Chapter 1: Data & Business Decisions

• Chapter 1 focuses on data and business decisions, emphasizing statistics, data analysis, and decision modeling.

Data, Information, and Analysis

• Data consists of numerical facts and figures gathered through a measurement process.
• Information is derived from the analysis of data.
• Organizations require quality data for performance evaluation and strategic decision-making.

Uses of Data

• Data is used in a variety of ways
• Annual reports
• Audits
• Financial analysis
• Market research
• Operations management
• Human resource management
• Economic analysis
• Regulatory compliance
• Budget allocation.

Decision Models

• A decision model is a logical or mathematical representation of a business problem developed from theory or observation.
• Data provides essential inputs for these decision models.

Statistics

• Statistics is the science of uncertainty and technology for extracting information from data.
• Statistics involve collecting, organizing, analyzing, interpreting, and presenting data.
• A statistic is a summary measure of data.

Data and Organizational Measurement

• The Malcolm Baldrige Criteria includes measurement categories for:
• Product and process outcomes
• Customer-focused outcomes
• Workforce-focused outcomes
• Leadership outcomes
• Financial and market outcomes
• Understanding relationships among measures leads to better decisions.

Sources of Data

• Internal data is obtained from company records and databases.
• External data is obtained from published sources, external databases, and the internet.
• Generated data is obtained from surveys and focus groups.

Metrics and Measurement

• A metric is a unit of measurement used to objectively quantify performance.
• Measurement involves obtaining data associated with a metric.
• Measures are numerical values linked to a metric.

Discrete and Continuous Metrics

• Discrete metrics are derived from counts, for example, defects per unit, on-time flight percentage or customer complaints.
• Continuous metrics are structured on a continuous scale, for example, delivery time, monthly revenues, or time spent on homework.

Data Classification

• Data can be classified by type
• Cross-sectional, collected at one time
• Time series, collected over time
• Data can be classified by the number of variables
• Univariate data has a single variable
• Multivariate data has two or more variables involved

Categorical (Nominal) Data

• This data is sorted into mutually exclusive categories
• Examples include geographical region, employee type, gender, birth state, or type of automobile owned
• There are no quantitative relationships.
• Statistics like averages are often meaningless.

Ordinal Data

• Ordinal data is ordered or ranked according to some relationship
• Examples include ranking colas in taste tests, employee performance appraisals, and satisfaction survey scales
• Categories can be compared, but statistics are meaningless due to the lack of fixed measurement units.

Interval Data

• Interval data is ordered and has a specified distance between observations but no natural zero.
• Examples include temperature scales, time, and some survey scales
• Ratios are meaningless, but differences can be compared.

Ratio Data

• Ratio data has a natural zero point
• Examples include sales dollars, length, weight, and time from the start of a process
• It is the strongest measurement form, where both ratios and differences are meaningful.

Statistical Thinking

• It utilizes a philosophy of learning and action for improvement based on three principles:
• All work exists in interconnected processes.
• Variation is present in all processes.
• Better performance arises from understanding and reducing variation.

Variation

• Common causes of variation are complex interactions of materials, tools, operators, and the environment
• Individual sources of this variation are not easily controlled or understood.
• Special causes of variation arise from external sources not inherent in a process
• These can be explained and controlled.
• Many mangers confuse these two sources and make poor decisions as result

Six Sigma

• Six Sigma is a process improvement approach focused on finding and eliminating causes of defects, reducing cycle times and operational costs
• It also improve productivity, customer satisfaction, and asset utilization
• "Six sigma" means at most 3.4 errors or defects per million opportunities.

Six Sigma Problem Solving: DMAIC

• DMAIC includes Define, Measure, Analyze, Improve, and Control
• Uses a variety of statistical and process improvement tools
• Many companies report positive financial results from Six Sigma initiatives.

Populations and Samples

• A population includes all items of interest,
• For example, all married drivers over 25 in the U.S.
• A sample is a subset of a population
• For example, Nielsen samples of TV viewers
• Samples are used to reduce data collection costs or when a full census is not feasible.

Statistics

• Are summary measures of population characteristics computed from samples.
• Used to describe population characteristics or to draw inferences.
• Descriptive statistics involve the collection, organization, and description of data.
• Statistical inference involves drawing conclusions about populations based on samples.
• Predictive statistics involves inferring future values based on historical data.

Basic Excel Skills

• Opening, saving, and printing files
• Moving around a spreadsheet
• Selecting ranges
• Inserting/deleting rows and columns
• Entering and editing text, data, and formulas
• Formatting data (number, currency, decimal)
• Working with text strings
• Performing basic arithmetic calculations
• Formatting text
• Modifying spreadsheet appearance
• Sorting data

Excel Functions

• MIN(range)= finds the smallest value in a range of cells
• MAX( range )= finds the largest value in a range of cells
• SUM(range)= finds the sum of values in a range of cells
• AVERAGE( range)= finds the average of the values in a range of cells
• COUNT(range)= finds the number of cells in a range that contain numbers
• COUNTIF( range, criteria)= finds the number of cells within a range that meet specified criteria
• AND( condition 1, condition 2 ...)= a logical function that returns TRUE if all conditions are true, and FALSE if not
• OR( condition 1, condition 2 ...)= a logical function that returns TRUE if any condition is true, and FALSE if not
• IF(condition, value if true, value if false)= a logical function that returns one value if the condition is true and another if the condition is false
• VLOOKUP( value, table range, column number)= looks up a value in a table

Charts

• Column and bar charts can be used for any measurement scale (nominal, ordinal, interval, or ratio).
• Line charts are useful for variables data, particularly over time.
• Pie charts are useful for displaying attributes to show relative proportions.
• Area charts combine features of pie and line charts.
• Scatter diagrams show relationships between two variables.

Explore the role of data in business decision-making, covering how data transforms into information, and its applications across various organizational functions. The chapter elucidates decision models and emphasizes importance of statistical analysis.