This presentation was made in front of CIO to sensibilize to the big data in practical terms and to the new usages of machine learning and analytics.

1. 1Dataiku6/4/2013

2. 6/4/2013Dataiku 2
Hi !
Current Life:
CEO, Dataiku
Tweet about this: @dataiku @club_dsi_gun
Past Life:
Criteo
IsCool Entertainment
Exalead
Florian
Douetteau
Available on Slide Share
http://www.slideshare.net/Dataiku
Goals Today:
• Concrete Feedback on Data Analytics
Projects
• Data Team in practice and Key technologies
• Motivate you to start a data science project
Slide deck allergic ? Check:
https://github.com/dataiku

3. 6/4/2013Dataiku 3
Dataiku
Dataiku : An open source platform
to help you build your data lab
‟
”

4. 6/4/2013Dataiku 4

5. Collocation
6/4/2013Dataiku 5
Big Apple
Big Mama
Big Data
A familiar grouping of words,
especially words that habitually appear
together and thereby convey meaning
by association.
C
o
l
l
o
c

6. “Big” Data in 1999
6/4/2013Dataiku 6
struct Element {
Key key;
void* stat_data ;
}
….
C
Optimized Data structures
Perfect Hashing
HP-UNIX Servers – 4GB Ram
100 GB data
Web Crawler – Socket reuse
HTTP 0.9
1 Month

7.  Hadoop
 Java / Pig / Hive / Scala /
Closure / …
 A Dozen NoSQL data store
 MPP Databases
 Real-Time
6/4/2013Dataiku 7
Big Data in 2013
1 Hour

8. Data Analytics: The Stakes
6/4/2013Dataiku 8
1 TB
? $
Social Gaming
2011Web Search
1999
Logistics
2004
Online
Advertising
2012
1 TB
100M $
E-
Commerce
2013
Banking
CRM
2008
1 TB
1B $
Web
Search
2010
100 TB
? $
10 TB
10M $
1000TB
500M $
50TB
1B$

9. Meet Hal Alowne
6/4/2013Dataiku - Data Tuesday 9
Big Guys
• 10B$+ Revenue
• 100M+ customers
• 100+ Data Scientist
Hal Alowne
BI Manager
Dim’s Private Showroom
Hey Hal ! We need
a big data platform
like the big guys.
Let’s just do as they do!
‟
”European E-commerce Web site
• 100M$ Revenue
• 1 Million customer
• 1 Data Analyst (Hal Himself)
Dim Sum
CEO & Founder
Dim’s Private Showroom
Big Data
Copy Cat
Project

10. Technology is complex
6/4/2013Dataiku 10
Hadoop
Ceph
Sphere
Cassandra
Spark
Scikit-Learn
Mahout
WEKA
MLBase
RapidMiner
Panda
D3
Crossfilter
InfiniDB
LucidDB
Impala
Elastic Search
SOLR
MongoDB
Riak
Membase
Pig
Hive
Cascading
Talend
Machine Learning
Mystery Land
Scalability CentralNoSQL-Slavia
SQL Colunnar Republic
Vizualization County
Data Clean Wasteland
Statistician Old
House
R

11. Statistics and Machine Learning is
complex !
6/4/2013Dataiku 11
 Try to understand
myself

12. (Some Book you might want to read)
6/4/2013Dataiku 12

13. Plumbing is not complex
(but difficult)
6/4/2013Dataiku 13
Implicit User Data
(Views, Searches…)
Content Data
(Title, Categories, Price, …)
Explicit User Data
(Click, Buy, …)
User Information
(Location, Graph…)
500TB
50TB
1TB
200GB
Transformation
Matrix
Transformation
Predictor
Per User Stats
Per Content Stats
User Similarity
Rank Predictor
Content Similarity

14. MERIT = TIME + ROI
6/4/2013Dataiku 14
Targeted
Newsletter
Recommender
Systems
Adapted Product
/ Promotions
TIME : 6 MONTHS ROI : APPS
 Build a lab in 6 months
(rather than 18 months)
Find the right
people
(6 months?)
Choose the
technology
(6 months?)
Make it work
(6 months?)
Build the lab
(6 months)
 Deploy apps
that actually deliver value
2013 2014
2013
• Train People
• Reuse working patterns

15. The Problem
6/4/2013Dataiku 15
It’s utterly complex and
unreasonable

16. Our Goal
6/4/2013Dataiku 16
Our Goal:
Change his perspective
on data science projects
(sorry, we couldn’t
find a picture of Hal
Smiling)

17.  Why and For What ?
◦ Business Theory
◦ Concrete Projects
 How people and project ?
◦ How to start
◦ Dedicated team ?
 What technologies ?
◦ Machine Learning
◦ Architecture
Agenda
6/4/2013Dataiku 17

18. Embodiment of Knowledge
6/4/2013Dataiku 18

19.  Product Success
driven by Quality !
 Margin / Customer
Value / Traffic /
Acquisition
6/4/2013Dataiku 19
Example: Launching an App
on the App Store

20.  Margin for new
customers might
decline …
 Margin for new
features might
decline …
 Is your business
really scalable ?
6/4/2013Dataiku 20
you continue growing ….

21.  Existing Customers
Profiles
 Existing Product Assets
 Existing Specific
Business Model
 And your KNOWLEDGE
of it
6/4/2013Dataiku 21
Where is your core business
advantage ?

22. 6/4/2013Dataiku 22
Data Driven Business
What your value ?
Number of
Customers
Customer Knowledge
Increase over time with:
- Time spend in your app
- User relationship (network effet)
- Partner / Other Apps Interactions
Your Value

23. Data Impact
Not all business equals
6/4/2013Dataiku 23
Online
Advertising
Telecommunication
Insurance
Ability
to Acquire
Margin
New
Services
Overall
Subscription
Market
Infrastructure
Driver
Selling Data
Risk / Price
Optimization
Subscription
Market
Subscription
Market

24. From Theory To Practice
6/4/2013Dataiku 24

25.  What should be free
in the application ?
 How to optimize
conversion ?
 How to plan and
create a business
model ?
Main Pain Point:
How to plan and
optimize pricing in
the application ?
6/4/2013Dataiku 25
Freemium Application

26. Example (Freemium Application)
Fremium Model Optimization
6/4/2013Dataiku 26
Business
Model
User
Cluster
Simulation
 Optimized Pricing: Margin
+23%
 Business Planning
Capability
1 month  9 months
 R + Python + InfiniDB
On-Premise
1TB Dataset
5 weeks project

27.  Business Intelligence
Stack as Scalability and
maintenance issues
 Backoffice implements
business rules that are
challenged
 Existing infrastructure
cannot cope with per-
user information
Main Pain Point:
23 hours 52 minutes to
compute Business Intelligence
aggregates for one day.
6/4/2013Dataiku 27
Large E-Retailer

28. • Relieve their current DWH and
accelerate production of some
aggregates/KPIs
• Be the backbone for new
personalized user experience
on their website: more
recommendations, more
profiling, etc.,
• Train existing people around
machine learning and
segmentation experience
 1h12 to perform the
aggregate, available every
morning
 New home page
personalization deployed in a
few weeks
 Hadoop Cluster (24 cores)
Google Compute Engine
Python + R + Vertica
12 TB dataset
6 weeks projects
6/4/2013Dataiku - Data Tuesday 28
Large E-Retailer : The Datalab

29.  BI performed directly on
production databases
 New reports required the
CTO direct work for
design and
implementation
 Each photo tag manually
validated and completed
Large Photo Bank
6/4/2013Dataiku - Data Tuesday 29
Main pain point:
No visibility on new users
behaviours

30.  Implementing a Cloud-based
data lab to :
• centralize all available data,
previously scattered between
SQL DB and file systems,
• improve web tracking
granularity to enhance
customer knowledge via
behavior modeling and
segmentation,
• create content-based
recommendation engines with
keywords clustering and
association.
6/4/2013Dataiku - Data Tuesday 30
Large Photo Bank : The Datalab
 R + Vertica + Hadoop
Amazon Web Services
8 weeks projects
 Automated content filtering
and recommendation

31.  Large set of
manually crafted
linguistic resources
for interpreting
users queries
 New Brands, rare
terms .. hard to
maintain
6/4/2013Dataiku 31
Large Online Directory
Main Pain Point:
Ability to maintain a very
large ontological knowledge
sets, with more than 100k
concepts

32.  Analyze clicks,
rephrasing navigation to
detect queries that
require specific
processing
 Gather web and external
data to enrich the
existing index
 Train team to Hadoop
and Machine Learning
 Continuous Relevance
Monitoring
 Automated enrichment 
2x more productivity
 Hadoop (48 cores)
Python
On Premise
10 weeks projects
6/4/2013Dataiku 32
Large Online Directory: The Data Lab

33.  Launch A Marketing
campaign
 After a few days
PREDICT based on
behaviours
◦  Total ARPU for users
after 3 months
◦  Efficiency of a campaign
◦ Continue or not ?
Example ( E-Application )
Marketing Campaign Prediction
Dataiku 33

34. A very large community
Some mid-size
communities
Lots of small clusters
mostly 2 players)
 Correlation
◦ between community size
and engagement / virality
 Meaningul patterns
◦ 2 players / Family / Group
 What is the minimum
number of friends to
have in the application
to get additional
engagement ?
Example (Social Gaming)
Social Gaming Communities
6/4/2013Dataiku 34

35.  What others do ?
◦ Concrete Projects
 How people and project ?
◦ How to start
◦ Dedicated team ?
 What technologies ?
◦ Machine Learning
◦ Architecture
Agenda
6/4/2013Dataiku 35

36. 6/4/2013Dataiku 36

37.  A / B Test
(or equivalent for your
business) is the first step to
get into a “data-driven”
mind set
 No advanced analytics
requires, some existing
tools can help
 Changing a color button
+21%
6/4/2013Dataiku 37
(1) Be Data Driven

38.  People  Microsoft Excel
6/4/2013Dataiku 38
(2) Use Excel

39.  Data Team  Data Tools
6/4/2013Dataiku 39
(3) Build a team
The Business Expert
who knows maths
The Analyst
that reveals patterns
The Coding Guy That
is enthusiastic

40.  data lab, (n. m): a small group
with all the expertise, including
business minded people,
machine learning knowledge and
the right technology
 A proven organization used by
successful data-driven
companies over the past few
years (eBay, LinkedIn, Walmart…)
TEAM + TOOLS = LAB
6/4/2013Dataiku 40

41. Organization
6/4/2013Dataiku 41
Targeted campaings
Price optimization
Personalized
experience
Quality Assurance
Workload and yield
management
User Feedback (A/B Test)
Continuous improvement
Data
Product
Designer
Business
&
Marketing
Engineers
User
Voice

42. Short Term Focus Long Term Drive
Business People Optimize Margin, …. Create new business
revenue streams
Marketing People Optimize click ratio Brand awareness and
impact
IT People Make IT work Clean and efficient
Architecture
Data People Get Stats Right, make
predictions
Create Data Driven
Features
It’s just a new team
6/4/2013Dataiku 42

43. Super Intern
6/4/2013Dataiku 43
What is your ability to integrate a new
smart guy and give him any
data he would need and any computing
power he would need to enhance
your product ?

44.  What others do ?
◦ Concrete Projects
 How people and project ?
◦ How to start
◦ Dedicated team ?
 What technologies ?
◦ Machine Learning
◦ Architecture
Agenda
6/4/2013Dataiku 44

45. An oversimplified view of big data architecture
6/4/2013Dataiku 45

46. 6/4/2013Dataiku 46
Database Business Layer Application

47. (What it really looks like)
6/4/2013Dataiku 47

48. What kind of scale?
6/4/2013Dataiku 48
Database Business Layer Application
Or
Data Science App
Or ?

49. What kind of interaction ?
6/4/2013Dataiku 49
Database Business Layer Application
Data Science App
?
?
? ? ?
?

50. Classic Columnar Architecture
6/4/2013Dataiku 50
Some data Some Place To
Pour It In
Some Tool To
To Some Maths And Graphs

51. Classic Columnar Architecture
6/4/2013Dataiku 51
Lots of data Some Place To
Pour It In
Some Tool To
To Some Maths And Graphs
Web Tracking Logs
Raw Server Logs
Order / Product / Customer
Facebook Info
Open Data (Weather, Currency …)

52. The Corinthian Architecture
6/4/2013Dataiku 52
Lots of data
Some Place
To Perform
Rapid Calculations
Some Tools To
Do Some Maths
And Charts
Some Place To
Pour It In And
Clean / Prepare It

53. Data Storage And Preparation
6/4/2013Dataiku 53
Large Scale:
Hadoop Cluster
Cassandra
MPP SQL Columnar
Medium/Large Scale:
CouchBase
MongoDB
….
Selection Drivers
Volume
Scalability

54. Calculations
6/4/2013Dataiku 54
Classic Database
• PostgresSQL
• MySQL
• ….
MPP SQL Database
• Vertica, Vectorwise, InfiniDB,
GreenplumHD….
Hadoop New Databases
• Impala
…
Selection Drivers:
Speed ( Interactivity )
Expressivity

55. The Corinthian Architecture
6/4/2013Dataiku 55
Lots of data
Some Place
To Perform
Rapid Calculations
Some Tools To
Do Some Maths
And Charts
Some Place To
Pour It In And
Clean / Prepare It
Statistics
Cohorts
Regressions
Bar Charts For Marketing
Nice Infography for you Company Board

56. The Corinthian Architecture
6/4/2013Dataiku 56
Lots of data
Some Database
To Perform
Rapid Calculations
Some Tools To
Do Some Maths
Some Other
To Do Some
Charts
Some Place To
Pour It In And
Clean / Prepare It

57. Statistical Tools
6/4/2013Dataiku 57
Open Source:
• IPython
• Rstudio
Commercial
• RapidMiner
• SAS
• RevolutionR
Selection Drivers
Existing Knowhow
Scalability

58. 6/4/2013Dataiku 58
What is a statistical tool ?
 Interact and explore
data
 Some stats
capabilities
 Some Graph
Capabilities

59. Visualization Tools
6/4/2013Dataiku 59
Open Source:
• SpotFire
• Tableau
• QlikView
SAAS
• BIME
• ChartIO
• RevolutionR
HTML5 / AdHoc
• D3
• GraphViz
Selection Drivers
How Many Contributors /
Readers ?
Scalability

60. The One Database won’t
make it all problem
6/4/2013Dataiku 60
Lots of data
Some Database
To Perform
Rapid Calculations
Some Tools To
Do Some Maths
Some Other
To Do Some
Charts
Some Place To
Pour It In And
Clean / Prepare It
JOIN / Aggregate
Rapid Goup By Computations
Direct Access to the computed Results
to production etc..

61. The Roman Social Forum
6/4/2013Dataiku 61
Lots of data
Some Database
To Perform
Rapid Calculations
And Some Database
For Graphs
Some Tools To
Do Some Maths
Some Other
To Do Some
Charts
Some Place To
Pour It In And
Clean / Prepare It

62. Graph
6/4/2013Dataiku 62
Databases
• Neo4J
• Titan
• OrientDB
• InfiniteGraph
Analytic / Visualization
• Gephi
Selection Drivers
Scalability
What Algorithms ?
Licensing Constraints

63. The Key Value Store
6/4/2013Dataiku 63
Lots of data
Some Database
To Perform
Rapid Calculations
And Some Database
For Graphs And
Some Distributed Key
Value Store
Some Tools To
Do Some Maths
Some Other
To Do Some
Charts
Some Place To
Pour It In And
Clean / Prepare It

64. NoSQL
6/4/2013Dataiku 64
Search
• SOLR
• ElasticSearch
Document
• MongoDB
• CouchDB
KeyValue
• Redis
• Hbase
…
Selection Drivers
Durability / Avaiability …
Performance
Ease of use and API
Indexing

65. Action requires Prediction
6/4/2013Dataiku 65
Lots of data
Some Database
To Perform
Rapid Calculations
And some database
for graphs And
Some Distributed Key
Value Store
Some Tools To
Do Some Maths
Some Other
To Do Some
Charts
Some Place To
Pour It In And
Clean / Prepare It
Draw A Line  For the future
What are my real users groups ?
Should I launch a discount offering or not ?
To everybody or to specific users only ?

66. The Medieval Fairy Land
6/4/2013Dataiku 66
Lots of data
Some Tools To
Do Some Maths
Some Other
To Do Some
Charts and some
MACHINE LEARNING
Some Place To
Pour It In And
Clean / Prepare It
Some Database
To Perform
Rapid Calculations
And Some Database
For Graphs And
Some Distributed Key
Value Store

67. Predictions
6/4/2013Dataiku 67
Java
• Mahout (Hadoop)
• WEKA
Python
• Scikit-Learn
• PyML
R
Commercial
• Kxen
• SAS
• SPSS…
Selection Drivers
Scalability
Black Box / White Box ?
Data Management Integration

68. Can be fun
6/4/2013Dataiku 68

69.  Exploratory Data Analysis
◦ Identifying and visualizing key patterns and correlations within the dataset
 Unsupervised Learning
◦ Create groups of similar observations sharing same patterns (aka Clustering, Segmentation)
 Supervised Learning
◦ Modeling a variable using independent features (aka Scoring, Predictive Modeling, Classification)
 Time Series Prevision
◦ Predict a time-dependent variable using its own history, and sometimes other covariates (variables)
 Graph Analysis
◦ Analyzing relationships between a set of “nodes”, linked by “edges”
 Associations / Sequences Mining
◦ Identifying frequently associated items within transactions/ events databases, sometimes ordered over time
 And many more…
Classes of Machine Learning Problems
04/06/2013Dataiku - Innovation Services 69

70. Mapping ML to Business Questions
04/06/2013Dataiku - Innovation Services 70
Class Sample Business Questions
Exploratory Data Analysis What does my dataset look like ? What are the key correlations in my data ?
Unsupervised Learning Can I create groups of users who share the same purchasing behavior ? The
same navigation behavior ?
Supervised Learning What users are likely to click on ad X ? What users are likely to convert to paying
users ? Who is going to leave my service ? What is the profile of the users who
do X ?
Time Series Prevision What is the prevision of my revenue next month ? Given the weather forecast,
can I also forecast my sales ?
Product Sale Forecast (for surbooking)
Graph Analysis Can I identify influencers in my users community ? Can I recommend new friends
to my users ?
Association & Sequences Mining Which products are frequently bought together ? What is the typical navigation
path on my website ?

71. Machine Learning Methods Detailed
04/06/2013Dataiku - Innovation Services 71
Analytical Task ML Task Sample Algorithms Shape of Dataset
Exploratory Data Analysis Univariate Analysis Distribution, frequencies, histogram, boxplots, fit tests... N obs. (1 row per obs.) * P features
Bivariate Analysis Scatterplots, correlations (Pearson, Spearman), GLM, Chi Square... N obs. (1 row per obs.) * P features
Multivariate Analysis Principal components analysis, multi-dimensional scaling
correspondence analysis, factor analysis…
N obs. (1 row per obs.) * P features
“Oriented” Data Analysis Unsupervised Learning K-means, K-medoids, hierarchical clustering, gaussian mixture
models, mean shift, dbscan, spectral clustering...
N obs. (1 row per obs.) * P features
Supervised Learning Linear & logistic regression, decision trees, neural networks, SVM,
naïve Bayes, K-NN, random forests…
N obs. (1 row per obs.) * P features
Time Series Prevision ARMA, VARMAX, ARIMA… Time Series (rows: time period,
columns: measures)
Graph Analysis Centrality (closeness, betweeness, Page Rank, HITS), modularity
(Louvain)…
Nodes and Edges lists (+
attributes)
Associations &
Sequences
Frequent Itemsets, A priori, Market Basket… (Timestamped) events or
transactions

72.  Cluster a dataset
into K Buckets by
choosing the
“closest”
neighbours
6/4/2013Dataiku 72
Unsupervised Method
K-Means

73.  Predict the color of
a point depending
on the colors of its
K closest
neighbours
6/4/2013Dataiku 73
Supervised
K-Nearest-Neighbours

74.  Find the most
“significant” input
variable and split
value
 Split the dataset
recursively
6/4/2013Dataiku 74
Supervised
Decision Tree

75. Several Paths to Machine Learning
04/06/2013Dataiku - Innovation Services 75
Analytical
Dataset
I’m looking
for clusters
I want to
predict a
variable
I’m looking
variable by
variable, or
pairs
I know how
many groups
to look for
HCA
…
Partitioning (K-
means…)
GMM
…
DP
GMM
…
K-means + Gap
| Silhouette | …
2-steps
clustering
I just want
to
explore
Yes
No
Ye
s
No
Small
Dataset
(<<1K)
Ye
s
No
Medium Dataset
(<<100K)
Ye
s
No
I can
sample
Ye
s
No
Affinity
Propagation,
Mean Shift…
Unsupervised Learning
Ye
s
No
All my
variables
are
numeric Ye
s
No
CA…
I have a
distance
matrix
Ye
s
No
MDS...
PCA
…
Exploratory Data Analysis Data
Viz...
Ye
s
Not
Only
I value
interpretability
Generalized
Linear
Model
Simple
Decision
Tree
Supervised Learning*
Correlation
Analysis
GLM
Parametric and non
parametric stat.
tests
* Methods generally working for both classification & regression
Support
Vector
Machines
Neural
Networks
K-Nearest
Neighbors
Ensembles (Random
Forest, Gradient
Boosted Tree
MARS
Generalized
Additive
Model

76. 6/4/2013Dataiku 76
Questions ?
 Take Away
◦ There are new ways to perform data
analytics that are within your reach and
can bring business value
 Some Additional Resources
◦ Open Source Projects
 Dataiku Cloud Transport Client
http://dctc.io
 Dataiku Web Tracker
https://github.com/dataiku/wt1
◦ Our Technical Blog
 http://www.dataiku.com/blog