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Transcript of BI_IBM
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IBM Software
Beyond smart meters:Taking analytics to utilities data
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Combine the power of Netezza with Advanced Content Analytics
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Contents
2 The wave of change
3 Business imperatives dene the analytical strategy
3 Customer optimization
4 Demand response optimization
5 Operational efciency
6 Bringing it all together
6 Glossary
Utility companies are facing a step change in the industry from
many factors at once: social and political pressures on
generation, increasing data volumes from smart grid and
advanced meter infrastructure (AMI) technologies and
sweeping regulatory changes amongst.
Evolving technology in the utilities space has generated
unprecedented data volume and complexity which will
continue to grow as the industry continues to transform andevolve. To remain competitive utility companies will need to
change from the traditional infrastructure-led business model
to one being more information- and services-led. But utilities
are also typically vertically siloed organizations with various
departments working - and managing their data - very
independently of each other. Even within a single department
it is likely that multiple operational systems and data stovepipes
exist, yet another challenge to utility companies trying to
transition to a more efficient and rational horizontally
integrated information ecosystem.
Given this highly challenging information and dataenvironment, utilities are focusing increased attention on
business intelligence and advanced analytics to provide
data-driven decision-making as they plan and manage change.
Demand for advanced analytics is growing, necessitating an
integrated view of company data across the disparate
operational silos. Utilities achieving this data integration and
ensuing analytical capabilities can gain productivity, increase
profitability, enhance efficiency, reduce the carbon footprint,
and improve customer satisfaction.
Analytics are technologies and applications including hardware
software and services that enable utilities to transform data into
actionable insights. Todays analytics are focused on leveraging
real-time data sources and analytics, bringing together multiple
data sources, predicting outcomes instead of just reportinginformation, merging new and existing data, creating flexible
applications, and better serving the data customer.1
Analytics has been successfully adopted and applied in various
industries historically from telecommunications to retail and
utilities can leverage technology and business process changes
learned from these other industries to successfully meet the
above challenges. This means building a 360 degree view of the
business and of the customer in an environment so that analysis
can be applied at the speed business is taking place. This must be
done with the simplicity to allow adaptation to future business
needs as they are discovered and understood.
The wave of changeThe utility industry has been plagued with unforeseen hype for
the past two to three years. The growing wave of smart meter
and smart grid pilots and implementations has been
accompanied by much media and analyst attention combined
with Smart conferences in every geography, every other week
Where the concept is in terms of the Gartner Hype Cycle is
open to conjecture and debate, but its clear that we are now
beyond the early adopter phase for the AMI (advanced meter
infrastructure)/meter data management system (MDMS)
portion of the architecture.
That particular concept is proven and workable and hopefully
sufficiently scalable, but there is now a growing industry
recognition that investment in smart meters and AMI/MDMS
doesnt necessarily change the game in terms of operational
improvements and providing real business benefit. Assuming
Utility Analytics Insti tute, Annual Market and Forecast, November
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the necessary integration has been achieved into the customer
information system (CIS) and billing processes, customers are
receiving more timely and accurate billing statements, and in
some cases can view detailed usage with web portals or
in-home displays. Providing more visibility of utilization is all
well and good but there isnt sufficient detail to enable all but
the most conscientious of customers to markedly change their
energy consumption. Knowing how many kilowatts of power
your home is consuming doesnt help you to understand thatyour ancient fridge/freezer is responsible for 30 percent of
your daily consumption.
Putting the usage data into context and applying analytics
unearths new opportunities for the utility company. This
cannot be readily achieved in situ on the MDMS. The MDMS
system is optimized to support the data ingestion, validation,
estimation and editing (VEE) and related core functions.
There is therefore a need to transform the data and add
contextual information to the validated usage data for
analytical purposes this is best done with a platform
optimized for high performance analytics. The frequency orlatency with which the usage data is made available to the
analytical environment can have a significant impact on the
potential business benefit. More on this to follow.
Business imperatives define the
analytical strategyHow do you decide which analytics are the most appropriate
for any particular utility? Fundamentally, the analytical
roadmap should be determined by alignment to the companys
overarching business imperatives and prevailing regulatory/
market model. Focusing on the end customer may be
inappropriate for a utility in a highly regulated monopolistic
environment. Improving security of supply and cost/quality of
service may be a better strategy.
Across the globe the main business imperatives of utilities can
be characterized as the following:
Customer optimization
Demand response optimization
Operational efficiency
While it is essential for a utility to be competent in each of
these disciplines, it is likely that an organization will prioritize
one particular area; the prioritization being determined by the
company itself or more likely influenced by the country or
state regulator. Each market discipline will be explored in
detail in a later section.
Customer optimization
Whether competing to attract and retain profitable customers in
a competitive and deregulated market or striving to improve
customer satisfaction and eliminating complaints to the
regulator, the relationships a utility establishes and maintains
with its customers is becoming an increasing priority. Customer
optimization isnt achieved by simply buying and implementing
a CRM application; there are capabilities that need to be
developed and combined to achieve the end goal. The above
figure illustrates some of the key capabilities required to achieve
an appropriate form of customer optimization.
A successful customer optimization program requires at its
foundation a full 360 degree view of the customer. This is
established by integrating customer data from all key
operational and business systems with additional relevant data
such as credit and geo-demographic data from external
agencies. It is imperative that utilities create a proper view of
their customers as opposed to viewing them as meter points.
Although the segmentation and valuation stages may seem
most appropriate to utilities operating in a competitive market,
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such customer insight is also equally important for utilities in a
monopolistic market. This customer insight capability enables
the utility to better understand past and predict future
utilization patterns, and enables credit risk management and an
understanding of what new offers and services may be most
appropriate to specific customers. It can also be used to
determine optimal tariff strategies and demand response
programs. The segmentation scheme can also provide the basis
for determining virtual power plant (VPP) allocations,ensuring customers with similar usage, curtailment and
contractual agreements are grouped together.
Demand response optimizationDemand response (DR) is becoming widely recognized as one
of the essential disciplines that all utility companies need to
embrace given the obvious benefits in peak load shifting and
potential elimination of capital investment in additional
generation capacity. Demand response is not a capability that
can be bought off the shelf and enabled, but similar to
customer optimization, demands a series of analytical processes
to fully achieve the optimization goals. The following figure
illustrates how effective demand response can be achieved by
building on other essential analytical processes.
Successful demand response requires accurate demand
forecasting models to enable the appropriate load shifting
optimizations to be identified and executed. Data latency has a
significant impact on the accuracy and granularity of the
forecasting models. Historically utilities have forecasted loads six
weeks in advance. Although historical trends and weather
forecasts are factored into predicted consumption, historical
consumption was calculated at an aggregated level and could not
be easily apportioned across the customer base. Smart meterdata will provide granular consumption data for the whole
customer base. This data will be required near to real time: both
for load forecasting and also for the monitoring and tracking of
the demand response program execution.
Course grained forecast models can be used initially to
determine where peak consumption may occur. These forecast
peaks will necessitate the execution of more granular models to
compare hourly load versus anticipated capacity. This in turn
will determine the load shedding requirement and drives the
demand response curtailment program that identifies which
VPP/customer segments (and ultimately customers) need toparticipate in the DR scheme. Once identified, the DR
program can be executed with the appropriate curtailment
requests being issued to customers. The take rate of the
program is then monitored to ensure the required load is shed
and additional target customers contacted if there is a shortfall
in take up.
While this process is similar in concept to some traditional
marketing style programs, there is an iterative real-time
requirement to monitor take-up or defection from the
program, and extend the campaign to additional customers
until the required capacity reduction has been achieved. Ideallythe DR signals should be directed to automated recipients in
the form of smart thermostats and appliances, but provision for
actual customer interaction is also required.
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At each stage identified there is a need to iteratively execute
advanced analytical models across a combination of customer,
consumption, weather and generation capacity data. To ensure
consistency it is vital that the various analytical models are
executed on the same set of data.
Operational efficiencyThe last discipline will be common to all utilities to a greater or
lesser extent. Reducing or optimizing the cost to serve customersis an industry-wide initiative in which attainment is severely
impacted by the many seismic changes the industry is facing.
Some of the key challenges faced by the utility industry
include:
Changes in the power generation to include more
renewables;
Switch to/from nuclear generation;
Huge growth in micro-generation;
Increasing ecological concerns and carbon emission taxes;
Emerging demand for and growth in electric vehicles; Increasing credit risk challenges;
Greater regulatory pressure on the industry;
Increasing incidence of energy theft;
Aging workforce
All these are contributing to a major shift in the previously
stable cost model which the industry has benefitted from since
its inception. To achieve ongoing operational efficiencies
companies will be required to encompass significant waves of
change to their business model while continuing to provide a
cost-effective service to its customers. The ability to flex and
adapt to new and changing market models will be an essentialfuture attribute. The long decision cycle around major capital
acquisition and implementation of plant equipment is being
challenged. New technologies such as solar and wind-based
generation are gaining widespread social and political
importance, and most importantly, technological investment;
the changes that have revolutionized telecommunications and
media are about to have a similar impact on utilities. The
incumbent players must move with the times or face aggressive
new entrants who will outmaneuver them.
Very few utilities have the ability today to easily determine how
their overall business is performing. When they do it is often at
an aggregated level. This is predominately a byproduct of the
historical approach to automation; specific point solutions havebeen implemented to address functions such as asset
management, workforce management and outage management.
Each solution has deployed its own independent data model,
including commonly used data. This has led to a very siloed
approach to data management with some degree of integration
through ESB/SOA architectures. The integration option works
to a certain scale but cannot address the future demands that
smart meters and a fully instrumented smart grid present. The
data volumes that the future smart world will produce preclude
the movement of detailed usage and event data to various
operational and business applications, in some cases the
applications mandate the use of aggregated or summarized data.
Future operational efficiency is best achieved on the bedrock of
a 360 degree view of the business, where all relevant business
data is placed in a single analytical environment and the various
analytical processes are brought to the data rather than piping
the data into different pseudo operational analytical systems.
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The 360 degree view of the business can combine financial data
with consumption data to provide differing perspectives of
consumption analysis. This will be used to address immediate and
emerging requirements. It can also readily provide insight into
micro generation and workforce management. Adopting the
credo of having all the data readily available and applying differing
forms of analytics means that emerging requirements to address
energy theft, revenue protection and risk management can be
readily accommodated on the same platform, generatingexponential value out of the same set of data.
Bringing it all togetherThe three disciplines outlined above have a common theme,
namely, identifying the need for a consolidated data store
which can support different types of analytics. Much of the
data required to support each discipline is common they are
not mutually exclusive. They all have some degree of
dependency on the anticipated deluge of smart meter
consumption data and smart grid events. While other
industries are beginning to focus on Big Data and its related
challenges, utilities are faced with their own step change which,
while significant from their perspective, is well within the
bounds of well established data warehousing solutions. What is
more challenging is the lack of expertise in the utility sector in
architecting, developing and managing those more traditional
data warehousing solutions.
There is an alternative option available which provides cost -
effective scalability combined with operational simplicity. The
IBMNetezzadata warehouse appliance changed the data
warehousing industry with the launch of its IBM Netezza
Analytical Appliance in 2001.
The IBM Netezza data warehouse appliance pioneered the data
warehouse appliance market by integrating database, server and
storage into a single, easy to manage massive parallel processing
appliance that requires minimal setup and ongoing
administration while delivering faster and more consistent
analytical performance. The new family of IBM Netezza data
warehouse appliances continue to set the standard for analytical
appliances by consolidating all analytical activity into the
appliance, right where the data resides, leveraging massive
parallel processing for blisteringly fast performance.
With deep integration into IBM information management and
business analytic products as well as leading third party
reporting and analytical tools, the IBM Netezza AnalyticalAppliance can provide the integration point for the utilities
analytical requirements now and the future.
GlossaryAMI: Advanced meter infrastructure
CIS: Customer information system
CRM: Customer relationship management
ESB: Enterprise service bus
MDMS: Meter data management system
SOA: Service oriented architecture
VEE: Validation, estimation and editing
VPP: Virtual power plant
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About IBM Netezza data warehouse
appliancesIBM Netezza data warehouse appliances revolutionized data
warehousing and advanced analytics by integrating database,
server and storage into a single, easy-to-manage appliance that
requires minimal set-up and ongoing administration while
producing faster and more consistent analytic performance.
The IBM Netezza data warehouse appliance family simplifies
business analytics dramatically by consolidating all analytic
activity in the appliance, right where the data resides, for
industry-leading performance. Visit ibm.com/software/data/
netezzato see how our family of data warehouse appliances
eliminates complexity at every step and helps you drive true
business value for your organization. For the latest data
warehouse and advanced analytics blogs, videos and more,
please visit: thinking.netezza.com.
About IBM Data Warehousing and
Analytics Solutions
IBM provides the broadest and most comprehensive portfolioof data warehousing, information management and business
analytic software, hardware and solutions to help customers
maximize the value of their information assets and discover
new insights to make better and faster decisions and optimize
their business outcomes.
For more informationTo learn more about the IBM Data Warehousing and Analytics
Solutions, please contact your IBM sales representative or IBM
Business Partner or visit: ibm.com/software/data/netezza.
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Copyright IBM Corporation 2011
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Somers, NY 10589U.S.A.
Produced in the United States of America
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