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Strategic Management of Information Systems

Fifth Edition

Business
Analytics and Knowledge Management

Keri
Pearlson

and
Carol Saunders

PowerPoint
®

f
iles

by Michelle M. Ramim

Huizenga School of Business and Entrepreneurship

Nova Southeastern University

(c) 2013 John Wiley & Sons, Inc.

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Chapter 11

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Learning Objectives


Understand the difference between data, information, and
knowledge.


Define how tacit knowledge differs from explicit knowledge.


Describe why knowledge management is so important.


Understand how knowledge is generated and captured.


Describe a knowledge map.

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Real World Example


Harrah’s found a way to double revenues by collecting and then
analyzing customer data.


They mine their customer data completely.


They use loyalty cards to track customer behavior and to identify high
-
revenue customers.


Harrah’s determined that these customers were motivated by reduced
hotel room rates and wanted quick service.


They found ways to reduce lines and wait time.


High
-
revenue customers rarely waited in any line.


They found ways to keep customers coming back.


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Knowledge Management, Business
Intelligence, and Business Analytics


Knowledge management has been invigorated and enabled by:

o
new technologies for
collaborative

systems
.

o
the emergence of the Internet and intranets.

o
large, geographically
-
distributed
knowledge

reposito
ries
.

o
well
-
publicized successes of companies using business analytics (e.g.,
Caesars).

o
Established sources.


Anthropology, cognitive psychology, management, sociology,
artificial intelligence, IT, and library science.


Knowledge management is an emerging discipline.


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Knowledge Management


Knowledge management includes the processes necessary to generate, capture,
codify, and transfer knowledge across the organization to achieve competitive
advantage.


Individuals

are the ultimate source of organizational knowledge.


To obtain the full value of knowledge, it must be captured and transferred across
the organization.


Business intelligence
(BI):

o
is a set of technologies and processes that use data to understand and analyze business
performance.

o
is a management strategy used to create a more structured approach to decision making.

o
analyzes information collected in company databases, extracting knowledge from data.

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Business Intelligence


Business intelligence can be considered a component of knowledge
management.


Davenport and Harris suggest that
business

analytics

refers to the use of
quantitative and predictive models as well as fact
-
based management to drive
decisions.


A sustainable competitive advantage lies in what
employees

know and how
they apply that knowledge to business problems.


Knowledge must serve the broader goals of the organization.

o
How the information is used and how the knowledge is linked back to
business processes are important components of knowledge management.

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Intellectual Property


Intellectual

capital

is knowledge that has been identified, captured, and
leveraged to produce higher
-
value goods or services or some other competitive
advantage for the firm.

o
Knowledge management and intellectual capital are often used imprecisely and
interchangeably.


Information technology (IT):

o
provides an infrastructure for capturing and transferring knowledge.

o
does not create knowledge.

o
cannot guarantee knowledge sharing or use.


Intellectual

property

allows individuals to own their creativity and
innovation in the same way that they can own physical property.

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Intellectual Property (Cont.)


Information
-
based property differs from physical property in two
ways:

o
It is non
-
exclusive.


When one person uses it, it can be used again by another person.

o
The marginal cost of producing additional copies of information
-
based
property is negligible compared with the cost of original production.


These characteristics create differences in the ethical treatment of
physical and information
-
based
intellectual

property
.

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Intellectual Property (Cont.)


Intellectual property enables
owners

to be rewarded for the use of their
ideas.

o
It allows them to have a say in how their ideas are used.


Owners are granted intellectual property rights.


Some protection such as
copyright

arises automatically.


Types of intellectual property:

o
Patents for inventions.

o
Trademarks for brand identity.

o
Designs for product appearance.

o
Copyrights.


Copyrights apply to literary and artistic material, music, films, sound
recordings, broadcasts, and software.


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Copyright Act


The

Digital Millennium Copyright Act
(DCMA) makes it a crime to
circumvent copy protection

even if that copy protection impairs rights
established by the Audio Home Recording Act.


The

Digital Tech Corps Act
of 2002 bans employees from revealing trade
secrets during their lifetime and imposes a criminal penalty of up to five years in
prison and a $50,000 fine.


The Coordinator for International Intellectual Property Enforcement in the U.S.
Department of Commerce coordinates the battle against global piracy of
intellectual property.


The

Stop Online Piracy Act
(SOPA) and the
Protect IP Act
(PIPA)
were introduced to protect intellectual property.

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Data, Information, and Knowledge


The terms data, information, and knowledge are often used interchangeably
(Figure 11.1).


Data

are specific, objective facts or observations.

o
Facts have no intrinsic meaning but can be easily captured, transmitted, and
stored electronically.


Information

is defined by Peter
Drucker

as “data endowed with relevance
and purpose.”


People turn data into information by organizing them into some unit of analysis.

o
This involves interpreting the context of the data and summarizing it into a more
condensed form.

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Figure 11.1 The relationships between data, information, and knowledge.

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Components of Knowledge


Knowledge is:

o
a
mix

of contextual information, experiences, rules, and values (Figure 12.2).

o
both richer and deeper than information.

o
more valuable because it includes someone’s unique experience, judgment,
and wisdom.


Three different types of knowing:

o
Knowing
what
:



based on assembling information and applying it.

o
Knowing
how
:


focuses on applying knowledge.

o
Knowing
why
:


is synthesized through a reasoning process.


is the casual knowledge of why something occurs.

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Know
-
Why
Know
-
What
Know
-
How
Application
Experience
Information
Procedure
Reasoning
Figure
11.2
Taxonomy of
knowledge.

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Components of Knowledge (Cont.)


Values

and
beliefs

determine the interpretation and the organization of
knowledge.


Davenport and
Prusak

say:

o
“The power of knowledge to organize, select, learn, and judge comes from values and
beliefs as much as, and probably more than, from information and logic.”


Computers work well for managing data but are less efficient at managing
information.


Managing knowledge has become far more complex because of:

o
a greater amount of knowledge to manage.

o
more powerful tools available to manage knowledge.


Managing knowledge provides
value

to organizations in many ways (Figure
11.3).

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Figure
11.3 The value of managing knowledge.

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Tacit Versus Explicit Knowledge


Tacit

knowledge:

o
was first described by Michael
Polyani
:


“We can know more than we can tell.”

o
is personal, context
-
specific, and hard to formalize and communicate.

o
consists of experiences, beliefs, and skills.

o
is entirely subjective.

o
is acquired through physically practicing a skill or activity.


Explicit

knowledge:

o
is the focus of IT.

o
is knowledge that can be easily collected, organized, and transferred through
digital means such as a memorandum or financial report.

o
gained
from reading this
textbook
is objective, theoretical, and codified for transmission
in a formal, systematic method using grammar, syntax, and the printed word.


Individuals possess both tacit and explicit knowledge (Figure 11.4).

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Figure 11.4 Examples of explicit and tacit knowledge.

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Knowledge Conversion


Knowledge
conversion

strategies are often of interest in the business
environment.


Companies want to:

o
take an expert’s tacit knowledge and make it explicit.

o
take a new hire’s explicit book
-
learning and make it tacit.


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Modes of Knowledge Conversion


Ikujiro

Nonaka

and
Hirotaka

Takeuchi developed four different modes of
knowledge conversion (Figure 11.5):

o
S
ocialization

-

from
tacit knowledge to tacit
knowledge.


Socialization is the process of sharing experiences.


It
occurs
through observation, imitation, and practice.


Common examples include sharing war stories, apprenticeships,
conferences, and casual, unstructured discussions in the office or
“at the water cooler.”

o
E
xternalization

-

from tacit knowledge to explicit knowledge.

o
C
ombination

-

from
explicit knowledge to explicit knowledge.

o
I
nternalization

-

from
explicit knowledge to tacit knowledge
.

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Figure 11.5 The four modes of knowledge conversion.

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Knowledge Management Processes


Four main knowledge management processes:

o
Knowledge generation
:


I
ncludes all activities that discover “new” knowledge

whether such
knowledge is new to the individual, the firm, or the entire discipline.

o
Knowledge capture
:


Involves continuous processes of scanning, organizing, and packaging
knowledge after it has been generated.

o
Knowledge codification
:


The representation of knowledge in a manner that can be easily
accessed and transferred.

o
Knowledge transfer
:


I
nvolves transmitting knowledge from one person or group to another
and the absorption of that knowledge.

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Knowledge Management Processes

(Cont.)


Without absorption, a transfer of knowledge does not occur.


Generation, codification, and transfer generally take place
constantly without management intervention.


Knowledge management systems:

o
seek to enhance the efficiency and effectiveness of these activities and
leverage their value for the firm and the individual.

o
continually evolve into new and more robust systems for managing and
using knowledge.


Knowledge management processes are different in the age of Web
2.0 and robust
search

tools

such as Google.

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Modern Knowledge Management

Systems


Traditional knowledge management systems had well
-
defined processes for
generation, capture, codification, and transfer.


Large data warehouses, ubiquitous websites, search tools, and tagging make it
possible to capture and find information without the formal processes.


Tagging

is:


o
when users list key words that codify the information or document at hand, creating an
ad
-
hoc codification system.

o
sometimes referred to as a
folksonomy
.


Modern technologies have replaced traditional knowledge management systems.


Individuals have the ability to find information that traditionally was locked
within structures that had to be designed, managed, and then taught to users.

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Business Intelligence


Traditional
business

intelligence

(BI)

provides
dashboards

and reports
to assist managers in monitoring key performance metrics.


BI systems include reporting, querying, dashboards, and scorecards.

o
Dashboards:


are simple online displays of key metrics often graphically displayed in
pie charts, bar charts, red
-
yellow
-
green coded data, and other images.


easily convey both the value of the metric and

via the color coding

if
the metric is within acceptable parameters.


BI is useful for strategic, tactical, and operational decisions.


BI 2.0, or
collaborative

BI
:


o
is the next generation of business intelligence.

o
incorporates a more proactive perspective
.

o
provides for querying of real
-
time data.

o
provides
visualization

and analytics tools.

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Competing with Business Analytics


Many companies in many industries offer similar products and use
comparable technologies.


Business

processes

are among the last remaining points of
differentiation
.


Davenport and Harris suggest

companies that successfully compete
using their business analytics skills have five capabilities:

o
Hard to duplicate.

o
Uniqueness.

o
Adaptability.

o
Better than the competition.

o
Renewability.

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Components of Business Analytics


Companies make a significant investment in their technologies, their people, and
their strategic decision
-
making processes.


Four components of business analytics (Figure 11.6):

o
A data repository.

o
Software tools.

o
An analytics environment.

o
A skilled workforce.


Data
Repositories
.

o
Data used in the analytical processes must be gathered, cleaned up, common, integrated,
and stored for easy access.


Data
warehouses
:

o
are collections of data designed to support management decision making.

o
sometimes serve as repositories of organizational knowledge.

o
contain a wide variety of data used to create a coherent picture of business conditions at
a single point in time.





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Figure 11.6 Components of business analytics.

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Software Tools


Data
mining
:

o
is the process of analyzing data warehouses for “gems” that can be used in management
decision making.

o
identifies previously unknown relationships among data.

o
refers to combing through massive amounts of customer data to understand buying
habits and to identify new products, features, and enhancements.


The
analysis

may help a business better understand its customers.


There are four categories of tools:

o
Statistical analysis.

o
Forecasting/extrapolation.

o
Predictive modeling.

o
Optimization.

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Analytics Environment



Building an environment that supports and encourages analytics is a critical
component.


o
IS strategy and organizational strategy

must be aligned with the business
strategy.

o
Corporate culture, incentive systems, metrics used to measure success of
initiatives, and processes for using analytics must be aligned with the
objective of building competitive advantage through analytics.


Leadership

plays a big role in creating a strong analytics environment.

o
Leaders must move the company’s culture toward an evidence
-
based
management approach in which evidence and facts are analyzed as the first
step in decision making.

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Skilled Workforce


It

is clear that to be successful with analytics, data and technology
must be used.

o
People

must be involved.


Managers must have enough knowledge of analytics to use them in
their decision making.


Leaders

must set an example for the organization.


Some hire
experts

to use analytics software.


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BI Competitive Advantage


Companies tend to fall into one of 5 levels of maturity with analytical capabilities
(Figure 11.7).


There is a very large amount of data amassing in databases.


Big

data
:

techniques and technologies that make it economical to deal with
very large datasets at the extreme end of the scale.

o
Large datasets are desirable because of the potential trends and analytics that
can be extracted.

o
Specialized computers and tools are needed to mine the data.

o
Big data is more common because of the rich, unstructured data streams that
are created by social IT.

o
Big data problems occur in
s
imulations
, scientific research, Internet
searches, customer data management, and financial market analytics.


Social

IT

supplies unique customer intelligence.



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Figure 11.7 Analytical capabilities maturity levels.

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Social Analytics or Social Media

Analytics


Social analytics:

o
Is a set of tools developed to measure the impact of the
social

IT

investments on the
business.

o
analyzes conversations, tweets, blogs, and other social IT data to create meaningful,
actionable facts.


Social analytics vendors include Google Analytics and Radian6 (Salesforce.com).

o
Radian6’s platform tools enable:


listening to the community.


learning who is in the community.


engaging people in the community.


tracking what is being said.

o
Google

Analytics

is a set of social analytics tools that enables organizations to analyze
their website and include:


website
testing and optimizing.


search
optimization.


search
term interest and insights.


advertising
support and
management.


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Caveats for Managing Knowledge and

Business Intelligence


Knowledge

management

and business intelligence continue to be
emerging disciplines.


Knowledge is not always visible or available.


Nurturing a culture that values learning and sharing of knowledge enables
effective and efficient knowledge management.


The success of knowledge management ultimately depends on a personal and
organizational willingness to learn.

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Chapter 11
-

Key Terms

Big data
(p. 341)
-

techniques and technologies that make it

economical to deal with very large datasets at the extreme end of the scale.

Business analytics
(p. 327)
-

the use of quantitative and predictive

models as well as fact
-
based management to drive decisions.

Business intelligence
(p. 327)
-

a set of technologies and processes that

use data to understand and analyze business performance.

Data

(p. 330)
-

specific, objective facts or observations.

Data mining
(p. 339)
-

the process of analyzing data warehouses for

“gems” that can be used in management decision making.



(c) 2013 John Wiley & Sons, Inc.

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Chapter 11
-

Key Terms (Cont.)

Data warehouses
(p. 338)
-

collections of data designed to support

management decision making; sometimes serve as repositories of

organizational knowledge.

Explicit knowledge
(p. 334)
-

knowledge that can be easily collected,

organized, and transferred through digital means such as a memorandum

or financial report.

Externalization
(p. 334)
-

articulating and thereby capturing tacit knowledge
through use of metaphors, analogies, and models.

Evidence
-
based management
(p. 341)
-

an approach in which evidence and

facts are analyzed as the first step in decision making.



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Chapter 11
-

Key Terms (Cont.)

Folksonomy

(p. 335)
-

an ad
-
hoc codification system created by users.

Information
(p. 330)
-

data with a context.

Intellectual capital
(p. 328)
-

knowledge that has been identified,

captured, and leveraged to produce higher
-
value goods or services or

some other competitive advantage for the firm.

Intellectual property
(p. 328)
-

allows individuals to own their creativity

and innovation in the same way that they can own physical property.

Knowledge

(p. 331)
-

mix of contextual information, experiences, rules, and

values. It is richer and deeper than information and more valuable because

someone has thought deeply about that information and added his or her own

unique experience, judgment, and wisdom.



(c) 2013 John Wiley & Sons, Inc.

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Chapter 11
-

Key Terms (Cont.)

Knowledge capture
(p. 335)
-

the continuous processes of scanning,

organizing, and packaging knowledge after it has been generated.

Knowledge codification

(p. 335)
-

the representation of knowledge in

a manner that can be easily accessed and transferred.

Knowledge generation
(p. 335)
-

all activities that discover “new”
knowledge

whether such knowledge is new to the individual, the firm, or
the entire discipline.

Knowledge management
(p.
327)
-

the processes necessary to generate,
capture, codify, and transfer knowledge across the organization to achieve
competitive advantage
.



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Chapter 11
-

Key Terms (Cont.)

Knowledge transfer
(p. 335)
-

involves transmitting
knowledge from one
person or group to
another
and the absorption of that knowledge.

Social analytics
(p. 342)
-

a set of tools developed to measure the impact of
the social IT investments on the business.

Socialization
(p. 335)
-

the process of sharing experiences; occurs through
observation, imitation, and practice.

Tacit knowledge
(p. 332)
-

knowledge that is personal, context
-
specific, and
hard to formalize and
communicate; consists
of experiences, beliefs, and skills.

Tagging
(p. 335)
-

users list key words that codify the information or
document at hand, creating an ad
-
hoc codification system.

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