by Rodger Smith, Oracle Corp.
Smart meter and smart grid technology deployments are creating exponentially more data for utilities, providing the opportunity for granular insight into customer usage patterns and the status of the distribution network. This will allow for better planning and optimization of utility supply chains, as well as pre-emptive network and outage management.
But according to an Oracle Utilities survey of 151 North American senior-level utility executives earlier this year, accessing, analyzing, managing and delivering this information—to optimize business operations and enhance customer relationships—is daunting.
The average utility with a smart meter program has increased the frequency of its data collection 180 times, collecting data once every four hours rather than once every month. In addition, outage management and distribution management systems, customer data and feedback, alternative energy sources, and advanced sensors, controls and grid-healing elements are all feeding information back to the utility, all contributing to the flood of big data that is surging into the utility.
Information to actionable intelligence
Nearly half of those surveyed—all of whom have implemented at least one smart metering pilot project—said they are pulling in the data but not using it effectively. Specifically, they identified two problems. Despite improvements, 45 percent of utilities said they still struggle to report information to management staff fast enough to make operational decisions and enhance customer experience, and 50 percent said they miss opportunities to deliver useful information back to their customers. Furthermore, 64 percent of those utilities surveyed said the need to improve their ability to translate information into actionable intelligence and leverage data for strategic decision-making is one of their top three priorities.
So what is holding utilities back? The No. 1 issue, they said, is a lack of talent within utilities to execute data analysis. Seventy-one percent of those utilities surveyed put this at the top of their lists, followed by visualizing and comprehending (69 percent) and analysis and processing speed (65 percent). This is a change from how utilities have done business and a big challenge. Those who take immediate steps to identify and hire those with the needed skill sets—a combination of industry expertise, as well as analytics skills—will be able to accelerate intelligence delivery, increase operational efficiencies, better understand the usage patterns of their customers and enhance customer experience and relationships as a result.
Some already are taking early steps. For example, one utility executive said his company hired a mathematician intern to assist with predictive analytics in an effort to boost operational change.
Understanding customer behavior
Utilities are beginning to make the move from descriptive analytics, or the rear-view mirror insight, to predictive analytics, including statistical analysis, forecasting, predictive modeling and optimization. The push for customer segmentation during the past few years is being refined further: The ability to leverage customer analytics and other customer insights allows for an even finer customer segmentation around value points, including usage information, socioeconomic information, customer service records, call center, other interaction and more.
Customer value is a large key business value segment that can give utilities with advanced metering infrastructure data some early wins in analyzing energy use data for customers and pinpointing potential causes for high energy bills. Analyzing customer payment patterns can pinpoint late-paying customers who might need assistance in early-win opportunities.
The 360-degree view of the customer starts with interval data, another utility executive confirmed. He said that with advanced analytics combined with a single view of the customer, utilities can use customer insights to drive informed, interactive and personalized customer service.
Data has provided a forensic look into the past, but utilities need to look forward to predict what their customers likely will do and when their assets likely will fail.
Improving operational excellence an economic imperative
Government and utilities touted the economic benefits, but as smart meter deployments began, mature projects eclipsed what was expected in the original business plans. At the beginning, the industry expected the greatest gains would be on the demand side, and those gains have been proven. But the industry underestimated efficiencies that would be gained in the operational side of the business.
To maximize those efficiencies, utilities must work across the historic silos. Meter data is useful to more than customer billing and metering departments. This is another change in how utilities do business. Operational efficiencies will require a cross-silo approach to data and processes and a focus on enterprise data and enterprise information management to get us there.
Many utilities are still all over the board in how they will use the information available to them. As the silos of application and expertise continue to grow, that one version of the truth is increasingly important. As well, securing and managing the data the same way one does with physical assets and understanding the life cycle of that data, keeping it current, and increasing its value requires a unified approach.
Low-hanging fruit, new opportunities
Early data analytics wins help build the business cases for further opportunities. Predictive analytics around customer behavior, made possible by a mashing up of meter data management information and other available customer data, offers low-hanging fruit.
Once one has applied analytics to improve customer empowerment, the next steps depend upon the individual utility’s greatest needs. Questions to consider include: If myriad devices and systems on your grid are providing big data, which is the most crucial data stream for your business to analyze? What data challenges—revenue protection, outage management, condition-based asset management, or the integration of distributed renewable resources—are the most cost-effective, and in what order?
The optimization of operations is one opportunity yet to be tapped by most utilities. Business values and needs drive the types of applications tapped first, and building short-term wins on a measured scale and scope has tended to be the norm.
The information the smart grid brings is only as good as the models and the data architecture we build. From those, proactive analysis and predictive assessment can grow.
Rodger E. Smith is senior vice president and general manager for the Oracle Corp. tax and utilities global business unit. He is the former president of Enterprise Management Solutions, the management consulting division of Black & Veatch, and previously held positions with PricewaterhouseCoopers and Southern Co.