,Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,Copyright 2014 Pearson Education,Inc.,Slide 6-,单击此处编辑母版标题样式,单击此处编辑母版文本样式,二级,三级,四级,五级,*,Chapter 6:,Big Data and Analytics,Business Intelligence:,A Managerial Perspective on Analytics(3,rd,Edition),Business Intelligence:A M,Learning Objectives,Learn what Big Data is and how it is changing 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,(Continued),Learning ObjectivesLearn what,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,Learning ObjectivesCompare and,Opening Vignette,Big Data Meets Big Science at CERN,Situation,Problem,Solution,Results,Answer&discuss the case questions.,Opening VignetteBig Data Meet,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?,Questions for the Opening Vign,Big Data-Definition and Concepts,Big Data means different things to people with different backgrounds and interests,Traditionally,“Big Data”=massive volumes of data,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,Big Data-Definition and Con,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 Kilometer Array telescope(the worlds proposed biggest telescope)1 EB/day,Technology Insights 6.1 The D,Big Data-Definition and Concepts,Big Data is a misnomer!,Big Data is more than just“big”,The Vs that define Big Data,Volume,Variety,Velocity,Veracity,Variability,Value,Big Data-Definition and Con,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,Big Data-Definition and Con,A High-Level Conceptual Architecture for Big Data Solutions,(by AsterData/Teradata),A High-Level Conceptual Archit,Application Case 6.1,Big Data Analytics Helps Luxottica Improve its Marketing Effectiveness,Questions for Discussion,What does“big data”mean to Luxottica?,What were their main challenges?,What were the proposed solution and the obtained results?,Application Case 6.1Big Data A,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),Fundamentals of Big Data Analy,Big Data Considerations,You cant process the amount of data that you want to because of the limitations of your current platform.,You cant 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 organizations doorstep that your traditional analytics platform cannot handle it.,Big Data ConsiderationsYou can,Critical Success Factors for Big Data Analytics,A clear business need(alignment with the vision and the strategy),Strong,committed sponsorship(executive champion),Alignment between the business and IT strategy,A fact-based decision-making culture,A strong data infrastructure,The right analytics tools,Right people with right skills,Critical Success F