Lecture 6
Database Design and
Management
Peter Flett
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Recap – why?
Data:
“ The raw facts or observations that are
considered to have little or no value until
they have been processed and
transformed into information”
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Data
1. A series of non-random symbols,
numbers, values or words.
1. A series of facts obtained by observation or
research.
2. A collection of non-random facts.
3. The record of an event or fact.
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Types of Data
Formatted
Free text
Images
Audio
Video
Models
‘Hard’ and ‘Soft’ data
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Information
1. Data that has been processed so that they
are meaningful
2. Data that has been processed for a purpose
3. Data that has been interpreted and
understood by the recipient
4. Information acts to reduce uncertainty
(risk) about a situation or event
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Examples of information
A bank statement
A sales forecast
A telephone directory
Management report
Financial report
MIS’s, DSS’s, ES’s, and ERP systems
Beware of paralysis by analysis
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Capturing Data
Many sources
Can often be problematic
Open to interpretation
• E.g. different types of research methodology
• Spin doctoring
• Lying with statistics
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Inputting Data
Inputting of data is tedious.
Hardware can help
Scanning information (still requires a
degree of data entry
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Creating Information
Data Process:
A process used to convert data into
information.
Examples include sorting, searching,
filtering, summarising, classifying,
calculating and combining
Data
Transformation
Process (the
data process)
Information
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Knowledge
“An accumulation of information, building on
existing ideas and experience”
This should be the result of information
Q. How does an organization retain
knowledge?
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Relating data, information
and knowledge
Learn
Interpret
Outcomes
Enhanced/Increased
Knowledge
Decisions/Actions
A cyclical
improvement process?
Data
Converts
Information
Understand
Interpret
Decide
Act upon
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Perspectives on
Information
Informative
• Type of information & what it tells us
Nature of form
• How is the information presented
Time interval
• When is the information communicated to us
Scope
• The part of the org to which the info relates
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Value of Information
Tangible value
• A value or benefit that can be measured
directly, usually in monetary terms
• Value of information minus cost of gathering
information
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Value of Information
Intangible value
• A value or benefit that is difficult or
impossible to quantify
• E.g. Improvement in decision behaviour
minus cost of gathering information
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Sources of Information
Formal communication
• reports, accounting statement, programs,
memos etc.
Informal communication
• Conversation, notes etc.
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Information Quality
Additional
Characteristics
Time
Content
Form
Timeliness
Accuracy
Clarity
Confidence in
source
Currency
Relevance
Detail
Reliability
Frequency
Completeness
Order
Appropriate
Time Period
Conciseness
Presentation
Received by
correct
person
Scope
Media
Sent by
correct
channels
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O’Brien (1993)
Summary
Information can be derived from data in many
different ways
Gathering and processing data costs money
Organizations use a wide variety of information
for different purposes
The characteristics of that information have a
major impact on organizational effectiveness
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The Design Process
Crucial, good design prevents,
Redundant data
Inconsistent data
Inflexibility of use
Limited sharing of data
Limited security
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For example
Deletion - if
student 12347
withdrew from
course we would
loose BUS fee
information
Student
Reg No
12345
12346
12347
12348
12349
Course Fee
ISM
MBA
BUS
ISM
MBA
4000
3500
4200
4000
3500
Redundancy
-course fee
repeated
Updating - If MBA fee
Insertion - A new course
cannot be added
until a student registers
changed we would have
to alter records of all MSC
students
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5 steps to database design
( Dowling)
1. What is the purpose of the database?
 SMART: Specfic, Measurable, Achievable,
Relevant, Time related
2. Determine the information requirements
of the database
( these stages are all key parts of the system analysis
that has to take place prior to implementation )
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5 steps to database design
( Dowling)
3. Produce a logical model of the information
requirements (E-R model) SSADM
4. Convert the logical data model to a physical
data model
I.e. go from the conceptual world to the real world
From the E-R model to the Relational Model
(normalisation)
5. Implement the physical design
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