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)