all data points
• Provides very accurate data because there’s no manual data entry, which is ripe for error
• For invoices, the data is available faster (no need to wait for the paper bills to arrive and be manually entered)

Because energy data-as-a-service vendors focus on the energy data itself, not the front-end analytical applications, their clients can focus on strategy rather than data management.

The bottom line is that in order to take advantage of big energy data, automating data collection is an investment that shouldn’t be over-looked. I’m often surprised and how many organizations invest heavily in a great analytical tool only to have limited data to feed it.

If you’re ready to take the next step in leveraging your utility data, contact Urjanet today.

Related Resources:

• An Open Letter to Sustainability Software Providers
• A Path to 100% Clean Energy for the City of Atlanta
• Breaking Through to Gen Z with Better Data

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