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? Copynight@ 2014 Pearson Education, Inc Slide 6-11
Copyright © 2014 Pearson Education, Inc. Slide 6 - 11 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?
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.) Copynight@ 2014 Pearson Education, Inc Slide 6-12
Copyright © 2014 Pearson Education, Inc. Slide 6 - 12 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 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 Copynight@ 2014 Pearson Education, Inc Slide 6-13
Copyright © 2014 Pearson Education, Inc. Slide 6 - 13 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. ▪ …
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 Copynight@ 2014 Pearson Education, Inc Slide 6-14
Copyright © 2014 Pearson Education, Inc. Slide 6 - 14 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 Factors for Big Data Analytics A Clear business need Personnel with Strong, analytical skills sponsorship Keys to Success ith Big Data Analyti Alignment ICS The right between the analytics tools business and IT A fact-based A strong data infrastructure decision-making Copynight@ 2014 Pearson Education, Inc Slide 6-15
Copyright © 2014 Pearson Education, Inc. Slide 6 - 15 Critical Success Factors for Big Data Analytics Keys to Success with Big Data Analytics A Clear business need Strong, committed sponsorship Alignment between the business and IT strategy A fact-based decision-making culture A strong data infrastructure The right analytics tools Personnel with advanced analytical skills