Chapter 12
Knowledge Management, Business
Intelligence, and Analytics
Opening Case: Netflix
• What gave Netflix assurance that House of Cards
would be a success?
• What gives Netflix a competitive advantage?
© 2016 John Wiley & Sons, Inc.
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More Real World Examples
• Caesar’s and Capital One both collect and analyze
customer data.
• Result: They can determine who are the most
profitable customers and then follow up with them.
• Caesar’s: frequent gamblers
• Capital One: charge a lot and pay off slowly
• They provide products that would appeal to the
profitable customers.
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A Real World Example from Sports
• Oakland As and Boston Red Sox baseball teams
• Crunched the numbers on the potential players, such
as on-base percentage
• Others who did not do the analysis failed to recognize
the talent
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Five Ways Data Analytics can Help an
Organization (McKinsey and Co.)
• Making data more transparent and usable more
quickly
• Exposing variability and boosting performance
• Tailoring products and services
• Improving decision-making
• Improving products
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Terminology
• Knowledge management: The processes needed
to generate, capture, codify and transfer
knowledge across the organization to achieve
competitive advantage
• Business intelligence: The set of technologies and
processes that use data to understand and analyze
business performance
• Business analytics: The use of quantitative and
predictive models, algorithms, and evidence-based
management to drive decisions
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Data, Information, and Knowledge
(reprise)
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The Value of Managing Knowledge
Value
Sources of Value
Sharing best practices
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•
Avoid reinventing the wheel
Build on valuable work and expertise
Sustainable competitive advantage
•
•
Shorten innovation life cycle
Promote long term results and returns
Managing overload
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Filter data to find relevant knowledge
Organize and store for easy retrieval
Rapid change
•
•
•
Build on/customize previous work for agility
Streamline and build dynamic processes
Quick response to changes
Embedded knowledge from
products
•
•
•
Smart products can gather information
Blur distinction between manufacturing/service
Add value to products
Globalization
•
•
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Decrease cycle times by sharing knowledge globally
Manage global competitive pressures
Adapt to local conditions
Insurance for downsizing
•
•
•
Protect against loss of knowledge when departures occur
Provide portability for workers who change roles
Reduce time to acquire knowledge
© 2016 John Wiley & Sons, Inc.
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Dimensions of Knowledge
Explicit
 Teachable
 Articulable
 Observable in use
 Scripted
 Simple
 Documented
Tacit
 Not teachable
 Not articulable
 Not observable
 Rich
 Complex
 Undocumented
Examples:
• Explicit steps
• Procedure manuals
Examples:
• Estimating work
• Deciding best action
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Four Modes of Knowledge Conversion
(and examples)
Transferring by
mentoring,
apprenticeship
Learning by doing;
studying manuals
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Transferring by
models,
metaphors
Obtaining and
following manuals
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Knowledge Management – Four Processes
• Generate – discover “new” knowledge
• Capture – scan, organize, and package it
• Codify – represent it for easy access and transfer
(even as simple as using hash tags to create a
folksonomy)
• Transfer – transmit it from one person to another to
absorb it
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Measures of KM Project Success
• Example of specific benefits of a KM project:
•
•
•
•
•
•
•
Enhanced effectiveness
Revenue generated from extant knowledge assets
Increased value of extant products and services
Increased organizational adaptability
More efficient re-use of knowledge assets
Reduced costs
Reduced cycle time
© 2016 John Wiley & Sons, Inc.
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Components of Business Analytics
Component
Definition
Example
Data Sources
Data streams and repositories
Applications and processes for
statistical analysis, forecasting,
predictive modeling, and
optimization
Organizational environment that
creates and sustains the use of
analytics tools
Data warehouses; weather data
Data mining process; forecasting
software package
Software Tools
Data-Driven
Environment
Skilled Workforce
Workforce that has the training,
experience, and capability to use
the analytics tools
© 2016 John Wiley & Sons, Inc.
Reward system that encourages
the use of the analytics tools;
willingness to test or
experiment
Data scientists, chief data
officers, chief analytics officers,
analysts, etc. Netflix, Caesars
and Capital One have these
skills
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Data Sources for Analytics
• Structured (customers, weather patterns) or
unstructured (Tweets, YouTube videos)
• Internal or external
• Data warehouses full of a variety of information
• Real-time information such as stock market prices
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Data Mining
• Combing through massive amounts of customer data,
usually focused on:
• Buying patterns/habits (for cross-selling)
• Preferences (to help identify new products/
features/enhancements to products)
• Unusual purchases (spotting theft)
• It also identifies previously unknown relationships
among data.
• Complex statistics can uncover clusters on many
dimensions not known previously
• (e.g., People who like movie x also like movie y)
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Four Categories of Data Mining Tools
• Statistical analysis: Answers questions such as
“Why is this happening?”
• Forecasting/Extrapolation: Answers questions
such as “What if these trends continue?”
• Predictive modeling: Answers questions such as
“What will happen next?”
• Optimization: Answers questions such as “What is
the best that can happen?”
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How to be Successful
• Achieve a data driven culture
• Develop skills for data mining
• Use a Chief Analytics Officer (CAO) or Chief Data
Officer (CDO)
• Shoot for high maturity level (see next slide)
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Five Maturity Levels of Analytical Capabilities
Level
Description
Source of Business Value
1 – Reporting
What
happened?
Reduce costs of summarizing,
printing
2 – Analyzing
Why did it
happen?
Understanding root causes
3 – Describing
What is
happening now
Real-time understanding &
corrective action
4 – Predicting
What will
happen?
Can take best action
5 – Prescribing
How should we
respond?
Dynamic correction
© 2016 John Wiley & Sons, Inc.
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BI and Competitive Advantage
• There is a very large amount of data in databases.
• Big data: techniques and technologies that make it
economical to deal with very large datasets at the
extreme end of the scale: e.g., 1021 data items
• Large datasets can uncover potential trends and causal
issues
• Specialized computers and tools are needed to mine
the data.
• Big data emerged because of the rich, unstructured
data streams that are created by social IT.
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Practical Example
• Asthma outbreaks can be predicted by U. of Arizona
researchers with 70% accuracy
• They examine tweets and Google searches for words
and phrases like
• “wheezing” “sneezing” “inhaler” “can’t breathe”
• Relatively rare words (1% of tweets) but 15,000/day
• They examine the context of the words:
• “It was so romantic I couldn’t catch my breath” vs
• “After a run I couldn’t catch my breath”
• Helps hospitals make work scheduling decisions
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Sentiment Analysis
• Can analyze tweets and Facebook likes for
• Real-time customer reactions to products
• Spotting trends in reactions
• Useful for politicians, advertisers, software
versions, sales opportunities
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Google Analytics and Salesforce.com
• Listening to the community: Identifying and monitoring all
conversations in the social Web on a particular topic or brand.
• Learning who is in the community: Identifying demographics such
as age, gender, location, and other trends to foster closer
relationships.
• Engaging people in the community: Communicating directly with
customers on social platforms such as Facebook, YouTube,
LinkedIn, and Twitter using a single app.
• Tracking what is being said: Measuring and tracking
demographics, conversations, sentiment, status, and customer
voice using a dashboard and other reporting tools.
• Building an audience: Using algorithms to analyze data from
internal and external sources to understand customer attributes,
behaviors, and profiles, then to find new similar customers
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Google Analytics
• Web site testing and optimizing: Understanding traffic to
Web sites and optimizing a site’s content and design for
increasing traffic.
• Search optimization: Understanding how Google sees an
organization’s Web site, how other sites link to it, and
how specific search queries drive traffic to it.
• Search term interest and insights: Understanding interests
in particular search terms globally, as well as regionally,
top searches for similar terms, and popularity over time.
• Advertising support and management: Identifying the
best ways to spend advertising resources for online
media.
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Internet of Things (IoT)
• Much big data comes from IoT
• Sensor data in products can allow the products to:
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•
•
•
•
Call for service (elevators, heart monitors)
Parallel park, identify location/speed (cars)
Alert you to the age of food (refrigerator)
Waters the lawn when soil is dry (sprinklers)
Self-driving cars find best route (Google)
© 2016 John Wiley & Sons, Inc.
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Intellectual Capital vs Intellectual
Property
• Intellectual Capital: the process for managing
knowledge
• Intellectual Property: the outputs; the desired
product for the process
• Intellectual Property rights differ remarkably by
country
© 2016 John Wiley & Sons, Inc.
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Closing Caveats
• These are emerging concepts and disciplines
• Sometimes knowledge should remain hidden
(tacit) for protection
• We should remain focused on future events,
not just look over the past
• A supportive culture is needed in a firm to
enable effective KM and BI
© 2016 John Wiley & Sons, Inc.
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