Highline Class, BI 348

1. Highline Class, BI 348 Basic Business Analytics using Excel, Chapter 01 Intro to Business Analytics

2. Topics • Raw Data Into Useful Information • Business Analytics (Textbook): • Descriptive Analytics • Predictive Analytics • Prescriptive Statistics • Big Data • Steps In Making A Decision • Types Of Decisions • Approaches To Decision Making

3. Raw Data Into Useful Information • Data Analysis (From Busn 216 and Busn 218): • Converting raw data into useful information for decision makers • Statistical Analysis (Busn 210): • Statistics is the art and science of collecting, analyzing, presenting and interpreting data to help make informed decisions. • Analysis (Merriam-Webster dictionary): • A careful study of something to learn about its parts, what they do and how they relate to each other • An explanation of the nature and meaning of something • Analytics (Merriam -Webster dictionary): • Information resulting from systematic analysis of data or statistics • Business Analytics (textbook): • Scientific process of transforming data into insight for better decisions

4. Business Analytics (Textbook): • Scientific process of transforming data into insight for better decisions • Data driven decision making • Fact-based decision making • Scientific process such as: Queries, Linear Regression, and Optimization • Business Analytics has three parts: • Descriptive Analytics • Describing the past • Predictive Analytics • Build models that help predict the unknown future • Prescriptive Analytics • Build models to help predict the best course of action

5. Descriptive Analytics • Set of techniques that describe what has happened in the past. • Examples: • Data Queries • Like a Filter in Excel or an Access Query • Reports • Like an Income Statement, a Regional Report or a PivotTable with multiple Criteria • Descriptive Statistics • Examples: Mean, Median Mode, Standard Deviation, Correlation • Data Visualization • Examples; Charts, Tables, Conditional Formatting • What if Excel models • Like Income Statement Budget with Assumption Table or a Fixed Variable Cost Analysis • Data Dashboards • Collection of items such as tables, charts and descriptive statistics that will update as new data arrives

6. Predictive Analytics • Set of techniques that use models constructed from past data to: • Predict the futureorAscertain impact of one variable on another • Examples: • Linear Regression • Models to help predict one variable based on a one or more other variables) • Time Series Analysis & Forecasting • Using data to make forecasts of unknown future • Data Mining (not covered in this class) • Methods to reveal patterns and relationships in data

7. Prescriptive Analytics • Set of techniques to indicate the best course of action; what decision to make to optimize outcome. • Examples: • Optimization models • A mathematical model that gives the best decision, subject to the situations constraints • We’ll use the Excel feature called “Solver” which can tell us things like what number of units to produce to maximize profit. • Simulation • Use Native Excel Functions to create a simulation • Decision Analysis (not covered in this class) • Advanced Analytics • Predictive Analytics and Prescriptive Analytics

8. Big Data • A set of data that cannot be managed, processed or analyzed with commonly available software in a reasonable amount of time. • Why do we have so much data now: • Every time you buy something, the scanner beep at the register records a lot of data such as price, product name, time, date, location, sales person and more. • All our personal devices collect vast amounts of data everyday • Social media • E-commerce data • Almost every click on the internet… • According to Google: Amount of data generated every 48 hours is equal to all data created from the beginning of civilization to 2003. • Business Analytical methods are used more often now because of: • Vast amount of data • Improved computational approaches and algorithms to handle the vast amounts of data • Faster computers and more ability to store vast amounts of data

9. Steps In Making A Decision: • Identify and define the problem • Determine criteria that will be used to evaluate alternative solutions • Determine the set of alternative solutions • Evaluate the alternatives with the criteria • Choose the alternative

10. Types Of Decisions: • Strategic Decisions • High-level manager decisions concerning the overall direction, goals and objectives of the organization (3 - 5 year time span) • Examples: • Does a local company try and sell out of the state or internationally? • Does an online only company try to open brick and mortar stores? • Tactical Decisions • Mid-level manager decisions about how organization can achieve the goals and objectives of the organization (1 year or 6 month time span) • Examples: • What states or cities or locations to sell in? • Operational • Decisions concerning day to day operations such as number of products to make or order, or how to schedule events.

11. Approaches To Decision Making: • Tradition • (Probably based on someone’s past experience from way back) • Intuition • (Probably based on persons unconscious past experiences) • Rule of Thumb • (Probably based on past experiences) • Data Based Decisions • (Based on past experiences, but in a more objective way)