EXECUTIVE SUMMARY: Smart Grid Data Analytics

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Nov 21, 2013 (3 years and 9 months ago)

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0



Marianne Hedin, Ph.D.
Industry Analyst
Clint Wheelock
President

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 sales@pikeresearch.com


Published 4Q10



Smart Grid Data Analytics

© 2010 Pike Research LLC.
All Rights Reserved. This publication may be used only as expressly permitted by license from Pike Research LLC and may not otherwise be accessed or used, without the
express written permission of Pike Research LLC.

1


Section 1
E
XECUTIVE
S
UMMARY

1.1 Introduction to Smart Grid Data Analytics
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.

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2


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
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
$4,500
2009 2010 2011 2012 2013 2014 2015
($ Millions)
Middle East/Africa
Asia Pacific
Europe
Latin America
North America

(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

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express written permission of Pike Research LLC.

3


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.

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express written permission of Pike Research LLC.

4


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.


Smart Grid Data Analytics

© 2010 Pike Research LLC.
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express written permission of Pike Research LLC.

70


Section 7
T
ABLE OF
C
ONTENTS

Section 1 ...................................................................................................................................................... 1
 
Executive Summary .................................................................................................................................... 1
 
1.1
 
Introduction to Smart Grid Data Analytics ..................................................................................... 1
 
1.2
 
Market Opportunities ..................................................................................................................... 1
 
1.3
 
Market Forces ............................................................................................................................... 2
 
1.4
 
Competitive Landscape ................................................................................................................. 3
 
Section 2 ...................................................................................................................................................... 5
 
Market Issues .............................................................................................................................................. 5
 
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
 
Market Drivers ............................................................................................................................... 7
 
2.2.1
 
Risk Mitigation ......................................................................................................................... 7
 
2.2.2
 
Request for Data Analysis from External Stakeholders .......................................................... 8
 
2.2.3
 
Customer Expectations ........................................................................................................... 8
 
2.2.4
 
Middle Market Prospects ......................................................................................................... 8
 
2.2.5
 
Cloud Computing ..................................................................................................................... 8
 
2.2.6
 
Smart Grid Data Analytics Benefits ......................................................................................... 9
 
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
 

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71


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
 
Section 3 .................................................................................................................................................... 22
 
Competitive Landscape ............................................................................................................................ 22
 
3.1
 
Market Fragmentation ................................................................................................................. 22
 
3.2
 
A Winning Value Proposition ....................................................................................................... 23
 
3.3
 
Software and Services Vendor Profiles ...................................................................................... 25
 
3.3.1
 
Accenture .............................................................................................................................. 25
 
3.3.2
 
Aclara Software ..................................................................................................................... 27
 
3.3.3
 
AT&T ..................................................................................................................................... 28
 
3.3.4
 
Capgemini ............................................................................................................................. 29
 
3.3.5
 
Ecologic Analytics ................................................................................................................. 30
 
3.3.6
 
eMeter ................................................................................................................................... 31
 
3.3.7
 
IBM ........................................................................................................................................ 32
 
3.3.8
 
Infosys ................................................................................................................................... 34
 
3.3.9
 
Itron ....................................................................................................................................... 36
 
3.3.10
 
KEMA ................................................................................................................................ 37
 
3.3.11
 
Martin Dawes Analytics ..................................................................................................... 38
 
3.3.12
 
Microsoft ............................................................................................................................ 39
 
3.3.13
 
NorthStar Utilities .............................................................................................................. 40
 
3.3.14
 
OPOWER .......................................................................................................................... 41
 
3.3.15
 
Oracle ................................................................................................................................ 42
 
3.3.16
 
OSIsoft .............................................................................................................................. 43
 
3.3.17
 
SAIC .................................................................................................................................. 44
 
3.3.18
 
SAP ................................................................................................................................... 45
 
3.3.19
 
Siemens ............................................................................................................................ 46
 
3.3.20
 
Telvent .............................................................................................................................. 47
 
3.3.21
 
Teradata ............................................................................................................................ 48
 
Section 4 .................................................................................................................................................... 50
 
Market Forecasts ....................................................................................................................................... 50
 
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
 

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express written permission of Pike Research LLC.

72


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
 
Section 10 .................................................................................................................................................. 78
 
Sources and Methodology ....................................................................................................................... 78
 
Notes .......................................................................................................................................................... 78
 


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© 2010 Pike Research LLC.
All Rights Reserved. This publication may be used only as expressly permitted by license from Pike Research LLC and may not otherwise be accessed or used, without the
express written permission of Pike Research LLC.

73


Section 8
T
ABLE OF
C
HARTS AND
F
IGURES

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
 



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express written permission of Pike Research LLC.

74


Section 9
S
COPE OF
S
TUDY

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.

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75


9.2 Defining the Electric Utility Market
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”:
 Consulting
 Implementation
 Outsourcing
 Software support and training

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76


9.4.1 Consulting
Consulting services typically consist of:
 Business strategy advice
 Process improvement
 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.

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9.4.4 Software Support and Training
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.


Smart Grid Data Analytics

© 2010 Pike Research LLC.
All Rights Reserved. This publication may be used only as expressly permitted by license from Pike Research LLC and may not otherwise be accessed or used, without the
express written permission of Pike Research LLC.

78


Section 10
S
OURCES AND
M
ETHODOLOGY

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.
N
OTES

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.


Smart Grid Data Analytics

© 2010 Pike Research LLC.
All Rights Reserved. This publication may be used only as expressly permitted by license from Pike Research LLC and may not otherwise be accessed or used, without the
express written permission of Pike Research LLC.

79

























Published 4Q 2010

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