Chapter Nine Enabling the Organization - Decision Making

Chapter Nine Enabling the Organization - Decision Making

Decision Making 

• Ø  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 model is a simplified representation or abstraction of reality.

 -Models can calculate risks, understand uncertainly , change variable, and manipulate time.

- Decision-making information systems work by building models out of organization information to lend insight into important business issues and opportunities.

Each system uses different models to assist in decision making, problem solving, and opportunity capturing. This system includes:

    -Transaction Processing System.

    -Decision Support Systems.

    -Executive Information Systems.   

• Ø  IT systems in an enterprise

•  Transaction Processing Systems

The structure of a typical organization is similar to a pyramid. Organizational activities occur at different levels of the pyramid. People in the organization have unique information needs and thus require various sets of IT tools.

   

 - Online transaction processing is the manipulation of information to create business intelligence in support of strategic decision making.

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
•   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

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 

• 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  Interaction between a TPS and a DSS

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

Artificial Intelligence (AI)
Executive information systems are starting to take advantage of artificial intelligence to help executives make strategic decisions.
Phili Lumish said that competing in the internet arena is competing with the entire world rather than a store down the block or a few miles away.
- Intelligent Systems are various commercial applications of artificial intelligence. --
- Artificial inteligence(AI) simulates human intelligence such as ability to reason and learn.

 AI systems dramatically increase the speed and consistency of decision making, solve problems with incomplete information, and solve complicated issues that cannot be solved by conventional computing. There are many categories of AI systems:

    -Expert System
    -Neural Networks
    -Genetic Algorithms
    -Intelligent agents

+ Expert system are computerized advisory program that imitate the reasoning processes of experts in solving difficult problems.
    

+A neural Network, also called an artificial neural network, is a category of AI that attempts to emulate the way the human brain works.
    - Fuzzy logic: It is an artificial method of handing imprecise or subjective information.
  

 +A genetic algorithms is an artificial intelligence system that mimic the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem.

+ Intelligent agents is a special-purpose knowledge-based information system that accomplished specific task as on behalf of its users. A shopping both is simple example of an intelligent agent.
    - Shopping bot is the software that will search several retailer websites and provide a comparison of each retailer's offering including price and availability.

Multi-Agent Systems and Agent Modeling

By observing the ecosystem like ant or bee colonies, artificial intelligence scientists can use hardware and software models that incorporate insect characteristics and behavior to:

 - Learn how people-based system behave

 - predict how they will behave under a given set of circumstances

 - improve human system to make them more efficient and effective.

This concept of learning from ecosystems and adapting their characteristics to human and organizational situations is called biomimicry.

+ Data Mining

- Data miming system sift instantly through the information to uncover patterns and relationships that would elude an army of human research.

- Data-mining software typically includes many forms of AI such us networks and expert system.

Chapter Eight Accessing Organizational Information - Data Warehouse

Chapter Eight Accessing Organizational Information - Data Warehouse

History of Data Warehouse

•          Data warehouses extend the transformation of data into information

•          In the 1990’s executives became less concerned with the day-to-day business operations and more concerned with overall business functions

•          The data warehouse provided the ability to support decision making without disrupting the day-to-day operations

Data Warehouse Fundamentals

•          Data warehouse – a logical collection of information – gathered from many different operational databases – that supports business analysis activities and decision-making tasks

•          The primary purpose of a data warehouse is to aggregate information throughout an organization into a single repository for decision-making purposes

•          The primary difference between a database and a data warehouse is that a database stores information for a single application, whereas a data warehouse stores information from multiple databases, or multiple applications, and external information such as industry information           

•          This enables cross-functional analysis, industry analysis, market analysis, etc., all from a single repository

•          Data warehouses support only analytical processing (OLAP)

•          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

•          The ETL process gathers data from the internal and external databases and passes it to the data warehouse

•          The ETL process also gathers data from the data warehouse and passes it to the data marts

•          Data mart – contains a subset of data warehouse information

• Data warehouse models

MULTIDIMENSIONAL ANALYSIS AND DATA MINING

•          Databases contain information in a series of two-dimensional tables

•          In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows

–      Dimension – a particular attribute of information

•          Each layer in a data warehouse or data mart represents information according to an additional dimension

•          Dimensions could include such things as:

Products

Promotions

Stores

Category

Region

Stock price

Date

Time

Weather

•          Why is the ability to look at information based on different dimensions critical to a business success?

–      Ans:  The ability to look at information from different dimensions can add tremendous business insight

–      By slicing-and-dicing the information a business can uncover great unexpected insights

•          Cube – common term for the representation of multidimensional information

•          Users can slice and dice the cube to drill down into the information

•          Cube A represents store information (the layers), product information (the rows), and promotion information (the columns)

•          Cube B represents a slice of information displaying promotion II for all products at all stores

•          Cube C represents a slice of information displaying promotion III for product B at store 2

•          Data mining – the process of analyzing data to extract information not offered by the raw data alone

•          Data mining can begin at a summary information level (coarse granularity) and progress through increasing levels of detail (drilling down), or the reverse (drilling up)

•          To perform data mining users need data-mining tools

Data-mining tool – uses a variety of techniques to find patterns and relationships in large volumes of information and infers rules that predict future behavior and guide decision making

Data-mining tools include query tools, reporting tools, multidimensional analysis tools, statistical tools, and intelligent agents

• Multidimensional Analysis and Data Mining - relational Database contain information in a series of two-dimensional tables

• In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows
• Dimension – a particular attribute of information

• Cube – common term for the representation of multidimensional information
• Once a cube of information is created, users can begin to slice and dice the cube to drill down into the information
• Users can analyse information in a number of different ways and with number of different dimensions

Multidimensional Analysis and Data Mining

• Data mining – the process of analysing 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 analysing vast data stores in order to find trends, patterns, and correlations that can guide decision making and increase understanding
• To perform data mining users need data-mining tools
• Data-mining tool – uses a variety of techniques to find patterns and relationships in large volumes of information. Examples: retailers can use knowledge of these patterns to improve the placement of items in the layout of a mail-order catalogue page or Web page

Information Cleansing or Scrubbing

• An organization must maintain high-quality data in the data warehouse
• Information cleansing or scrubbing – a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information
• Occur during ETL process and second on the information once if is in the data warehouse
• Contact information in an operational system

• Standardizing Customer name from Operational Systems

• Information cleansing activities

• Accurate and complete information

BUSINESS INTELLIGENCE

BI is information that people use to support their decision-making efforts

Principle BI enablers include:

•          Technology

•          Even the smallest company with BI software can do sophisticated analyses today that were unavailable to the largest organizations a generation ago. The largest companies today can create enterprisewide BI systems that compute and monitor metrics on virtually every variable important for managing the company. How is this possible? The answer is technology—the most significant enabler of business intelligence.

•          People

•          Understanding the role of people in BI allows organizations to systematically create insight and turn these insights into actions. Organizations can improve their decision making by having the right people making the decisions. This usually means a manager who is in the field and close to the customer rather than an analyst rich in data but poor in experience. In recent years “business intelligence for the masses” has been an important trend, and many organizations have made great strides in providing sophisticated yet simple analytical tools and information to a much larger user population than previously possible.

•          Culture

•          A key responsibility of executives is to shape and manage corporate culture. The extent to which the BI attitude flourishes in an organization depends in large part on the organization’s culture. Perhaps the most important step an organization can take to encourage BI is to measure the performance of the organization against a set of key indicators. The actions of publishing what the organization thinks are the most important indicators, measuring these indicators, and analyzing the results to guide improvement display a strong commitment to BI throughout the organization.