managing knowledge and data
Difficulties in managing data
amount of data increase exponentially
data are scattered and collected by many individuals using various methods and devices
data come from several resources
data security, quality and integrity are critical
The data base approach
data base management system(DBMS) : provides all users with access to the all data
DBMSs minimize the following problems
data redundancy
data isolation
data inconsistency
DBMSs maximize the following issues
data security
data integrity
data independence
Data Hierarchy
bit,byte,field,record,table,database file
Data Model
entity,attribute,primery key,secondry keys
Entity Relationship (ER) modeling
database designers plan the database design in a process
ER diagrams consist of entities, attributes and relationships
Relational database model
structured query language
query by example
Normalization is a method for analysing and reducing a relational database to its most streamlined form for
minimum redundancy
maximum data integrity
best processing performance
Data warehousing
data warehouses are organised bu business dimensions or subject
data warehouses are multidimensional
a data cube
data warehousing are historical
data warehouses use online analytical processing
benefits of data warehousing
end users can access data quickly and easily via web browsers because they are located in one place
end users can conduct extensive analysis with data in ways that may not have been possible before
end users have a consolidated view of organizational data
data marts is a small data warehouse designed for the end user needs in a strategic business unit or a department
Data goverance consist of master data management and master data
There are two types of Knowledge Management
explicit knowledge
tacit knowledge
Knowledge Management System Cycle
create, capture, refine, store, manage, disseminate
