First, difficulties
in Managing Data
Ø 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
Ø Data are scattered and
collected by many individuals using
various methods and devices
Ø Data degrades overtime
Examples: customers
move to a new address
employees are hired and fired
Ø Data rot: problems with
media on which the data are stored
Data
Governance: an approach to managing data across an entire
organization.
ü Formal sets of policies
that are designed to ensure that the data are collected, handled
and protected in a certain, well-defined fashion .
ü 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 .
Master data: a set of core
data [customer, employee, vendor, geographic location] that span all enterprise
information systems.
Transaction data: data that
are generated and captured by operational systems
Second: The Database Approach
Ø Database management system
(DBMS) provides all users with access to all the data.
Ø DBMSs minimize the
following problems:
Ø Data redundancy: The same
data are stored in many places
Ø Data isolation:
Applications cannot access data associated with other applications
Ø Data inconsistency:
Various copies of the data do not agree.
Ø DBMSs maximize the
following issues:
o
Data Security: keeping the organization’s data safe from theft, modification, and/or
destruction.
o
Data integrity: Data
must meet constraints (e.g., student grade point averages cannot be negative).
o
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.
Database Management Systems
Data Hierarchy:
Bit: a binary digit, or a “0” or a “1” - The
smallest unit of data a computer can handle
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:
Ø Data model
Ø a diagram that
represents the entities in the database and
Ø their relationships
o Entity: a person,
place, thing, or event about which information is maintained. [A record
generally describes an entity]
o Attribute: a particular
characteristic of a particular entity
o Primary key (Key
field): a field that uniquely identifies a record, so that it can be retrieved
and updated
o Secondary Key
Entity-Relationship Modeling:
Ø Database designers plan
and create the database through a process called entity-relationship (ER)
modeling.
Ø ER diagrams consists of
entities, attributes and relationships. [illustrating relationships between
database entities]
o Entity classes: groups
of entities of a certain type
o Instance: the
representation of a particular entity
o Identifiers: attributes
that are unique to that entity instance
Third, 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.
SELECT (Student Name) FROM (Student) Database
WHERE (GPA) > 3.4
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
Normalization:
Ø Normalization is a
method for analyzing and reducing a relational database to its most streamlined
form for:
o Minimum redundancy
o Maximum data integrity
o Best processing
performance
Ø Normalized data is when
attributes in the table depend only on the primary key.
Fourth, Data Warehouses and Data
Mart:
Ø Data warehouse: a repository of current and historical data to support
decision makers in the organization.
o Organized by business
dimension or subject [for example, by customer, product, price and region)
o Consistent
o Historical: can be used
for identifying trends, forecasting, and making comparisons over time.
o 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.
Problems with Data Warehousing:
Ø Very expensive to build
and to maintain [ around R.O. 400, 000]
Ø Incorporating data from
obsolete (old) mainframe systems can be difficult and expensive
Ø People in one
department may be reluctant to share data with other department
Data
mart:
Ø Data mart: a small data
warehouse, designed for the end-user needs in a strategic business unit (SBU)
or a department.
Example:
Marketing and sale data mart to deal with customer information
Ø Far less costly than a
data warehouse (around R.O. 40, 000)
Ø Can be implemented more
quickly (around 3 months)
Ø More rapid response and
easier to learn and navigate
Fifth: Knowledge Management:
Ø Knowledge: information
that is contextual, relevant, and actionable
*Intellectual
capital
*intellectual assets
Ø Explicit knowledge: codified
(documented) in a form that can be distributed to others (CEPS student’s
handbook)
Ø Tacit knowledge: a set
of insights, expertise and skills
Knowledge
that people carry in their heads, but difficult to write down
in a
document
Ø Best Practices: the
most effective and efficient ways of doing things
Ø Knowledge
management (KM): a process
of accumulating and creating knowledge efficiently, so that it can be applied
effectively throughout the organization
Ø KM is not a technology. It a process supported by
IS
Benefits of KM:
Ø KM fosters innovation by encouraging the free flow of
ideas, novel approaches and better ways of solving problems
Ø KM improves customer
service by streamlining response time
Ø KM boosts revenue by
getting products and services to market faster
Ø KM enhance employee
retention rates by recognizing the value of employees’ knowledge
Knowledge Management System (KMS)
Ø KMS: the use of information technologies to
systematize, enhance, and expedite intrafirm and interfirm knowledge management
and knowledge sharing .
Knowledge Sharing:
Nothing is more frustrating for a manager than
the situation in which one employee struggles with a problem that another
employee knows how to solve it easily
v Knowledge
Sharing tools
v Portals
v Discussion groups -
FAQa
v E-mail
v Blogs/ wikis
v Podcasts
Resistance to sharing knowledge
-
Reluctant to show that they do not know
-
Employee competition
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