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RESEARCH BRIEF

Smart Grid Data Analytics for Business Intelligence

Daniella Muallem Bob Lockhart

Associate Analyst Research Director Published 3Q 2013

1. EXECUTIVE SUMMARY

Faced with the prospect of a data deluge resulting from the deployment of smart grid technologies, utilities are trying to understand the potential for data analytics applications for business intelligence beyond those provided by traditional business intelligence (BI) tools. BI analytics can leverage smart meter data and asset monitoring data to improve efficiency of meter-to-cash operations, enhance revenue assurance, provide greater visibility into system performance and asset utilization, and enable load and generation forecasting for energy trading or production planning activities.

To date, smart meter deployments have given the biggest push for BI analytics. Utilities that have deployed smart meters and already have access to massive volumes of new data are looking for ways to add value to costly implementations. While meter data management (MDM) continues to be the preferred choice for utilities to manage smart meter data, MDM vendors have moved to differentiate themselves by offering enhanced BI analytics. As such, BI analytics will tend to follow adoption of MDM (Chart 1). Navigant Research expects North America and Europe to remain major MDM markets with significant activity likely to occur in Asia Pacific as a result of smart meter deployments in China, India, and Japan. In the longer term, MDM will only be part of the story. To fully realize the potential for data analytics, utilities will need to overcome the challenges for information technology (IT) and operations technology (OT) data integration, and determine the best approach to manage not only the growing volume, but also the variety of data coming in. The most innovative utilities are starting to investigate alternative approaches to big data management.

Chart 1 Meter Data Management Installed Base by Region, World Markets: 2012-2020

(Source: Navigant Research)

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2. MARKET UPDATE

2.1 Introduction

Business analytics, used to guide business planning and identify inefficient business processes, represents an important component in the BI toolbox for utilities. Within the utilities sector, business analytics have most commonly been used for financial and production planning applications, analyzing historical data to provide information to business users around a set of pre-defined key performance indicators (KPIs) via reports, dashboards, and web-portals. Typical applications include financial and compliance reporting, demand forecasting for energy trading, and customer operations.

The advent of smart grid technologies, which are generating new volumes and variety of data, are opening up opportunities to use data analytics for more advanced BI applications across the energy value chain. The more advanced applications move from a predominantly descriptive approach toward more predictive analytics that will enable utilities to become proactive in their strategies. Smart grid data analytics can enhance forecasting and revenue assurance capabilities, provide greater visibility into system performance and asset utilization, track the effectiveness of DR programs, and improve efficiency of important business metrics such as meter-to-cash operations.

Utilities looking to add value to smart grid deployments and prepare for future developments in smart grid are recognizing the potential for data analytics, but deployments of BI analytics are still largely at the descriptive and reporting stage. Most utilities are only beginning to develop a strategy for smart grid big data that will enable more advanced analytics.

2.2 Drivers and Inhibitors

Utilities globally are starting to consider and implement smart grid technologies, driving the industry toward an era of big data. Data analytics can provide a means to improve the return on smart grid investments by generating actionable intelligence to improve the efficiency of business operations. However, it is not only the volume of data, but also the variety and speed of data coming in that utilities will need to manage, and there remain IT and business culture challenges to overcome.

2.2.1 Market Drivers

» Financial pressures. The bottom line counts for utilities globally. Whether in regulated or deregulated markets, large investor-owned or smaller municipal or cooperative organizations, tough economic conditions put pressure on utilities to reduce operating costs. Many utilities are also subject to regulations that govern how pricing is set in relation to investments made, putting further pressure to justify expenditure. Utilities will look for analytics solutions that improve fraud detection, protect revenue, and support asset management and investment planning to avoid unnecessary CAPEX.

» Growth in smart grid technologies. Navigant Research’s Smart Grid Technologies report forecast that global smart grid technology revenue will reach a cumulative total of $494 billion during the period from 2012 to 2020. Utilities will look to analytics to help maximize their return on costly investments.


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» New smart grid data is big data. The massive volumes of high-granularity data generated from smart meters and devices deployed in the grid, as well as growth in social media and other sources of third-party unstructured data, provide an opportunity for more advanced big data analytics. Unstructured data, such as customer call center data or text data from social media platforms, can provide vital information about issues in the network, but will require a different data management approach than that used to extract data from traditional relational databases typically used by utilities. There is an opportunity for vendors that can bring big data technologies, such as Hadoop, to the utility table. A growing number of vendors across the smart grid ecosystem are looking to add analytics capabilities, increasing availability of solutions in the market.

» Energy efficiency and carbon reduction regulations. Although the regulatory environment varies across different regions, utilities in many countries are subject to regulations to encourage reduced carbon emissions and improved energy efficiency. Advanced analytics solutions will enable utilities to improve business processes and workflow management for regulatory compliance reporting and assessing the effectiveness of energy efficiency programs.

» Changing energy mix and demands. Deployment of large-scale renewable generation, and distributed generation and loads, including renewables, cogeneration, and electric vehicles (EVs) will increase the challenges and risks for utilities to balance supply and demand requiring more advanced load and generation forecasting.

» Integration of information technology (IT) and operations technology (OT). Utilities recognize that the move to a smarter grid will require greater integration and cooperation between IT and OT departments. Although utilities are typically making baby steps in this direction, deployment of analytics solutions that leverage combined data from IT and OT systems will be both a consequence and a driver for IT-OT integration.

2.2.2 Market Inhibitors

» Expense. The high IT costs associated with implementing an analytics strategy – which likely requires new infrastructure for data collection, storage, and processing – not to mention the labor costs associated with complex data integration projects, can be a significant barrier. This is particularly the case where utilities lack a clear understanding of how the strategy could contribute to achieving business goals.

» Lack of data integration. Advanced analytics applications depend on data from both IT and OT systems, as well as third-party, public data sources. Data in utilities is traditionally siloed in different systems and under different departments where legacy assets and IT systems are still very much in use. The complexity of data integration is a major challenge for the smart grid and, consequently, smart grid data analytics.

» Concerns about data quality. Achieving accuracy with predictive analytics requires good data quality. Utilities are sometimes reluctant to embark on an analytics project until they have addressed concerns about the quality of their data.


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» Lack of relevant skilled staff. Existing managers at utilities are unaccustomed to managing the challenges associated with implementing a big data strategy. Moreover, utilities face the looming retirement of many skilled and veteran employees, which implies that they will have a battle to attract and retain qualified workers. Many utilities may lack the personnel to implement an effective analytics strategy.

» Change management. Utilities are increasingly recognizing the need for greater IT and OT integration but this requires a significant cultural change. In a typically conservative industry, there is still uncertainty over how IT-OT integration should be achieved. Moreover, on the front line, workers need to become more aware of the importance of quality of data they supply. Strong leadership will be needed to realize the potential for data analytics.

2.3 Technology and Solutions

The capability continuum for smart grid data analytics for BI moves from descriptive analytics, delivered via traditional BI tools, to prescriptive and then predictive modeling to enable more accurate tools for forecasting, simulation, and optimized planning (Figure 1). Most utilities have yet to move beyond descriptive analytics, but analytics are a key priority for utility IT departments globally. Depending on specific needs, different approaches are being taken from utility to utility and across different regions. While many utilities are still approaching analytics in an application-by-application way, some are starting to look for solutions that can deliver value now, while enabling potential for expansion as data volumes grow.

2.3.1 Technology Issues

A variety of data sources can be leveraged as the basis for BI analytics. Currently, many of the more advanced BI analytics applications are being developed as a consequence of smart meter and AMI deployments, and rely to some extent on smart meter data, frequently sitting on top of an MDM system. Already, a number of meter analytics solutions for BI also rely on data pulled in from other utility and third-party sources, in particular customer information systems (CISs). Moreover, the potential for BI analytics that leverage data from sensors and devices elsewhere in the grid is increasingly being recognized. In particular, data from transformer monitoring systems, to assess and predict transformer health, can provide useful intelligence for investment planners, as well as support operations. In the future, as deployment of intermittent renewables and distributed energy resources (DER) increases, utilities will need enhanced forecasting capabilities, including renewable generation forecasting that takes into account third-party weather data, to support energy trading activities and production planning. BI analytics applications can deliver intelligence through BI dashboards and more advanced visualization tools, as well as web and mobile reports.

Whatever the application, advanced BI analytics require financial data to be linked with operational data, making data integration a central technology issue to address. Until now, this has mostly been handled on an application-by-application basis, with MDM serving as the data repository for meter-based BI analytics applications. As the volume and variety of structured and unstructured data increases, the data management layer will become more critical and the limitations of transaction-based MDM systems will become more apparent. Many approaches and models exist for handling big data, and the more forward-thinking utilities are beginning to examine how their data is managed in that context.


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Figure 1 BI Analytics Capability Continuum


2.3.2 Use Cases

Most BI analytics deployed to date provide tools for standard and ad-hoc reporting, queries, and alerts. However, some more innovative utilities are starting to trial and implement more advanced predictive solutions to support utility back-office processes and financial management across the energy value chain. Customer operations, asset management, and renewables integration are all targets for BI analytics applications that provide input for revenue protection, financial forecasting, investment planning, and reliability performance analysis and reporting, as well as feeding back into other analytical practices that enable consumer engagement and/or grid optimization.

Some BI analytics use cases currently being targeted are:

» Enhanced billing and collections operations: Analysis of smart meter consumption data, combined with pricing information and other customer data including credit rating reports, can enable utilities to offer customized payment plans and ensure that assigned rate classifications match consumption. More advanced analytics, also taking into account seasonal and weather effects, can help utilities forecast customer revenues and identify customers most at risk of nonpayment.

» Fraud detection: Non-technical revenue losses are an ongoing concern for utilities, and analytics can support theft detection. Analysis of meter events including detection of unusual load events can help detect whether meter tampering or energy diversion has occurred. At a more advanced level, analysis of consumption patterns from endpoint and feeder meters can help identify

DescriptiveCalculating billing determinants

Reporting unusual load events

Transformer load monitoring

Meter-tamper detection

Outage reporting

PrescriptiveOutage and outage restoration management analysis

Load forecasting for energy trading

PredictiveRenewable generation forecasting for production planning

Asset health analysis for investment planning

Fraud detection based on customer analysis


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irregularities that might be a result of fraud, providing intelligence on which cases should be considered priority.

» Outage management reporting: Utilities require detailed information on outages for reporting to customers and regulators. Analysis of smart meter data can provide information on number and duration of outages.

» Load forecasting to support energy trading: Analysis of smart meter data can enable predictive modeling of customer demand to support day-ahead purchases and planning.

» Renewable generation forecasting to support production planning: Predictive analytics that take into account weather effects on renewable generation capacity can help align capacity with demand and reduce market uncertainty associated with managing intermittent renewables.

» Operationally focused applications: Transformer load monitoring and asset health analytics can also provide useful information on asset performance to support investment planning.

2.4 Key Players

A number of players are looking to develop business intelligence analytics capabilities within the wider context of smart grid data analytics. At present, the BI analytics vendor landscape is led by MDM players, IT giants, including Oracle, SAP, and SAS, and established players such as Ventyx (ABB), which have a strong presence providing enterprise software to the utility industry. While some MDM players are shifting their analytics focus more toward grid optimization or consumer engagement applications, BI analytics applications still constitute the core analytics offerings of MDM players such as eMeter and Itron. Oracle and SAP, though more focused on applying their core data management and software tools to provide an underlying smart grid data platform, also provide BI analytics applications of varying sophistication. As BI analytics move up the capability continuum, requiring more advanced analytic algorithms and greater integration of IT and OT data, there will be more partnerships between software and data management platform vendors, such as between Teradata and Itron, and/or specialty analytics applications players, such as the partnership between eMeter and Detectent.

2.4.1 Aclara

Aclara, based in St. Louis, Missouri, is part of the Utility Solutions Group of ESCO Technologies, Inc. (NYSE: ESE). The company provides advanced metering infrastructure (AMI), data management, and consumer engagement solutions for water, gas, and electric utilities, primarily in North America. Aclara’s product offerings include power line communications (PLC) and radio frequency (RF) communications technology platforms, MDM, and customer presentment software. Additionally, in January 2013, ESCO Technologies acquired Metrum Technologies to add cellular communications technology to the Aclara offering.

Along with other MDM players, Aclara has made the move into the analytics space. Although Aclara has become better known for its consumer engagement applications, the company also provides a software suite of applications aimed at the business user, including load research and forecasting, complex billing for a variety of pricing structures, distribution asset analysis, revenue assurance, and AMI device management. These applications employ analytics, leveraging data stored in the Aclara MDM system. The Aclara MDM system is an Oracle-based data repository that can be configured to pull in additional data from systems apart from the meter, including CISs, supervisory control and data acquisition (SCADA), geographic information systems (GISs), load research, load schedule systems, weather, and other legacy and middleware systems.


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In contrast to other MDM players, Aclara has focused its attentions on the smaller municipal and cooperative utility market. It will likely continue to develop its analytics capabilities to expand its product offering to help utilities improve ROI on smart meter deployments and to achieve further growth in the Tier Two utility market in water and gas as well as electricity sectors.

2.4.2 eMeter (Siemens)

Siemens Industry (NYSE: SI) completed its acquisition of San Mateo, California-based MDM company eMeter in January 2012. eMeter’s MDM product, EnergyIP, is now part of the Smart Grid Division of the Siemens Infrastructure & Cities group based in the United States. EnergyIP also serves as an integration platform for data management applications, supporting integration with AMI, data collection, and utility back office enterprise systems. eMeter moved into the data analytics space with the launch of its prepackaged analytics solution, Analytics Foundation for EnergyIP in January 2012.

Analytics Foundation processes raw data separate from the EnergyIP core transaction database, ensuring that basic billing functionality will not be compromised. It provides applications for revenue protection, AMI health, event analysis, outage analysis, and load monitoring. Data can be analyzed and reported on to business users using the EnergyIP Graphical Reporting Framework or other third-party reporting/BI tools. As one of the leading MDM players, the company is providing its analytics services to MDM customers including: Eidsiva Nett in Norway, CenterPoint Energy in Houston, Texas, and Westar Energy in Lawrence, Kansas. eMeter will look to enhance its analytics capabilities through partnerships, via the eMeter Alliance Program. In September 2012, it announced a partnership with Detectent, a provider of advanced revenue protection analytics. eMeter also has key partnerships with Accenture, CSC, HCL, IBM, and SAP, and offers managed services through a partnership with Verizon.

Itron (NASDAQ: ITRI) is headquartered in Liberty Lake, Washington and is a global supplier of metering devices, data collection and communications systems, data management and analysis software, and professional services for electricity, gas, and water utilities. .

Itron moved into the smart grid data analytics space with the launch of its analytics platform, Active Smart Grid Analytics (ASA) in February 2012. The ASA platform consists of off-the-shelf analytics applications, called Analytic Accelerators, which sit on top of the ASA data warehouse. The ASA data warehouse, developed in partnership with Teradata, integrates data from multiple sources across the enterprise including smart grid operational data, customer data, and financial information. Customization of analytics applications can be achieved via ASA Extension Toolkits. Itron’s analytics applications are primarily designed to help utilities optimize meter-to-cash operations, energy delivery, measurement and validations, and manage DR programs.

Itron currently offers off-the-shelf analytics applications for power quality analytics, energy diversion detection, and transformer load management. It also has separate software applications for short-term small area load forecasting. The company is also working on further refinements for analytics aimed at optimizing AMI operations and enabling more network diagnostics beyond tamer detection. Other areas of focus include customer segmentation analytics to help utilities target DR and EE programs. In addition, it offers financial analytics through consulting services, using meter data and existing forecasting tools to provide more accurate revenue estimates, and is looking to productize this application. Itron is adopting a strategy of integrating its analytics solutions into its overall AMI offering. In March 2013, it announced a new release of its MDM with analytics pre-integrated. Although the company has few sales in analytics, it enjoys an advantage given its


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deep utility relationships and strong partnerships, and this strategy should see its analytics deployment increase. To date, Itron has a number of customers using its forecasting applications. Avista Utilities is its most significant customer for analytics that leverage smart meter data.

2.4.3 Oracle

Founded in 1977 and based in Redwood Shores, California, Oracle (NYSE: ORCL) is a Fortune 500 company offering enterprise software, services, and hardware systems. Known for its database management and applications software, the company provides a broad range of products that includes enterprise resource planning tools, Oracle Fusion Middleware, and business applications for data warehousing and business intelligence.

Oracle has a long history of serving the utility industry and has an application stack that includes customer care and billing, MDM, mobile workforce management, work and asset management, Oracle Utilities Business Intelligence and the Oracle Utility Network Management system. It currently offers two off-the-shelf data analytics solutions – meter data analytics and outage analytics, which use the Oracle BI platform. The meter data analytics application is designed to provide decision support for managers and executive users dealing with consumption, load, and customer service issues. It uses data from the MDM and customer care and billing to analyze consumption trends and monitor meter performance for customer segmentation and maintenance planning. The outage analytics application provides executive users with outage trends to help monitor reliability. The Oracle Advanced Spatial Analytics solution allows key performance indicators to be viewed geospatially.

Oracle sees a significant opportunity in the provision of large-scale and real-time data analytics to support smart grid deployments. In February 2012, Oracle announced the availability of the Oracle Exalytics in-memory machine, engineered for in-memory decision support, online analytical processing, forecasting, and planning. In December 2012, it acquired DataRaker, and now offers the Oracle Utilities Analytics Cloud Service, which provides intelligence for meter operations, revenue protection, energy efficiency and DR, call center support, distribution planning, and financial analytics and reports. Oracle is currently working to integrate its cloud analytics applications with Oracle transactional systems, and is investing in improving existing analytics models and expanding the range of use cases targeted. Oracle works with integration and technology partners including IBM, Tata Consultancy Services, Ltd., Wipro, Accenture, Capgemini, and Infosys. Oracle is likely to play a role worldwide in all aspects of smart grid data analytics including meter, operations, asset, and renewables integration.

2.4.4 SAP

SAP AG, founded in 1972 and headquartered in Walldorf, Germany, is a leading provider of enterprise application software. The company is known for its business applications, which include enterprise resource planning (ERP), customer relationship management (CRM), enterprise asset management (EAM), supply chain management (SCM), and product lifecycle management (PLM) software products, as well as database technology. In the last 2 to 3 years the company has also expanded its mobile product offerings and launched its in-memory database technology platform, SAP HANA.

SAP is a major player in the utility industry where it has a long history providing ERP, CRM, and billing solutions. Another key area for SAP’s utility business is the provision of advanced BI and analytical applications. SAP is increasingly focused on leveraging its SAP HANA platform to address issues with data


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latency and provide utilities with more advanced, real-time data analytics, including predictive analytics instead of just historic reporting.

Currently SAP has two solutions for smart grid data analytics, which run on the SAP HANA platform: smart meter analytics and grid infrastructure analytics. The smart meter analytics applications are built around analyzing smart meter interval data and enable customer segmentation, energy efficiency benchmarking, and customer self-service applications. Its grid infrastructure analytics applications are aimed at grid operators and maintenance engineers and focused on transformer asset health. SAP continues to develop smart grid data analytics solutions on an application by application basis to support business operations, including for revenue protection, load forecasting, and predictive maintenance. Also, in July 2013, SAP announced a partnership with GIS provider, Esri, to integrate GIS information with SAP HANA and SAP BI solutions. Overall, the company is less focused on providing out-of-the-box analytics solutions, and more focused on the development of the end-to-end SAP HANA-based data platform, which also integrates Sybase event processing capabilities. The company is partnering with dedicated analytics startups, as well as major software and hardware partners, to provide richer analytics capabilities and enable integration of OT data.

2.4.5 SAS

Among the world’s largest privately held firms, SAS was founded in 1976 and is headquartered in Cary, North Carolina. The company is a leading provider of business intelligence and analytics solutions and services across industries. In the utilities sector, SAS offers solutions in four key areas: customer intelligence, energy portfolio optimization, network optimization, and smart grid data management.

Analytics applications use meter data, asset data (e.g., from transformers and feeder lines), and third-party data such as weather or customer data to address a number of use cases. These include predictive asset maintenance, customer segmentation, fraud detection, energy trading risk analysis, and predictive grid analytics for storm-related outage management. SAS also recently launched a forecasting application, SAS Energy Forecasting, which currently provides load forecasting for the very short-term (hours) to long-term (years), as well as geographical forecasting, to support energy trading activities and system planning. In the future, SAS plans to add renewable generation, price, and spatial load forecasting capabilities. Analytics-based intelligence can be delivered to users via the cross-industry, SAS in-memory visualization solution, SAS Visual Analytics, which enables users to load and sift through massive volumes of data, generate ad-hoc reports, identify opportunities for further analysis, and deliver the results via the web or even a tablet device.

In general SAS is adopting the approach of working with utility customers to identify common problems and develop solutions on an application-by-application basis. SAS customers are concentrated in the United States and Western Europe, but the company has also had some success in Australia and New Zealand.

2.4.6 Ventyx (ABB)

Ventyx, an ABB company (NYSE: ABB), founded in 1997 and headquartered in Atlanta, Georgia, is a global supplier of industrial enterprise software for asset-intensive industries, such as energy and utilities, mining, and public infrastructure. Following ABB’s acquisition of Ventyx in June 2010, the ABB group went on to acquire software companies Obvient Strategies and Mincom in 2011, which were integrated into the Ventyx organization, expanding Ventyx’s capabilities in business intelligence, as well as enterprise asset and work management.


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Ventyx delivers analytics through its BI analytics solution, Ventyx FocalPoint, a suite of packaged modules that provides information via dashboards and graphical interfaces for business and operational users. Also integrated with Ventyx’s network management/distribution management system (DMS) and mobile workforce management software, it delivers applications including meter performance analysis, revenue protection, outage analysis, and workforce analytics. In addition to the FocalPoint suite, Ventyx provides load, revenue, and price forecasting solutions across the energy value chain.

Going forward, the company considers that analytics will be one of the key growth areas within its software initiatives, and is moving toward predictive analytics applications, as well as analytics focused on past performance. It is concentrating its advanced analytics efforts in two directions: asset health and outage lifecycle management. In June 2013, Ventyx announced the availability of a new asset health solution, the Ventyx Asset Health Center, and the Ventyx Outage Lifecycle Management solution. The Asset Health Center, developed in partnership with American Electric Power (AEP), is currently aimed at electricity transmission companies. It applies advanced predictive analytics to historical and real-time data integrated from enterprise and OT systems, including work management and financial systems, SCADA, and sensors, to predict asset performance and failures, which support transmission operators and system and investment planners. On the outage side, analytics are aimed at providing more real-time alerts, and enabling outage restoration, particularly in extreme weather events. Ventyx will likely leverage these solutions to expand its transmission and distribution client base over the next year.

2.5 Global Trends

Many utilities deploy some form of BI analytics via traditional BI tools, but more advanced, big data BI analytics have so far tended to follow smart grid deployments, in particular smart meters. As a result, BI analytics have attracted the most attention in regions where smart meters have already been deployed, such as in North America and parts of Europe. Although the drivers vary across different regions, BI analytics will be considered as part of the approach to maximizing value from smart grid investments including smart meters and other sensors and devices in the grid, and BI analytics are expected to be considered wherever smart grid technologies are being implemented. Applications aimed at revenue assurance or optimizing asset utilization are likely to gain interest across global markets.

2.5.1 North America

North America offers the most mature market in terms of smart grid deployments, particularly smart meter and AMI deployments. Consequently, many utilities in North America already have access to massive volumes of data and are looking for solutions to gain value from it. For the most part, utilities have adopted MDM systems to manage smart meter data, and many will look to add BI analytics that leverage those MDM deployments. Even though the pace of AMI deployments is expected to slow down in North America, there will be a continued push for BI analytics that leverage smart meter data, particularly as utilities look to enable new billing use cases to support dynamic pricing and ensure performance of smart meter programs.

2.5.2 Europe

Apart from Sweden and Italy, which have already completed national smart meter rollouts, Europe still tends to lag behind North America in terms of smart grid deployments and, as yet, many utilities have more limited data available. However, utilities across Europe are facing challenging conditions, including competitive deregulated markets, tough economic conditions, and flat energy consumption, which are putting utility IT


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departments under increasing pressure to drive improvements in business performance and reduce costs, in turn driving interest in BI analytics. As smart grid deployments grow, driven by the 20-20-20 mandates, and utilities gain access to increasing volumes and variety of smart grid data, European utilities are expected to deploy BI analytics to support activities across various lines of business, including smart meter programs, energy trading activities, capital and asset investment planning, and renewable integration.

2.5.3 Asia Pacific

Opportunities for BI analytics in the Asia Pacific region will be driven by smart meter deployments in China, India, Japan, South Korea, Australia, and New Zealand. In particular, China has adopted an aggressive strategy to move toward a smart grid, and will account for the largest share of smart meter installations in the Asia Pacific region. In Japan and South Korea, there is growing pressure to balance supply and demand with pressures on peak capacity and integration of renewable energy and distributed loads (for example, when EVs become more widespread). As such, BI analytics solutions that provide load and generation forecasting applications to support investment planning will attract interest. In India, revenue loss protection is the key driver for BI analytics but, like Japan and South Korea, India also faces a challenge to cope with growing peak demand.

2.5.4 Latin America

Non-technical losses are a serious concern for utilities in Latin America and are a major driver for smart meter deployments in countries such as Brazil, Mexico, and Chile. BI analytics applications that leverage smart meter data to improve revenue collection and reduce energy theft will attract interest.

2.5.5 Middle East & Africa

With the exception of South Africa and Israel, which have shown commitment to smart meter implementations, there has been limited interest in smart grid technologies across the Middle East & Africa. Given the very slow pace of smart grid deployments, there are limited opportunities for BI analytics.

2.6 Market Forecast

A majority of BI analytics applications in use or in development fall under the umbrella of meter analytics. As such, the adoption of data analytics for BI follows the adoption curve of smart meter technology as utilities look to gain value from smart meter data. More specifically, BI analytics, which are increasingly being offered by MDM vendors, will follow adoption of MDM, which continues to be utilities’ preferred approach for managing smart meter data. Although meter analytics will increasingly aim to address operational issues, BI analytics applications aimed at improving the efficiency of everyday back-office processes, including meter-to-cash operations, revenue assurance, and outage management reporting, will still be key meter analytics applications.

Beyond smart metering, implementation of monitoring devices in the grid, particularly in transformer substations, are driving asset analytics to provide BI for investment planners and reliability managers, enabling asset performance and reliability reporting. Later in the forecast period, from 2017 to 2018, as deployment of renewable generation, EVs, and DER becomes more significant, BI analytics applications that provide both long-term and near real-time load and generation forecasting to support production planning and energy trading activities will be a bigger contributor.


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Worldwide spending on BI analytics solutions is expected to grow at 22% compound annual growth rate (CAGR) from $250 million in 2012, to $1.21 billion in 2020 (Chart 2.1). Globally, this represents a significant share of the total smart grid data analytics spend, which is forecasted to reach over $6 billion by 2020 in Navigant Research’s Smart Grid Data Analytics report.

Chart 2 BI Analytics Spending by Region, World Markets: 2012-2020


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3. CONCLUSIONS AND RECOMMENDATIONS

BI analytics present an opportunity for utilities to capitalize on the data being generated from expensive smart meter and smart grid deployments. Given the pressures on utilities to reduce costs and improve business efficiency, BI analytics should be a priority for utilities implementing smart grid technologies. For the most part, utilities that are engaging with data analytics are moving on an application-by-application basis. The challenge for vendors is to identify applications that can be productized and sold to utilities globally.

3.1 Recommendations for Utilities

» Prioritize business goals. Utilities should define business objectives and require use cases before embarking on a smart meter or MDM project. Utilities should consider what data analytics can deliver within the broader business goals.

» Learn from the experience of other utilities. Many utilities in North America took the opportunity presented by ARRA funds to deploy smart meters without considering what they would do with the data beyond providing more accurate bills. Utilities that are still in the planning stage for smart meter rollouts should address issues around MDM and analytics in the preparation phase.

» Prepare for the future. Whether preparing for the next challenge beyond smart metering or still in the middle of planning a smart meter rollout, utilities should consider the strategy for data capture, storage, and management (e.g., whether MDM will be sufficient for big data analytics requirements).

» Push greater communication and collaboration between IT and OT departments. Advanced BI analytics will depend on structured and unstructured data being pulled in from multiple systems from across different lines of business.


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3.2 Recommendations for Vendors

» Be able to demonstrate a clear business case. Utilities need to understand how any solution can deliver a return on investment.

» Differentiate yourself. Offerings from a growing number of vendors appear to be converging. Be able to articulate how your offering stands out.

» Cost is a major concern for most utilities. Vendors should have a plug-and-play solution for utilities that do not want to spend time and money on a customized solution.

» Build partnerships. Vendors that engage in partnerships will be best able to provide utilities with a holistic approach to data integration, processing, and storage, as well as advanced analytics applications.

» Leverage alliances. Vendor partnerships can help enhance brand awareness and gain access to new markets.

» Know the regulatory environment. Regulations vary significantly across geographies, and can change in short time frames compared with utility sales cycles.

» Be flexible and recognize that there is no one-size-fits-all approach. Some utilities will look to implement a unified analytics approach, while many others will move in an incremental way, application by application.

» Leverage existing relationships with utilities. Utilities are the ultimate decision makers in this market, so vendors that already have good presence at utilities can use that presence to best understand what applications should be productized.


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Published 3Q 2013

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