Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition BUSINESS INTELLIGENCE ANALYTICS Chapter 7 AND DATA SCIENCE Big Data Concepts A Managerial and tools Ramesh Sharda Dursun Delen Efraim Turban PEarson Pearson Copyright 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition Chapter 7 Big Data Concepts and Tools Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Slides in this presentation contain hyperlinks. JAWS users should be able to get a list of links by using INSERT+F7
Learning Objectives (1 of2 7.1 Learn what Big Data is and how it is changing the world of analytics 7. 2 Understand the motivation for and business drivers of Big Data analytics 7.3 Become familiar with the wide range of enabling technologies for big data analytics 7.4 Learn about Hadoop, MapReduce, and NosQL as they relate to Big Data analytics 7.5 Compare and contrast the complementary uses of data warehousing and Big Data technologies Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (1 of 2) 7.1 Learn what Big Data is and how it is changing the world of analytics 7.2 Understand the motivation for and business drivers of Big Data analytics 7.3 Become familiar with the wide range of enabling technologies for Big Data analytics 7.4 Learn about Hadoop, MapReduce, and NoSQL as they relate to Big Data analytics 7.5 Compare and contrast the complementary uses of data warehousing and Big Data technologies
Learning Objectives (2 of 2) 7.6 Become familiar with select Big Data platforms and services 7. 7 Understand the need for and appreciate the capabilities of stream analytics 7.8 Learn about the applications of stream analytics Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (2 of 2) 7.6 Become familiar with select Big Data platforms and services 7.7 Understand the need for and appreciate the capabilities of stream analytics 7.8 Learn about the applications of stream analytics
Opening vignette (1 of4 Analyzing Customer Churn in a Telecom Company Using Big data Methods Telecom -a highly competitive market segment Customer churn rate is higher than most other markets a good example of Big Data analytics Challenges Data from multiple sources Data volume is higher than usual Solution Results Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (1 of 4) Analyzing Customer Churn in a Telecom Company Using Big Data Methods • Telecom – a highly competitive market segment • Customer churn rate is higher than most other markets • A good example of Big Data analytics • Challenges – Data from multiple sources – Data volume is higher than usual • Solution • Results
Opening Vignette (2 of 4) TERADAD ASTER SOLH connector Load from teradata CAtalog t metadata I and Data on HDFS TERAD Callcenter Data Data on ASTER Online Data =---------=--- Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (2 of 4)
Opening Vignette(3 of 4) Callcenter Dispute Callcenter cancel Callcenter: Bill Service Callcenter Service Store: Cancel Servi Callcenter. service Callcenter: Service Complaint Online: Canc Store: Bill Dispute sare排 Dispute Store: New Account Store: Cancel Service Store Service Store: service Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (3 of 4)
Opening Vignette (4 of4 Discussion Questions 1. What problem did customer service cancellation pose to ATs business survival? 2. Identify and explain the technical hurdles presented by the nature and characteristics of at's data 3. What is sessionizing? Why was it necessary for At to sessionize its data? 4 Research other studies where customer churn models have been employed. What types of variables were used in those studies? How is this vignette different? Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (4 of 4) Discussion Questions 1. What problem did customer service cancellation pose to AT’s business survival? 2. Identify and explain the technical hurdles presented by the nature and characteristics of AT’s data. 3. What is sessionizing? Why was it necessary for AT to sessionize its data? 4. Research other studies where customer churn models have been employed. What types of variables were used in those studies? How is this vignette different?
Big Data- Definition and Concepts (I of 2) Big Data means different things to people with different backgrounds and interests Traditionally, Big Data=massive volumes of data Example, volume of data at CerN, NASA, Google Where does the big data come from? Everywhere! Web logs, RFID, GPS systems, sensor networks. social networks, Internet-based text documents Internet search indexes, detail call records, astronomy, atmospheric science, biology, genomics, nuclear physics biochemical experiments, medical records, scientific research, military surveillance, multimedia archives Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Big Data - Definition and Concepts (1 of 2) • Big Data means different things to people with different backgrounds and interests • Traditionally, “Big Data” = massive volumes of data – Example, volume of data at CERN, NASA, Google, … • Where does the Big Data come from? – Everywhere! Web logs, RFID, GPS systems, sensor networks, social networks, Internet-based text documents, Internet search indexes, detail call records, astronomy, atmospheric science, biology, genomics, nuclear physics, biochemical experiments, medical records, scientific research, military surveillance, multimedia archives, …
Technology Insights 7.1 (1 of 2) The Data Size Is Getting Big, Bigger, and Bigger Hadron Collider-1 PB/sec ° Boeing jet-20TB/hr ° Facebook-500TB/day YouTube -1 TB/4 min The proposed Square Kilometer Array telescope( the world's proposed biggest telescope)-1EB/day Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Technology Insights 7.1 (1 of 2) The Data Size Is Getting Big, Bigger, and Bigger • Hadron Collider - 1 PB/sec • Boeing jet - 20 TB/hr • Facebook - 500 TB/day • YouTube – 1 TB/4 min • The proposed Square Kilometer Array telescope (the world’s proposed biggest telescope) – 1EB/day
Technology Insights 7.1( of2) Name Symbol Value Kilobyte y e KB 10 Megabyte MB 106 Gigabyte GB 10s Terabyte TB 1012 Petabyte PB 1015 Exabyte EB 1018 Zettabyte ZB 102 Yottabyte YB 1024 Brontobyte BB 10 Gegobyte GeB 1030 Not an official SI (International System of Units)name/symbol, yet Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Technology Insights 7.1 (2 of 2) Name Symbol Value Kilobyte kB 103 Megabyte MB 106 Gigabyte GB 109 Terabyte TB 1012 Petabyte PB 1015 Exabyte EB 1018 Zettabyte ZB 1021 Yottabyte YB 1024 Brontobyte* BB 1027 Gegobyte* GeB 1030 *Not an official SI (International System of Units) name/symbol, yet
