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The 3 Key Barriers Keeping Companies
from Acting Upon the Possibilities
That Big Data has to Offer
A little bit about me…
•  Born & raised in Palo Alto, California

•  BA in European History From Columbia University

•  Masters in Marketing & Communication from Sciences
Po Paris

•  Director of Marketing at Dataiku

•  Currently living in Paris
Shift from
Uomo Universale
« A man can do all things if he will. »
-Leon Battista Alberti (1404-72)
Excel at all things:
•  Intellect
•  Mathematics
•  Science
•  Art
•  Social
•  Physical
Shift from
Uomo Universale To Expert
Required Assets:
•  Hacker mindset
•  Logic
•  Statistics
•  Polyglot Programmer
•  Mathematics
•  Algorithmics
•  Engineering
•  Databases
•  Machine Learning
•  Strong creativity
•  Strategical thinker
•  Business understanding
•  Strong communication skills
•  Project management
Data Science Superstar
Are we in some sort
of Renaissance Era 
of Big Data?
If so, what’s next?
Investigation Part 1:
What is a Data Product?
Data Products
Data Products
Data Products
Data Products
Data Products
Data Products
Data Products
Data Products
Data Products
Data Products
Data Products
Data Products
=

Data + Technology + Data Scientist? + End User
Investigation Part 2:
What goes on behind the scenes?
Building a Data Product
User	
  Interface	
  Stream / Real-time
 Query
 Data
Preprocessing
Building a (Predictive) Data Product
Predicted Data
Historical Data
 Machine Learning
 Model
Preprocessing
Building a (Predictive) Data Product
Predicted Data
Historical Data
 Machine Learning
 Model
Preprocessing
Pre-processing & cleaning data alone can take 
up to 80% of the time spent on a data project
Building a (Predictive) Data Product
Predicted Data
Historical Data
 Machine Learning
 Model
Preprocessing
Building a (Predictive) Data Product
Predicted Data
Historical Data
 Machine Learning
 Model
Preprocessing
Running a (Predictive) Data Product
Deployment
Real-time / Stream
Model
Preprocessing	
  
Predicted Data
Investigation Part 3:
Who does what?
Customer Data!
Machine Data!
System Data!
Graph Data!
Structured Data!
Unstructured Data!
Transactional Data!
Catalogue Data!
Web Log Data!
RAW DATA
System Architect /
IT Team / Data Engineer
RAWDATA
Data Product
= 
Business Incentive
Mathematics / statistics
Data Product
= 
Business Incentive
Data Product
= 
Business Incentive
Mathematics / statistics / Business
« Data Scientist »
Data Product
= 
Business Incentive
« Data Scientist »?
Data Engineers
« Data Scientist »
What I’ve Learned
Fact 1: The Skill Sets Exist
Business Statistics Math
 Data Engineering
Build
 Maintain
Fact 1: The Skill Sets Exist (& your company probably already has them)
Business Analyst
 Data Engineer
Build
 Maintain
Mathematician / Statistician
Fact 2: The Technologies Exist
(and some are free!)
Fact 3: The Data Exists
Why is Production & Industrialisation of
(Predictive) Data Products Important?
Those who win are those who deliver
new data products continuously
Those who win are those
who deliver data products
Data products are supposed to deliver
business value… if you don’t deploy them,
where’s the long term value?
No Industrialisation =
Limited ROI
Those who win are those
who deliver data products
It’s like building your dream house but
never moving in.
No Industrialisation =
Limited ROI
Those who win are those
who deliver data products
It’s like building your dream house but
never moving in. Absurd!
So Why Aren’t More Companies Deploying
(Predictive) Data Products?
A Data Product must be 
business focused (ROI) & mathematically accurate (RELIABLE)
1° Business Analytic & Algorithmic Minds Are Different…
1° Business Analytic & Algorithmic Minds Are Different…
The Business Analysts Brain
Patterns. Patterns. Patterns.
The Algorithmic Brain
Performance. Truth. Anomaly.
1° Business Analytic & Algorithmic Minds Are Different…
…But Your Data Product Needs Both
Patterns, patterns, patterns
Performance, Truth, Anomaly
BUSINESS
KNOWLEDGE
MATHEMATICAL
ACCURACY
1° Business Analytic & Algorithmic Minds Are Different…
MINDSET
•  Project alignment from conception to execution – install team mindset with
common goal – even if the paths to get there are different
FRAMEWORK
•  One common platform with enough flexibility for both mindsets to fully
exercise their individual skill and expertise on a common project
Resolving the Skill Gap
2° So Many Technologies, Languages, and Needs
2° So Many Technologies, Languages, and Needs
R	
  /	
  Python	
  
2° So Many Technologies, Languages, and Needs
Code-­‐free	
  
R	
  /	
  Python	
  
2° So Many Technologies, Languages, and Needs
Code-­‐free	
  
SQL	
  
R	
  /	
  Python	
  
2° So Many Technologies, Languages, and Needs
Code-­‐free	
  
R	
  /	
  Python	
  
Hadoop	
  
SQL	
  
…Or As My Boss Calls It: Technoslavia
Florian Douetteau
Dataiku CEO
…Or As My Boss Calls It: Technoslavia
Florian Douetteau
Dataiku CEO
…Or As My Boss Calls It: Technoslavia
Florian Douetteau
Dataiku CEO
…Or As My Boss Calls It: Technoslavia
Florian Douetteau
Dataiku CEO
OPTION 1:
Enterprise dictatorship
Living in Harmony with Technoslavia
OPTION 2 (my personal favorite):
Accept a polyglot approach
Living in Harmony with Technoslavia
3° Production and Industrialisation is Complex
Data reliability is hard to guarantee 


Technological Complexity
3° Production and Industrialisation is Complex
Technological Complexity
The assumption that production will identically reproduce the analysis phase 
is a hard promise to make 


3° Production and Industrialisation is Complex
Technological Complexity
Monitoring a predictive model’s life cycle is a tedious and continuous task


3° Production and Industrialisation is Complex
Human & Organisational Complexity
BUILDING
 MAINTAINING
3° Production and Industrialisation is Complex
Human & Organisational Complexity
BUILDING
 MAINTAINING
Business Analyst
•  patterns
3° Production and Industrialisation is Complex
Human & Organisational Complexity
BUILDING
 MAINTAINING
Business Analyst / Algorithmic 
•  patterns
•  performance 
•  truth	
  
3° Production and Industrialisation is Complex
Business Analyst / Algorithmic 
•  patterns
•  performance 
•  truth	
  
Data engineers 
•  stability 
•  reliability 
•  cost of ownership	
  
Human & Organisational Complexity
BUILDING
 MAINTAINING
3° Production and Industrialisation is Complex
Business Analyst / Algorithmic 
•  patterns
•  performance 
•  truth	
  
Data engineers 
•  stability 
•  reliability 
•  cost of ownership	
  
Human & Organisational Complexity
BUILDING
 MAINTAINING
3° Production and Industrialisation is Complex
TIP #1: Invest in a platform where development and production are the same

DEVELOPMENT
 TEST
 PRODUCTION
Making Complexity Work for You
TIP #2: Invest in monitoring capabilities & strategies
Making Complexity Work for You
Data Engineers must have visibility and understanding of the key business metrics
TIP #3: Name your Data Engineer(s) Wisely & Define Responsibilities
Making Complexity Work for You
Data Engineers must know if (and when) a model is diverging
TIP #3: Name your Data Engineer(s) Wisely & Define Responsibilities
Making Complexity Work for You
Data Engineer must be responsible for quality of service
TIP #3: Name your Data Engineer(s) Wisely & Define Responsibilities
Making Complexity Work for You
Differentiate builders of new data products 
from those that maintain them. 
TIP #4: It’s Not a One Man Show
Making Complexity Work for You
What To Expect?
From the Renaissance of Big Data…
Where the Data Science Superstar is one person that excels at all skill sets…
…and where actual data products are rarely deployed and maintained
To the Enlightenment of (Big) Data
Where the Data Science Superstar is a team of complimentary skill sets…
… and where data products are designed, built, tested, and deployed by a
team of skilled individuals that each have a distinct role.
TEAM	
  
SPOTLIGHT on the
Data Science Team Manager
The Rise of the
Data Science Team Manager
The Data Science Team Manager must understand the stakeholders’ needs,
translate them into a business need that can be answered with a data
product…
The Rise of the
Data Science Team Manager
The Data Science Team Manager must permit and enable collaboration
between business analysts, statisticians, & engineers… 

Collaboratively 
design, build, & deploy 
Data Products
The Rise of the
Collaborative Data Science Team
…all the while maintaining the distinction between each individual role 
and each individual skill set.
Business
 Mathematics
Data 
Engineering
The Secret to Building and Industrializing Data Products 
is Collaboration.

Today, collaboration between different 
skill sets, technologies, and data is finally possible.
Data Science Studio:
One Platform for Development and Industrialization
Thank You!
Pauline Brown
Dataiku, Director of Marketing
Pauline.brown@dataiku.com
@pauline8brown
www.dataiku.com
The 3 Key Barriers Keeping Companies from Deploying Data Products

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The 3 Key Barriers Keeping Companies from Deploying Data Products

  • 1. The 3 Key Barriers Keeping Companies from Acting Upon the Possibilities That Big Data has to Offer
  • 2. A little bit about me… •  Born & raised in Palo Alto, California •  BA in European History From Columbia University •  Masters in Marketing & Communication from Sciences Po Paris •  Director of Marketing at Dataiku •  Currently living in Paris
  • 3. Shift from Uomo Universale « A man can do all things if he will. » -Leon Battista Alberti (1404-72) Excel at all things: •  Intellect •  Mathematics •  Science •  Art •  Social •  Physical
  • 5. Required Assets: •  Hacker mindset •  Logic •  Statistics •  Polyglot Programmer •  Mathematics •  Algorithmics •  Engineering •  Databases •  Machine Learning •  Strong creativity •  Strategical thinker •  Business understanding •  Strong communication skills •  Project management Data Science Superstar
  • 6.
  • 7. Are we in some sort of Renaissance Era of Big Data?
  • 9. Investigation Part 1: What is a Data Product?
  • 21. Data Products = Data + Technology + Data Scientist? + End User
  • 22. Investigation Part 2: What goes on behind the scenes?
  • 23. Building a Data Product User  Interface  Stream / Real-time Query Data Preprocessing
  • 24. Building a (Predictive) Data Product Predicted Data Historical Data Machine Learning Model Preprocessing
  • 25. Building a (Predictive) Data Product Predicted Data Historical Data Machine Learning Model Preprocessing Pre-processing & cleaning data alone can take up to 80% of the time spent on a data project
  • 26. Building a (Predictive) Data Product Predicted Data Historical Data Machine Learning Model Preprocessing
  • 27. Building a (Predictive) Data Product Predicted Data Historical Data Machine Learning Model Preprocessing
  • 28. Running a (Predictive) Data Product Deployment Real-time / Stream Model Preprocessing   Predicted Data
  • 30. Customer Data! Machine Data! System Data! Graph Data! Structured Data! Unstructured Data! Transactional Data! Catalogue Data! Web Log Data! RAW DATA
  • 31. System Architect / IT Team / Data Engineer RAWDATA Data Product = Business Incentive
  • 32. Mathematics / statistics Data Product = Business Incentive
  • 33. Data Product = Business Incentive Mathematics / statistics / Business
  • 38. Fact 1: The Skill Sets Exist Business Statistics Math Data Engineering Build Maintain
  • 39. Fact 1: The Skill Sets Exist (& your company probably already has them) Business Analyst Data Engineer Build Maintain Mathematician / Statistician
  • 40. Fact 2: The Technologies Exist (and some are free!)
  • 41. Fact 3: The Data Exists
  • 42. Why is Production & Industrialisation of (Predictive) Data Products Important?
  • 43. Those who win are those who deliver new data products continuously
  • 44. Those who win are those who deliver data products Data products are supposed to deliver business value… if you don’t deploy them, where’s the long term value?
  • 45. No Industrialisation = Limited ROI Those who win are those who deliver data products It’s like building your dream house but never moving in.
  • 46. No Industrialisation = Limited ROI Those who win are those who deliver data products It’s like building your dream house but never moving in. Absurd!
  • 47. So Why Aren’t More Companies Deploying (Predictive) Data Products?
  • 48. A Data Product must be business focused (ROI) & mathematically accurate (RELIABLE)
  • 49. 1° Business Analytic & Algorithmic Minds Are Different…
  • 50. 1° Business Analytic & Algorithmic Minds Are Different… The Business Analysts Brain Patterns. Patterns. Patterns.
  • 51. The Algorithmic Brain Performance. Truth. Anomaly. 1° Business Analytic & Algorithmic Minds Are Different…
  • 52. …But Your Data Product Needs Both Patterns, patterns, patterns Performance, Truth, Anomaly BUSINESS KNOWLEDGE MATHEMATICAL ACCURACY 1° Business Analytic & Algorithmic Minds Are Different…
  • 53. MINDSET •  Project alignment from conception to execution – install team mindset with common goal – even if the paths to get there are different FRAMEWORK •  One common platform with enough flexibility for both mindsets to fully exercise their individual skill and expertise on a common project Resolving the Skill Gap
  • 54. 2° So Many Technologies, Languages, and Needs
  • 55. 2° So Many Technologies, Languages, and Needs R  /  Python  
  • 56. 2° So Many Technologies, Languages, and Needs Code-­‐free   R  /  Python  
  • 57. 2° So Many Technologies, Languages, and Needs Code-­‐free   SQL   R  /  Python  
  • 58. 2° So Many Technologies, Languages, and Needs Code-­‐free   R  /  Python   Hadoop   SQL  
  • 59. …Or As My Boss Calls It: Technoslavia Florian Douetteau Dataiku CEO
  • 60. …Or As My Boss Calls It: Technoslavia Florian Douetteau Dataiku CEO
  • 61. …Or As My Boss Calls It: Technoslavia Florian Douetteau Dataiku CEO
  • 62. …Or As My Boss Calls It: Technoslavia Florian Douetteau Dataiku CEO
  • 63. OPTION 1: Enterprise dictatorship Living in Harmony with Technoslavia
  • 64. OPTION 2 (my personal favorite): Accept a polyglot approach Living in Harmony with Technoslavia
  • 65. 3° Production and Industrialisation is Complex
  • 66. Data reliability is hard to guarantee Technological Complexity 3° Production and Industrialisation is Complex
  • 67. Technological Complexity The assumption that production will identically reproduce the analysis phase is a hard promise to make 3° Production and Industrialisation is Complex
  • 68. Technological Complexity Monitoring a predictive model’s life cycle is a tedious and continuous task 3° Production and Industrialisation is Complex
  • 69. Human & Organisational Complexity BUILDING MAINTAINING 3° Production and Industrialisation is Complex
  • 70. Human & Organisational Complexity BUILDING MAINTAINING Business Analyst •  patterns 3° Production and Industrialisation is Complex
  • 71. Human & Organisational Complexity BUILDING MAINTAINING Business Analyst / Algorithmic •  patterns •  performance •  truth   3° Production and Industrialisation is Complex
  • 72. Business Analyst / Algorithmic •  patterns •  performance •  truth   Data engineers •  stability •  reliability •  cost of ownership   Human & Organisational Complexity BUILDING MAINTAINING 3° Production and Industrialisation is Complex
  • 73. Business Analyst / Algorithmic •  patterns •  performance •  truth   Data engineers •  stability •  reliability •  cost of ownership   Human & Organisational Complexity BUILDING MAINTAINING 3° Production and Industrialisation is Complex
  • 74. TIP #1: Invest in a platform where development and production are the same DEVELOPMENT TEST PRODUCTION Making Complexity Work for You
  • 75. TIP #2: Invest in monitoring capabilities & strategies Making Complexity Work for You
  • 76. Data Engineers must have visibility and understanding of the key business metrics TIP #3: Name your Data Engineer(s) Wisely & Define Responsibilities Making Complexity Work for You
  • 77. Data Engineers must know if (and when) a model is diverging TIP #3: Name your Data Engineer(s) Wisely & Define Responsibilities Making Complexity Work for You
  • 78. Data Engineer must be responsible for quality of service TIP #3: Name your Data Engineer(s) Wisely & Define Responsibilities Making Complexity Work for You
  • 79. Differentiate builders of new data products from those that maintain them. TIP #4: It’s Not a One Man Show Making Complexity Work for You
  • 81. From the Renaissance of Big Data… Where the Data Science Superstar is one person that excels at all skill sets… …and where actual data products are rarely deployed and maintained
  • 82. To the Enlightenment of (Big) Data Where the Data Science Superstar is a team of complimentary skill sets… … and where data products are designed, built, tested, and deployed by a team of skilled individuals that each have a distinct role. TEAM  
  • 83. SPOTLIGHT on the Data Science Team Manager
  • 84. The Rise of the Data Science Team Manager The Data Science Team Manager must understand the stakeholders’ needs, translate them into a business need that can be answered with a data product…
  • 85. The Rise of the Data Science Team Manager The Data Science Team Manager must permit and enable collaboration between business analysts, statisticians, & engineers… Collaboratively design, build, & deploy Data Products
  • 86. The Rise of the Collaborative Data Science Team …all the while maintaining the distinction between each individual role and each individual skill set. Business Mathematics Data Engineering
  • 87. The Secret to Building and Industrializing Data Products is Collaboration. Today, collaboration between different skill sets, technologies, and data is finally possible.
  • 88. Data Science Studio: One Platform for Development and Industrialization
  • 89. Thank You! Pauline Brown Dataiku, Director of Marketing Pauline.brown@dataiku.com @pauline8brown www.dataiku.com