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Knowledge Management 2011-2012

Introduction to the course. Definition of Knowledge Management and Knowledge. Example applications.
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Valentina Presutti

on 12 March 2013

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Transcript of Knowledge Management 2011-2012

Knowledge Management
- Course Program 2013 -

Laurea Magistrale in Scienze di Internet
a.a. 2012/2013
Dott.ssa Valentina Presutti
Definition: Knowledge Management (KM)

There is not an official definition
Try to query Google with "define:" command Definition: Knowledge Management (KM)

EFFICIENT HANDLING of information and resources within a commercial organization (New Oxford American Dictionary)
KM comprises a range of STRATEGIES and PRACTICES used in an organization to identify, create, represent, distribute, and ENABLE ADOPTION of INSIGHTS and EXPERIENCES. Such insights and experiences comprise knowledge, either embodied in INDIVIDUALs or embedded in organizational PROCESSEs or PRACTICEs (Wikipedia) Definition: Knowledge Management (KM)

The process of capturing, organizing, and storing INFORMATION and EXPERIENCEs of workers and groups within an organization and MAKING IT AVAILABLE to others. By collecting those artifacts in a central or distributed electronic environment (often in a DATABASE called a KNOWLEDGE BASE) KM aims to help a company gain competitive advantage. (London Imperial College - eLearning glossary)
The way a company STORES ORGANIZES and ACCESSES internal and external information. (MIT - Center for Coordination Science) Some good reasons for KM

We spend 20-30% of our time searching for information
High turnover of personnel
Leverage wisdom of the entire organization
Connect those who know with those that need to know
Leverage intangible assets of an organization
Prevent re-inventing the wheel
Make organizations learning organizations – create, acquire, transfer and retain knowledge
Improve efficiency and enhance organizational performance while reducing costs
Create and foster Communities of Practice Defining Knowledge

"Understanding gained from experience."

(Webster’s Standard Dictionary Definition) Knowledge Management (KM)
Frequent questions

What is knowledge management?
Why should we care about this topic?
What is knowledge and how does it differ from information (or does it)?
Where does knowledge come from?
How can knowledge be managed (or can it be)? Andrew Carnegie (November 25, 1835 – August 11, 1919)
He was Scottish-American
Industrialist, businessman, entrepreneur
Founder of Carnegie Corporation of New York, Carnegie Endowment for International Peace, Carnegie Institution of Washington, Carnegie Mellon University and the Carnegie Museums of Pittsburgh. Andrew Carnegie

The only irreplaceable capital an organization possesses is the KNOWLEDGE and ABILITY of its PEOPLE.

The PRODUCTIVITY of that capital DEPENDS on how EFFECTIVELY people SHARE their COMPETENCE with those who can USE it Defining Knowledge

Knowledge is understanding gained from experience, analysis and sharing. It gives us power to do something with data and information. (Douglas Weidner, KM Institute)
Knowledge is Information Combined with Experience, Context, interpretation, and reflection. (Tom Davenport) Explicit vs. Tacit Knowledge Tacit Knowledge

Knowledge for which we do not have words
Automatic, requires little or no time or thought
Knowing more than we can tell, or knowing how to do something without thinking about it, like ride a bicycle.
Tacit knowledge tends to be local. It is not found in manuals, books, databases or files
Technical or cognitive
Made up of mental models, values, beliefs, perceptions, insights and assumptions. Tacit Knowledge

People use metaphors, analogies, demonstrations and stories to convey their tacit knowledge to others
Nearly two-thirds of work-related information that is gradually transformed into tacit knowledge comes from face-to-face contacts, like casual conversations, stories, mentoring, internships and apprenticeships. KM components

Effective KM depends on People and Processes as well as Technology and Content
In today’s world Technology is an essential enabler

Effective KM:
70% “People”
10% “Processes”
10% “Technology”
10% "Content Know what vs. Know how

Expertise (know-what)
Goal: knowing things
Concernces explicit knowledge
Focus on analysis
Success = “turning chaos into options”
Emphasizes information/ facts
Can be learned
Objective (mostly)
Danger: losing sight of the big picture

Experience (know-how)
Goal: understanding things
Concerns tacit knowledge
Focus on synthesis
Success = getting things done
Emphasizes judgment/ insight
Must be earned
Subjective
Danger: losing sight of field realities EFFICIENT HANDLING: depend on tasks
STRATEGIES AND PRACTICES: procedural knowledge
MAKING IT AVAILABLE: Web-based tools
KNOWLEDGE BASE
STORING, ORGANIZING, ACCESSING Explicit Knowledge

Most explicit knowledge is technical or academic data
It is described in rigorous or formal language e.g., manuals, mathematical expressions, copyright and patents
This “know-what” is readily communicated and shared through print, electronic methods, etc.
It is technical and requires a level of academic knowledge or understanding that is gained through formal education, or structured study. Historical perspective

KM is an established discipline since 1991.
Scandia (Insurance company) hired Leif Edvinsson as the world’s first Chief Knowledge Officer (CKO)
"The Knowledge-Creating Company" by Ikujiro Nonaka The knowledge creating company

Successful companies: constantly create new knowledge and reuse it for continuous innovation
As opposed to Taylor's view: an organization is a machine for "information processing"

High successful Japanese companies have the ability to RESPOND QUICKLY to customers, CREATE new markets, RAPIDLY DEVELOP new products, and dominate EMERGENT TECHNOLOGIES.
Why? The knowledge creating company

Use individual's tacit knowledge and make it available for testing and use by the company as a whole
Personal commitment
A company is a living organism
Inventing new knowledge is not a specialized activity The knowledge creating company - a true story

Developing a new bread-making machine
After long time and big effort the result was disappointing
The compared X ray of dough kneaded by the machine and that kneaded by professional bakers, but nothing
A software developer had an intuition: to be trained by the best bakery of Osaka to make bread
She studied its kneading technique
After one year she worked out (with project's engineers) a product specification
The result: a unique method and a record for sale of a new kitchen appliance What happened?

A movement between two different types of knowledge
The end point is explicit knowledge: the product specification
The starting point is tacit knowledge: the chief baker knowledge Four basic patterns for creating knowledge

From tacit to tacit
From tacit to explicit
From explicit to explicit
From explicit to tacit From tacit to tacit (socialization)

She learns the tacit secrets of the chief baker
Direct sharing of knowledge between individuals
Through observation, imitation, and practice From tacit to explicit (externalization)

She translates these secrets into explcit knowledge - product's specification
Articulation of tacit knowledge foundations
Their explicit expression in formal or structured form From explicit to explicit (combination)

The team standardizes this knowledge into a manual and embodied it in a product
Combine different pieces of knowledge into a new one
Example: a budgeting report that synthesizes information from different sources
Combination does not extend the company's knowledge base Nonaka knowledge spiral Objectives of KM mentioned by corporations KM core concepts

Knowledge is a useable asset
It can be (and should be) managed
It can drive decisions and add business value
Includes both Explicit and Tacit, unstructured knowledge
Emphasis on people (internal & external) as well as tools
Knowledge grows when shared What damages KM

Focus on technology to the exclusion of the people and culture factor
KM is about adoption and use

Turning it into yet another project
KM needs to be inculcated into the organization

Failing to handle the rewards/incentives issues
You must address the WIIFM factor (what’s in it for me?) For KM to be successful

KM must create value (make the user’s life easier/more efficient/save time)
Collect useful knowledge - not all knowledge is useful
Must be integrated into key business processes
Focuses on people What is Knowledge and how does it differ from information?

Data
symbols

Information
data that are processed to be useful
it provides answers to "who", "what", "where", and "when" questions

Knowledge
application of data and information
it answers "how" questions Summary

Definition of KM and Knolwledge
Good reasons for KM
Explicit vs. Tacit knowledge
Knowledge vs. information
Creation of new knowledge Introduction From explicit to tacit (internalization)

They enrich their own tacit knowledge through the experience of creating a new product
New explicit knowledge is available and shared throughout an organization
People in the organization begin to internalize it References (Dispense da Bononia)

Web references
KmWiki: http://kmwiki.wikispaces.com/
http://www.dmoz.org/Reference/Knowledge_Management/

Articles and books
Alavi, Maryam; Leidner, Dorothy E. (1999). "Knowledge management systems: issues, challenges, and benefits". Communications of the AIS 1 (2).
McInerney, Claire (2002). "Knowledge Management and the Dynamic Nature of Knowledge". Journal of the American Society for Information Science and Technology 53 (12): 1009–1018. doi:10.1002/asi.10109.
Nonaka, Ikujiro (1991). "The knowledge creating company". Harvard Business Review 69 (6 Nov-Dec): 96–104.
Nonaka, Ikujiro; Takeuchi, Hirotaka (1995). The knowledge creating company: how Japanese companies create the dynamics of innovation. New York: Oxford University Press. p. 284. ISBN 9780195092691. Mandatory attendance?
YES! KM Rules Communications by mailing list Exam structure
Oral interview + project (in group)
Assign project specification
2 Partial exams
Positive average grade gives the right to skip the oral interview
Dates are NOT communicated in advance Project delivery has its own deadlines
Access to oral interview only after project has been delivered
The project is valid for one academic year Tests/oral interview: what to study?
Content of lectures
Seminars are included
Slide content is not enough
Additional materials will be provided Teacher available
By appointment
Typically on Wednesday 17-19
Send an email the day before Sharing KNOWLEDGE (COMPETENCE) between PEOPLE
Using them EFFECTIVELY In this course we study technologies
for supporting such processes Tacit Knowledge

Knowledge embedded in individual experience and involving intangible factors such as personal beliefs, perspectives and values Explicit Knowledge

Knowledge that is codified, documented, articulated in a formal way Technologies and applications
Data analytics
Tweet analysis for predicting movie income -> to adjust marketing policies
Social network analysis for identifying most influent people in certain domains -> for predicting trends
Processing of natural language text (e.g. news articles) -> for adding links to Wikipedia automatically
Mash-up of sensor data -> for building graphical summaries
Processing of information about musical artists -> for recommending “related” content on YouTube
Monitoring user behavior in Amazon/YouTube -> for recommending “relevant” content on Amazon/YouTube
Mash-up of sensor data and automatic reasoning -> for detecting risk situations, for suggesting contextual decisions
Extracting knowledge from legal documents -> for helping lawyers in preparing trials
Extracting structured knowledge from Wikipedia -> for supporting exploratory search
Processing heterogeneous resources -> for collecting only useful knowledge
.... Technological perspective
Content Management Systems
Social networks
Databases
Semantic technologies
Semantic Web and Linked Data
Ontologies
Knowledge extraction techniques
.... Course content
Brief history of knowledge management
Main concepts and definitions
Related technologies and disciplines
Content Management
Semantic Web and Linked Data
Ontologies
Knowledge patterns
Applications and tools for developing semantic content management
Full transcript