Utilities know what they want to accomplish and are confident analytics can deliver, but the jury is still out.
In late 2016, Utility Analytics Institute conducted a survey (full report), sponsored by ABB, that examined utilities’ use of and approach to analytics, and the results were presented in a webinar in September.
Among the main takeaways was an almost single-minded focus on what challenges utilities are using analytics to address. The top response wasn’t improving reliability, integrating renewables or customer service, though one could make an argument that all of these play into what respondents identified as their top pick.
By far the most common answer was “operational efficiency and cost reduction.” As UAI executive director Mark Johnson noted upon seeing the results, this should not be a surprise.
“Economic uncertainty, flat to declining revenues, changing rate structures, the increasing adoption of solar, and evolving utility revenue models explain why survey participants would put improving operational efficiency and cost reduction at the top the list of utility challenges,” he said.
So, how are utilities doing in their analytics journey? The survey provided several encouraging data points, but these were tempered by other findings. Some of the usual suspects like budget, executive sponsorship and IT support were all relative non-issues, ranking well below other road blocks. Respondents also showed a high level of confidence that analytics can help address their business challenges. A large majority (86%) of respondents said they were “completely” to “somewhat” confident.
That’s good, but even those in the “confident” camp also reported difficulty in moving beyond simple data collection and analysis to actually applying what they’ve learned. While 24% of respondents said they can answer “what happened,” only 9% can speak to “why.”
Among the obstacles identified, data availability and/or access ranked first with 23% of respondents flagging this as a problem, followed by lack of centralized data storage (19%) and skilled staff (19%) tying for second. The silver lining here could be that the first two are addressable through capex spending, whereas staffing could remain a sore point for the foreseeable future. It’s a tough sell, after all, to convince a data scientist to come to work for a utility instead of a Google or Facebook.
It’s early days for analytics in the utility sector. Many companies have yet to make any formal organizational changes to accommodate analytics as a function with 24% of respondents saying it was still too early in the process to do so. There is also an understandable gap between large IOUs, which have already made significant investments in analytics, and smaller utilities that perhaps struggle with the business case and internal capability.
Still, given the how industry trends are driving the imperatives of operational efficiency and cost reduction, it’s likely that even smaller players will at some point look to analytics to deliver them into the power industry’s new reality.