Chapter 5
Managing
Knowledge and Data
*Managing
Data :
1. Difficulties in
Managing Data
o
Amount of data increases
exponentially
§ According
to the annual survey of the global digital output by International Data Corporation, the total amount
of global data was expected to pass 1.2 zettabytes
o
Data are scattered and
collected by many individuals using
various methods and devices.
o
Data degrades overtime.
Examples:
§ customers
move to a new address
§ employees
are hired and fired
o
Data rot: problems with media on which the data
are stored
o
Data come from many sources (e.g. Clickstream
data )
o
Data security, quality and integrity are
critical.
Ø Data
Governance:
an approach to managing data
across an entire organization.
Formal sets of policies that are designed to ensure that the data are
collected, handled and protected in a certain, well-defined fashion
Ø Master
data management:
a process/method that provides an organizations with the ability to
store, maintain, exchange and synchronize a consistent, accurate and timely ‘single
version of the truth’ for the organization's core master data
Ø Master
data:
Aset of core data [customer, employee, vendor, geographic location]
that span all enterprise information systems.
Ø Transaction
data:
Data that are generated and captured by operational systems .
*The Database Approach:
o
Database management system (DBMS) provides all users
with access to all the data.
o
DBMSs minimize the following problems:
ü Data
redundancy:
The same data are stored in many places
ü Data
isolation:
Applications cannot access data associated with other applications
ü Data
inconsistency:
Various copies of the data do not agree.
o
DBMSs
maximize the following issues:
ü
Data
Security: keeping the organization’s data safe from theft,
modification, and/or destruction.
ü
Data
integrity: Data must meet constraints (e.g., student grade point
averages cannot be negative).
ü
Data
independence: Applications and data are independent of one another.
Applications and data are not linked to each other, meaning that applications
are able to access the same data.
*Data
Hierarchy:
·
Bit: a binary digit, or a “0” or a “1”
·
Byte: eight bits and represents a single character
(e.g., a letter, number or symbol)
·
Field: is a group of related characters (e.g.,
student’s name, age, mobile number)
·
Record: a group of logically related fields (e.g.,
student in a university database
·
File (or
table): a group
of related records
·
Database: a group of related files.
*Designing
the Database:
·
Data model : a diagram that represents the entities in the database
and their relationships
ü Entity: a person, place, thing, or event
about which information is maintained. [A record generally describes an entity]
ü Attribute: a particular characteristic of a
particular entity
ü Primary
key (Key
field): a field that uniquely identifies a record, so that it can be retrieved
and updated
ü Secondary
Key
*Entity-Relationship
Modeling:
·
Database designers plan and create the database through a
process called entity-relationship (ER)
modeling.
·
ER
diagrams: consists of entities, attributes and relationships. [illustrating relationships between database entities]
ü Entity classes: groups of
entities of a certain type
ü Instance: the
representation of a particular entity
ü Identifiers: attributes
that are unique to that entity instance
·
Type of relation :
1. One-to-One [1:1]
2. One-to-Many [1:M]
3. Many-to-Many [M:M]
*Database
Management Systems :
·
Database
management system (DBMS):
a software that provides users with tools to add, delete,
access, and analyze data stored in one location
Examples:
v
Microsoft
Access
v
Oracle
·
Relational
database model:
based on the concept of two-dimensional tables
·
Requesting
Data from a database
·
Structured
Query Language (SQL):
allows users to perform complicated searches (request
information) by using relatively simple statements or keywords.
·
Query
by Example (QBE):
allows users to fill out a grid or template to construct
a sample or description of the data he or she wants
*Data
Dictionary:
Ø Defines the format necessary to
enter the data into the database
Ø Provides information on each
attributes
Ø Provides information on how often
the attribute should be updated
*Normalization:
Is a method
for analyzing and reducing a relational database to its most streamlined form
for:
v
Minimum redundancy
v
Maximum data integrity
v
Best processing performance
·
Normalized data is when attributes in the
table depend only on the primary key.
*Data Warehouses and Data Mart:
·
Data warehouse: a repository of current and
historical data to support decision makers in the organization.
§ Organized
by business dimension or subject [for example, by customer, product, price and
region)
§ Consistent
§ Historical:
can be used for identifying trends, forecasting, and making comparisons over
time.
§ Multidimensional
*Benefits of Data Warehousing:
Ø
End
users can access data quickly and easily via Web browsers because they are
located in one place.
Ø
End
users can conduct extensive analysis with data in ways that may not have been
possible before.
Ø End users have a consolidated view of
organizational data.
* Data Marts:
Data
mart: a small data
warehouse, designed for the end-user needs in a strategic business unit (SBU)
or a department.
Ø Example: Marketing and sale data mart to deal with
customer information
v Far less
costly than a data warehouse
v Can be
implemented more quickly
v More
rapid response and easier to learn and navigate
*Knowledge Management:
v
Knowledge: information that is contextual,
relevant, and actionable
Intellectual capital intellectual assets
v
Explicit knowledge: codified (documented) in a form
that can be distributed to others
v
Tacit knowledge: a set of insights, expertise and skills
v
Best Practices: the most effective and efficient
ways of doing things
v Knowledge
management (KM):
a process of
accumulating and creating knowledge efficiently, so that it can be applied
effectively throughout the organization
*Knowledge Management System (KMS): the use of
information technologies to systematize, enhance, and expedite intrafirm and
interfirm knowledge management and knowledge sharing.
