knowledge management

KNOWLEDGE MANAGEMENT : 

KNOWLEDGE MANAGEMENT

Slide 2: 

1200 BC        Clay tablets Papyrus scrolls  700  BC       Bunko literary storehouses Parchment 400 BC  Library at Alexandria  300   BC       Roman private & public libraries 200 BC       Qin Dynasty Imperial Library History of Knowledge Sharing

Slide 3: 

1000     Movable type Monasteries Universities 1300  Libraries in Europe  1400     Printing press Imperial Library  1600     Harvard University Library  1800     Library of Congress Boston History of Knowledge Sharing

Slide 4: 

1920-1940   Electronic mass media (radio) Paperbacks    1940-1960 Digital computing TV mass media  Cryptography 1960-1980   Text searching   1980-2003   Personal computing   Internet, Mass media Search engines, Digital libraries, eCommerce, Portals, eMail History of Knowledge Sharing

Knowledge Management : 

Knowledge Management AIM A Crash Course ?

PHASES : 

PHASES KNOWLEDGE & ITS MANAGEMENT. DATA MINING. APPLICATION TO THE ARMED FORCES. MODEL OF KM IN USE. PHASE I PHASE II PHASE III PHASE IV

PHASE I : 

PHASE I

Slide 8: 

“If HP knew what HP knows, we would be three times as profitable” Lew Platt, HP CEO

What is Knowledge? : 

Data What is Knowledge?

Data Pyramid : 

Data Pyramid Data Information Knowledge Wisdom Data + context Information + rules Knowledge + experience

Explicit and Tacit knowledge : 

Explicit and Tacit knowledge Explicit Expressed in words Found in documents Shareable Static Tacit Uncodified Experience Difficult to share Dynamic Explicit Tacit

KM - Definitions : 

KM - Definitions “a discipline that promotes an integrated approach to identifying, managing and sharing all of an enterprise’s information assets. These information assets may include databases, documents, policies and procedures, as well as previously unarticulated expertise and experience resident in individual workers” Gartner Group Inc, 1996

KM - Definitions : 

KM - Definitions “Knowledge management is the explicit and systematic management of vital knowledge and its associated processes of creating, gathering, organising, diffusion, use and exploitation. It requires turning personal knowledge into corporate knowledge that can be widely shared throughout an organisation and appropriately applied.” David Skyrme Associates, 1997

KM Concepts : 

KM Concepts From data through information to knowledge, but one person’s knowledge is another’s data Explicit knowledge is formal and systematic Tacit knowledge is unrecorded and unarticulated, also known as implicit or experimental knowledge Intellectual capital is a firm’s intangible assets, includes competence, innovation, learning, processes, relationships, systems, etc Making formal knowledge more visible and usable, making the informal more public and useful

Technology : 

Technology Good technology is hard to do But technology is the easy part

KM and Information Technology : 

KM and Information Technology Suite of tools - online databases, groupware, document management systems, web/intranets, specialist search engines, video-conferencing Significant contribution - rapidly disseminating information, integrating and linking knowledge and facilitating cross-functional teamwork Seen as ‘people and process’ issue - not as an expansion of the information systems function

Culture + Technology : 

Culture + Technology Without a committed culture Technology cannot succeed Without dedicated technology Only the most extraordinary culture can succeed Culture is the biggest hurdle

The Information Management Challenge : 

The Information Management Challenge Knowledge Workers

Action Steps : 

Action Steps Appoint a Chief Knowledge Officer – CKO Get management on side by showing other firm’s successes Integrate knowledge management into work processes - capture knowledge Create a culture of trust and learning Ensure quality content Deploy technologies Measure the effort and the benefits

Knowledge Harvesting : 

Knowledge Harvesting Elicit Tacit Knowledge one-to-one interviews focus groups Mining Explicit Knowledge documents processes Knowledge on Demand Improved Performance New capabilities Strategic changes Creating Knowledge Assets Organizing & Packaging Knowledge

KM is best seen as a Convergence : 

KM is best seen as a Convergence total quality management resource-based view of the firm information mapping benchmarking the learning organisation core competence communities-of-practice business re-engineering intellectual capital

Slide 22: 

PHASE II

Slide 23: 

The process of exploration and analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns and rules. Knowledge Discovery. Data Mining is not only Data Warehousing, OLAP etc. Data Mining

From Data to Knowledge : 

From Data to Knowledge Data mining: the core of knowledge discovery process. Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation

Process : 

Process The Virtuous Cycle

Business Problem : 

Define the business problem Understand the business and the rules Determine if Data Mining fits the need Understand the value to the business of solving the problem Business Problem

Transform Data : 

Transform Data Steps

Transform Data : 

Transform Data Identify Data 1 Step 1 - Identify Data What data is required to meet the modeling need? What data is available?

Transform Data : 

Transform Data Step2 - Obtain Data Identify Data Obtain Data 1 2 Data Warehouse Data Marts and OLAP Self Reported External

Transform Data : 

Transform Data Step 3: Validate & Clean Identify Data Obtain Data Validate & Clean 1 3 2 Data Issues: Missing Fuzzy Incorrect Outliers Solutions: Change Source Filter Out Ignore Integrate Predict Derive a New Variable

Transform Data : 

Transform Data Step 4: Transpose to right granularity Identify Data Obtain Data Transpose to Right Granularity Validate & Clean 1 4 3 2 Data sets for Data Mining need one view, one record Grain must be consistent throughout Training data sets cast from point in time of event looking back

Transform Data : 

Transform Data Step 5: Add Derived Variables Identify Data Obtain Data Transpose to Right Granularity Validate & Clean Add Derived Variables 1 5 4 3 2 Combined Columns Summarizations Features from Columns Time Series

Transform Data : 

Transform Data Step 6: Prepare Model Set Identify Data Obtain Data Transpose to Right Granularity Validate & Clean Add Derived Variables Prepare Model Set 1 6 5 4 3 2 The Actual Input to the modeling

Transform Data : 

Transform Data Step 7: Conduct Modeling Identify Data Obtain Data Transpose to Right Granularity Validate & Clean Add Derived Variables Prepare Model Set Conduct Modeling 1 6 5 4 3 2 7 Get our result Decision Trees Neural Networks Clustering Nearest Neighbour Rule Induction

Slide 35: 

Not an ER type Data Model A data mining model is computational, full of algorithms A model can be descriptive or predictive. A descriptive model helps in understanding underlying processes or behavior. A predictive model uses known values (input) to predict an unknown value (output) Modeling

Modeling Techniques : 

Modeling Techniques

Modeling Techniques : 

Modeling Techniques Decision Trees The tree is built based on the input of a training data set Each record of the Model Set is run through the branches of the tree until the record reaches a leaf

Modeling Techniques : 

Modeling Techniques Decision Trees Age < 23 Y N Income < 12 000 N N Y Y Male ? 28% 37%

Modeling Techniques : 

Modeling Techniques Neural Networks Neural networks are a nonlinear model -similar to a ‘brain’. The network is built based on the input of a training set. Model sets run through this network will return accurate results based on the patterns identified in the training set. Very Complex

Modeling Techniques : 

Modeling Techniques Clustering Clustering finds groups of records that are similar. For example, customers can be clustered by: income age

Modeling Techniques : 

Modeling Techniques Clustering Male Income < 12 000 Age < 23 Coke Buyers Male Income < 12 000 Age < 23 Non Coke Buyers

Modeling Techniques : 

Modeling Techniques Nearest Neighbour Model is built based on the input of a training set. Classifies a record by calculating the distances between the record criteria and the training data set Then it assigns the record to the class that is most common among its nearest neighbours

Modeling Techniques : 

Modeling Techniques Nearest Neighbour Records plotted based on: IncomeGenderAge Bought Coke Bought Coke Bought Coke Did Not Did Not

Act : 

Act The Organisation has to actually do something with the results.

Measure : 

Measure Answer 2 Questions Was the Data Mining effort accurate? Were the Business Actions successful? Use different sets of data to compare real results Actioned Customers vs. Non Actioned Accuracy Types Absolute Our prediction was 80% of Group D would buy Coke and 78% really did Relative Our prediction was 80% of Group D would buy Coke but 57 % really did, however Group C which we predicted had a 60% propensity to buy Coke actually bought Coke 42% of the time

Process : 

Process The Virtuous Cycle

Data Mining and Business Intelligence : 

Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP

Slide 48: 

PHASE III

APPLICATION OF KM TO THE ARMY : 

APPLICATION OF KM TO THE ARMY Military Law. Orders and Instructions. R & D. Regulations. Educational Institutes.

PHASE IV : 

PHASE IV

The Microsoft Knowledge Management Platform : 

The Microsoft Knowledge Management Platform ExternalApplications Legacy Systems Databases Thin Client Rich Client VB Outlook Web ActiveX OLAP SQL Server Exchange Site Server

Sample KM Architecture : 

Sample KM Architecture Office & HTML Docs Internet Explorer Office HTML Video Audio Publishing w/ Tagging Indexing Internet Information Server Site Server Exchange Public Folders SQL Server Other Web Sites

The Digital Dashboard : 

The Digital Dashboard A KM user interface Runs inside Outlook 2000 or IE Successor to Outlook Today Browser inside Outlook Enhanced offline features Based on ActiveX

The Army Digital Dashboard : 

The Army Digital Dashboard

Knowledge Management : 

Knowledge Management Leadership & strategy Processes Technology Culture People

Knowledge Management : 

Knowledge Management A means to an end Not an end in itself

Slide 57: 

In the end, the locationof the new economyis not in the technology,be it the microchip or theInternet. It is in the human mind. Alan Webber

Slide 58: 

"Real knowledge is to know the extent of one's ignorance." -Confucius