I N D U S T R Y S P O T L I G H T. T h e Grow i n g Appeal of Ad va n c e d a n d P r e d i c ti ve Analytics f o r the Utility I n d u s t r y

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1 (% of respondents) I N D U S T R Y S P O T L I G H T T h e Grow i n g Appeal of Ad va n c e d a n d P r e d i c ti ve Analytics f o r the Utility I n d u s t r y October 2014 Sponsored by SAP Advanced and Predictive Analytics in the Utility Industry Utilities are still maturing in their use of analytics, as reflected in the distribution of utilities across the stages of IDC's Big Data and Analytics (BDA) Maturity Model. IDC's research shows that while 64% of utilities that have embarked on an analytics initiative experienced benefits that met or exceeded expectations, more than four times as many utilities are in the lowest two stages of maturity as in the highest two stages of maturity, with 63% of utilities in the middle stage of maturity (see Figure 1). F i g u r e 1 Big Data and Analytics Maturity Distribution Across Stages: Utilities Ad hoc Opportunistic Repeatable Managed Optimized n = 59 Source: IDC's Big Data and Analytics Maturity Benchmark Survey, 2013 While the utility industry is continuing to discover and leverage the power of current analytics technologies, the technology sector is continuing to develop new technologies that offer great promise. Predictive analytics, in particular, has the potential to deliver critical capabilities to the utility industry in the area of assets, operating efficiency, new revenue opportunity discovery, and customer engagement. IDC 1786

2 Predictive analytics is in a category of what IDC calls advanced and predictive analytics (APA). (See Worldwide Advanced and Predictive Analytics Software Forecast and 2013 Vendor Shares, IDC #249054, June 2014.) Advanced and predictive analytics software, which includes data mining and statistical software, uses a range of techniques to create, test, and execute statistical models to discover relationships in data and make predictions that are hidden, not apparent, or too complex to be extracted using query, reporting, and multidimensional analysis software. The adoption of these technologies continues to vary with the availability of data, data quality, analytic orientation, and analytic skills in industries, but the case for predictive analytics continues to grow. The advent of in-memory technologies now means that predictive models can produce analysis and reporting in time frames that are much closer to real time than those of more traditional advanced and predictive analytics, adding to the value proposition of APA solutions. We are beginning to see, too, a growing focus by vendors on prepackaging and automating more of their functionality for the broader base of business users, which makes these solutions even more interesting. As utilities come under increasing pressure from a set of converging trends unique to their industry (increased data volumes, pressure for increasing operating efficiency, growth of distributed generation, rising extreme weather events, evolving customer expectations), they will increasingly turn to advanced and predictive analytics technologies. Predictive Analytics in the Utility Industry Asset Management Over the past decade, there has been an evolution in thinking about the best approaches to asset management (break/fix, preventive, condition based, predictive, reliability centered, and criticality based). The holy grail of asset management has been the capability to gauge asset health, predict in real time the likelihood and timing of asset failure, and execute the maintenance plan most appropriate to a particular asset or group of assets (run to failure, repair, replace, invest in redundant systems, etc.). Predictive analytics offers the potential to improve on the reactive approach by using more powerful analytics methods and technology to go beyond assessing what has happened to what is likely to happen. This changes the game from addressing maintenance issues and failures more rapidly to addressing asset and maintenance issues before they arise and before failures occur. The approach changes from time-based maintenance to risk-based asset management identifying where the most critical risks are and allocating resources to the areas of greatest need. Utilities are just beginning to deploy predictive analytics for asset management; for an industry with an aging infrastructure and a strong interest in efficiency, this use of predictive analytics is sure to grow. Transformer Loading The transformer is a critical asset for utilities that deserves special attention. Many utilities have been working on improving capital planning and transformer loading for some time. The ability to apply predictive analytics to transformers bears particular promise. Transformer loading has garnered considerable attention for some time. The advent of analytics, as well as the availability of smart meter data, has enabled more accurate transformer load management. Advanced and predictive analytics now extend these capabilities to allow the increased use of leading indicators (such as meter data, weather patterns and forecasts, and equipment specifications) to more accurately predict future performance and transformer issues and failures. The operation of an electrical grid puts stress on numerous assets, with transformers bearing the brunt of this stress. Therefore, being able to assess the condition and operational characteristics of IDC

3 transformers and then apply predictive analytics to these operating parameters could enable much greater predictive capabilities about the asset health of the grid and presage likely future issues and even outages. If a utility extends this predictive visibility across the enterprise, then the utility has the capability to more accurately direct maintenance and control resources or reduce unplanned outages, as well as increase resource and operating efficiency. This starts with better visibility and predictability with regard to transformer loading, monitoring transformer operating conditions, and applying advanced analytics for better predictability of transformer failure. Transformer loading is an area where the traditional approaches to asset management are likely to increasingly include the use of predictive analytics. Outage Analytics As the threat and experience of severe weather increase, unplanned weather-related outage management is a hot-button issue that is garnering increasing attention. Utilities are expected to meet service objectives, from both regulators and increasingly demanding consumers. If severe weather trends continue to rise, with an increase in the frequency, severity, and unpredictability of weatherrelated disruptions, utilities will experience increasing impacts to revenues, operational costs, and customer satisfaction. Enter predictive analytics. The opportunities for outage-related predictive analytics span transmission and distribution. Predictive analytics will be increasingly used to provide better asset management and more sophisticated real-time weather forecasting and storm tracking as well as for pre-storm preparations and post-storm cleanup. Analytics in weather forecasting will give way to the use of predictive analytics in more geospecific, real-time storm tracking that is integrated with real-time outage reporting to fine-tune storm tracking and the projected number and locations of outages. The predictive analytics system will use real-time storm tracking together with detailed asset information (including predictive failure data) to identify likely impacts on assets. As these predictive analytics capabilities improve, the utility will have access to tools and technologies that provide greater detail on asset health in real time and the impact of severe weather conditions and events. The focus shifts from reacting to unanticipated weather events to anticipation, predictability, and proactive, micro-targeted preparation and staging. Customer Engagement As the utility industry goes through its present evolution, part of this is a transformation in the relationship that most utilities will need to have with a customer base that has increasing expectations for lower energy costs and better service. The first phase for most utilities has been applying analytics to smart meter data to harvest available insights around, for example, individual customer energy usage or customer segmentation by consumption patterns. The next phase is the multichannel phenomenon, which is quickly changing the game for utilities. New mobile devices have entered the picture, giving customers more ways of interacting with utilities. Customers have rising expectations for when and how they will interact with their utilities with much more change still to come. Utilities, for their part, have to focus on increasing customer satisfaction even while customer expectations are rising. The nature of the customer relationship is evolving at the same time that 2014 IDC 3

4 utilities are going through their transformation. While multiple pieces are moving on this industry chessboard, utilities need to enhance their capabilities to understand, relate to, and respond to customers and their interests and issues. This goes beyond the challenge of optimizing the call center. Predictive analytics offers the possibility of even greater customer insights than utilities are getting from smart meter data or from the call center. As channels of engagement multiply, and social channels and technologies (such as gamification) play a greater role, utilities will find it even more important to be able to leverage the customer insights that can be gained from across the multiple channels of engagement. Beyond using advanced analytics for more sophisticated insights around energy usage, utilities will be looking at predictive analytics for better call center utilization, early identification and intervention of potential customer credit issues, and harvesting better customer insights from a broadening set of channels to create better customer engagement. The Business Value of Predictive Analytics in Utilities Calculating return on investment (ROI) is notoriously difficult in predictive analytics because of the number of cost factors; the varying assumptions that have to be made; the unanticipated, but real, benefits that may result; and the difficulty of assigning a value to the ability to make a better decision. There may be considerable concern about the return on investment or payback in any particular predictive analytics scenario, but there is little question that if a utility can peer further into the future with the enablement of the right algorithms and modeling, this improved visibility and vision can deliver better decision making and execution. At a time when asset management is more important than ever for utilities, predictive asset management means that utilities can make much better decisions to assess asset health, predict the likelihood and timing of asset failures, and better tailor the appropriate maintenance approach to particular assets or groups of assets. One utility used predictive analytics to achieve a productivity gain of 20% for service trucks and an additional 20% reduction in fuel costs because of fewer truck rolls. Other utilities are likely to find business cases in reduced equipment failures, better prioritization of capital maintenance and replacement, reduced or shortened outages, better use of capital, and increased productivity in other areas. In the area of outage analytics, predictive analytics can be used not only to anticipate where and when outages will occur but also to better assess the impacts of outages. One U.S. utility used advanced analytics to trace the cause of failure rates of equipment to the relative familiarity of host and guest crews with the installed equipment, ruling out manufacturing and other issues through a root cause process. Many utilities will continue to insist on calculating an ROI for their analytics project. Other utilities will approach the ROI exercise with some skepticism and undoubtedly look more favorably upon the value proposition of deeper insights that predictive analytics delivers and the better decision making that can result from these deeper insights. The next phase of maturity is using predictive analytics to sense customer sentiment from the range of social channels to predict future customer wants and needs, engage customers with more pinpoint precision, and create a much richer customer experience IDC

5 Looking Forward The inevitable question is whether utilities will have the requisite set of internal skills to leverage these advanced and predictive technologies. While advanced analytics tools and technologies require advanced analytics skills to realize their full potential, other technologies on the market today are designed to deliver faster time to value from a broader range of analytics skills. This lowers the skills barrier to entry and enables a wider internal business constituency to take advantage of the power of some of these analytics technologies. Converging trends (increased data volumes, increasing pressure on the efficiency front, distributed generation, extreme weather events, rising customer expectations, increased use of electric vehicles, and renewable energy resources) are on the horizon, if not the doorstep, for utilities. The convergence of these trends is serving to create increasing pressure on the utility industry in a multitude of ways. Predictive analytics has the potential to deliver critical capabilities in the area of assets, operating efficiency, new revenue opportunity discovery, and customer engagement. Although the utility industry has not always been as fast as other industries to adopt leading-edge technology, advanced and predictive analytics address some critical issues for the industry. Technologies with a lower barrier to entry that can deliver quick time to value may be just the ticket for many utilities. Recommendations for Utilities Consider including advanced and predictive analytics in your technology strategies and analytics road maps. Assess how you are handling asset management and maintenance today and how well your current processes provide visibility into the future performance and time to failure of critical assets. Review your strategy for monitoring the condition and life-cycle stage of transformers. Are your existing capabilities for monitoring the condition and performance of transformers meeting current needs? Develop a plan for leveraging analytics for outage planning, preparation, and remediation that extends out at least two years. Will you have the capabilities in two years to respond to rising severe weather trends and rising customer expectations? Will you be leading or chasing the state of the art for outage analytics? Take a frank and aggressive look at how well you are engaging customers today, what you know and don't know about your customer base, and where you need to be in three years to deliver exceptional customer engagement. Start developing a plan to be there. A B O U T T H I S P U B L I C A T I ON This publication was produced by IDC Custom Solutions. The opinion, analysis, and research results presented herein are drawn from more detailed research and analysis independently conducted and published by IDC Energy Insights, unless specific vendor sponsorship is noted. IDC Custom Solutions makes IDC Energy Insights content available in a wide range of formats for distribution by various companies. A license to distribute IDC Energy Insights content does not imply endorsement of or opinion about the licensee. C O P Y R I G H T A N D R E S T R I C T I O N S Any IDC Energy Insights information or reference to IDC Energy Insights that is to be used in advertising, press releases, or promotional materials requires prior written approval from IDC Energy Insights. For permission requests, contact the IDC Custom Solutions information line at or Translation and/or localization of this document requires an additional license from IDC Energy Insights. For more information on IDC Energy Insights, an IDC company, visit For more information on IDC, visit or for more information on Custom Solutions, visit Global Headquarters: 5 Speen Street Framingham, MA USA P F IDC 5

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