Knowledge Management

1
Knowledge Management

• (Selected discussion topics related to and
  enhancing Pearlson Saunders Chapter 12)

2
Our Three Discussion Goals

• A slightly deeper look into data, information,
  knowledge, and wisdom
• The latest in business use of the above
  Competing on Analytics
• The latest in Web use of the aboveThe Semantic
  Web

3
Knowledge Management

• Processes necessary to generate, capture,
  codify, and transfer knowledge across the
  organization to achieve competitive advantage.
  (p. 314)
• Related concepts
• Knowledge bases, knowledge management . . .
• Intellectual capital
• Expertise
• Etc.

4
Lets look deeper into . . .

• Data
• Facts, observations (p. 315)
• Non-random symbols or signals
• Information
• Data endowed with relevance and purpose
• Data interpreted or processed to make it useful
  for decision making
• Detected non-random patterns in data
• To be useful it must have surprise value

(Many sources see work by Ackoff, Drucker,
Shannon . . .)
5
Examples Data versus Information

• What is a tell in poker?
• What is the data?
• What is the information?
• Customer loyalty cards
• Collect what?
• And can be used to generate what?
• That can then be used to do what?

6
Lets look deeper into . . .

• Knowledge
• Information human added value reflection,
  synthesis, context, process, causality . . . (p.
  315-316)
• Explicit (easier to structure represent)
• Tacit (implicit, harder to structure represent)
• Organized, applied, operationalizable, storable,
  shareable information
• Whatever happened to expert systems?

7
Lets look deeper into . . .

• Wisdom
• Knowledge plus meta-knowledge
• Knowing whether/where knowledge applies
• Combining knowledge
• Extrapolating knowledge
• Recognizing and defining principles
• Applying values and value judgments
• Will we ever see wise IT?

Notice that we have been increasing the level of
understanding and added value as we move from
data to information to knowledge to wisdom.
8
Competing on Analytics

• Analytical management is impeded by
• Conventional wisdom
• Lack of rigor and dispassionate analysis
• Lack of people willing able to do analytical
  work
• The dominance of people over ideas
• Guess what? Theres actually evidence that
  decisions based on analytics are more likely to
  be correct than those based on intuition!

Davenport Harris, Competing on Analytics, HBS
Press, 2007
9
Example Netflix

• The Cinematch recommendation engine
• Contains over 1 billion reviews
• Throttling
• Favors the most profitable infrequent-use
  customers
• Competitive advantages keep coming
• Better decisions on distribution rightswhat to
  pay, how many to license
• Leverage knowledge as a bridge to inevitable
  online distribution of movies

10
Business Intelligence and Analytics

• Optimization
• Predictive modeling
• Forecasting
• Statistical analysis
• Alerts
• Query/drill down
• Ad hoc reports
• Standard reports

• Whats the best that can happen?
• What will happen next?
• What if these trends continue?
• Why is this happening?
• What actions are needed?
• Where exactly is the problem?
• How many, how often, where?
• What happened?

Analytics
Competitive advantage
Access reporting
Degree of intelligence
11
Why Compete on Analytics?

• Business processes are among the last
  differentiators
• Analytical competitors wring every last drop of
  value from business processes and key decisions.
• There are now huge pools of data to draw on
• POS
• ERP
• RFID

12
More Examples

• Airline yield management (actually pre-dates many
  modern analysis techniques)
• Insurance companies using FICO scores
• Harrahs gaming loyalty cards
• UPS delivery routing (remember right turn only?)
• Amazon.com mass customization
• Even now used by (gasp) winemakers!

13
Four Pillars of Analytical Competition

• Distinctive capability
• Enterprise-wide analysis
• Senior management commitment
• Large-scale ambition

14
The Vision of the Semantic Web

• I have a dream for the Web in which computers
  become capable of analyzing all the data on the
  Web the content, links, and transactions
  between people and computers. A Semantic Web,
  which should make this possible, has yet to
  emerge, but when it does, the day-to-day
  mechanisms of trade, bureaucracy and our daily
  lives will be handled by machines talking to
  machines. The intelligent agents people have
  touted for ages will finally materialize.

Tim Berners-Lee as quoted at http//en.wikipedia
.org/wiki/Semantic_web_note-5
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What is a Semantic Web?(A Picture is Worth 1000
Words)
Current Web
Semantic Web
http//www.w3.org/2004/Talks/0120-semweb-umich/Ove
rview.html
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Evolution of Markup

• HTML formatting of content (data)
• lth1gtThis is a level one headinglt/h1gt
• XML labeling of content (information)
• ltsymptomsgtRashlt/symptomsgt
• RDF (Resource Definition Framework) meaning and
  relationships of content (knowledge)
• ltthis presentationgt
• ltsubjectgt
• ltknowledge managementgt
• . . .

See the World Wide Web Consortiums Semantic Web
Activity Page http//www.w3.org/2001/sw/
17
Summary

• Knowledge is (competitive) power built upon data
  and information and worthy of management
  attention
• Competing on Analytics attempts to exploit data,
  information, and knowledge for business
  competitive advantage
• The Semantic Web attempts to extend the current
  Web to incorporate knowledge and relationships

18
Something Fun and Relevant(Can you say why?)

• Google Image Labeler
• (Not always available to anyonesee
  http//en.wikipedia.org/wiki/Google_Image_Labeler
  for more information)
• Can a CS Professor give an accessible and
  interesting talk?
• Watch http//video.google.com/videoplay?docid-824
  6463980976635143 for at least 5 minutes and let
  me know. (No, it wont be on the test.)