Monday, 19 August 2013



Reason for Growth of Decision Making Information System
1. People need to analyze large amounts of information :- improvement in technology itself, innovations in communication, and globalisation have resulted in a dramatic increase in the alternatives and dimension people need to consider when making a decision or appraising an opportunity.
2. People must make decision quickly :- time is of the essence and people simply do not have time to sift through all the information manually.
3. People must apply sophisticated analysis technique such as modelling and forecasting to make good decision :- information system substantially reduce the time required to perform this sophisticated analysis technique.
4. People must protect the corporate asset of organizational information :- information systems offer the security required to ensure organization information remains safe.
Model - a simplified representation or abstraction of reality.

Transaction Processing System

Ø  Moving up through the organizational pyramid users move from requiring transactional information to analytical information

 Ø  Transaction processing system – the basic business system that serves the operational level (analysis) in an organization
Ø  Online transaction processing (OLTP) – the capturing of transaction and event information using technology to (1) process the information according to defined business rules, (2) store the information, (3) update existing information to reflect the new information
Ø  Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making

Decision support systems
Ø  Decision support system (DSS) – models information to support managers and business professionals during the decision-making process
Ø  Three quantitative models used by DSSs include;
1.       Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model
2.       What-if analysis – checks the impact of a change in an assumption on the proposed solution
Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of outputs

Executive information system
Ø  Executive information system (EIS) – A specialized DSS that supports senior level executives within the organization
Ø  Most EISs offering the following capabilities;
-          Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information
-          Drill-down – enables users to get details, and details of information
-          Slice-and-dice – looks at information from different perspectives
Ø  Interaction between a TPS and an EIS
Ø  Digital dashboard – integrates information from multiple components and presents it in a united display
Artificial intelligence (AI)
Ø  The ultimate goal of AI is the ability to build a system that can mimic human intelligence
Ø  Intelligent system – various commercial applications of artificial intelligence
Ø  Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn

Four most common categories of AI include;

1.       Expert system – computerized advisory programs that imitate the reasoning processes of experts in               solving difficult problems
2.       Neural network – attempts to emulate the way the human brain works
          Fuzzy logic – a mathematical method of handling imprecise or subjective information
3.       Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest                process to generate increasingly better solutions to a problem
4.       Intelligent agent – special-purposed knowledge-based information system that accomplishes specific             tasks on behalf of its users

Data Mining

Ø  Data-mining software includes many forms of AI such as neutral networks and expert systems



What is Data Warehouse?

Ø  Defined in many different ways, but not rigorously
-          A decision support database that is maintained separately from the organization’s operational database.
-          A consistent database source that bring together information from multiple sources for decision support queries.
-          Support information processing by providing a solid platform of consolidated, historical data for analysis.
History of Data Warehousing
Ø  In the 1990’s executives became less concerned with the day-to-day business operations and more concerned with overall business functions
Ø  The data warehouse provided the ability to support decision making without disrupting the day-to-day operations, because;
-          Operational information is mainly current – does not include the history for better decision making
-          Issues of quality information
-          Without information history, it is difficult to tell how and why things change over time
Data warehouse fundamentals
Ø  Data warehouse – A logical collection of information – gathered from many different operational databases – that supports business analysis activities and decision-making takes
Ø  The primary purpose of a data warehouse is to combined information throughout an organization into a single repository for decision-making purposes – data warehouse support only analytical processing
Data warehouse model
Ø  Extraction, transformation and loading (ETL) – A process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse.
Ø  Data warehouse then send subsets of the information to data mart.

Ø  Data mart – contains a subset of data warehouse information.

Multidimensional Analysis and Data Mining
Ø  Relational Database contains information in a series of two-dimensional tables.
Ø  In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows
-          Dimension – A particular attribute of information
Ø  Once a cube of information is created, users can begin to slice and dice the cube to drill down into the information.
Ø  Users can analyze information in a number of different ways and with number of different dimensions.
Ø  Data Mining – the process of analyzing data to extract information not offered by the raw data alone. Also known as “knowledge discovery” – computer-assisted tools and techniques for sifting through and analyzing vast data stores in order to finds trends, patterns and correlations that can guide decision making and increase understanding
Ø  To perform data mining users need data-mining tools
-          Data-mining tool – uses a variety of techniques to finds patterns and relationships in large volumes of information. Eg: retailers and use knowledge of these patterns to improve the placement of items in the layout of a mail-order catalog page or Web page.
Information Cleansing or Scrubbing
Ø  An organization must maintain high-quality data in the data warehouse
Ø  Information cleansing or scrubbing – A process that weeds out and fixes or discards inconsistent, incorrect or incomplete information
Ø  Occurs during ETL process and second on the information once if is in the data warehouse
Ø  Contract information in an operational system
Ø  Standardizing Customer  name from Operational Systems
Ø  Information cleansing activities
-          Missing Records or Attributes
-          Redundant Records
-          Missing Keys or Other Required Data
-          Erroneous Relationships or References
-          Inaccurate Data

Business Intelligence
Ø  Business Intelligence – refers to applications and technologies that are used to gather, provides access, analyze data and information to support decision making efforts
Ø  These systems will illustrate business intelligence in the areas of customer profiling, customer support, market research, market segmentation, product profitability, statistical analysis, and inventory and distribution analysis to name a few

Ø  Eg; Excel, Access

Chapter 7 - Storing Organizational Information - Database

Chapter 7 - Storing Organizational Information - Database


 -    Information is everywhere in an organization
-  Information is stored in databases
Ø   Database – maintains information about various types of objects (inventory), events (transactions), people (employees), and places (warehouses)
 -     Database models include;
Ø   Hierarchical database model – information is organized into a tree-like structure (using parent/child relationships) in such a way that it cannot have too many relationships
 Ø  Network database model – a flexible way of representing objects and their relationship
  Ø  Relational database model – stores information in the form of logically related two-dimensional tables


-    Entity – a person, place, thing, transaction, or event about which information is stored
Ø  The rows in each table contains the entities
-    Attributes (fields, columns) – characteristics or properties of an entity class
Ø  The columns in each table contain the attributes


-    Primary keys and foreign keys identity the various entity classes (tables) in the database
Ø  Primary key – a fields (or group of fields) that uniquely identities a given entity in a table
Ø  Foreign key – a primary key of one table that appears an attribute in another table and acts to provide a logical relationships among the two tables


-    Database advantages from a business perspective include;
Ø  Increased flexibility
Ø  Increased scalability and performance
Ø  Reduced information redundancy
Ø  Increased information integrity (quality)
Ø  Increased information security

-     A well-designed database should;
Ø  Handle changes quickly and easily
Ø  Provide users with different views
Ø  Have only one physical views
§  Physical view – deals with the physical storage of information on a storage device
Ø  Have multiple logical views
§  Logical view – focuses on how users logically access information


-      A database must scale to meet increased demand, while maintaining acceptable performance levels
Ø  Scalability – refers to how well a system can adapt to increased demands
Ø  Performance – measures how quickly a system performs a certain process or transaction


-      Databases reduce information redundancy
Ø  Redundancy – the duplication of information or storing the same information in multiple places
-     Inconsistency is one of the primary problems with redundant information


-      Information is an organization asset and must be protected
-      Databases offer several security features including;
Ø  Password – provides authentication of the user
Ø  Access level – determines who has access to the different types of information

-     Database management systems (DBMS) – software through which users and application programs interact with a database

-       Data-driven Web sites – an interactive Web site kept constantly updated and relevant to the needs of its   customers through the use of database


-         Development
-         Content Management
-         Future Expandability
-         Minimizing Human Error
-         Cutting Production and Update Costs
-         More Efficient
-         Improved Stability

-         BI in a data-driven Web site

-      Integration – allows separate systems to communicate directly with each other
Ø  Forward integration – takes information entered into a given system and sends it automatically to all downstream systems and processes