MBA 8011.

 

Data Analysis and Decision Making

 

Summery of Chapter 1

 

1.1 Introduction

Two important implications for everyone entering the business world in the age of technology:

1)      Technology has made it possible to collect huge amounts of data.

2)      Technology has given many more people the power and responsibility to analyze data and make decisions on the basis of quantitative analysis.

 

1.2 An Overview of the Book.

 

1.2.1 The Methods

The book concentrates on three themes: Data Analysis, Decision Making, and Dealing with Uncertainty.

 

1)      Data Analysis

o       Data Description

o       Data inference

o       Search for Relationships in Data

 

 

2)      Decision Making

o       Optimization Techniques for problems with No uncertainty

o       Decision Analysis for Problems with Uncertainty

o       Structured Sensitivity Analysis

 

 

3)      Dealing with Uncertainty

o       Measuring Uncertainty

o       Modeling Uncertainty Explicitly into the Analysis

 

 

1.2.2 The Software

 Problems that contemporary business encounters require the use of powerful software.

 Excel – Powerful, flexible, and easy to use, it is the most heavily used spreadsheet package on    

 the market.

Built in Excel Features  

 

Solver Add-in

StatPro Add-in

SolverTable Add-in

 

Decision Tools Suite

@Risk

PrecisionTree

Toprank

BestFit

RiskView

 

 

1.3 A sampling of Examples

Preview of a few examples from later chapters.

 

 

1.4 Modeling and Models

A model is an abstraction of real problem.

 

 

1.4.1 Graphical Models

Graphical models are the most intuitive and least quantitative type of model.

They attempt to portray how different elements of a problem are related – what affects what.

 

 

1.4.2 Algebraic Models

Algebraic models specify a set of relationships in a very precise way by means of algebraic equations and inequalities.

 

 

1.4.3 Spreadsheet Models

An alternative to algebraic modeling is spreadsheet modeling. Instead of being related with algebraic equations and inequalities, various quantities are related with cell formulas.

 

 

1.4.4 The Seven-Step Modeling Process 

      

1. Define the Problem

The modeling process begins by identifying an underlying problem.

 

2. Collect and summarize data.     

Collecting the data typically requires asking questions of key people through organization, studying existing organizational database, and performing time-consuming observational studies of the organization’s processes.

 

3. Formulate a Model.

A model should capture the key elements of the business problem in such way that it is understandable by all parties involved.

 

4. Verify the model.

Verification is process when analyst tries to determine whether the model developed in the previous step is an accurate representation of reality.

 

 

5. Select one or more suitable decision.

If model is working correctly, it is used to determine which decisions produce the best outputs.

 

 

6. Present the results to the organizations.

Include relevant people through the company in the modeling process.

It is preferable to use a spreadsheet model in order to more successfully “sell” the model to management team.

 

 

7. Implement the model and update it through the time.

It is important to develop a model that can be modified easily as the need arises.