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Tdworld 9578 Data Metrics Analytics Nicoelnino
Tdworld 9578 Data Metrics Analytics Nicoelnino
Tdworld 9578 Data Metrics Analytics Nicoelnino
Tdworld 9578 Data Metrics Analytics Nicoelnino
Tdworld 9578 Data Metrics Analytics Nicoelnino

How Do We Build the Long, Winding Road to Data & Analytics?

Aug. 23, 2017
Part 2: SAS Institute recently completed a global survey of utility analytics leaders

As utilities face a daunting set of challenges ranging from renewables to shifting customer demands and more, analytics is proving to be an important part of how utilities are meeting these challenges. This article is Part II of a two-part series that looks at the results of a global utility industry survey conducted earlier this year that set out to answer some of the questions around the current and developing state of utility analytics.

In Part I of this series we looked at how utilities are organizing and using new tools and technologies to leverage the power of analytics. In Part II we will be looking at which business areas are showing the most potential for how analytics can be an important part of solving problems and driving value for both the utility itself and its customers and stakeholders.

One of the crossroads that utility analytics leaders often come to in their analytics journeys is that of how, if, or when to build an enterprise analytics platform. Many of the earliest utility analytics efforts could be characterized as “point solutions”, and while there have clearly been some successes with this approach, a better long-term, corporate ROI-centric approach is typically found the development of an enterprise analytics platform that can be leveraged across multiple business units in a utility.

Figure 1 below shows us that nearly two-thirds of utility analytics leaders surveyed are firmly on a path to enterprise analytics. It is worth noting here that this enterprise approach does not preclude utilities from developing business area-specific analytics applications, but this approach will enable many other benefits, including a better long-term cost basis, data sharing, and a better opportunity to use analytics resources like staff expertise across traditional organizational boundaries.

Figure 1. What is your utility’s current status regarding implementation of an enterprise analytics platform?

Looking more specifically at analytics application in the business units, Figures 2 and 3 below look at the relative and absolute importance of analytics application areas.

Figure 2. Please indicate the relative priority to your utility of each of the following analytics application areas.

Note that energy forecasting has bubbled to the top in both the relative and absolute scales. This is likely driven by the influx of smart meter data, creating opportunities for more accurate forecasts that can save a utility literally millions of dollars in a relatively short period of time.

Smart meter analytics is also consistent, in second place on both scales. This will likely evolve as utility operations and customer analytics leaders look to leverage smart meter data in new ways to improve their business operations, possibly starting with asset management on the operations side and more granular customer segmentation on the customer side.

Figure 3. Of the following high-priority analytical application areas, which is the one most important to your utility?

Given all of the data presented in this article series, where on the analytics path does this leave the utility industry?  The findings suggest that:

  • Open source tools and cloud-based solutions and tool all have a place in a utility’s analytics approach. They keys are understanding both the benefits (typically lower initial costs and readily available) and the limits (compliance, scalability, productivity, total cost of ownership) of these technologies.
  • Building an enterprise analytics platform is often the best approach for a long-term corporate ROI, while also providing opportunities for leveraging data and skill sets that have historically been siloed.
  • Energy forecasting is a leading example of how utility organizations can leverage their newly data-rich environments for both operational and financial improvements. Grid and customer analytics applications follow closely behind.

As you move forward in your analytics journey, keep these guideposts in mind, and we’d love to learn more about your journey!

The Utility Analytics Institute (UAI) is a corporate membership-based organization consisting of more than 110 operating utility companies and leading analytics solution providers. UAI’s mission is to enable its members to realize desired business outcomes using data analytics. SAS is a long-standing member and highly valued UAI member.

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