Big Data- Definition and Concepts (2 of 2) Big Data is a misnomer! Big Data is more than just"big The Vs that define big data Volume Variety Velocity Veracity Variability Value 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 (2 of 2) • Big Data is a misnomer! • Big Data is more than just “big” • The Vs that define Big Data – Volume – Variety – Velocity – Veracity – Variability – Value – …
A High-Level Conceptual Architecture for Big data Solutions (by Aster Data /Teradata) UNIFIED DATA ARCHITECTURE System Conceptual View MOVE ACCESS Executives DATA PLATFORM NTEGRATED DATA WAREHOUSE Customers DISCOVERY PLATFORM EVENT PROCESSING Languages Engineers Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved A High-Level Conceptual Architecture for Big Data Solutions (by AsterData / Teradata) Math and Stats Data Mining Business Intelligence Applications Languages Marketing ANALYTIC TOOLS & APPS USERS DISCOVERY PLATFORM INTEGRATED DATA WAREHOUSE DATA PLATFORM MOVE MANAGE ACCESS UNIFIED DATA ARCHITECTURE System Conceptual View Marketing Executives Operational Systems Frontline Workers Customers Partners Engineers Data Scientists Business Analysts EVENT PROCESSING ERP SCM CRM Images Audio and Video Machine Logs Text Web and Social BIG DATA SOURCES ERP
Application Case 7.1 Alternative Data for Market analysis or Forecasts Questions for Discussion 1. What is a common thread in the examples discussed in this application case? 2. Can you think of other data streams that might help give an early indication of sales at a retailer? 3. Can you think of other applications along the lines presented in this application case? Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Application Case 7.1 Alternative Data for Market Analysis or Forecasts Questions for Discussion 1. What is a common thread in the examples discussed in this application case? 2. Can you think of other data streams that might help give an early indication of sales at a retailer? 3. Can you think of other applications along the lines presented in this application case?
Fundamentals of Big Data Analytics Big Data by itself, regardless of the size, type, or speed is worthless Big data+ big analytics value With the value proposition, Big Data also brought about big challenges Effectively and efficiently capturing, storing, and analyzing Big Data New breed of technologies needed(developed or purchased or hired or outsourced.) Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Fundamentals of Big Data Analytics • Big Data by itself, regardless of the size, type, or speed, is worthless • Big Data + “big” analytics = value • With the value proposition, Big Data also brought about big challenges – Effectively and efficiently capturing, storing, and analyzing Big Data – New breed of technologies needed (developed or purchased or hired or outsourced …)
Big Data Considerations You can't process the amount of data that you want to because of the limitations of your current platform You cant include new/contemporary data sources(example, social media, RFID, Sensory, Web, GPS, textual data)because it does not comply with the data storage schema You need to(or want to) integrate data as quickly as possible to be current on your analysis You want to work with a schema-on-demand data storage paradigm because the variety of data types involved The data is arriving so fast at your organizations doorstep that your traditional analytics platform cannot handle it Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Big Data Considerations • You can’t process the amount of data that you want to because of the limitations of your current platform. • You can’t include new/contemporary data sources (example, social media, RFID, Sensory, Web, GPS, textual data) because it does not comply with the data storage schema • You need to (or want to) integrate data as quickly as possible to be current on your analysis. • You want to work with a schema-on-demand data storage paradigm because the variety of data types involved. • The data is arriving so fast at your organization’s doorstep that your traditional analytics platform cannot handle it. • …