Data & Business Decision

1. Data & Business Decision (Lesson - 01) Role of Data in modern Organization Dr. C. Ertuna

2. Management by Fact • As a manager you observed a decline in Stockholder satisfaction. (the Business Situation) • You believe that the Stockholder satisfaction is a function of Revenues, Profits Before Taxes, Return on Assets and Earnings per Share. (the Model, and the variables) • You analyze the data on these variables and decide on increasing Earnings per Share. (the Decision) • You are managing by “Fact” Dr. C. Ertuna

3. Management by Fact Managing based on models that explain the relationship between • the Business Situation and • Decision is called “Management by Fact.” Dr. C. Ertuna

4. Management by Fact Tools • Data Analysis* • Decision Modeling * One of the most important tool for data analysis is Statistics. Dr. C. Ertuna

5. Role of Data Analysis in Business is to extract larger meaning from data in order to support • Evaluation and • Decision-making. Dr. C. Ertuna

6. Types of Business Data • Customer Satisfaction Data. • Financial Data • Market Performance Data • Human Resources Data • Supplier and Partner Performance Data • Organizational Effectiveness Data. End-User Satisfaction, etc. EPS, ROI, etc. Market share, Sales, etc Employee satisfaction etc Defects, etc. Response Time, etc. Dr. C. Ertuna

7. Statistics • - Definition • Role • Types is science of • Collecting, • Organizing, • Interpreting, and • Presenting data. Dr. C. Ertuna

8. Statistics • - Definition • Role • Types • toDraw Inferences (Increases in Sales are not stable) • to Monitor the Effectiveness of business processes. (Customer Satisfaction: Customer Satisfaction Index) Dr. C. Ertuna

9. Statistics • - Definition • Role • Types • Descriptive Statistics (Gender distribution of customers) • Statistical Inference (Impact of age on color preference) • Predictive Statistics. (Number of expected visitors next year) Dr. C. Ertuna

10. Decision Model • - Definition • Role • Types is • Logical or • Mathematical representation of a business situation (or problem) Room_Sales = f(Room_Price, Advertisement, Service_Quality, etc.). Dr. C. Ertuna

11. Decision Model • - Definition • Role • Types is to establish a relationship between • Actions and • Results. ( 5% ↓ Room_Rates → 3% ↑ Room_Sales ) Dr. C. Ertuna

12. Decision Model • - Definition • Role • Types • Regression Models • Forecasting Models • Selection Models • Simulation Models • Optimization Models. Dr. C. Ertuna

13. Data Scales Data scales can be defined two ways: • In terms of quality of data scale, such as Ratio, Interval, Ordinal, Nominal • In terms of time of data scale, such as time series , cross-sectional Dr. C. Ertuna

14. Scales of Measurement • Ratio( natural zero, fixed unite of measure, Sales) • Interval(no natural zero, fixed unite of measure, C) • Ordinal(no fixed unite of measurement, ranking, Priorities) • Categorical/ Nominal(no ranking, Religion) No meaningful comparison of ranges, averages, and other statistics. Dr. C. Ertuna

15. Scales of Measurement • Ratio • natural zero, constant scale, ranked • e.g., number of rooms sales, length of stay, age of guests, • Interval • no natural zero but constant scale, ranked, • differences make sense, but ratios do not (e.g., 30°-20°=20°-10°, but 20°/10° is not twice as hot! • e.g., temperature (C,F), longitudes, dates • Ordinal • No constant scale, ranked but differences between values are not important • e.g., hotel ratings • Surway data although ordinal could be treated like interval • e.g., degree of guest satisfaction; Likert scales, rank on a scale of 1..7 • Categorical/ Nominal • classification data, e.g. m/f, • no ranking, e.g. it makes no sense to state that M > F • arbitrary labels, e.g., m/f, 0/1, etc • also called count data since only numerical aspect is the number of observations Dr. C. Ertuna

16. Time Scale of Data • Time Series • Different dates different observation. Room sales of one hotel over many months • Ctross Sectional • On one date there are many observation Room sales of many hotels for a particular month. Dr. C. Ertuna

17. Time Series Data Time Series Data means that each observation is link to one unique time period different from the rest. • One date - one observation • The sequence of the data cannot be changed. Dr. C. Ertuna

18. Cross-sectional Data Cross Sectional Data means that there is one time period at which many observations are recorder. • One date - many observations • The sequence of the data can be changed. Dr. C. Ertuna

19. Data Scales in SPSS • Scale Data (both Ratio or Interval data are grouped under this name) • Ordinal Data • Categorical Data • Numeric Data (Scale & Ordinal data) • String Data (Categorical data) Dr. C. Ertuna

20. Example:Types of Data Scales • Trees are surveyed and their species and height are measured. The levels of measurement for the two variables (species and height) are; • Nominal (Categorical) and Interval • Ratio and Interval • Categorical (Nominal) and Ratio • Ordinal and Full Dr. C. Ertuna

21. Example:Types of Data Scales (cont.) • Which of the following variables is measured at the Ordinal level? • Mean annual rainfall (e.g. millimeters) • Town Post-codes (e.g. 2840) • Accommodation ratings (e.g. 5 star, 4 star) • Temperature (e.g. degrees Celsius) Dr. C. Ertuna

22. Example:Types of Data Scales (cont.) • Which of the following is NOT measured at the Nominal level of measurement? • Eye Color • Income Tax Bracket • Country of birth • Post-code Dr. C. Ertuna

23. How Data are used in Evaluating and Solving Business Problems Role of Data Analysis in Business Is to extract larger meaning from data to support evaluation and decision making. One of the most important tool for data analysis is statistics. Role of Statistics in Business Is to draw inferences and to monitor the effectiveness of business processes. Statistics is science of Collecting, Organizing, Interpreting, and Presenting data. Role of Decision Modeling in Business Is to establish a relationship between actions and results.Decision Model is logical or mathematical relation of a business situation (or problem). Regression Models Forecasting Models Selection Models Simulation Models Optimization Models Descriptive Statistics Statistical Inference Predictive Statistics Customer Satisfaction Data Market Performance Data Human Resources Data Supplier Performance Data Effectiveness Data Dr. C. Ertuna

24. Next Lesson (Lesson - 02) Displaying & Summarizing Data Dr. C. Ertuna