Business Analytics Lesson 2 PDF

Business Analytics Lesson 2 What is Analytics? Contents: Tools Data Models Problem Solving with analytics Some common types of decisions that can be enhanced by using analytics includes: Pricing Customer segmentation Merchandising Location Social Media Scope of Business Analyti...

Business Analytics Lesson 2 What is Analytics? Contents: Tools Data Models Problem Solving with analytics Some common types of decisions that can be enhanced by using analytics includes: Pricing Customer segmentation Merchandising Location Social Media Scope of Business Analytics Descriptive analytics – the use of data to understand past and current business performance and make informed decisions Predictive analytics – predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time. Prescriptive analytics – identify the best alternatives to minimize or maximize some objective Example 1.1: Retail Markdown Decisions Most department stores clear seasonal inventory by reducing prices Key question: When to reduce the price and by how much to maximize revenue? Example 1.1: Retail Markdown Decisions Potential applications of analytics: Descriptive analytics - examine historical data for similar products (prices, units sold, advertising, etc.) Predictive analytics - predict sales based on price Prescriptive analytics – find the best sets of pricing and advertising to maximize sales revenue Tools Database queries and analysis Spreadsheets Data visualization Dashboards to reports key performance measures Data and Statistical methods Data Mining basics (predictive models) Data for Business Analytics Data – numerical or textual facts and figures that are collected through some type of measurement process. Information – result of analyzing data; that is, extracting meaning from data to support evaluation and decision making. Examples of Data Sources and Uses Internal Annual reports Accounting audits Financial profitability analysis Operations management performance Human resource measurements External Economic trends Marketing research Examples of Data Sources and Uses New developments: Web behavior – Social Media – Mobile – IOT Page views, visitor’s country, time of view, length of time, origin and destination paths, products they searched for and viewed, products purchased, what reviews they read, and many more. Big Data Big Data – refers to massive amounts of business data from a wide variety of sources, much of which is available in real time, and much of which is uncertain or unpredictable. IBM calls these characteristics volume, variety, velocity and veracity. Big Data Big Data Apache Hadoop Ecosystem for Big Data Types of Data Discrete – derived from counting something. For example, a delivery is either on time or not; an order is complete or incomplete; or an invoice can have one, two, three, or any number of errors. Some discrete metrics would be the proportion of on-time deliveries; the number of incomplete orders each day, and the number of errors per invoice. Continuous – based on a continuous scale of measurement Any metrics involving dollars, length, time, volume, or weight, for example, are continuous.