By Bradley Williams, Oracle Utilities vice president, industry strategy
As operational processes and tools within the electric grid have increased and matured, so too has the amount of data being collected by intelligent grid devices. Focused analysis of this data is providing utilities with many opportunities to better manage the enterprise based on data-driven decisions.
There are a number of key opportunities in which data analytics can play a pivotal role in improving a utility’s overall focus on its mandate of safety, reliability, operational excellence and enhanced customer service. Analytics provides utilities with the increased ability to better manage their businesses, and their customer relationships, across the enterprise.
Step 1: Ask the right questions
But first, utilities need to decide what they want analytics to do for them. A good, enterprise-wide exercise begins with questions like these: “What are our current business needs? Where are our areas of opportunity? What else can we do? How else can we achieve value? How can we leverage our analytics to change our business processes? How can we better drive our decision-making?”
Answering these questions will mean looking more deeply at what you want analytics to do for you, and then sharing data and collaborating across the utility enterprise silos and experimenting with mash-ups of the disparate types of data you have collected. For example:
• What data can we aggregate and then analyse across all silos to give us a deeper, more holistic understanding of our businesses?
• How can we use data to better optimise our assets?
• How is customer segmentation impacted by load profile?
• How can we best use meter data to identify overloaded distribution transformers and other assets at risk?
The possible questions are limited only by an individual utility’s needs, interests, and even imagination. These “what if” questions lay the groundwork to provide increasingly stronger business cases for the utility to pursue.
Step 2: Define and build your use/business cases
There are vast opportunities for the use of analytics in all areas of the utility value chain, with utilities finding solid returns in everything from customer service and billing to non-technical losses (theft) and power distribution.
It is important to point out that there is no one analytics solution for all utilities. While adding analytics to the enterprise can seem like an overwhelming task, there are many ways to go about it. For example, some utilities have used cloud solutions to go after quick wins first, tackling revenue protection or reliability issues, and then moved on to other areas within the business. One of the distinct advantages of using a cloud solution at the outset is that it is less expensive and faster to implement than traditional approaches to utility data analytics.
Examining other utilities’ best practices in analytics is a practical first step, in order to be able to show investors, regulators, customers and other shareholders the intrinsic value of the project. This is the value of collaborative analytics.
Whether you want to increase customer satisfaction through more clearly targeted interactions, improve reliability through more effective monitoring and proactive maintenance, enhance operational efficiency aided by better planning and execution, or increase safety through a clearer understanding and mitigation of hidden risks, there are compelling arguments for leveraging analytic processes in order to create value-added insights on both the customer and operational sides of the enterprise.
• Improved customer satisfaction through segmentation and communication personalisation. Customer data analytics, encompassing both structured meter data and more unstructured customer data (such as social media and customer relationship data); can provide the utility with a means to better provide its customers with information about their usage patterns. Analytics can also provide information to enable the utility to be more proactive with its customer service.
• Improved reliability through monitoring and proactive maintenance. Operational data analytics can aid a utility by providing both an historic and a real-time view of the utility’s operations. Bring predictive analytics into the mix, and the utility can then begin to compare historical data to identify trends in usage and asset health, overlay weather maps and forecasts, and forecast demand to more accurately predict energy or water usage, grid impact of renewable generation, and more. Being able to better analyse and predict asset health and manage potential outages or leaks can turn what has historically been a reactive, “run to failure” utility approach to asset and outage management into a much more proactive, predictable, cost-reductive process.
• Improve operational efficiencies through better planning and execution. From revenue assurance and employee utilisation and prioritised field work to the reduction of infrastructure and asset replacement costs, predictive analytics can leverage data from multiple sources, across organisational departments, for new insights into utility operational performance.
•Improve safety by understanding and mitigating risks. Analytics can be used to proactively approach vegetation management, as well as asset management, eliminating unnecessary outages. Public safety is improved, as well. Besides being able to analyse usage spikes for the benefit of the customer (a potential water or gas leak, or a malfunctioning appliance), usage spikes can also indicate a potential public safety hazard a utility can act upon quickly as soon as it is identified.
Step 3: Consider an enterprise analytics strategy
Just as the specific drivers for each utility will be different, so, too, will its approach to enterprise analytics. Again, it will involve questioning the usefulness of your utility’s historic approach to data: How do we approach data as a valuable enterprise resource in a project-oriented culture?
Many utilities complete their advanced metering infrastructure (AMI) deployments, and begin to bring more frequent interval data back to the enterprise to better feed transactional applications such as customer information systems, customer care and billing, and operational applications such as the advanced distribution management and outage management systems, it becomes increasingly more important that the “whole picture” is seen and drives additional value, rather than simply the siloed needs within each particular program or project.
Beyond the overall need to utilise the new data available to improve the utility’s enterprise capabilities, utilities’ specific data analytics needs and approaches will likely remain as individual as the utility itself, whether that is in terms of specific projects or its approach to enterprise analytics as a whole. Utility leaders must create a culture that embraces analytics to drive continuous improvement.
Technology integration, including a strong foundational information management platform and business infrastructure for analytics, has become increasingly important in this enterprise-wide approach.
In time, new analytics processes will migrate into standard operating procedures, and will be replaced by even more potentially complex and compelling analytics issues, as utilities continue to more finely hone their processes and pursue new opportunities.