EXECUTIVE SUMMARY: Smart Grid Data Analytics Business Intelligence, Situational Awareness, and Predictive Analytics for Utility Customer Information and Grid Operations: Market Analysis and Forecasts NOTE: This document is a free excerpt of a larger research report. If you are interested in purchasing the full report, please contact Pike Research at [email protected] Published 4Q10
Data is the lifeblood of any business. Without the information or “intelligence” that can be derived from smart meters and other smart grid devices, utilities cannot derive the substantial benefits that their smart grid deployments can deliver. As these deployments significantly increase data quantity and availability, data analytics will become essential. Indeed, data analytics sets a utility organization apart from its competition, allowing it to identify its most profitable customers and products and handle billing issues more effectively and efficiently. Smart grid data analytics also enables a utility to gain insights into the energy use behavior of their customers to manage peak demand, forecast load, and determine business risks, such as revenue loss. Moreover, smart grid data analytics offers valuable information about the performance of the distribution system and its various assets in order to help avoid power failures.
When utilities adopt smart grid technology, they will undergo a paradigm shift. That is, instead of solely being distributors of power, they will become brokers of information. The traditional and rather simple transactions involved in the meter-to-cash function will change completely, as they will become a lot more complex. Data will pour in from an ever-increasing number of smart meters and numerous other functions on the grid, such as outage management and demand response events.
The challenge for utilities in maximizing the benefits from smart grid data analytics is the ability to turn the huge volume of smart grid data into value. As utilities move to the smart grid and expand it over time with the installation of thousands and sometimes millions of smart meters, they must address the most challenging question: How will they be able to manage and take advantage of the surge of data resulting from these smart meters and other intelligent devices on the smart grid?
As soon as a utility company begins to receive data, it must be able to transform the raw data into useful information. For instance, it must be able to review the data for any changes or events in the grid that trigger alarms within outage management systems and other real-time systems. In short, an organization can be very data rich, yet very information poor. As a result, data analytics plays a major role – from the very beginning of a smart grid deployment.
1.2 Market Opportunities
Software and services providers, especially those with a long legacy of data analytics experience, can find very attractive market opportunities in the burgeoning and fast-growing smart grid data analytics market. Although this market is at a very early stage of development, it is expected to grow extremely quickly. Utilities with smart grid programs are finding it increasingly necessary to turn the massive amount of data that they are receiving from the smart grid into valuable business information to guide their decision making and actions. The large utilities, especially the investor owned utilities (IOUs) in the United States, are spearheading the adoption of smart grid data analytics. Yet, the smaller and midsize utility companies are expected to follow their larger peers and invest in software and services that will enable them to convert data into “smart” and actionable information.
Pike Research forecasts the smart grid data analytics market will enjoy very robust growth at a compound annual growth rate (CAGR) of over 65% – from $356 million in 2010 to $4.2 billion in 2015. Although the European market will offer the best opportunity in the early years of this forecast period, North America is expected to become the leading market in 2011.
Chart 1.1 Smart Grid Data Analytics External Spending by Region, World Markets: 2009-2015
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($ M
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Middle East/Africa
Asia Pacific
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Latin America
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(Source: Pike Research)
Both software and services providers will benefit from this opportunity, though we expect that demand for service offerings will be somewhat stronger than software throughout the forecast period. Essentially, utilities will increasingly seek consulting and implementation assistance from services providers. Pike Research also anticipates that more and more utilities will take advantage of the availability of outsourcing for their various data needs by using data analytics hosted services online in the cloud.
1.3 Market Forces
The smart grid data analytics market is influenced by an array of different market forces. The fact that utilities must address a slew of different risks – many of which could have serious consequences if not managed properly – has heightened the need for data analytics when they transition to a smart grid operation.
Additionally, regulators, government agencies, environmental groups, and even shareholders are becoming more and more interested in the data that utilities are collecting from smart meters and are asking them to share the information and the results of their data analyses. In particular, they are interested in finding out if the utilities are meeting their energy management goals, such as reduced carbon emissions and increased energy efficiency. Moreover, consumers with smart meters will increasingly want to have access to their energy use data. With the installation of smart meters, especially advanced smart meters, customers will expect to receive (as well as perform) their own data analysis so they can reduce their energy consumption and cost. They will also become progressively
more interested in dynamic pricing or time-of-use (TOU) billing. In other words, consumers will seek to take advantage of the different options made possible by smart meters and the related available data.
Among the biggest drivers of the smart grid data analytics market, however, are the various business benefits that utility companies realize they can derive from the smart meter and grid data. Data analytics will not only help them improve their customer relationships, but will also enhance their ability to run a more efficient and effective grid operation than ever before – especially with respect to revenue loss management, load management, outage management, asset management, and energy management.
Aside from these benefits, utility companies are also motivated to procure smart grid data analytics software and services to help them address the challenges they must face when installing an ever-increasing number of smart meters and devices. The most serious of these challenges is related to the huge volume of data that is being produced by these assets – often referred to as a “tsunami” of data. Along with this challenge is the complexity of data that must be managed and analyzed. There are different data structures and types of data that must be considered.
Despite strong positive market forces, there are also many inhibiting factors that hamper the utility industry from taking full advantage of smart grid data analytics. The most significant one is probably the lack of knowledge and understanding of what needs to be done, coupled with a lack of skills and staff resources to tackle the various smart meter data challenges. As a result, many utility companies are taking a “wait-and-see” attitude and are postponing any action. Some are waiting to see what actions the large IOUs are taking and what business results they are achieving through smart grid data analytics.
1.4 Competitive Landscape
As in most nascent technology sectors, the smart grid data analytics market can be characterized as a rather fragmented marketplace with a mix of large, established players and smaller, specialized firms that come from many different industries. While the majority of the vendors, both large and small, come from the information technology (IT) sector, others may come from the telecom or even the automobile sector. Among the large group of IT players, there are the big, established companies like Accenture, Capgemini, HP, IBM, Microsoft, Oracle, SAIC, SAP, Siemens, and Teradata. Another large technology group is represented by the Indian service companies such as Infosys and the “pure plays” – mostly with meter data management (MDM) expertise – like Aclara Software, Ecologic Analytics, eMeter, Itron, Olameter, and NorthStar Utilities. Telvent is a relatively large IT provider that offers a portfolio of MDM software, as well as distribution management system (DMS) and outage management system (OMS) solutions with data analytics capability.
Interestingly, there is no pure niche smart grid data analytics vendor in the marketplace. However, there is a small group of software companies that have developed a special niche area that they have found can be leveraged in the smart grid data analytics market. OPOWER and OSIsoft represent this market segment.
Although telecom companies play a fairly large role in the smart grid market, they do not appear to be prominent players with respect to smart grid data analytics – at least at this time. However, it would not surprise Pike Research if they soon make inroads into this particular sub-segment of the smart grid market since they can leverage a long history of managing and analyzing reams of data. AT&T is a good example of such a vendor.
The auto companies, offering plug-in hybrid and electric vehicles, could also become serious competitors in the smart grid data analytics marketplace. For example, Toyota plans to launch a home energy system in 2012 to help individuals monitor and manage (even remotely) their energy use.
Pike Research believes that when the vendors begin to face the issue of scale, this competitive landscape will shift. The increasing need for scalability to handle ever-increasing amounts and complexity of data will provide a major advantage to those vendors that have the current software and other resource capabilities to handle the explosion of data. Once scale and scalability become the key issue for utilities, many smaller vendors could lose their competitive advantage.
To compete effectively in this marketplace, vendors must demonstrate that they possess deep utility industry know-how and understanding of the different technological and data analytics challenges that utilities face when transitioning to a smart grid operation. A solid background in managing and interpreting data for other industry clients, especially in the telecom, banking, or retail sectors – be it in the area of business intelligence, information management, data correlation and modeling techniques, data mining, database/data warehousing, or predictive analytics and forecasting – will be considered an advantage by potential utility clients as they try to address their smart grid data analytics issues.
Moreover, in this early adoption phase, vendors need to be sensitive to the fact that many utilities are not ready and willing to handle too much change at once, preferring instead a more cautious, incremental approach. The ability to integrate data and the results of analytics into business processes is another key competitive factor. Similarly, it is important that vendors can offer visualization along with location and geospatial enablement of data sets. Since data security and privacy is of such a significant concern among utility companies when deploying smart grid technology, vendors with a strong background in dealing with these issues tend to enjoy a competitive edge.
As the volume of data escalates, scalability and speed of analysis through in-memory analytics will also matter a great deal to utility clients. When the quantity of data becomes overwhelming for utilities to handle, outsourcing will become an attractive option. Utilities will be inclined to contract out the management of their data, including data analytics, on an ongoing basis to a vendor. In such a case, the outsourcers, especially those with cloud computing capabilities, will have a competitive advantage.
2.1 Introduction and Background ........................................................................................................ 5 2.1.1 Meter Types ............................................................................................................................. 6 2.1.2 Definition of Smart Meter ......................................................................................................... 6 2.1.3 Definition of Smart Meter Data Analytics ................................................................................ 7
2.2.6.1 Customer Management Data Analytics .......................................................................... 9 2.2.6.1.1. Billing Data ................................................................................................................. 9 2.2.6.1.2. Revenue Data ............................................................................................................ 9 2.2.6.1.3. Usage Data .............................................................................................................. 10 2.2.6.1.4. Demand Response .................................................................................................. 10
2.2.6.2 Grid Operation Data Analytics ...................................................................................... 10 2.2.6.2.1. Outage Management and Distribution Optimization ................................................ 10 2.2.6.2.2. Asset Management .................................................................................................. 11 2.2.6.2.3. Energy Management Systems ................................................................................. 11
2.2.7 Smart Grid Data Challenges ................................................................................................. 11 2.2.7.1 The Data Tsunami ........................................................................................................ 12 2.2.7.2 Complexity of Data Analytics – Data Rich and Information Poor ................................. 13
2.2.7.2.1. Lack of Data Integration ........................................................................................... 13 2.2.7.2.2. Meter Data Management ......................................................................................... 13 2.2.7.2.3. Situational Awareness .............................................................................................. 14 2.2.7.2.4. Data Quality and Integrity......................................................................................... 14 2.2.7.2.5. A Plethora of Different Types of Data ...................................................................... 14 2.2.7.2.6. Structured and Unstructured Data ........................................................................... 14 2.2.7.2.7. New and Old Data .................................................................................................... 15 2.2.7.2.8. Event Data ............................................................................................................... 15
2.2.7.3 Turning Data into Usable and Actionable Information .................................................. 15 2.2.7.3.1. Predictive Data Analytics ......................................................................................... 15
2.3 Market Inhibitors .......................................................................................................................... 16 2.3.1 Lack of Knowledge ................................................................................................................ 16 2.3.2 Shortage of Skills and Talent Plus an Aging and Retiring Workforce ................................... 16 2.3.3 Concern about Data Privacy and Cyber Security .................................................................. 16 2.3.4 Stringent Data Analytics Requirements ................................................................................ 17 2.3.5 Lack of Standards ................................................................................................................. 17
2.3.6 Transformational Change ...................................................................................................... 18 2.4 Market Adoption of the Smart Grid.............................................................................................. 18
2.4.1 The Americas ........................................................................................................................ 18 2.4.1.1 United States ................................................................................................................ 18 2.4.1.2 Canada ......................................................................................................................... 19 2.4.1.3 Latin America ................................................................................................................ 20
2.4.2 Europe ................................................................................................................................... 20 2.4.3 Asia Pacific ............................................................................................................................ 21
2.4.3.1 Australia ........................................................................................................................ 21 2.4.3.2 China ............................................................................................................................. 21 2.4.3.3 South Korea .................................................................................................................. 21
4.1 Forecast Introduction .................................................................................................................. 50 4.2 The Utility Environment ............................................................................................................... 50 4.3 Assumptions Determining this Forecast ..................................................................................... 50 4.4 Worldwide Smart Meter Installed Base by Region ...................................................................... 52 4.5 Worldwide Smart Meter Data Analytics by Region ..................................................................... 54 4.6 Smart Grid Data Analytics Software versus Services Spending ................................................. 58
4.6.1 Smart Grid Data Analytics Software Spending ..................................................................... 60 4.6.2 Smart Grid Data Analytics Services Spending ...................................................................... 61 4.6.3 Smart Grid Data Analytics Services Spending by Service Segment .................................... 62
Section 5 .................................................................................................................................................... 64 Company Directory ................................................................................................................................... 64 Section 6 .................................................................................................................................................... 66 Acronym and Abbreviation List ............................................................................................................... 66 Section 7 .................................................................................................................................................... 70
Table of Contents ...................................................................................................................................... 70 Section 8 .................................................................................................................................................... 73 Table of Charts and Figures..................................................................................................................... 73 Section 9 .................................................................................................................................................... 74 Scope of Study .......................................................................................................................................... 74
9.1 Data Collection ............................................................................................................................ 74 9.2 Defining the Electric Utility Market .............................................................................................. 75 9.3 Defining the Smart Grid Data Analytics Market .......................................................................... 75 9.4 Defining Service Offerings .......................................................................................................... 75
9.4.1 Consulting .............................................................................................................................. 76 9.4.2 Implementation ...................................................................................................................... 76 9.4.3 Outsourcing ........................................................................................................................... 76 9.4.4 Software Support and Training .............................................................................................. 77
Chart 1.1 Smart Grid Data Analytics External Spending by Region, World Markets: 2009-2015 ......... 2 Chart 2.1 Smart Meter Penetration Rate of All Electrical Meters by Region, World Markets: 2008-2015 ................................................................................................... 19 Chart 4.1 Smart Meter Installed Base by Region, World Markets: 2008-2015 .................................... 53 Chart 4.2 Advanced Smart Meter Installed Base by Region, World Markets: 2008-2015 ................... 54 Chart 4.3 Smart Grid Data Analytics External Spending by Region, World Markets: 2009-2015 ....... 56 Chart 4.4 Smart Grid Data Analytics External Spending Share by Region: 2010 and 2015 ............... 57 Chart 4.5 Percentage of Total Smart Grid Data Analytics Spending for Services and Software: 2009-2015 ............................................................................................................................. 58 Chart 4.6 Smart Grid Data Analytics Services and Software Spending Growth, World Markets: 2010-2015 ............................................................................................................................. 59 Chart 4.7 Percentage of Smart Grid Data Analytics External Spending on Services by Service Segment: 2009-2015 ............................................................................................... 63
Table 4.1 Smart Grid Data Analytics External Spending by Region, World Markets: 2009-2015 ....... 55 Table 4.2 Smart Grid Data Analytics External Spending Share by Region, World Markets: 2009-2015 ............................................................................................................................ 57 Table 4.3 Smart Grid Data Analytics External Spending on Software by Region, World Markets: 2009-2015 ............................................................................................................................. 60 Table 4.4 Smart Grid Data Analytics External Spending on Services by Region, World Markets: 2009-2015 ............................................................................................................................. 61 Table 4.5 Smart Grid Data Analytics External Spending on Services by Service Segment, World Markets: 2009-2015 ................................................................................................... 62
This Pike Research report examines the smart grid data analytics software and services markets and provides a 7-year forecast and market sizing from 2009 through 2015. The five major regions covered include:
North America
Latin America
Europe
Asia Pacific
Middle East/Africa
In addition to a total view of the smart grid data analytics market, Pike Research provides separate market sizing and forecasts for the software and services markets. We also present global market sizing and a 7-year forecast for four discrete service engagement types with a focus on the following service offerings:
Consulting
Implementation
Outsourcing
Software support and training
Pike Research looks at the entire smart grid data analytics services market, including services that are provided as “standalone,” or discrete data analytics-specific service offerings, and those that are embedded in software or other service engagements.
In addition, this study looks at the smart grid data analytics software and services competitive landscape to identify and highlight the key players in this market. Pike Research interviewed a mix of 21 software and services vendors and presents a profile of each company in this report.
9.1 Data Collection
The forecasts provided in this study represent Pike Research’s best estimates and projections for 2010-2015, where the base year is 2009. These estimates are based on primary and secondary information obtained in the fall of 2010. During these months, interviews were conducted with 21 major smart grid data analytics providers.
Secondary research information was collected from a wide range of sources, such as Smart Grid Today, SmartGridNews.com, Environmental Leader, white papers from vendor websites and press releases, newspaper articles, and Pike Research smart grid-related research reports.
The electric power sector consists of those entities whose primary business is the production of electricity. This sector encompasses power generation, transmission, and distribution and retail activities.
9.3 Defining the Smart Grid Data Analytics Market
Smart grid data analytics is the process and activity of collecting, aggregating, inspecting, cleaning, interpreting, visualizing, and modeling smart grid data from smart meters and other smart grid devices. The goal of this process is to highlight useful information, suggest conclusions, and facilitate decision making for utility companies. Smart grid data analytics also includes data mining, a data analysis technique that focuses on modeling and knowledge discovery for predictive – rather than solely descriptive – purposes.
Data analytics can be a “standalone,” or discrete, offering, but it is also frequently embedded as a value-added feature in a wide range of smart grid-related software applications (e.g., MDM, DMS, and OMS solutions). As such, defining the smart grid data analytics market is especially challenging. For the purposes of this report, if data analytics is a key feature of a smart grid solution (i.e., comprises over 50% of an application’s functionality), Pike Research classifies the solution as a component of the smart grid data analytics market.
Data analytics has multiple approaches and encompasses a variety of techniques under different names, depending upon the particular business or process it supports. Business intelligence, for example, is a term used often in conjunction with data analytics, as it has a strong focus on the aggregation of business information. In fact, some smart grid vendors refer to their “smart analytics” tools as new business intelligence solutions for utilities.
As part of the data analytics definition, it is also necessary to consider the activities that must take place before an analysis can be done. Data integration is one of these essential pre-activities because data cannot be properly analyzed unless it has been pulled together and aggregated from multiple disparate sources inside and even outside an organization. The same is true for VEE-related activities, whereby data is validated, estimated, and edited before it is analyzed. Data visualization is also very much a part of data analytics because it facilitates the ability to analyze.
9.4 Defining Service Offerings
Pike Research refers to four major service segments in the use of the term “service offerings” or just “services”:
Business process reengineering (an approach to restructure a business process in a significant way)
Operations assessment (an assessment of how effectively an organization uses resources and how well operating units perform)
Benchmarking
Needs assessment
Change management (includes a communications plan)
IT strategy advice
IT design
Capacity and maintenance planning (future support requirements for IT)
Supplier analysis
In the early years of the forecast period, demand for consulting assistance will primarily be generated by clients’ need to understand their business case for smart grid data analytics. Additionally, clients will seek advice on how to address various data challenges, particularly the growing amount and complexity of data.
9.4.2 Implementation
Implementation services focus on executing the business vision or strategy that has been set forth with respect to smart grid data analytics. They often entail the following:
Installing and configuring the software
Testing the software and the quality of the data
Integration of data from multiple disparate sources, internal and external to the organization
Creating the data extract, transform, clean, and load data
Preparing and automating custom reports
9.4.3 Outsourcing
IT and business process outsourcing involves the contracting out – on an ongoing basis for several years – of technology (i.e., application/s, IT, and network infrastructure), an entire function (e.g., financial accounting), or a particular business process within an organization to an external provider. Managing a utility’s smart grid data on an ongoing basis, for example, presents a significant business process outsourcing (BPO) opportunity for services providers. Many are getting ready to provide such services in the form of cloud computing.
Software support involves after-sales services provided by a software vendor in solving software conflicts and usability problems. Such services also entail supplying updates and patches for bugs and security holes in the solution to enhance the performance and availability of software.
Training includes a combination of online and in-classroom training courses, as well as hands-on workshops.
Pike Research’s industry analysts utilize a variety of research sources in preparing Research Reports. The key component of Pike Research’s analysis is primary research gained from phone and in-person interviews with industry leaders, including executives, engineers, and marketing professionals. Analysts are diligent in ensuring that they speak with representatives from every part of the value chain, including but not limited to technology companies, utilities and other services providers, industry associations, government agencies, and the investment community.
Additional analysis includes secondary research conducted by Pike Research’s analysts and the firm’s staff of research assistants. Where applicable, all secondary research sources are appropriately cited within this report.
These primary and secondary research sources, combined with the analyst’s industry expertise, are synthesized into the qualitative and quantitative analysis presented in Pike Research’s reports. Great care is taken in making sure that all analysis is well supported by facts, but where the facts are unknown and assumptions must be made, analysts document their assumptions and are prepared to explain their methodology, both within the body of a report and in direct conversations with clients.
Pike Research is an independent market research firm whose goal is to present an objective, unbiased view of market opportunities within its coverage areas. The firm is not beholden to any special interests and is thus able to offer clear, actionable advice to help clients succeed in the industry, unfettered by technology hype, political agendas, or emotional factors that are inherent in cleantech markets.
NOTES
CAGR refers to compound average annual growth rate, using the formula:
CAGR = (End Year Value ÷ Start Year Value)(1/steps) – 1.
CAGRs presented in the tables are for the entire timeframe in the title. Where data for fewer years are given, the CAGR is for the range presented. Where relevant, CAGRs for shorter timeframes may be given as well.
Figures are based on the best estimates available at the time of calculation. Annual revenues, shipments, and sales are based on end-of-year figures unless otherwise noted. All values are expressed in year 2010 U.S. dollars unless otherwise noted. Percentages may not add up to 100 due to rounding.
This publication is provided by Pike Research LLC (“Pike”). This publication may be used only as expressly permitted by license from Pike and may not otherwise be reproduced, recorded, photocopied, distributed, displayed, modified, extracted, accessed or used without the express written permission of Pike. Notwithstanding the foregoing, Pike makes no claim to any Government data and other data obtained from public sources found in this publication (whether or not the owners of such data are noted in this publication). If you do not have a license from Pike covering this publication, please refrain from accessing or using this publication. Please contact Pike to obtain a license to this publication.
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