1. Strategic Management of Information Systems Fifth Edition
Keri Pearlson and Carol Saunders
Chapter 11
Business Analytics and Knowledge Management
PowerPoint® files by Michelle M. Ramim
Huizenga School of Business and Entrepreneurship
Nova Southeastern University
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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.
Duplicate this slide as necessary.
This and related slides can be moved to the appendix or hidden if necessary.
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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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4. Knowledge Management, Business Intelligence, and Business Analytics
Knowledge management has been invigorated and enabled by:
new technologies for collaborative systems.
the emergence of the Internet and intranets.
large, geographically-distributed knowledge repositories.
well-publicized successes of companies using business analytics (e.g., Caesars).
Established sources.
Anthropology, cognitive psychology, management, sociology, artificial intelligence, IT, and library science.
Knowledge management is an emerging discipline.
Managing knowledge is not a new concept.
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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):
is a set of technologies and processes that use data to understand and analyze business performance.
is a management strategy used to create a more structured approach to decision making.
analyzes information collected in company databases, extracting knowledge from data.
The organization gains only limited benefit from knowledge isolated within individuals or among workgroups.
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6. 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.
How the information is used and how the knowledge is linked back to business processes are important components of knowledge management.
Some use the terms BI and analytics interchangeably.
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7. 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.
Knowledge management and intellectual capital are often used imprecisely and interchangeably.
Information technology (IT):
provides an infrastructure for capturing and transferring knowledge.
does not create knowledge.
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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8. Intellectual Property (Cont.)
Information-based property differs from physical property in two ways:
It is non-exclusive.
When one person uses it, it can be used again by another person.
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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9. Intellectual Property (Cont.)
Intellectual property enables owners to be rewarded for the use of their ideas.
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:
Patents for inventions.
Trademarks for brand identity.
Designs for product appearance.
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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11. Data, Information, and Knowledge
The terms data, information, and knowledge are often used interchangeably (Figure 11.1).
Data are specific, objective facts or observations.
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.
This involves interpreting the context of the data and summarizing it into a more condensed form.
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Figure The relationships between data, information, and knowledge.
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13. Components of Knowledge
Knowledge is:
a mix of contextual information, experiences, rules, and values (Figure 12.2).
both richer and deeper than information.
more valuable because it includes someone’s unique experience, judgment, and wisdom.
Three different types of knowing:
Knowing what:
based on assembling information and applying it.
Knowing how:
focuses on applying knowledge.
Knowing why:
is synthesized through a reasoning process.
is the casual knowledge of why something occurs.
Knowing what: The ability to recognize, describe, and classify concepts and things. Knowing how: An understanding of an appropriate sequence of events (procedures, routines, and rules). The ability to perform a particular set of actions.
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14. Figure 11.2 Taxonomy of knowledge.
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15. Components of Knowledge (Cont.)
Values and beliefs determine the interpretation and the organization of knowledge.
Davenport and Prusak say:
“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:
a greater amount of knowledge to manage.
more powerful tools available to manage knowledge.
Managing knowledge provides value to organizations in many ways (Figure 11.3).
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16. Figure 11.3 The value of managing knowledge.
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17. Tacit Versus Explicit Knowledge
Tacit knowledge:
was first described by Michael Polyani:
“We can know more than we can tell.”
is personal, context-specific, and hard to formalize and communicate.
consists of experiences, beliefs, and skills.
is entirely subjective.
is acquired through physically practicing a skill or activity.
Explicit knowledge:
is the focus of IT.
is knowledge that can be easily collected, organized, and transferred through digital means such as a memorandum or financial report.
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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18. 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:
take an expert’s tacit knowledge and make it explicit.
take a new hire’s explicit book-learning and make it tacit.
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20. Modes of Knowledge Conversion
Ikujiro Nonaka and Hirotaka Takeuchi developed four different modes of knowledge conversion (Figure 11.5):
Socialization - 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.”
Externalization - from tacit knowledge to explicit knowledge.
Combination - from explicit knowledge to explicit knowledge.
Internalization - from explicit knowledge to tacit knowledge.
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21. Figure 11.5 The four modes of knowledge conversion.
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22. Knowledge Management Processes
Four main knowledge management processes:
Knowledge generation:
Includes all activities that discover “new” knowledge—whether such knowledge is new to the individual, the firm, or the entire discipline.
Knowledge capture:
Involves continuous processes of scanning, organizing, and packaging knowledge after it has been generated.
Knowledge codification:
The representation of knowledge in a manner that can be easily accessed and transferred.
Knowledge transfer:
Involves transmitting knowledge from one person or group to another and the absorption of that knowledge.
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23. 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:
seek to enhance the efficiency and effectiveness of these activities and leverage their value for the firm and the individual.
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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24. 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:
when users list key words that codify the information or document at hand, creating an ad-hoc codification system.
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.
(c) 2013 John Wiley & Sons, Inc.

25. 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.
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:
is the next generation of business intelligence.
incorporates a more proactive perspective.
provides for querying of real-time data.
provides visualization and analytics tools.
Managers could drill down into each region by clicking on the state and see the next level of detail, which provided information by
region. BI is noted for its visualizations and simulation capabilites.
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26. 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:
Hard to duplicate.
Uniqueness.
Adaptability.
Better than the competition.
Renewability.
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27. 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):
A data repository.
Software tools.
An analytics environment.
A skilled workforce.
Data Repositories.
Data used in the analytical processes must be gathered, cleaned up, common, integrated, and stored for easy access.
Data warehouses:
are collections of data designed to support management decision making.
sometimes serve as repositories of organizational knowledge.
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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28. Figure 11.6 Components of business analytics.
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Software Tools
Data mining:
is the process of analyzing data warehouses for “gems” that can be used in management decision making.
identifies previously unknown relationships among data.
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:
Statistical analysis.
Forecasting/extrapolation.
Predictive modeling.
Optimization.
These tools are used with the data in the data warehouse to gain insight and support decision making.
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30. Analytics Environment
Building an environment that supports and encourages analytics is a critical component.
IS strategy and organizational strategy must be aligned with the business strategy.
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.
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.
Use of evidence-based management encourages decisions based on data and analysis rather than on experience and intuition.
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Skilled Workforce
It is clear that to be successful with analytics, data and technology must be used.
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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32. 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.
Large datasets are desirable because of the potential trends and analytics that can be extracted.
Specialized computers and tools are needed to mine the data.
Big data is more common because of the rich, unstructured data streams that are created by social IT.
Big data problems occur in simulations, scientific research, Internet searches, customer data management, and financial market analytics.
Social IT supplies unique customer intelligence.
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33. Figure 11.7 Analytical capabilities maturity levels.
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34. Social Analytics or Social Media Analytics
Is a set of tools developed to measure the impact of the social IT investments on the business.
analyzes conversations, tweets, blogs, and other social IT data to create meaningful, actionable facts.
Social analytics vendors include Google Analytics and Radian6 (Salesforce.com).
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.
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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35. 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.
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37. 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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38. 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.
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39. 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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40. 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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