MIS2502: Data Analytics Introduction to Advanced Analytics

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Presentation transcript:

MIS2502: Data Analytics Introduction to Advanced Analytics Aaron Zhi Cheng http://community.mis.temple.edu/zcheng/ acheng@temple.edu Acknowledgement: David Schuff

The Information Architecture of an Organization Now we’re here… Data entry Transactional Database Data extraction Analytical Data Store Data analysis Stores real-time transactional data Stores historical transactional and summary data

What is Advanced Data Analytics? The examination of data or content using sophisticated techniques and tools, to discover deeper insights, make predictions, or generate recommendations. Goals: Extraction of implicit, previously unknown, and potentially useful information from data Exploration and analysis of large data sets to discover meaningful patterns Prediction of future events based on historical data

The difference between OLAP and advanced data analytics OLAP can tell you what is happening, or what has happened Analytical Data Store …like a pivot table Advanced data analytics can tell you why it is happening, and help predict what will happen The (dimensional) data warehouse feed both… …like what we’ll do with R

What advanced data analytics is not… How do sales compare in two different stores in the same state? Sales analysis Which product lines are the highest revenue producers this year? Profitability analysis Did salesperson X meet this quarter’s target? Sales force analysis

Example: Smarter Customer Retention Consider a marketing manager for a brokerage company Problem: High churn (customers leave) Customers get an average reward of $160 to open an account 40% of customers leave after the 6 month introductory period Giving incentives to everyone who might leave is expensive Getting a customer back after they leave is expensive

Answer: Not all customers have the same value One month before the end of the introductory period, predict which customers will leave Offer those customers something based on their future value Ignore the ones that are not predicted to churn

Three Analytics Tasks We Will Be Doing in this Class Classification Clustering Association Rule Mining

Uses Classification Predict whether a customer should receive a loan Used to classify data according to a pre-defined outcome Based on characteristics of that data Can be used to predict future outcomes http://www.mindtoss.com/2010/01/25/five-second-rule-decision-chart/ Uses Predict whether a customer should receive a loan Flag a credit card charge as legitimate Determine whether an investment will pay off

Uses Clustering Used to determine distinct groups of data Based on data across multiple dimensions Uses Customer segmentation Identifying patient care groups Performance of business sectors http://www.datadrivesmedia.com/two-ways-performance-increases-targeting-precision-and-response-rates/

Association Rule Mining Find out which events predict the occurrence of other events Often used to see which products are bought together Uses What products are bought together? Amazon’s recommendation engine Telephone calling patterns