This article originally appeared on Forbes.com.
Most utility executives know there’s tremendous value in the data they have on hand, but few are acting aggressively to make use of it now.
They can learn from the examples of market leaders who are figuring out how to use what they have in hand as they begin to build their broader analytics programs.
For one North American utility, the opportunity came as they sought to improve their ability to forecast power restoration after an outage. Executives wanted to narrow the four-hour window that, on average, they used to predict when power would be restored—not only so that they could make better use of resources, but also to provide more accurate information to customers waiting for their electricity to come back on.
It found the solution in looking at more data that teams already had access to but weren’t using to estimate restoration times. By including weather and real-time locations of crews in its forecasting model, teams were able to make better informed and more realistic decisions about who was available for repairs and when they could complete them. Their new model doubled their accuracy, resulting not only in more efficient deployment of field staff but also in higher customer satisfaction scores.
Utility executives around the world recognize this kind of potential in data and analytics, but most don’t know where to begin. More data flows their way all the time, from smart meters and other sensors on the increasingly intelligent grid, as well as from traditional sources of data about their operations. But many are waiting when they could be acting.
Some utilities, like the North American one that improved its restoration forecasting, are already getting started, using the data and the tools that they have in hand to improve the ways they work, in at least three ways.
One of these is reducing costs. Analytics can help large utilities save up to tens of millions of dollars in capex and operational and maintenance expenditures by helping them improve operations (which can reduce call center volumes or help manage vegetation better along their power lines), optimize their capital deployment (by identifying the most efficient ways to reduce risk) or understand their procurement better (weighing spending against value).
Better data can also improve reliability. Advanced analytics can help prevent outages through more accurate predictions about when to replace failing equipment or through quicker, sometimes automated, dispatch of repair personnel thanks to real-time identification of problems.
A third way that data can deliver value is through better customer engagement. Data analytics can help utilities understand customers and their energy use better—insights that help utilities design new products and services that fit customers’ needs, such as demand-side management programs that reduce electricity use at peak times. And, as noted, more sophisticated analytics can help provide more accurate information to customers about power outages, grid updates, and repair work by field crews, all of which can raise customer satisfaction.
For executives who want to get started, the first important step in unlocking that value is to realize the potential and begin to experiment with the tools they already have. Some utilities are already creating small centers of excellence tasked with advanced analytics projects. These teams typically combine skills from the business with more advanced data-science capabilities. Once teams are in place, they begin to identify and act on the opportunities with the highest potential value.
As with any fledgling effort, it’s important to secure a few quick wins to build momentum. Teams should think about using existing data and off-the-shelf analytical tools—as the North American utility did to improve its outage restoration predictions. From these initial explorations, they will begin to build up their capabilities and extend their growing expertise to more of their business.
It can be helpful to think about three levels of complexity—basic descriptive systems, moderately complex predictive systems, and more advanced prescriptive systems—as a way of setting aspirations and developing strategic plans to build up capabilities that will help utilities achieve their data goals.
As they develop their analytics capabilities, utilities will find they need to adopt more rigorous standards for capturing, storing and managing data. Cleaning up data is a major challenge, requiring painstaking work to rationalize what is frequently a haphazard collection of systems and restructuring them along common lines so they can share and better use the data at hand.
Utilities will also have to get better at understanding and using advanced modeling techniques to discover insights in the data. To help with this task, most utilities are beginning to complement their workforces with data-savvy talent that brings advanced analytic, modeling and visualization skills to bear on these efforts.
Utilities should start by picking a key objective or subject area and developing targeted analytics to build momentum. Such areas may include outage processes, materials management, demand-side management or asset analytics. Focusing on a single area can help in several ways.
- First, it focuses the organization on exploring advanced analytics, but within the framework of a single design issue, with fewer stakeholders than will be required as the programs broaden.
- Second, the initial data engineering represents the heavy lifting that can produce results elsewhere in the organization. Once data sets are cleaned up and merged, additional and more advanced applications for the data can proceed quicker.
- Finally, a single effort can begin to generate momentum, and the organization can apply the lessons learned to future projects.
The journey may take years, but the benefits are sizable: millions in savings that would not be possible without big data and advanced analytics.
Stephan Zech is a partner with Bain and Company in Los Angeles. Christophe Guille is a principal in Bain’s San Francisco office.