商务智能：数据分析的管理视角_06
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Copyright © 2014 Pearson Education, Inc.
Slide 6 - 6
Technology Insights 6.1 The Data Size Is Getting Big, Bigger, …
Hadron Collider - 1 PB/sec
Boeing jet - 20 TB/hr Facebook - 500 TB/day YouTube – 1 TB/4 min The proposed Square
Big Data is a misnomer! Big Data is more than just “big” The Vs that define Big Data Volume Variety Velocity Veracity Variability Value …
Images
Business Intelligence
Customers Partners
Audio and Video
Data Mining
Frontline Workers
Machine Logs
DISCOVERY PLATFORM
Math and Stats
Business Analysts
Text
Application Case 6.1
Big Data Analytics Helps Luxottica Improve its Marketing Effectiveness
Questions for Discussion 1. What does “big data” mean to Luxottica? 2. What were their main challenges? 3. What were the proposed solution and the obtained results?
the world of analytics Understand the motivation for and business drivers of Big Data analytics Become familiar with the wide range of enabling technologies for Big Data analytics Learn about Hadoop, MapReduce, and NoSQL as they relate to Big Data analytics Understand the role of and capabilities/skills for data scientist as a new analytics profession
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 11
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
System Conceptual View
ERP ERP
MOVE
MANAGE
ACCESS
Marketing
Marketing Executives
SCM
DATA PLATFORM INTEGRATED DATA WAREHOUSE
Applications
Operational Systems
CRM
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 12
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 (e.g., 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 of 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. …
E.g., 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, …
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 5
Big Data - Definition and Concepts
Big Data means different things to people with
different backgrounds and interests Traditionally, “Big Data” = massive volumes of data
Business Intelligence:
A Managerial Perspective on Analytics (3rd Edition)
Chapter 6: Big Data and AnalytiБайду номын сангаасs
Learning Objectives
Learn what Big Data is and how it is changing
Copyright © 2014 Pearson Education, Inc.
(Continued…)
Slide 6 - 2
Learning Objectives
Compare and contrast the complementary uses
of data warehousing and Big Data Become familiar with the vendors of Big Data tools and services Understand the need for and appreciate the capabilities of stream analytics Learn about the applications of stream analytics
Effectively and efficiently capturing, storing,
and analyzing Big Data New breed of technologies needed (developed or purchased or hired or outsourced …)
Kilometer Array telescope (the world’s proposed biggest telescope) – 1 EB/day
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 7
Big Data - Definition and Concepts
EVENT PROCESSING
Data Scientists Languages Engineers
Web and Social
BIG DATA SOURCES
ANALYTIC TOOLS & APPS
USERS
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 10
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 4
Questions for the Opening Vignette
What is CERN, and why is it important to the world
of science? How does the Large Hadron Collider work? What does it produce? What is the essence of the data challenge at CERN? How significant is it? What was the solution? How were the Big Data challenges addressed with this solution? What were the results? Do you think the current solution is sufficient?
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 3
Opening Vignette…
Big Data Meets Big Science at CERN
Situation Problem Solution Results Answer & discuss the case questions.
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 8
Big Data - Definition and Concepts
Big Data is not new! Traditionally, “Big Data” = massive volumes of data 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, …
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 9
A High-Level Conceptual Architecture for Big Data Solutions (by AsterData / Teradata)
UNIFIED DATA ARCHITECTURE
Slide 6 - 6
Technology Insights 6.1 The Data Size Is Getting Big, Bigger, …
Hadron Collider - 1 PB/sec
Boeing jet - 20 TB/hr Facebook - 500 TB/day YouTube – 1 TB/4 min The proposed Square
Big Data is a misnomer! Big Data is more than just “big” The Vs that define Big Data Volume Variety Velocity Veracity Variability Value …
Images
Business Intelligence
Customers Partners
Audio and Video
Data Mining
Frontline Workers
Machine Logs
DISCOVERY PLATFORM
Math and Stats
Business Analysts
Text
Application Case 6.1
Big Data Analytics Helps Luxottica Improve its Marketing Effectiveness
Questions for Discussion 1. What does “big data” mean to Luxottica? 2. What were their main challenges? 3. What were the proposed solution and the obtained results?
the world of analytics Understand the motivation for and business drivers of Big Data analytics Become familiar with the wide range of enabling technologies for Big Data analytics Learn about Hadoop, MapReduce, and NoSQL as they relate to Big Data analytics Understand the role of and capabilities/skills for data scientist as a new analytics profession
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 11
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
System Conceptual View
ERP ERP
MOVE
MANAGE
ACCESS
Marketing
Marketing Executives
SCM
DATA PLATFORM INTEGRATED DATA WAREHOUSE
Applications
Operational Systems
CRM
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 12
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 (e.g., 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 of 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. …
E.g., 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, …
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 5
Big Data - Definition and Concepts
Big Data means different things to people with
different backgrounds and interests Traditionally, “Big Data” = massive volumes of data
Business Intelligence:
A Managerial Perspective on Analytics (3rd Edition)
Chapter 6: Big Data and AnalytiБайду номын сангаасs
Learning Objectives
Learn what Big Data is and how it is changing
Copyright © 2014 Pearson Education, Inc.
(Continued…)
Slide 6 - 2
Learning Objectives
Compare and contrast the complementary uses
of data warehousing and Big Data Become familiar with the vendors of Big Data tools and services Understand the need for and appreciate the capabilities of stream analytics Learn about the applications of stream analytics
Effectively and efficiently capturing, storing,
and analyzing Big Data New breed of technologies needed (developed or purchased or hired or outsourced …)
Kilometer Array telescope (the world’s proposed biggest telescope) – 1 EB/day
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 7
Big Data - Definition and Concepts
EVENT PROCESSING
Data Scientists Languages Engineers
Web and Social
BIG DATA SOURCES
ANALYTIC TOOLS & APPS
USERS
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 10
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 4
Questions for the Opening Vignette
What is CERN, and why is it important to the world
of science? How does the Large Hadron Collider work? What does it produce? What is the essence of the data challenge at CERN? How significant is it? What was the solution? How were the Big Data challenges addressed with this solution? What were the results? Do you think the current solution is sufficient?
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 3
Opening Vignette…
Big Data Meets Big Science at CERN
Situation Problem Solution Results Answer & discuss the case questions.
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 8
Big Data - Definition and Concepts
Big Data is not new! Traditionally, “Big Data” = massive volumes of data 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, …
Copyright © 2014 Pearson Education, Inc.
Slide 6 - 9
A High-Level Conceptual Architecture for Big Data Solutions (by AsterData / Teradata)
UNIFIED DATA ARCHITECTURE