This document discusses the challenges faced by a data team manager named Hal in developing a data science software platform for his company. It describes Hal's background in technical fields like functional programming. It then outlines some of the disconnects Hal experienced in determining the appropriate technologies, hiring the right people, accessing needed data, and involving product teams. The document provides suggestions for how Hal can find solutions, such as taking a polyglot approach using open source technologies, creating an API culture, and focusing on solving big business problems to gain support.

1. Data Science
Software Platform
The Solitude
Of The
Data Team Manager
Data Driven NYC
04/11/16

2. My nerdy background
Type Systems Automated Proving Abstract Program Interpretation Functional Programming Garbage Collection
and Vms
Graph Analytics Chess IA Natural Language Processing 80% Emacs /20% VIM

3. So to sum it up …
3
I (USED TO)
HATE
GUIs

4. …and our software is
A supercharged Visual IDE for Data Teams
Deployment

5. Meet HAL
Hal AlowneBI Manager
Dim’s Private Showroom

6. Meet Hal’s Boss, DIM
Hey Hal !
We need
a big data platform
like the big guys.
Just do what they’re doing!
‟
”
Big Data
Copy Cat
Project

7. TECHNOLOGY
Disconnect
What technologies should I
use ?‟
”

8. Welcome to Technoslavia !
8

9. TOY PLATFORM ANTI-PATTERN
9
Test and Invest in Infrastructure == Skilled People
or
Go For Cloud / Packaged Infrastructure
Your Brand New Hadoop Cluster
is perceived as slow, not so used
and not reliable

10. TECHNO MISMATCH ANTI-PATTERN
10
Assume Being Polyglot
or
Be a Dictator
VS
VS
The Python
Clan
The R
Tribe
The Old Elephant
Fraternity
The New Elephant
Club

11. PREDICTIVE ANALYTICS DEPLOYMENT STRATEGY
11
Website 2000’ winners
Companies that were able to release fast
"Artificial Intelligence with Data for
Internet of Things" 2010’ winners
Companies able to put intelligence in production
?
Design a way to put “PREDITICTIVE MODELS”
IN PRODUCTION

12. PEOPLE
Disconnect
Who should I hire ?
‟ ”

13. Classic Business Intelligence Team Organization
Business Leader
Data Consumer
Line-of-business
Data Consumer Business Project
Sponsor
BI Solution Architect
Model Designer
ETL Developer
Dashboard / Report Designer
Specs
Dim
Big Boss

14. Data Science Team Organization
Business Leader
Data Consumer
Line-of-business
Data Consumer
Business Project
Sponsor
Data Engineer
Data Analyst
System Engineer /
Data Architect
Business
Needs
Data Scientist
IT
Constraints
I.T.

15. Manage Expectations
15
Data
Plumberer
Data
Engineer
Data
Scientist
Data
Waiter
Data
Cleaner
Data
Analyst
REAL
JOB
DREAM
JOB

16. Managing Extreme Personalities
16
Data Scientist
Highly Creative
Passionate
Hard to hire?
Hard to manage?
Want to take
Hal’s job?Ambitious
Hard to retain?

17. Paired for Data
17
Data Analyst
Discover Patterns
Data Engineer
Make things work
Fight
data
entropy
Fight
tech
entropy

18. What do you prefer?
18
One Analyst
One Engineer
One Data Scientist
Four data scientists
OR

19. Two Mindsets Can Coexist
CLICKERS CODERS

20. DATA
Disconnect
What about data ?
‟ ”

21. What is the main reason for data project to fail ?
21
> DATA NOT
AVAILABLE

22. BUT FOR ONLY INCREMENTAL GAIN
50 30 20
0% 25% 50% 75% 100%
Contribution to the overall project performance
Business Goal Definition and Data Feature Engineering Algorithm

23. How to Get Data if you don’t have it
23
THE CICADA THE SPIDER THE FOX
Be Optimistic !
Wait for Open Data Initiatives
And data available in the
Enterprise Hub / Data Lake !
Create Network !
Create a set of trackers or
Addictive Data Collection
internally
To get Data on your side !
Hunt for Big Problem!
Convince the CEO that you can
Solve a Business Critical problem
And use it as an excuse to get all
The data you want !

24. PRODUCT
Disconnect

25. What is Big Data about ?
25

26. The Age Of Distributed Intelligence
26
Global, Personalised
and Real Time Data
Driven Services

27. Data to Visualize or Data to Automate ?
2013 2014 2015 2016 2017 2018
Moving to a world of automated decision making
27
DATA
FOR MORE INSIGHTS
DATA
FOR AUTOMATED DECISIONS

28. Involve Product Team
28
Product Feature
Personalised Item Ranking
Product Feature
Notify User Only when Needed
Product Feature:
Historical Data For Path Optimisation
Have Product Management Deeply Involved
In the Data Team

29. Focus on your added value
29
Build by the
DataTeam
Is the problem at the
Core of my BusinessProcess?
Is it a common
problem / with share data?
Can i solve it on my own?
Really?
Hire Consultant
and Learn
Build by
the Data Team
Go for Best of Breed
SaaS Solution
Build by
the Data Team?
YesNo
No Yes No Yes
No Yes

30. Create an API culture
Do not share
o Random Piece of Code
o Flat File
o Email
Do share
ü Reproductible documentedworkflows
ü Clean, documentedAPIs

31. Did Hal found his solutions ?
Technology
Data
People
Product
Polyglot on top of open source
Find a way to make clickers and coders work together
Create an API culture and involve the product teams
Hunt for Big Problems and Convince the CEO
Is this the end ?‟
”
Hal Alowne
BI Manager
Dim’s Private Showroom

32. That was the (romanced) story…
data scientists
and engineers25
2 locations
1 software
by the numbers
For a simple software for clickers and coders

33. Car Sharing
Worldwide
Leader
FlashSales
Worldwide
Leader
One Mission :
Never leave Hal ALONE
3,700
Hotels
Worldwide
2500 lovely
users
by the numbers
70 customers

34. Food for thoughts
www.dataiku.com/blog
THANK YOU !
FREE (as in Beer) Software
www.dataiku.com/dss