Business analytics data analysis and decision making 5th by wayne l winston chapter 01

BUSINESS ANALYTICS: DATA ANALYSIS AND DECISION MAKING Introduction to Data Analysis and Decision Making 1...  Technology has given more people the power and responsibility to analyze d

BUSINESS ANALYTICS: DATA ANALYSIS AND

DECISION MAKING

Introduction to Data Analysis and Decision Making

1

(slide 1 of 2)

 Living in the age of technology has

implications for everyone entering the

business world.

 Technology makes it possible to collect

huge amounts of data.

 Technology has given more people the

power and responsibility to analyze data

and make decisions.

 A large amount of data already exists

and will only increase in the future.

(slide 2 of 2)

 One of the hottest topics in today’s

business world is business analytics

 This term encompasses all of the types of

analysis discussed in this book.

 It also typically implies the analysis of very

large data sets.

 By using quantitative methods to uncover the information in these data sets and

then acting on this information,

companies are able to gain a competitive advantage.

The Methods

(slide 1 of 2)

 This book combines topics from two separate fields: statistics and management science.

 Statistics is the study of data analysis.

 Management science is the study of model building,

optimization, and decision making

 Three important themes run through this book:

 Data analysis—includes data description, data inference, and the search for relationships in data.

 Decision making—includes optimization techniques for problems with no uncertainty, decision analysis for

problems with uncertainty, and structured sensitivity

analysis.

 Dealing with uncertainty—includes measuring

uncertainty and modeling uncertainty explicitly.

The Methods

(slide 2 of 2)

themes and subthemes are discussed in the book.

The Software

(slide 1 of 3)

 The software included in new copies of this

book, together with Microsoft Excel ® , provides

a powerful combination that can be used to

analyze a wide variety of business problems.

 Excel—the most heavily used spreadsheet

package on the market

 The file excel_tutorial.xlsm explains many of the

features of Excel.

 Solver Add-in—uses powerful algorithms to

perform spreadsheet optimization.

 SolverTable Add-in—shows how the optimal

solution changes when certain inputs change.

The Software

(slide 2 of 3)

 DecisionTools ® Suite—Excel add-ins, including:

 @RISK—can run multiple replications of a

spreadsheet simulation, perform a sensitivity analysis, and generate random numbers from a variety of probability distributions.

 RISKOptimizer combines optimization with simulation.

 StatTools—generates statistical output quickly in

an easily interpretable form.

 PrecisionTree—used to analyze decisions with

uncertainty.

brain to find “neural networks” that quantify complex nonlinear relationships.

The Software

(slide 3 of 3)

 The figure below illustrates how these

add-ins are used throughout the book

Modeling and Models

 A model is an abstraction of a real

problem that tries to capture the

essence and key features of the

problem.

 There are different types of models, and each can be a valuable aid in solving a real problem:

 Graphical models

 Algebraic models

 Spreadsheet models

Graphical Models

 Graphical models attempt to portray

graphically how different elements of a problem are related—what effects what.

 A very simple graphical model, called an

influence diagram , is shown below.

Algebraic Models

 Algebraic models use algebraic

equations and inequalities to specify a set of relationships in a very precise way.

 A typical example is the “product mix”

model shown below.

Spreadsheet Models

(slide 1 of 2)

 Spreadsheet modeling is an alternative

to algebraic modeling that relates

various quantities in a spreadsheet with cell formulas.

 Instant feedback is available from

spreadsheets, so if a formula is entered

incorrectly, it is often immediately obvious.

 Developing good spreadsheet models is not easy

 They must be correct, well designed and well

documented.

Spreadsheet Models

(slide 2 of 2)

 A spreadsheet model for a specific example

of the product mix problem is shown below.

A Seven-Step Modeling

Process

seven-step process, but not all problems require all seven steps.

1 Define the problem.

2 Collect and summarize data.

3 Develop a model.

4 Verify the model.

5 Select one or more suitable decisions.

6 Present the results to the organization.

7 Implement the model and update it over

time