Business Analytics Chapter 1

Introductory Business Analytics :)

Business Analytics

Use of data and models for business insights.

Data Mining

Extracting patterns from large datasets.

Predictive Analytics

Forecasting future trends using historical data.

Descriptive Analytics

Analyzing past performance for informed decisions.

Prescriptive Analytics

Recommending actions based on data analysis.

Business Intelligence (BI)

Tools for analyzing business data.

Decision Support Systems (DSS)

Computer-based systems aiding decision-making.

Statistical Analysis

Using statistics to interpret data.

Quantitative Methods

Mathematical techniques for data analysis.

Cloud Drives

Online storage accessible from any device.

Local Drives

Physical storage on a specific device.

Excel 2019 for Windows

Spreadsheet software for data manipulation.

Excel 2019 for Mac

Mac version of Excel for data analysis.

SAS Analytics

Software for advanced analytics and data management.

Tableau

Data visualization tool for business intelligence.

Supply Chain Design

Optimizing sourcing and transportation strategies.

Customer Segmentation

Identifying key customer groups for targeting.

Staffing

Ensuring adequate workforce levels and skills.

Merchandising

Deciding on product brands and quantities.

Location Analysis

Finding optimal sites for business operations.

Impacts of Analytics

Benefits include cost reduction and improved decisions.

Challenges of Analytics

Issues like data quality and skill shortages.

Technical Issues

Problems related to software and hardware compatibility.

Prescriptive Analytics

Identifies best alternatives to optimize objectives.

Descriptive Analytics

Analyzes historical data for insights.

Predictive Analytics

Forecasts future sales based on data.

Data

Collected numbers or text from measurements.

Information

Meaning extracted from analyzed data.

Big Data

Massive data sets from diverse sources.

Data Volume

Amount of data collected and processed.

Data Variety

Diverse types of data from various sources.

Data Velocity

Speed of data generation and processing.

Data Veracity

Accuracy and trustworthiness of data.

Data Reliability

Consistency and accuracy of data measurements.

Data Validity

Correctness of data measuring intended concepts.

Model

Representation of a real system or idea.

Visual Representation

Graphical depiction of data or models.

Mathematical Formula

Equation representing relationships in data.

Spreadsheet

Digital tool for organizing and analyzing data.

Retail Markdown Decisions

Pricing strategy to maximize revenue from sales.

Customer Service Calls

Measured for reliability but may lack validity.

Survey Question

May be unreliable or invalid for measuring satisfaction.

Sales Growth Pattern

Initial slow sales, increasing, then saturation.

Economic Trends

Patterns in economic data over time.

Marketing Research

Analysis of market conditions and consumer behavior.

S-shaped curve

Graphical representation of sales growth over time.

Sales (S)

Total revenue generated from products sold.

Time (t)

Duration over which sales are measured.

Natural logarithm (e)

Base of natural logarithms, approximately 2.718.

Decision Model

Logical representation for analyzing business decisions.

Inputs

Data and variables used in decision models.

Uncontrollable inputs

Variables that can change but cannot be managed.

Decision options

Choices available to decision makers.

Descriptive Models

Models that explain behavior and evaluate decisions.

What-if questions

Hypothetical scenarios to analyze potential outcomes.

Gasoline Usage Model

Calculates fuel consumption based on driving habits.

Gallons (G)

Volume of fuel consumed per month.

Miles driven (m)

Distance traveled daily for work or school.

Driving days (d)

Number of days driving in a month.

Fuel economy (f)

Efficiency measured in miles per gallon (mpg).

Total miles driven

Sum of commuting and additional leisure miles.

Total cost (TC)

Overall expense of production or outsourcing.

Breakeven Point

Production volume where costs of both options equal.

Predictive Models

Forecast future outcomes based on historical data.

Sales-Promotion Decision Model

Analyzes impact of marketing strategies on sales.

Objective function

Equation that defines the goal of optimization.

Optimal solution

Best values for decision variables at extremes.

Sales Model

Sales = -2.9485 x Price + 3,240.9

Total Revenue Formula

Total Revenue = Price x Sales

Maximizing Total Revenue

Identify price for highest total revenue.

Model Assumptions

Simplifications for easier analysis and representation.

Price Elasticity

Ratio of percentage change in demand to price.

Linear Demand Model

D = a - bP, where D is demand.

Nonlinear Demand Model

D = cP^-d, with constant price elasticity.

Uncertainty

Imperfect knowledge of future events.

Risk

Consequences of actual outcomes versus expectations.

Peter Drucker Quote

Risk is inherent in resource commitment.

Problem Recognition

Gap between current and expected outcomes.

Defining the Problem

Complexity increases with multiple actions and objectives.

Structuring the Problem

Goals, decisions, constraints must be stated.

Analyzing the Problem

Involves experimentation and evaluating scenarios.

Interpreting Results

Understanding model limitations for decision-making.

Implementing the Solution

Translating model results into real-world actions.

Demand Function Constant

a estimates demand when price is zero.

Demand Function Slope

b represents the slope of demand function.

Demand at Zero Price

c is demand when price equals zero.

Price Elasticity Parameter

d > 0 indicates price elasticity in nonlinear model.

Decision Alternatives

Various options evaluated during problem analysis.

Resource Commitment

Allocating resources based on future expectations.