Utility analytics best practices
- Posted on October 9, 2012
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Editor note: This article was originally published in Intelligent Utility Magazine September / October 2012
When I mentioned that the Utility Analytics Institute was working on the Analytics Case Study and Best Practices report, some folks quipped, "Utility analytics best practices? Isn't that an oxymoron?"
Despite the naysayers, we managed to pull off a report that does indeed discuss best practices in utility analytics. Of course, most utilities are still in the early stages of advanced analytics, so the best practices today may not be best practices tomorrow. Effective practices will evolve over time as utilities gain increased confidence and capabilities with advanced analytics.
The full report gets into all of the details along with multiple case studies, but here is a quick snapshot of key best practices utilities should consider for various analytics initiatives, including grid optimization, asset optimization, customer operations and customer engagement.
Grid optimization analytics assist with optimizing the operation of the grid in order to minimize power losses and maximize efficiency and quality. With the influx of real-time smart grid and smart meter data, utilities are moving toward predictive analytics that leverage real and near-real-time data. Although utilities are leveraging more real-time information, they are still in the early phases of grid optimization.
In terms of best practices, look at the possibilities to leverage existing systems and data and work with vendors of your existing technologies to improve their analytics capabilities and data support. Be prepared to handle other business processes that will be affected by the need to operate and maintain a smart network and advanced analytics. Leverage new-found analytics capabilities to identify and evaluate new opportunities and pursue only those options that provide tangible value and that make economic sense based on your analysis. And be prepared to address concerns in-house staff may have about relinquishing control of the grid and turning it over to automation.
Asset optimization analytics assist with optimizing the performance and reliability of grid assets. With asset optimization, the analytics focus centers on getting the right data in place and ensuring its quality. Ongoing smart grid and grid optimization analytics efforts may move utilities into more advanced stages of the analytics, but for now, most utilities are squarely focused on asset data and its organization and accessibility.
In terms of best practices, ensure that assets are categorized in a way that can be maintained effectively and that your technologies can support the level of granularity needed. Analysis must be applied to information to make it useful, but you must ensure that the quality of the information is good. You also should make plans for maintaining and analyzing new digital assets. To help gain traction, tie your asset management analytics work into smart grid efforts, if possible. Change management is a particularly important component for asset optimization analytics, with buy-in from the field being paramount.
Customer operations analytics focus internally on improving the efficiency and effectiveness of a utility's customer operations. Essentially, meter data rules, and companies are figuring out how to best manage the onslaught of meter data. However, even though data and data integration are important, customer operations efforts are also focused on the predictive and real-time uses of analytics.
In terms of best practices, meter data is important, but also consider other data sources and technologies that support customer operations analytics, including those in engineering, operations, dispatch and so forth. Determine what really requires a "set of eyes," and use analytics to automate that analysis so employees are freed up to focus on more complex customer cases or problems. Customer satisfaction rates are important. And, although you may be early in the process, aligning the right team and dedicated resources will be important as customer operations analytics efforts expand.
Customer engagement analytics support utility interactions with customers and improve their relationships with the utility through improved service, lower costs and better customer experiences. As with customer operations, some utilities are actually on the cusp of a significant business transformation as they change the way they engage with customers. Data is important for customer engagement analytics, but many companies are massaging data that they already have and are drawing upon outside data resources.
In terms of best practices, analytics should help determine precisely which combination of attributes is most predictive for customer behavior. In addition to internal analytics, look at technologies that empower consumers to access and use analytics the way they want, in the format they want. No more mass mailings. Determine the best possible target customers for each enrollment-based program and service offered. This will take more time and resources up front to invest in these areas, but over time, you can expect operational efficiency gains. View analytics not as a strategic goal, but rather as a useful tool for supporting strategic goals. Consider not just the cost, but the effect that such things as unwanted email can have on customer satisfaction.
H. Christine Richards is the director of knowledge services for the Utility Analytics Institute, a division of Energy Central.
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