Chapter 4 Operations and Transactions The Strategic Management of Information Systems.

Chapter 4Chapter 4Operations and TransactionsOperations and Transactions

The StrategicManagement of

InformationSystems

Transaction Processing Transaction Processing SystemSystem

Input Output

Process

Two Levels of PlanningTwo Levels of Planning Systems Planning

– Gives Managers, Users, and Information Systems Personnel Projects

– Establishes what should be done– Sets a budget for the total cost of these projects

Systems Project Planning– Setting a plan for the development of each

specific systems project

Systems Professional SkillsSystems Professional Skills Systems Planning

– Form project team after proposed systems project is cleared for development

Systems Analysis– Business Systems Analysts knowledgeable in business

General Systems Design– Business Systems Analysts

Systems Evaluation and Selection– Business Systems Analysts

Detailed Systems Design– Wide Range of Systems and Technical Designers

Systems Implementation– Systems analysts, programmers, and special technicians

Effective Leadership StyleEffective Leadership Style Autocratic Style

– Crisis-Style Management– Used to Correct Major Problem, such as Schedule

Slippage Democratic Style

– Team-oriented Leadership– Gives each team member the freedom to achieve goals

which he/she helped set Laissez-Faire Style

– Highly-motivated, Highly-Skilled Team Members– People who work best alone

Project Management SkillsProject Management Skills Planning

– States what should be done– Estimates how long it will take– Estimates what it will cost

Leading– Adapts to dynamics of enterprise and deals with setbacks– Guides and induces people to perform at maximum abilities

Controlling– Monitors Progress Reports and Documented Deliverables– Compares Plans with Actuals

Organizing– Staffs a Systems Project Team– Brings together users, managers, and team members

Project ManagementProject Management

Gantt Chart Pert Chart

Gantt ChartGantt Chart

Compares Planned Performance against actual performance to determine whether the project is ahead of, behind, or on schedule

Schedule a complete systems project by phases

PERT ChartPERT Chart Four Steps

– Identify Tasks– Determine Proper Sequence of Tasks– Estimate the Time Required to Perform

each Task– Prepare Time-Scaled Chart of Tasks and

Events to Determine the Critical Path

PERT ChartPERT Chart Estimate, Schedule, and Control a network of

interdependent tasks Shown by arrows, nodes, or circles Determine minimum time needed to complete a

project, phase, or task Critical Path

– Minimum time needed to complete a project or phase

Program, Evaluation and Renew Technique– Total of the most time-consuming chain of events

CASECASE

Computer-Aided Systems and Software Engineering

Increase Productivity of Systems Professionals

Improve the Quality of Systems Produced

Improve Software Maintenance Issue

CASECASE

Includes:– workstations– central repository– numerous modeling tools– project management– Systems Development Life Cycle Support– Prototyping Applications– Software Design Features

Central RepositoryCentral Repository Models Derived from Modeling Tools Project Management Elements Documented Deliverables Screen Prototypes and Report Designs Software Code from Automatic Code Generator Module and Object Libraries of Reusable Code Reverse Engineering, Reengineering, and

Restructuring Features

Software MaintenanceSoftware Maintenance Reverse Engineering

– Extract original design from spaghetti-like, undocumented code to make maintenance change request

– Abstract meaningful design specifications that can be used by maintenance programmers to perform maintenance tasks

Reengineering– Examination and changing of a system to reconstitute it in

form and functionality– Reimplementation

Restructuring– Restructures code into standard control constructs

sequence, selection, repetition

Business Rules For Data Basic selection of what data elements are of

interest, what are their characteristics (data type and acceptable range - also called syntactic structure)

How they are related to, or dependent on, each other in a business sense (key, foreign key and referential constraint rule - also called the semantic structure)

Data Integrity Rules

Data RationalizationData Rationalization

Identification of data synonyms and homonyms across multiple and disparate data sources and the creation of a map that points back to their original sources.

Data Access GatewayData Access Gateway

A system that sits between end users (usually in PC networks) and a legacy database, that accepts data read requests (expressed as SQL statements), converts the requests to legacy access method instructions, and then provides the resulting data to the users. The data flow is one-way read-only.

Structured Analysis Identifies

the functions or activities which are to be handled by the system

the external entities which interact with the system

the logical data stores, and the data flows among all the the above Data flow diagrams (DFD) are used to

diagrammatically describe the elements.

Conversion into Normalized Record Types

For every data flow which either enters or emanates from a data store (in the leaf level DFDs), the integral data elements are identified

For every data store, a list of the data elements which are entering and emanating are drawn up

The dependencies among all the data elements are analyzed, and the normalization rules are applied in steps so that at every step a given relation is split into more “simple” relations

– Every relation has a key which consists of one or more data elements

– Every non-key data element functionally depends on that entire key and not on part of it

– No non-key data element depends on any other non-key data element in the relation (there are no transitive dependencies)

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Conversion into Normalized Record Types

Part Description for ModelPart Description for Modelfor General Motorsfor General Motors

“Part #123 that is supplied by GM was assembled on bus 456 on May 28, 1996” is decomposed into the following elementary sentences:

a). A part... is supplied by a manufacturer...

b). A part... was assembled on a bus...

c). The assembly [part*bus] was performed on a date...

Manufacturer (name)

Supplier of

Supplied of

Part (p#)

Part Distribution Modelfor General Motors

Relationship TypesRelationship Typesa). One-to-one (1:1): means that an occurrence if

one OT uniquely determines an occurrence of other OT - and vice-versa

b). One-to-many (1:n): means that an occurrence of one OT determines an occurrence of the other OT - but not vice-versa

c). Many-to-many (n:m):means that an occurrence of one OT can be related to many occurrences of other OT - and vice-versa

Bus (License #) Part

(p#)Supplier

Manu-facturer (name)

Date of Assembly

Date (Calc. date)

Assembly Distribution Model

Normalization ModelNormalization Model The SA/Normalization method is based on the use of

decomposition rules, which enable one to decompose tables/relations. – Database design starts with flat tables/relations, each of which is

created out of a data stores in the DFDs and then decomposed into the normal form relations. No conceptual schema of the enterprise is created to express the semantics of its information structure.

The SA/IA method is based on the use of grouping rules which map simple relationships in the binary-relationship data model onto normal form relationships. – The relational model and the normalization method have been criticized

for being too detailed to use at the initial design stage, and for lacking a semantic structure for making unambiguous choices in modeling the enterprise.

– The IA method incorporates a semantic model of the enterprise which captures its essential semantic features from which the normal form relations are derived.

Basic selection of what data elements are of interest, what are their characteristics (data type and acceptable range - also called syntactic structure)

How they are related to, or dependent on, each other in a business sense (key, foreign key and referential constraint rule - also called the semantic structure)

Data Integrity Rules

Business Rules For Data

Data RationalizationData Rationalization

Identification of data synonyms and homonyms across multiple and disparate data sources and the creation of a map that points back to their original sources.

Data Access GatewayData Access Gateway sits between end users (usually in PC

networks) and a legacy database accepts data read requests (expressed as SQL

statements) converts the requests to legacy access method

instructions provides the resulting data to the users data flow is one-way read-only.

Data DesignData Design Define all the entities to be dealt with and the relationships

between them Transform the conceptual design into logical design wherein all

the views are combined and all the resulting data elements are defined and the data structure is syntactically and semantically determined

Normalize this logical design for mathematically minimized redundancy and maximized integrity

Transform this logical design to a physical design where the underlying RDBMS, hardware, and use patterns are taken into account

Develop the SQL DDL code specific to each RDBMS vendor’s product is generated

Data WarehouseData Warehouse

An intermediate, read-only store (usually based in a purchased RDBMS product) and the programs that manage it.

Contains recent and summarized data extracted from across some or all of the legacy data systems

Presents a subject-based view

De-NormalizationDe-Normalization

The process of selectively – combining two or more normalized tables into

one, or – decomposing one normalized table into two or

more

Entity Relationship Diagrams Entity Relationship Diagrams (ERDs)(ERDs)

A method of documenting and visualizing a conceptual data model.

Functional DependencyFunctional Dependency

Mathematical term for the key relationship (using rational terminology) between data elements. A data element (attribute) that is functionally dependent on another data element (the key) will always exist in a relation (table) such that a unique value for the key will always “determine” or “locate” or “define a unique value of” the dependent.

MetadataMetadata Data about data that is generally extracted from an existing system or

created for a new system and stored in a design repository for developers to use in maintaining or extending the system during its lifecycle

Metadata refers to the table, attribute, and key definitions contained in the catalog of a relational database. It can also mean the business rules for data designed for a new design, or the business rules for data thought to be enforced in a legacy system (semantic data structure, sometimes called meta-data, or meta2 data).

The actual syntactic and semantic data structure (not just what the documentation might say), including a complete synonym and homonym map, plus the business rules for data that are actually being enforced in the legacy system.

NormalizationNormalization

The process based on the business rules for data– a set of data elements (attributes) are arranged

in a mathematically minimum set of tables (relations), within which all the attributes are dependent on a primary key attribute (the key).

Relational ModelRelational Model

The Relational Model for data design is the foundation of the relational database and the industry that produces the “engines” that run them.

It puts data design (and data modeling) on a formal, mathematical footing.

Advantages of Data QueryAdvantages of Data Query “slice and dice” dynamic query support standard high-level access language (SQL) minimum data redundancy self-protecting data integrity

– no insert, delete and update anomalies

GM Parts ExampleGM Parts Example“Part #123 that is supplied by GM was

assembled on bus 456 on May 28, 1996” is decomposed into the following elementary sentences:

a). A part... is supplied by a manufacturer...

b). A part... was assembled on a bus...

c). The assembly [part*bus] was performed on a date...

Manufacturer (name)

Supplier of

Supplied of

Part (p#)

GM Parts ExampleGM Parts Example

Relationship TypesRelationship Typesa). One-to-one (1:1): means that an occurrence

if one OT uniquely determines an occurrence of other OT - and vice-versa

b). One-to-many (1:n): means that an occurrence of one OT determines an occurrence of the other OT - but not vice-versa

c). Many-to-many (n:m):means that an occurrence of one OT can be related to many occurrences of other OT - and vice-versa

Bus (License #) Part

(p#)Supplier

Manu-facturer (name)

Date of Assembly

Date (Calc. date)

GM Parts Assembly ExampleGM Parts Assembly Example