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 ...
MK99 – Big Data 
5 
Another look at the digitalization of the economy: products and distribution channels 
Before 
After 
...
MK99 – Big Data 
6 
2. Low prices made data a cheaper commodity 
•Larger Storage and processing power (computers!) 
–Price...
MK99 – Big Data 
7 
3. More stuff count as “data” now 
In the 1990’s 
Standardized texts and numbers 
In the 2010’s 
Stand...
MK99 – Big Data 
8 
4. Growing expectations about the value of large datasets 
With big data, you hope you can… 
-Detect t...
MK99 – Big Data 
9 
5. More data to come! 
•Internet of Things (“connected objects”) 
–Connected camera, phone, toothbrush...
MK99 – Big Data 
10 
Next steps 
•Watch the video clip on 2 popular expressions: 
–“the cloud” and “Hadoop” 
–Continue the...
MK99 – Big Data 
11 
This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com) 
Cont...
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What is big data?

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Slides of the course on big data by C. Levallois from EMLYON Business School.
For business students. Check the online video connected with these slides.

-> The 5 driving forces behind big data.

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What is big data?

  1. 1. MK99 – Big Data 1 Big data & cross-platform analytics MOOC lectures Pr. Clement Levallois
  2. 2. MK99 – Big Data 2 What is big data? •You should have watched the video clip about “What is data?” first.
  3. 3. MK99 – Big Data 3 Big data is a mess. … but we can find 5 key points helping us understand what it is.
  4. 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. 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. 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. 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. 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. 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. 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. 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.

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