Utilities taking a comprehensive approach to data analytics

I've often discussed the energy industry's use of big data to better allocate electricity output from green technologies and enhance smart grid solution functionality. For example, if a substation automation program can process more detailed reports on consumption, it can make more "intelligent" decisions. 

What about the side of utility operations that isn't as "sexy," so to speak? I'm talking about stuff as ordinary as invoicing and billing. As power companies have continued to use big data analytics tools to support their central operations, many users have realized the same technology can be applied to day-to-day internal processes such as sales, customer service and human resources. 

"Smart grid analytics will grow at a compound annual growth rate of 25 percent over the next four years."

Looking at the big picture 
Providing visualization tools to different departments within a utility creates a business that ultimately considers data intelligence to be a fundamental component of its continuity. Think of it this way: If the sales team can correlate its own dashboards with visualizations provided by the customer service group, it can determine whether the services the utility delivers are matching up with consumer expectations. 

MarketsandMarkets conducted a study on the power industry's use of data analysis software, estimating the technology's adoption will increase at a compound annual growth rate of 25 percent over the next four years, reaching $5.5 billion by 2019. This activity is caused by a number of factors, such as:

  • Continuous installation of homeowner smart meters
  • An uptick in supervisory control and data acquisition system implementation
  • Consistent use of outage management solutions 
  • Increasing interest in customer information systems and geographic data applications

The deployment of myriad programs means utilities have more data on their hands. Whether they make use of this information depends on how well they integrate data analysis tools into their day-to-day workflows. Let's take a look at some of these solutions. 

Geographic information systems 
Depending on the size of the organization, a utility's customer base may span across 500 miles. For example, if a storm is due to impact New England, homeowners residing in eastern Massachusetts may be more affected than those living in northern Vermont.

By setting up a data analysis solution that compares real-time data from weather.com, consumer energy demand and geographic information, the utility provider can prepare crews to handle certain jobs. For instance, a heavily wooded region exposed to high winds may cause power line disruptions. 

"A CIS is primarily used to inform utilities how much electricity specific homeowners and businesses consume on a regular basis."

Customer information systems (CIS)
These particular programs are multi-faceted, and can be connected to customer relationship management software through application programming interfaces. A CIS is primarily used to inform utilities how much electricity specific homeowners and businesses consume on a daily, weekly, monthly and yearly basis. 

However, CIS use cases are diversifying as more consumers continue to install solar panels, making them direct contributors to utility energy distribution networks. To avoid complications associated with this behavior, utilities are integrating back-end data analytics functions into their grid management systems so that electricity generated by homeowners and businesses can be allocated across the network based on incremental demand. For instance, consumer power can be redirected toward a feeder where back-up electricity is required or areas that need voltage support. 

With further data analysis integration, it's likely that automated functions will become a regular component of grid operations. If one device can transmit electricity based on input from dozens of sensors, manual interference isn't necessarily required.