Big data lessons: How to apply analytics to improve utility distribution operations
- Jan 14, 2015 7:00 am GMT
- 286 views
By now, we’ve all heard the term “big data.” Big data refers to the vast amounts of data and information generated and collected both inside and outside of utilities. The rise of big data presents the opportunity to transform business practices across industries ranging from healthcare to agriculture to public services and more. By taking a closer look and analyzing the collected data, we can enhance visibility, discover new insights, improve our decision-making, and optimize processes and, ultimately, outcomes.
But how do we go from collecting enormous quantities of information to building a better business? To get to that critical point, businesses must first educate themselves about analytics and how data can help identify bottlenecks and challenges and highlight key inefficiencies and issues. With the continued deployment of advanced systems, sensors and other data sources, including hundreds of millions of smart meters, utilities are faced with an expanding universe of data that can lead down myriad roads, many of which are unproductive, confusing and downright distracting.
The key is learning from those who came before to zero in on the right information that’s critical to driving program success.
At Nexant, we’ve helped hundreds of utilities around the world deploy software and best practices to improve visibility and operations with data analytics. The benefits for utilities can be significant and include improving visibility, operational and financial efficiency, reducing risk and enhancing customer satisfaction. This overview presents the key lessons and best practices we’ve learned over time to achieve these results, organized around four critical steps:
- Develop a team of experts
- Choose a partner
- Assess existing business processes
- Identify core challenges and questions
This piece explores these vital steps and then outlines five key areas specific to distribution planning and operations where data analytics can provide greater value.
Develop a team of experts
It is critical that a utility forms a team of subject matter experts across all aspects of the organization to identify all available business challenges and pain points. By gathering experts from distribution planning, distribution operations, customer service, marketing, billing and additional business units, utilities can develop a more robust picture of system-wide challenges and identify unsolved problems that affect multiple aspects of the organization. Additionally, by including executives across the organization, utilities can groom “evangelists” to spread the word across the organization and ease adoption of analytics processes. To accelerate this process, utilities may need to hire people who specialize in analytical skills or train current employees in the use of data analytics.
Choose a partner
Once the utility has developed an internal team and educated them on best practices for deploying analytics, I strongly recommend picking a partner to help guide the process. This can be essential for utilities that lack in-house data analytics experience or seek an outside view. Utility partners have worked with a range of utility customers and can implement battle-tested best practices and processes that make a significant difference in smoothly deploying analytics software systems across distribution operational groups, departments and geographies. Utilities should examine a partner’s software and services offerings, previous experience with relevant utilities, and specific end-business results. After all, analytics aren’t worth the investment unless they ultimately help utilities achieve a targeted and specific goal.
Assess existing business processes
To derive business benefits from a sea of information, utilities must identify and organize critical information about customer base, usage, current and planned distribution infrastructure and existing business processes. Much of this information may already exist in different silos; in this case, the key will be to organize the data in a common location and format for all parties to review and assess. In some cases, utilities will have to work with stakeholders to uncover key patterns, trends and deviations. Your partner can play a key role in helping you gather and organize internal data as well as reviewing external data resources.
Identify core challenges
Using analytics, utilities can review information from all areas of the business involved in distribution to identify relationships, normal and abnormal operational patterns, hot spots and key issues in order to create resolution plans for consideration. From there, utilities can enhance operational procedures and guidelines to address challenges.
Understanding the fine grain challenges in the power grid can be complex and complicated but is key to resolving higher-level business challenges. In our 15 years working with utilities, we have seen a few challenges consistently rise to the top, such as managing outages, rising regulatory pressure and grid modernization challenges around the impact of distributed energy resources and demand side customer initiatives. It can be difficult to know what steps to take to resolve these challenges, so we encourage utilities to work with their partner to identify the best approach for their unique business model.
This is part one of a two-part series. Learn how to apply analytics to deliver results in part two next week in our Intelligent Utility newsletter.
Milani is Nexant's senior vice president of software and chief technology officer. He directs the strategy, development and delivery of Nexant’s customer-centric software platforms that help utilities align strategic planning, grid operations and demand side management to improve customer engagement, boost operational efficiency, reduce risk and deliver improved business results. Milani is an industry-recognized thought-leader and expert on cloud computing and SOA with more than 20 years of experience in product design, development and distributed systems architecture spanning the evolution of distributed systems.