1. Evolution of Business Analytics:
1.1. Business intelligence, Information Systems, Statistics, Operations research/Management Science, Decision support systems
2. Scope of Business Analytics: Descriptive Analytics, Predictive Analytics and Prescriptive Analytics
2.1. 1. Descriptive Analytics- the use of data to understand past and current business performance and make informed decisions
2.2. 2. Predictive Analytics- predict the future by examining historical data, detecting patterns or relationships in these data and then extrapolating these relationships forward in time.
2.2.1. Example 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?
2.2.1.1. Potential applications of analytics: 1. Descriptive Analytics: examine historical data for similar products (prices, unit sold, advertising etc), 2. Predictive Analytics: predict sales based on price, 3. Prescriptive Analytics: find the best sets of pricing and advertising to maximize sales revenue.
2.3. 3. Prescriptive Analytics- identify the best alternatives to minimize or maximize some objective.
3. Software Support
3.1. 1.IBM Cognos Express- integrated business intelligence and planning solution designed to meet the needs of midsize companies, provides reporting, analysis, dashboard, scorecard, planning, budgeting and forecasting capabilities
3.2. 2. SAS Analytics- predictive modelling and data mining, visualization, forecasting, optimization and model management, statistical analysis, text analytics and more.
3.3. 3. Tableau Software- simple drag and drop tools for visualizing data from spreadsheets and other databases.
4. Models in Business Analytics
4.1. Model: an abstraction or representation of a real system, idea or object.
4.1.1. Captures the most important features and can be a written or verbal description, a visual representation, a mathematical formula or a spreadsheet.
5. : the use of data, information technology, statistical analysis, quantitative methods and mathematical or computer-based models
5.1. To help managers gain improved insight about their business operations and make better, fact-based decisions.
6. Examples of Applications: 1.Pricing, 2. Customer segmentation , 3. Merchandising, 4. Location, 5. Social media
6.1. 1. Pricing - setting prices for consumer and industrial goods, government contracts and maintenance contracts
6.2. 2. Customer segmentation- identifying and targeting key customer groups in retail, insurance and credit card industries
6.3. 3. Merchandising- determining the brands to buy, quantities and allocations
6.4. 4. Location- finding the best location for bank branches and ATMs or where to service industrial equipment
6.5. 5. Social media - understand trends and customer perceptions; assist marketing managers and product designers.
7. Data for Business Analytics
7.1. Data: numerical or textual facts and figures that are collected through some type of measurement process.
7.1.1. Examples of Data Sources and Uses: Annual reports, Accounting audits, Financial profitability analysis, Economics trends, Marketing research, Operations management performance, Human resource measurements, Web Behavior.
7.1.1.1. Web Behavior: 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 others.
7.2. Information: result of analyzing data; that is extracting meaning from data to support evaluation and decision making.
7.3. Data Sets: a collection of data. Examples: marketing survey responses, a table of historical stock prices, and a collection of measurements of dimensions of a manufactured item.
7.4. Database: a collection of related files containing records on people, places or things.
7.4.1. Database file is usually organized in a two-dimensional table where the columns correspond to each individual element of data (called fields, or attributes), and the rows represent records of related data elements.
7.5. Big Data: refer 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.
7.5.1. IBM calls these characteristics VOLUME, VARIETY, VELOCITY and VERACITY.
7.5.1.1. The effective use of big data has a potential to transform economies, delivering a new wave of productivity growth and consumer surplus.
7.5.1.2. Using the big data will become a key basis of competition for existing companies, and will create new competitors who are able to attract employees that have the critical skills for a big data world. - McKinsey Global Institute,2011.