Chapter 9 - Enabling The Organization - Decision Making

Chapter 9 - Enabling The Organization - Decision Making

Decision Making 

•      Reasons for Growth of Decision Making Information System

-          People need to analyze large amounts of information 
– Improvements in technology itself, innovations in communication, and globalization have resulted in a dramatic increase in the alternatives and dimensions people need to consider when making a decision or appraising an opportunity

-          People must make decisions quickly – Time is of the essence and people simply do not have time to sift through all the information manually

-          People must apply sophisticated analysis techniques, such as modeling and forecasting, to  make good decisions 
– Information systems substantially reduce the time required to perform these sophisticated analysis techniques

-          People must protect the corporate asset of organizational information 
– Information systems offer the security required to ensure organizational information remains safe.

•       Model – A simplified representation or abstraction of reality

•       IT systems in an enterprise
•  
  

Transaction Processing System

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

•       Transaction processing system – the basic business system that serves the operational level (analysis) in an organization
•      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
•      Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making

Decision support systems

•      Decision support system (DSS) – models information to support managers and business professionals during the decision-making process
•       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

3.       Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of outputs

What-if analysis

Goal-seeking analysis

Executive information system 

•       Executive information system (EIS) – A specialized DSS that supports senior level executives within the organization
•       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

•       Interaction between a TPS and an EIS

•      Interaction between a TPS and a DSS

•       Digital dashboard – integrates information from multiple components and presents it in a united display

Artificial intelligence (AI)

•       The ultimate goal of AI is the ability to build a system that can mimic human intelligence
•       Intelligent system – various commercial applications of artificial intelligence
•      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

o   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

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

Chapter 8

Chapter 8

Chapter 8 – Accessing Organizational Information – Data Warehouse

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  Cube – common term for the representation of multidimensional 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

Ø  Accurate and complete information

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

Question?

1.       Describe the roles and purposes of data warehouse and data marts in an organization

2.       Compare the multidimensional nature of data warehouses (and data  marts) with the two-dimensional nature of databases

3.       Identify the information of ensuring the cleanliness of information throughout an organization

4.       Explain the relationship between business intelligence and a data warehouse

CHAPTER 7 : STORING ORGANIZATIONAL INFORMATION

CHAPTER 7 : STORING ORGANIZATIONAL INFORMATION

This is CHAPTER 7 : STORING ORGANIZATIONAL INFORMATION

- Information is stored in database.

 Relational Database Fundamentals
 - Database : maintain information about various types of objects (inventory), events (transaction),        people(employees), and     places(warehouses).
 - Hierarchical database model : information is organised into a tree-like structure that allows repeating using parent/child                 relationship.
 - Network database model : a flexible way of representing objects and their relationship.
- Relational database model : type of database that stores information in the form of logically related two-dimensional tables.

 Entities And Attributes 

 - Entity : is a person, place, thing, transaction, or event about which information is stored.
- Attributes : is fields or columns, are characteristics or properties of an entity class.

 Relational Database Advantages 

- Increased flexibility
 : a good database and any good business must able to handle changes quickly and easily.
 : physical view – information deals with physical storage of information on a storage device.
: logical view – information focuses on how users logically access information to meet their particular business needs.

 - Increased scalability and performance
: scalability – refers to how well a system can adapt to increased demand.
: performance – measures how quickly a system performs a certain process or transaction.
: database today scale to exceptional levels, allowing all types of users and programs to perform information-processing and information-searching tasks.

 - Reduced information redundancy
 :redundancy – is the duplication of information or storing the same information in multiple places.
 :database is to eliminate information redundancy

 - Increased information and integrity (quality)
 : information integrity – measure of the quality of information : information constraints – rules that help ensure the quality of information
: relational integrity constraints – rules that enforce basic and fundamental information-based constraints. :business-critical integrity constraints – enforce business rules vital to an organization’s success and often requires more insight and knowledge.

 - Increased information security : information is an organizational asset. : passwords provide authentication of the user who is gaining access to the system.
 : Access levels determine who has access to the different types of information.
 : Access controls determine what types of access they have to the information.

 Database Management System 
 - Database management system (DBMS) : is a software through which users and application programmes interact with the database.

Chapter 6 – Valuing Organizational Information

Chapter 6 – Valuing Organizational Information

ORGANIZATIONAL INFORMATION

• Information is everywhere in an organization
• Employees must be able to obtain and analyze the many different levels, formats and granularity of organizational information to make decisions
• Successfully collecting, compiling, sorting and analyzing information can provide tremendous insight into how an organization is performing
•  Levels, formats and granularity of organizational information

THE VALUE OF TRANSNATIONAL AND ANALYTICALLY INFORMATION

• Transaction information verses analytically information
• 
  
  
  
  
  
  
  THE VALUE OF TIMELY INFORMATION
  
  -   Timeliness is an aspect of information that depends on the situation
  -   Real-time information – immediate, up-to-date information
  -   Real-time system – provides real-time information in response to query requests
  
  
  THE VALUE OF QUALITY INFORMATION
  
  
  -    Business decisions are only as good as the quality of the information used to make the decisions
  -   You never want to find yourself using technology to help you make a bad decision faster
  -   Characteristics of high-quality information include;
  
  
  
  
  
  -   Low quality information example;
  
  
   
  
  
  
  
  UNDERSTANDING THE COSTS OF POOR INFORMATION
  
  
  -   The four primary sources of low quality information include;
  
  

  - Online customers intentionally enter inaccurate information to protect their privacy 

  - Information from different systems have different entry standards and formats 

  - Call center operators enter abbreviated or erroneous information by accident or to save time 

  - Third party and external information contains inconsistencies, inaccuracies and errors

  
  
  -   Potential business effects resulting from low quality information include;
  
  

  - Inability to accurately track customers

  - Difficulty identifying valuable customers 

  - Inability to identify selling opportunities 

  - Marketing to nonexistent customers 

  - Difficulty tracking revenue due to inaccurate invoices 

  - Inability to build strong customer relationships

  
  
  UNDERSTANDING THE BENEFITS OF GOOD INFORMATION
  
  
  -   High quality information can significantly improve the chances of making a good decision
  -   Good decisions can directly impact an organization’s bottom line