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Hightower – The analytical organization
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But Netflix uses analytics in many other ways as well. For
example, Netflix uses analytics to determine how much to pay
for the distribution rights to DVDs, and to prioritize shipping to
infrequent users, who are their most profitable customers. The
use of analytics has allowed Netflix to stay ahead of competitor
Blockbuster, which also delivers DVDs through the mail but has
the advantage of brick and mortar stores as well.
Although they undoubtedly increased the awareness of the
value of analytics, Davenport and Harris did not create the
phenomenon. The term business intelligence (BI) appeared in
the early 1990s, but its roots go back to the 1960s with systems
such as decision support systems (DSS) and executive information systems (EIS). The details of the technologies have evolved
but the goals of using data to gain insight and improve performance are not new. What has changed is the pervasiveness of the
technology. Today, almost every Fortune 2000 company has a
data warehouse (Erickson, 2006), a key BI enabler. Furthermore,
BI is one of the fastest and most consistently growing areas of
the information technology industry. The worldwide market for
BI technologies is nearly $100 billion, and growing at almost 10%
per year (The Economist, 2010). Unlike many other segments of
the information systems industry, the BI market continued to
grow during the recession that began in 2008 (Henschen, 2009).
Harrah’s uses data from their Total Rewards customer loyalty
program to create individual relationships with the more than 40
million visitors to their properties. This individualized approach
allows Harrah’s to create customized incentive plans for each
member of the program. The result is that Harrah’s share of
their customers discretionary gaming dollars spent versus their
competitors has risen from 30% to 50%.
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Netflix has used analytics extensively since the company was
founded. Netflix is best known for their Cinematch algorithm
which matches customers’ movie rankings to clusters of movies
in order make personalized recommendations. Netflix creates a
personalized web page for every user based on these recommendations.
This growth has a number of drivers. One important factor is
the maturity of enabling technologies. Increased sophistication
of information technology (IT) combined with lower costs has
made it much easier to collect, access, distribute, and analyze
data. Many of the components of the BI infrastructure, such as
data warehouses and data networks, are already in place or are
considered relatively low risk investments. Many vendors offer
out-of-the-box solutions that can be deployed with minimum
effort (Henschen, 2009). The key question remaining for companies is the best way to capitalize on BI and analytics, rather than
how to implement the technology required.
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In 1994 Continental Airlines was ranked 10th out of 10 major
U.S. airlines by the U.S. Department of Transportation in several
quality categories. Today Continental is considered one of the
best run airlines. Continental Airlines brought itself back from
near bankruptcy, in part by investing in real-time business intelligence that affected nearly every aspect of their business. For
example, managers can see real-time revenue projections for
every flight, and can identify who their most valuable customers are, and which ones are encountering delays. Using real-time
information, managers can run what-if scenarios to determine
the best way to respond to customer service and scheduling
issues.
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With their book, Competing on Analytics, Davenport and Harris
(2007) shown a spotlight on companies who have based their
strategies on analytical technologies and methods. The authors
called these companies analytical competitors, and defined
them as companies that make “… extensive use of data, statistical and quantitative analysis, explanatory and predictive
models, and fact-based management to drive decisions and
actions (Davenport and Harris, 2007, pg 7).” Analytical competitors don’t just use analytics to enhance their operations, but as
their primary competitive differentiator. The use of analytics is
actively encouraged by management and is embedded within
the organizational culture. By now the success stories of many
analytical competitors are well known.
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15.1 Introduction
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This chapter describes the attributes of companies that Davenport & Harris (2007) called analytical competitors. Business intelligence is one of the fastest growing technology areas, but many organizations are not able to exploit the technology to its fullest.
The attributes described in this chapter will help any organization get the most value from their business intelligence investments.
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Ross Hightower (Texas A&M)
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Chapter 15 : The Analytical Organization
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BI technologies are often divided into two broad categories :
infrastructure and access. Infrastructure includes all the technologies required to manage, extract, transform, and organize data
so that it can be accessed by users. Included in this category are
master data management (MDM), extraction, transformation,
and loading (ETL) technologies, enterprise data warehouses
(EDW) and data marts.
Also driving the adoption of BI is that efforts to improve
performance using conventional information technology have
reached the point of diminishing returns (Williams & Williams,
2007). Over the past two decades organizations have invested
billions of dollars on ERP systems to automate their business
processes. There remains little room for improvement through
this means. Organizations are looking to analytics as a means to
boost performance by leveraging information.
Access technologies include the tools used to access, report,
and analyze data. Davenport and Harris, as well as others,
further divide this category into reporting and analytical.
Reporting tools include standard reports, alerts, ad hoc reports,
and dashboards. Analytical tools include data mining, statistical
analysis, forecasting, and optimization. It is this latter category
that Davenport and Harris called business analytics (BA) (2007).
These factors, combined with a growing awareness, have driven
the interest in business intelligence and business analytics.
A study by Computer Economics found that 84% of organizations reported some level of business intelligence program
(Computer Economics, 2008). Yet, many of these organizations
have not realised significant benefits from these investments
(Williams & Williams, 2007). What’s the secret ? What separates
the few companies that are able, not only to capitalize on their
BI investments, but are able to separate themselves from their
competition by applying analytics ? Fortunately, the capabilities that allow organizations to successfully apply BI are easy to
identify, if not easy to implement. This chapter describes what
it takes for an organization to become an analytical competitor.
While it’s not necessary or desirable for every organization to
make the commitment required to be an analytical competitor,
every organization can benefit from becoming more analytical.
Developing the capabilities described in this chapter will help
them do that.
One way to understand the difference between reporting and
analytical tools is the time frame of the data used, and the
questions answered. Reporting tools are suitable for presenting
lag information that reflects past performance. Lag information is useful for answering questions focused on “what is” or
“what happened”, requiring little or no analysis. Lag information
is useful as a means to monitor performance and make evolutionary improvements in existing business processes, but is
not suitable for making the sort of revolutionary changes that
lead to competitive advantage (Laursen & Thorlund, 2010). The
majority of organizations that have deployed BI limit themselves
to reporting tools (Davenport & Harris, 2007).
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Many authors agree that business intelligence refers to all of
the technologies and methods used to organize and analyze
data in order to measure performance, improve operations, and
guide planning. According to this broad definition, BI includes
not only the technologies required to extract, organize, report,
and analyze data, but also the management processes and
governance structures required to make use of the information
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Questions such as “why” or “what’s next” can only be answered
by lead information. Lead information results from analysis of
lag information using analytical tools. Lead information can be
used to create new processes, or to re-engineer existing processes in ways that provide a competitive advantage. It is this type
of information that analytical competitors exploit so successfully. The use of analytical tools is usually the culmination of an
organization’s evolution with BI. To successfully apply analytics,
the technical infrastructure must be in place, analytical talent
must be available, and users must understand how to capitalize
on the information provided by the tools. Figure 1 shows the
relationship between access tools, information, and competitive
advantage.
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15.2 Business Intelligence and Analytics
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gained from the technology. In the broadest sense of the term,
BI includes Business Analytics (BA), Corporate Performance
Management (CPM), Business Performance Management (BPM),
Enterprise Information Management (EIM) and other related
areas.
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Information technology has also enabled most organizations to
accumulate large data repositories to exploit. ERP, CRM, pointof-sale, and web sites all generate a wealth of data. According
to Jim Goodnight, CEO of SAS Institute, in 2010 the amount of
digital data in the world doubles every 11 hours (Mohammad
Ali Khan, et al., 2009). This is a little easier to believe when you
consider that Wal-Mart alone processes more than 1 million
customer transactions per hour, storing the data in databases
estimated at more than 2.5 petabytes (The Economist, 2010).
The need to manage this flood of data and a rising awareness of
the value that can be mined from it has helped drive BI adoption.
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Hightower – The analytical organization
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CHAPTER 15
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What’s the best we can do?
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Data mining
Statistical analysis
Forecasting
Optimization
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Standard reports
Dashboards
Ad hoc reports
Alerts
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What will happen next?
What happened?
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Why is this happening?
What is?
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Figure 1 – Analytical Tools, Questions Asked ,and Information Types
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CHAPTER 15
analytics
reporting
competitive potential
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15.3 Becoming an Analytical Organization
It’s no coincidence that analytical competitors are among the
most innovative and successful companies in their industries. This success is only partly the result of using analytical
techniques and decision making methods. Competing on
analytics requires organizations to have a rigorously rational
approach to running their companies, and embrace a culture
of innovation and continuous improvement. There are no short
cuts. Becoming an analytical competitor requires a long term
commitment to cultural change, investments in people and
infrastructure, and intense focus on extracting business value
using analytical methods. Organizations should be prepared to
maintain a focused, sustained effort over years to develop the
technical infrastructure, internal skills, and organizational structures required.
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This long-term view of BI development is reflected in the models
used to classify organizations’ BI maturity or readiness. One such
model is the maturity model developed by Davenport & Harris
(2007). This model defines five stages of maturity based on
organizational, human, and technological factors. Organizations
should expect to progress through the stages in order, learning
lessons and building capabilities along the way. Skipping stages
is difficult and often leads to failure and regression. The characteristics of each stage are described in Table 1.
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On the other hand, it’s not necessary for a company to make
such a commitment to benefit from BI. Much of what makes
analytical competitors so successful can be applied on a smaller scale. The problem is most companies aren’t able to extract
sufficient value from their BI investments because their efforts
are uncoordinated, underfunded and not focused on decisions
that provide significant business value. According to Williams &
Williams (2007), BI provides value when it combines products,
technology, processes, and people to organize information
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that management needs to improve profit and performance.
Analytics should be embedded in core business processes using
measures that are fact-based, analytically rigorous, and repeatable. Decisions must be linked to actions that have measurable
impacts on performance. Dabbling in BI with only a vague idea
of what the benefits will be is a recipe for failure. To achieve
success, organizations must take a systematic approach to
laying the groundwork, developing managerial and technical
readiness, and choosing targets that will deliver value. This is
true whether BI is applied enterprise wide or on a much smaller
scale.
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Desire for more
objective data,
successes from
point use of
analytics start
to get attention
Recent transaction
data un-integrated,
missing important
information, isolated
BI / analytics efforts
Mostly
separate
analytic
processes,
Building
enterpriselevel plan
Analysts
in multiple
areas of
business but
with limited
interaction
Executive
support for
fact-based
culture-may
meet considerable resistance
Proliferation of BI
tools, Data marts /
data warehouse
established expands
Change program
to develop
integrated
analytical
processes and
applications build
analytical
capabilities
Some
embedded
analytics
processes
Skills exist,
Broad C-suite
but often not support
aligned to
right level /
right role
Change
management to
build a
fact-based
culture
High-quality data,
have an enterprise
BI plan / strategy,
IT processes and
governance
principles in place
Deep strategic
insights,
continuous
renewal and
improvement
Fully
embedded
and much
more highly
integrated
Highly
skilled,
leveraged,
mobilized,
centralized,
out-sourced
grunt work
Broadly
supported
fact-based
culture, testing
and learning
culture
Enterprise-wide
BI/BA architecture
largely implemented
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Figure 2 shows the Analytical Capability Assessment (ACA)
model developed by Lundgren & Larsson (2009). The ACA builds
on the work of several authors and is one of the most complete
frameworks available for identifying the essential ingredients
for becoming an analytical competitor.
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15.4 Analytical Capability Assessment
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So what are the characteristics of analytical competitors ? What
capabilities must organizations develop to progress through
the stages of BI maturity ? Fortunately, there has been enough
research on this topic that the answers to these questions
are known, though, not easy to implement. The next section
describes a model that answers these questions.
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CEO passion,
broad-based
management
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Pockets of
Functional and
isolated
tactical
analysis (may
be in finance,
SCM, or
marketing /
CRM)
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Coordinated,
established
enterprise
performance
metrics, build
analytically based
insights
5- Analytical
competitors
Missing/poor-quality
data, multiple definitions, un-integrated
systems
5
3- Analytical
aspirations
4- Analytical
companies
Knowledge
allergic – pride
on gut-based
decisions
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Disconnected,
very narrow
focus
None
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Autonomous
activity builds
experience and
confidence using
analytics ; creates
new analytically
based insights
None
Culture
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2- Localized
analytics
TECHNOLOGY
Sponsorship
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Doesn’t exist
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Limited insight
into customers,
markets,
competitors
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3
1- Analytically
impaired
Skills
HUMAN
M
pr
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Tags:
Morgan State University

Strategic Alignment

business intelligence and analytics

Standard reports

analytical competitors

Analytical Capability Assessment

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