1 Lecture 6 Database Design and Management Peter Flett

2 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”

3 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.

4 Types of Data Formatted Free text Images Audio Video Models ‘Hard’ and ‘Soft’ data

5 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

6 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

7 Capturing Data Many sources Can often be problematic Open to interpretation E.g. different types of research methodology Spin doctoring Lying with statistics

8 Inputting Data Inputting of data is tedious. Hardware can help Scanning information (still requires a degree of data entry

9 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

10 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?

11 Relating data, information and knowledge Data Information Understand Interpret Decide Act upon Decisions/Actions Outcomes Learn Interpret Enhanced/Increased Knowledge Converts A cyclical improvement process?

12 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

13 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

14 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

15 Sources of Information Formal communication reports, accounting statement, programs, memos etc. Informal communication Conversation, notes etc.

16 Information Quality TimeContentForm Additional Characteristics TimelinessAccuracy Clarity Confidence in source CurrencyRelevanceDetailReliability FrequencyCompletenessOrderAppropriate Time PeriodConcisenessPresentationReceived by correct person ScopeMediaSent by correct channels O’Brien (1993)

17 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

18 The Design Process Crucial, good design prevents, Redundant data Inconsistent data Inflexibility of use Limited sharing of data Limited security

19 Deletion - if student withdrew from course we would loose BUS fee information Redundancy -course fee repeated Insertion - A new course cannot be added until a student registers Updating - If MBA fee changed we would have to alter records of all MSC students For example Student Reg No CourseFee 12345ISM MBA BUS ISM MBA3500

20 5 steps to database design ( Dowling) 1.What is the purpose of the database? zSMART: 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 )

21 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