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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
FormattedFree textImagesAudioVideoModels‘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 statementA sales forecastA telephone directoryManagement reportFinancial reportMIS’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
TransformationProcess (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
Data
Information
UnderstandInterpretDecide
Act upon
Decisions/Actions
Outcomes
LearnInterpret
Enhanced/Increased Knowledge
Converts
A cyclical improvement process?
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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
Time Content Form
Additional Characteristi
cs
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 channelsO’Brien (1993)
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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 dataInconsistent dataInflexibility of useLimited sharing of dataLimited security
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Deletion - if student 12347 withdrew from course we would loose BUS fee information
Redundancy -course feerepeated
Insertion - A new course cannot be added until a student registers
Updating - If MBA fee changed we would have to alter records of all MSCstudents
For example
Student Reg No
Course
Fee
12345 ISM 400012346 MBA 350012347 BUS 420012348 ISM 400012349 MBA 3500
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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 worldFrom the E-R model to the Relational Model (normalisation)
5. Implement the physical design