SlideShare a Scribd company logo
1 of 11
Download to read offline
MK99 – Big Data 
1 
Big data & cross-platform analytics 
MOOC lectures Pr. Clement Levallois
MK99 – Big Data 
2 
What is big data? 
•You should have watched the video clip about “What is data?” first.
MK99 – Big Data 
3 
Big data is a mess. 
… but we can find 5 key points helping us understand what it is.
MK99 – Big Data 
4 
1. Data gets generated in bigger volumes because of the digitalization of the economy 
Data generated by a movie-goer: 
1.On the box office ticket: movie title, date, price 
In a movie theater 
Watching through Netflix 
1.Login to Netflix: age, name, gender, location + preferences for movie genres? 
2.Browsing / purchasing history for movies 
3.Movie title, date and price for the movie 
4.Data on movie paused / interrupted? 
5.Comments / ratings posted 
6.Follow / Friends activities 
7.If Netflix account connected to FB: personal info, etc.
MK99 – Big Data 
5 
Another look at the digitalization of the economy: products and distribution channels 
Before 
After 
Source: “B4B” by Wood, Hewlin & Lah (2013)
MK99 – Big Data 
6 
2. Low prices made data a cheaper commodity 
•Larger Storage and processing power (computers!) 
–Prices for hard drives and processors get regularly cut by two 
–Personal anecdote: in 1994 my parents paid ~ 1,600€ a computer with a *450Mb* hard drive. 
•Quicker data communication (Internet! Optical Fiber!) 
–Broadband connections become mainstream 
•More powerful, free software for analysis 
–Ex: Excel can now analyze 1,000,000 rows x 1,600 col: it was just 16,000 x 256 cols 5 years ago. 
–New Open source software and packages such as R provide free software solutions for analysis 
=> In practice, this means that SME can afford to generate, store and explore datasets.
MK99 – Big Data 
7 
3. More stuff count as “data” now 
In the 1990’s 
Standardized texts and numbers 
In the 2010’s 
Standardized texts and numbers + 
- Places (info about distance, proximity, etc.) 
- Networks (info about who is connected with whom) 
- Free text (semantics and meaning) 
Typical query would be: 
In my database, find all customers living in the city “Paris”, which bought at least one product last month. 
Example of a query: “In my database, find all customers living between Dijon and Paris, who made a negative comment about one of our products and who are popular in their network of friends”. 
This is much richer than the query you see on the left, because it deals with geographical distances, opinions and connections between stuff – these are not simple operations on text and numbers! 
Today, space, semantics and networks count as data with business relevance, that needs to be stored and queried. 
Big players in these new dbs are Neo4J, CartoDB, MongoDB. Search also for SPARQL.
MK99 – Big Data 
8 
4. Growing expectations about the value of large datasets 
With big data, you hope you can… 
-Detect things before they are reported (crimes, epidemics, change in consumer tastes) 
-Have a 360 view on each person in your db (customer, patient, citizen…) 
-and create the perfect response to that (personalized products and services)
MK99 – Big Data 
9 
5. More data to come! 
•Internet of Things (“connected objects”) 
–Connected camera, phone, toothbrush, watch, shoes, car, scale, aircon, jewelry, etc. 
-> All these objects generate data about speed, temperature, behavior, etc. 
•Open data movement 
–Governments, cities, NGOs and firms open up their data to users. 
•Quantified self movement 
–People wearing connected objects (bracelets, shoes, phones, etc.) to track their biometrics and possibly sharing them.
MK99 – Big Data 
10 
Next steps 
•Watch the video clip on 2 popular expressions: 
–“the cloud” and “Hadoop” 
–Continue the readings for week 1
MK99 – Big Data 
11 
This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com) 
Contact Clement Levallois (levallois [at] em-lyon.com) for more information.

More Related Content

What's hot

Data mining for social media
Data mining for social mediaData mining for social media
Data mining for social mediarangesharp
 
Evolving social data mining and affective analysis
Evolving social data mining and affective analysis  Evolving social data mining and affective analysis
Evolving social data mining and affective analysis Athena Vakali
 
Social Media Data Mining
Social Media Data MiningSocial Media Data Mining
Social Media Data MiningRyan Reede
 
Pie chart or pizza: identifying chart types and their virality on Twitter
Pie chart or pizza: identifying chart types and their virality on TwitterPie chart or pizza: identifying chart types and their virality on Twitter
Pie chart or pizza: identifying chart types and their virality on TwitterElena Simperl
 
Social Media Mining - Chapter 5 (Data Mining Essentials)
Social Media Mining - Chapter 5 (Data Mining Essentials)Social Media Mining - Chapter 5 (Data Mining Essentials)
Social Media Mining - Chapter 5 (Data Mining Essentials)SocialMediaMining
 
Incentive compatible privacy preserving data analysis
Incentive compatible privacy preserving data analysisIncentive compatible privacy preserving data analysis
Incentive compatible privacy preserving data analysisJPINFOTECH JAYAPRAKASH
 
The profile of the management (data) scientist: Potential scenarios and skill...
The profile of the management (data) scientist: Potential scenarios and skill...The profile of the management (data) scientist: Potential scenarios and skill...
The profile of the management (data) scientist: Potential scenarios and skill...Juan Mateos-Garcia
 
Social Targeting: Understanding Social Media Data Mining & Analysis
Social Targeting: Understanding Social Media Data Mining & AnalysisSocial Targeting: Understanding Social Media Data Mining & Analysis
Social Targeting: Understanding Social Media Data Mining & AnalysisInfini Graph
 
Social media analytics - Making sense of Big Data
Social media analytics - Making sense of Big DataSocial media analytics - Making sense of Big Data
Social media analytics - Making sense of Big DataHenrik Hammer Eliassen
 
Big Data Analytics : Understanding for Research Activity
Big Data Analytics : Understanding for Research ActivityBig Data Analytics : Understanding for Research Activity
Big Data Analytics : Understanding for Research ActivityAndry Alamsyah
 
#opendata Back to the future
#opendata Back to the future#opendata Back to the future
#opendata Back to the futureSlim Turki, Dr.
 
Big Data presentation for Statistics Canada
Big Data presentation for Statistics CanadaBig Data presentation for Statistics Canada
Big Data presentation for Statistics CanadaPiet J.H. Daas
 
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)paperpublications3
 
Presentation big data and social media final_video
Presentation big data and social media final_videoPresentation big data and social media final_video
Presentation big data and social media final_videoramikaurraminder
 
Social Network Analysis for Telecoms
Social Network Analysis for TelecomsSocial Network Analysis for Telecoms
Social Network Analysis for TelecomsDataspora
 

What's hot (20)

Data mining for social media
Data mining for social mediaData mining for social media
Data mining for social media
 
Evolving social data mining and affective analysis
Evolving social data mining and affective analysis  Evolving social data mining and affective analysis
Evolving social data mining and affective analysis
 
Social Data Mining
Social Data MiningSocial Data Mining
Social Data Mining
 
Overview of Big Data
Overview of Big DataOverview of Big Data
Overview of Big Data
 
Unit 1
Unit 1Unit 1
Unit 1
 
Social Media Data Mining
Social Media Data MiningSocial Media Data Mining
Social Media Data Mining
 
Pie chart or pizza: identifying chart types and their virality on Twitter
Pie chart or pizza: identifying chart types and their virality on TwitterPie chart or pizza: identifying chart types and their virality on Twitter
Pie chart or pizza: identifying chart types and their virality on Twitter
 
Social Media Mining - Chapter 5 (Data Mining Essentials)
Social Media Mining - Chapter 5 (Data Mining Essentials)Social Media Mining - Chapter 5 (Data Mining Essentials)
Social Media Mining - Chapter 5 (Data Mining Essentials)
 
Incentive compatible privacy preserving data analysis
Incentive compatible privacy preserving data analysisIncentive compatible privacy preserving data analysis
Incentive compatible privacy preserving data analysis
 
The profile of the management (data) scientist: Potential scenarios and skill...
The profile of the management (data) scientist: Potential scenarios and skill...The profile of the management (data) scientist: Potential scenarios and skill...
The profile of the management (data) scientist: Potential scenarios and skill...
 
Social Targeting: Understanding Social Media Data Mining & Analysis
Social Targeting: Understanding Social Media Data Mining & AnalysisSocial Targeting: Understanding Social Media Data Mining & Analysis
Social Targeting: Understanding Social Media Data Mining & Analysis
 
Social media with big data analytics
Social media with big data analyticsSocial media with big data analytics
Social media with big data analytics
 
Social media analytics - Making sense of Big Data
Social media analytics - Making sense of Big DataSocial media analytics - Making sense of Big Data
Social media analytics - Making sense of Big Data
 
Big Data Analytics : Understanding for Research Activity
Big Data Analytics : Understanding for Research ActivityBig Data Analytics : Understanding for Research Activity
Big Data Analytics : Understanding for Research Activity
 
#opendata Back to the future
#opendata Back to the future#opendata Back to the future
#opendata Back to the future
 
Big Data presentation for Statistics Canada
Big Data presentation for Statistics CanadaBig Data presentation for Statistics Canada
Big Data presentation for Statistics Canada
 
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
 
Unit 2
Unit 2Unit 2
Unit 2
 
Presentation big data and social media final_video
Presentation big data and social media final_videoPresentation big data and social media final_video
Presentation big data and social media final_video
 
Social Network Analysis for Telecoms
Social Network Analysis for TelecomsSocial Network Analysis for Telecoms
Social Network Analysis for Telecoms
 

Similar to What is big data?

data analytics lecture2.pptx
data analytics lecture2.pptxdata analytics lecture2.pptx
data analytics lecture2.pptxNamrataBhatt8
 
What Is That DMP Good For, Anyway?
What Is That DMP Good For, Anyway?What Is That DMP Good For, Anyway?
What Is That DMP Good For, Anyway?MediaPost
 
Big data and Internet
Big data and InternetBig data and Internet
Big data and InternetSanoj Kumar
 
2007 presentation to the exec board of a high street bank - the workplace of...
2007 presentation to the exec board of a high street bank -  the workplace of...2007 presentation to the exec board of a high street bank -  the workplace of...
2007 presentation to the exec board of a high street bank - the workplace of...Jerry Fishenden
 
SoBigData. European Research Infrastructure for Big Data and Social Mining
SoBigData. European Research Infrastructure for Big Data and Social MiningSoBigData. European Research Infrastructure for Big Data and Social Mining
SoBigData. European Research Infrastructure for Big Data and Social MiningResearch Data Alliance
 
Notes from the Observation Deck // A Data Revolution
Notes from the Observation Deck // A Data Revolution Notes from the Observation Deck // A Data Revolution
Notes from the Observation Deck // A Data Revolution gngeorge
 
Data mining with big data implementation
Data mining with big data implementationData mining with big data implementation
Data mining with big data implementationSandip Tipayle Patil
 
Gadgets, Digital Media and your Library.
Gadgets, Digital Media and your Library.Gadgets, Digital Media and your Library.
Gadgets, Digital Media and your Library.Scott Kehoe
 
The Future of Research: Inspiring, Always on, Anytime-Anywhere, Observing, Pa...
The Future of Research: Inspiring, Always on, Anytime-Anywhere, Observing, Pa...The Future of Research: Inspiring, Always on, Anytime-Anywhere, Observing, Pa...
The Future of Research: Inspiring, Always on, Anytime-Anywhere, Observing, Pa...InsightInnovation
 
Data Cloud - Yury Lifshits - Yahoo! Research
Data Cloud - Yury Lifshits - Yahoo! ResearchData Cloud - Yury Lifshits - Yahoo! Research
Data Cloud - Yury Lifshits - Yahoo! ResearchYury Lifshits
 
Big data lecture notes
Big data lecture notesBig data lecture notes
Big data lecture notesMohit Saini
 
Research issues in the big data and its Challenges
Research issues in the big data and its ChallengesResearch issues in the big data and its Challenges
Research issues in the big data and its ChallengesKathirvel Ayyaswamy
 
Qu'est ce que le Big Data ? Avec Victoria Galano Data Scientist chez Air France
Qu'est ce que le Big Data ? Avec Victoria Galano Data Scientist chez Air FranceQu'est ce que le Big Data ? Avec Victoria Galano Data Scientist chez Air France
Qu'est ce que le Big Data ? Avec Victoria Galano Data Scientist chez Air FranceJedha Bootcamp
 

Similar to What is big data? (20)

data analytics lecture2.pptx
data analytics lecture2.pptxdata analytics lecture2.pptx
data analytics lecture2.pptx
 
What Is That DMP Good For, Anyway?
What Is That DMP Good For, Anyway?What Is That DMP Good For, Anyway?
What Is That DMP Good For, Anyway?
 
Big data and Internet
Big data and InternetBig data and Internet
Big data and Internet
 
2007 presentation to the exec board of a high street bank - the workplace of...
2007 presentation to the exec board of a high street bank -  the workplace of...2007 presentation to the exec board of a high street bank -  the workplace of...
2007 presentation to the exec board of a high street bank - the workplace of...
 
Ictam big data
Ictam big dataIctam big data
Ictam big data
 
Big Data World
Big Data WorldBig Data World
Big Data World
 
SoBigData. European Research Infrastructure for Big Data and Social Mining
SoBigData. European Research Infrastructure for Big Data and Social MiningSoBigData. European Research Infrastructure for Big Data and Social Mining
SoBigData. European Research Infrastructure for Big Data and Social Mining
 
Notes from the Observation Deck // A Data Revolution
Notes from the Observation Deck // A Data Revolution Notes from the Observation Deck // A Data Revolution
Notes from the Observation Deck // A Data Revolution
 
Data mining with big data implementation
Data mining with big data implementationData mining with big data implementation
Data mining with big data implementation
 
Identifying the new frontier of big data as an enabler for T&T industries: Re...
Identifying the new frontier of big data as an enabler for T&T industries: Re...Identifying the new frontier of big data as an enabler for T&T industries: Re...
Identifying the new frontier of big data as an enabler for T&T industries: Re...
 
Gadgets, Digital Media and your Library.
Gadgets, Digital Media and your Library.Gadgets, Digital Media and your Library.
Gadgets, Digital Media and your Library.
 
The Future of Research: Inspiring, Always on, Anytime-Anywhere, Observing, Pa...
The Future of Research: Inspiring, Always on, Anytime-Anywhere, Observing, Pa...The Future of Research: Inspiring, Always on, Anytime-Anywhere, Observing, Pa...
The Future of Research: Inspiring, Always on, Anytime-Anywhere, Observing, Pa...
 
SKILLWISE-BIGDATA ANALYSIS
SKILLWISE-BIGDATA ANALYSISSKILLWISE-BIGDATA ANALYSIS
SKILLWISE-BIGDATA ANALYSIS
 
Data Cloud - Yury Lifshits - Yahoo! Research
Data Cloud - Yury Lifshits - Yahoo! ResearchData Cloud - Yury Lifshits - Yahoo! Research
Data Cloud - Yury Lifshits - Yahoo! Research
 
Business with Big data
Business with Big dataBusiness with Big data
Business with Big data
 
Big data lecture notes
Big data lecture notesBig data lecture notes
Big data lecture notes
 
Research issues in the big data and its Challenges
Research issues in the big data and its ChallengesResearch issues in the big data and its Challenges
Research issues in the big data and its Challenges
 
Datascience
DatascienceDatascience
Datascience
 
Qu'est ce que le Big Data ? Avec Victoria Galano Data Scientist chez Air France
Qu'est ce que le Big Data ? Avec Victoria Galano Data Scientist chez Air FranceQu'est ce que le Big Data ? Avec Victoria Galano Data Scientist chez Air France
Qu'est ce que le Big Data ? Avec Victoria Galano Data Scientist chez Air France
 
130214 copy
130214   copy130214   copy
130214 copy
 

More from Clement Levallois

Part 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accountsPart 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accountsClement Levallois
 
Education et intelligence artificielle
Education et intelligence artificielleEducation et intelligence artificielle
Education et intelligence artificielleClement Levallois
 
3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications business3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications businessClement Levallois
 
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?Clement Levallois
 
Presentation of programming languages for beginners
Presentation of programming languages for beginnersPresentation of programming languages for beginners
Presentation of programming languages for beginnersClement Levallois
 
Umigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroomUmigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroomClement Levallois
 
Data visualization: enjeux pour le business
Data visualization: enjeux pour le businessData visualization: enjeux pour le business
Data visualization: enjeux pour le businessClement Levallois
 
An explanation of machine learning for business
An explanation of machine learning for businessAn explanation of machine learning for business
An explanation of machine learning for businessClement Levallois
 

More from Clement Levallois (9)

Part 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accountsPart 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accounts
 
Education et intelligence artificielle
Education et intelligence artificielleEducation et intelligence artificielle
Education et intelligence artificielle
 
3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications business3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications business
 
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
 
Presentation of programming languages for beginners
Presentation of programming languages for beginnersPresentation of programming languages for beginners
Presentation of programming languages for beginners
 
Umigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroomUmigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroom
 
Data visualization: enjeux pour le business
Data visualization: enjeux pour le businessData visualization: enjeux pour le business
Data visualization: enjeux pour le business
 
Twitter for beginners
Twitter for beginnersTwitter for beginners
Twitter for beginners
 
An explanation of machine learning for business
An explanation of machine learning for businessAn explanation of machine learning for business
An explanation of machine learning for business
 

Recently uploaded

Team Lead Succeed – Helping you and your team achieve high-performance teamwo...
Team Lead Succeed – Helping you and your team achieve high-performance teamwo...Team Lead Succeed – Helping you and your team achieve high-performance teamwo...
Team Lead Succeed – Helping you and your team achieve high-performance teamwo...Association for Project Management
 
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptx
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptxBIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptx
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptxSayali Powar
 
Satirical Depths - A Study of Gabriel Okara's Poem - 'You Laughed and Laughed...
Satirical Depths - A Study of Gabriel Okara's Poem - 'You Laughed and Laughed...Satirical Depths - A Study of Gabriel Okara's Poem - 'You Laughed and Laughed...
Satirical Depths - A Study of Gabriel Okara's Poem - 'You Laughed and Laughed...HetalPathak10
 
Grade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptxGrade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptxkarenfajardo43
 
Healthy Minds, Flourishing Lives: A Philosophical Approach to Mental Health a...
Healthy Minds, Flourishing Lives: A Philosophical Approach to Mental Health a...Healthy Minds, Flourishing Lives: A Philosophical Approach to Mental Health a...
Healthy Minds, Flourishing Lives: A Philosophical Approach to Mental Health a...Osopher
 
An Overview of the Calendar App in Odoo 17 ERP
An Overview of the Calendar App in Odoo 17 ERPAn Overview of the Calendar App in Odoo 17 ERP
An Overview of the Calendar App in Odoo 17 ERPCeline George
 
How to Manage Buy 3 Get 1 Free in Odoo 17
How to Manage Buy 3 Get 1 Free in Odoo 17How to Manage Buy 3 Get 1 Free in Odoo 17
How to Manage Buy 3 Get 1 Free in Odoo 17Celine George
 
Q-Factor HISPOL Quiz-6th April 2024, Quiz Club NITW
Q-Factor HISPOL Quiz-6th April 2024, Quiz Club NITWQ-Factor HISPOL Quiz-6th April 2024, Quiz Club NITW
Q-Factor HISPOL Quiz-6th April 2024, Quiz Club NITWQuiz Club NITW
 
Mythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWMythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWQuiz Club NITW
 
Scientific Writing :Research Discourse
Scientific  Writing :Research  DiscourseScientific  Writing :Research  Discourse
Scientific Writing :Research DiscourseAnita GoswamiGiri
 
CLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptxCLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptxAnupam32727
 
Sulphonamides, mechanisms and their uses
Sulphonamides, mechanisms and their usesSulphonamides, mechanisms and their uses
Sulphonamides, mechanisms and their usesVijayaLaxmi84
 
Shark introduction Morphology and its behaviour characteristics
Shark introduction Morphology and its behaviour characteristicsShark introduction Morphology and its behaviour characteristics
Shark introduction Morphology and its behaviour characteristicsArubSultan
 
Narcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdfNarcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdfPrerana Jadhav
 
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDecoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDhatriParmar
 
Comparative Literature in India by Amiya dev.pptx
Comparative Literature in India by Amiya dev.pptxComparative Literature in India by Amiya dev.pptx
Comparative Literature in India by Amiya dev.pptxAvaniJani1
 
DBMSArchitecture_QueryProcessingandOptimization.pdf
DBMSArchitecture_QueryProcessingandOptimization.pdfDBMSArchitecture_QueryProcessingandOptimization.pdf
DBMSArchitecture_QueryProcessingandOptimization.pdfChristalin Nelson
 

Recently uploaded (20)

Team Lead Succeed – Helping you and your team achieve high-performance teamwo...
Team Lead Succeed – Helping you and your team achieve high-performance teamwo...Team Lead Succeed – Helping you and your team achieve high-performance teamwo...
Team Lead Succeed – Helping you and your team achieve high-performance teamwo...
 
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptx
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptxBIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptx
BIOCHEMISTRY-CARBOHYDRATE METABOLISM CHAPTER 2.pptx
 
Satirical Depths - A Study of Gabriel Okara's Poem - 'You Laughed and Laughed...
Satirical Depths - A Study of Gabriel Okara's Poem - 'You Laughed and Laughed...Satirical Depths - A Study of Gabriel Okara's Poem - 'You Laughed and Laughed...
Satirical Depths - A Study of Gabriel Okara's Poem - 'You Laughed and Laughed...
 
Grade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptxGrade Three -ELLNA-REVIEWER-ENGLISH.pptx
Grade Three -ELLNA-REVIEWER-ENGLISH.pptx
 
Healthy Minds, Flourishing Lives: A Philosophical Approach to Mental Health a...
Healthy Minds, Flourishing Lives: A Philosophical Approach to Mental Health a...Healthy Minds, Flourishing Lives: A Philosophical Approach to Mental Health a...
Healthy Minds, Flourishing Lives: A Philosophical Approach to Mental Health a...
 
An Overview of the Calendar App in Odoo 17 ERP
An Overview of the Calendar App in Odoo 17 ERPAn Overview of the Calendar App in Odoo 17 ERP
An Overview of the Calendar App in Odoo 17 ERP
 
How to Manage Buy 3 Get 1 Free in Odoo 17
How to Manage Buy 3 Get 1 Free in Odoo 17How to Manage Buy 3 Get 1 Free in Odoo 17
How to Manage Buy 3 Get 1 Free in Odoo 17
 
Q-Factor HISPOL Quiz-6th April 2024, Quiz Club NITW
Q-Factor HISPOL Quiz-6th April 2024, Quiz Club NITWQ-Factor HISPOL Quiz-6th April 2024, Quiz Club NITW
Q-Factor HISPOL Quiz-6th April 2024, Quiz Club NITW
 
Mattingly "AI & Prompt Design" - Introduction to Machine Learning"
Mattingly "AI & Prompt Design" - Introduction to Machine Learning"Mattingly "AI & Prompt Design" - Introduction to Machine Learning"
Mattingly "AI & Prompt Design" - Introduction to Machine Learning"
 
Mythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWMythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITW
 
Scientific Writing :Research Discourse
Scientific  Writing :Research  DiscourseScientific  Writing :Research  Discourse
Scientific Writing :Research Discourse
 
CLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptxCLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptx
 
CARNAVAL COM MAGIA E EUFORIA _
CARNAVAL COM MAGIA E EUFORIA            _CARNAVAL COM MAGIA E EUFORIA            _
CARNAVAL COM MAGIA E EUFORIA _
 
Sulphonamides, mechanisms and their uses
Sulphonamides, mechanisms and their usesSulphonamides, mechanisms and their uses
Sulphonamides, mechanisms and their uses
 
Shark introduction Morphology and its behaviour characteristics
Shark introduction Morphology and its behaviour characteristicsShark introduction Morphology and its behaviour characteristics
Shark introduction Morphology and its behaviour characteristics
 
Narcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdfNarcotic and Non Narcotic Analgesic..pdf
Narcotic and Non Narcotic Analgesic..pdf
 
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDecoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
 
Comparative Literature in India by Amiya dev.pptx
Comparative Literature in India by Amiya dev.pptxComparative Literature in India by Amiya dev.pptx
Comparative Literature in India by Amiya dev.pptx
 
Plagiarism,forms,understand about plagiarism,avoid plagiarism,key significanc...
Plagiarism,forms,understand about plagiarism,avoid plagiarism,key significanc...Plagiarism,forms,understand about plagiarism,avoid plagiarism,key significanc...
Plagiarism,forms,understand about plagiarism,avoid plagiarism,key significanc...
 
DBMSArchitecture_QueryProcessingandOptimization.pdf
DBMSArchitecture_QueryProcessingandOptimization.pdfDBMSArchitecture_QueryProcessingandOptimization.pdf
DBMSArchitecture_QueryProcessingandOptimization.pdf
 

What is big data?

  • 1. MK99 – Big Data 1 Big data & cross-platform analytics MOOC lectures Pr. Clement Levallois
  • 2. MK99 – Big Data 2 What is big data? •You should have watched the video clip about “What is data?” first.
  • 3. MK99 – Big Data 3 Big data is a mess. … but we can find 5 key points helping us understand what it is.
  • 4. MK99 – Big Data 4 1. Data gets generated in bigger volumes because of the digitalization of the economy Data generated by a movie-goer: 1.On the box office ticket: movie title, date, price In a movie theater Watching through Netflix 1.Login to Netflix: age, name, gender, location + preferences for movie genres? 2.Browsing / purchasing history for movies 3.Movie title, date and price for the movie 4.Data on movie paused / interrupted? 5.Comments / ratings posted 6.Follow / Friends activities 7.If Netflix account connected to FB: personal info, etc.
  • 5. MK99 – Big Data 5 Another look at the digitalization of the economy: products and distribution channels Before After Source: “B4B” by Wood, Hewlin & Lah (2013)
  • 6. MK99 – Big Data 6 2. Low prices made data a cheaper commodity •Larger Storage and processing power (computers!) –Prices for hard drives and processors get regularly cut by two –Personal anecdote: in 1994 my parents paid ~ 1,600€ a computer with a *450Mb* hard drive. •Quicker data communication (Internet! Optical Fiber!) –Broadband connections become mainstream •More powerful, free software for analysis –Ex: Excel can now analyze 1,000,000 rows x 1,600 col: it was just 16,000 x 256 cols 5 years ago. –New Open source software and packages such as R provide free software solutions for analysis => In practice, this means that SME can afford to generate, store and explore datasets.
  • 7. MK99 – Big Data 7 3. More stuff count as “data” now In the 1990’s Standardized texts and numbers In the 2010’s Standardized texts and numbers + - Places (info about distance, proximity, etc.) - Networks (info about who is connected with whom) - Free text (semantics and meaning) Typical query would be: In my database, find all customers living in the city “Paris”, which bought at least one product last month. Example of a query: “In my database, find all customers living between Dijon and Paris, who made a negative comment about one of our products and who are popular in their network of friends”. This is much richer than the query you see on the left, because it deals with geographical distances, opinions and connections between stuff – these are not simple operations on text and numbers! Today, space, semantics and networks count as data with business relevance, that needs to be stored and queried. Big players in these new dbs are Neo4J, CartoDB, MongoDB. Search also for SPARQL.
  • 8. MK99 – Big Data 8 4. Growing expectations about the value of large datasets With big data, you hope you can… -Detect things before they are reported (crimes, epidemics, change in consumer tastes) -Have a 360 view on each person in your db (customer, patient, citizen…) -and create the perfect response to that (personalized products and services)
  • 9. MK99 – Big Data 9 5. More data to come! •Internet of Things (“connected objects”) –Connected camera, phone, toothbrush, watch, shoes, car, scale, aircon, jewelry, etc. -> All these objects generate data about speed, temperature, behavior, etc. •Open data movement –Governments, cities, NGOs and firms open up their data to users. •Quantified self movement –People wearing connected objects (bracelets, shoes, phones, etc.) to track their biometrics and possibly sharing them.
  • 10. MK99 – Big Data 10 Next steps •Watch the video clip on 2 popular expressions: –“the cloud” and “Hadoop” –Continue the readings for week 1
  • 11. MK99 – Big Data 11 This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com) Contact Clement Levallois (levallois [at] em-lyon.com) for more information.