This document discusses predictive modeling and big data solutions from Timi Americas. It begins by introducing Timi as focused on faster predictions and better decisions through predictive models. It then discusses challenges with traditional predictive modeling approaches like time requirements and scalability issues. The document promotes Timi's approach of using distributed, efficient code to build predictive models in days rather than months. It also demonstrates Timi's ability to handle large datasets on a single laptop through its compressed data format and optimized algorithms.

1. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Big Data
Modelos Predictivos
Retos y generación de nuevas soluciones

2. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
360 Customer
View
Connections
without boundaries
Incredible Speed
and Reliability
The promise…

3. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
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Cambie sus creencias!
+ preguntas, + modelos,
+ Citizen Data Scientists
Menos Servidores,
Mas Distributed Poweful Laptops
- Hadoop, - Spark+,
- CODIGO!

4. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Part 1: Who are we?

5. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Dr. Ir. Frank Vanden Berghen
Phd. In Applied-Sciences with specialization in applied-
mathematics (mathematical optimization)
Building predictive models for more than 15 years, all around
the world.
Top winners at various world-level datamining competitions:
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6. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
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Automation
Moore’s Law
Lifeofa
Modelos Predictivos

7. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
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Objectives & Motivations

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At University
IRIDIA=Research in Artificial Intelligence
Neural Networks
One Tree
Forest of Trees

9. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
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In consulting… Far from AI…
VS
Now: Manual & Archaic
(1 to 3 months to get 1 Model)
Self-Learning &
Auto-Adaptative
Artificial Intelligence

10. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
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Market Reality: all is Scoring
Quien? Que?
X SELL
UP SELL
CHURN

11. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Typical difficulties:
• High Bayes-Error Levels
• Small Target Size (<1%)
• Scalability Issues
• Before all: TIME!
•AUTOMATED?
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Churn, cross-sell, and up-sell models aren’t easy
TIME! THERE IS NO TIME!

12. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Inventsomething

13. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Usual time to create one model for a datamining competition: 1 to 3 months
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Ultimate Accuracy & No Free Lunch
Competition Metric Winner
TIMi
(or similar
automated tool)
Difference
Heritage Health Price Some kind of R² 46.12% 46.24% 0.12%
AUSDM2009 (following Netflix) AUC 69.41% 69.24% 0.17%
Kaggle Axa Telematics 2015 AUC 96.35% 95.97% 0.38%
PAKDD2007 AUC 70.01% 69.28% 0.73%
PAKDD2010* AUC 64.10% 63.30% 0.80%
KDD2009-upselling AUC 90.92% 89.94% 0.98%
Datascience.net Axacross-selling2015 Lift at 10% 26.09% 24.74% 1.35%
KDD2009-churn AUC 76.51% 74.74% 1.77%
KDD2009-appetency AUC 88.19% 86.31% 1.88%
“No free lunch”: There will always exists a specific modeling algorithm
that surpasses a “generic, general-purpose & automated tool”
*: winning entry removed because it used additional external data and that’s cheating
Look here

14. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Sales
A typical time-line for a SAS,IBM,R project:
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Back in 2010
A typical time-line for a TIMi project:
idea
Data Preparation Model
Total: 1 – 3 meses. Modelo: 2-6 weeks
Total: 2 – 6 WEEKS. Modelo: 1-5 DAYSidea
ETL
MODEL
CREATI
VITY
RUNTI
ME
Sales
ETL
MODEL
CREATI
VITY
RUNTI
ME
Sales
FUN

15. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Part 2:Moore’s Law…
…In real life

16. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Moore’s Law:
“The number of transistors that can
be fit on a computer chip will double
every two years, resulting in periodic
increases in computing power.”
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I despise Moore’s law!
 FAST implementation, slow runtime
 No attention to code quality
 “Algorithmic complexity” ?
 Efficient code = LOSING TIME
Direct
consequence of
Moore’s Law:

17. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
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An observation: Moore’s Law is (somewhat) “broken”
Source: “Computing Performance: Game Over or Next Level?” from
Samuel H. Fuller, Analog Devices Inc. and Lynette I. Millett, National
Research Council

18. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
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Moore’s Law is broken: …and it’s terrible!
Exponential Growth
of Data Volumes
Technologies such as RFID and smartphones as well as the increasing use
of social media application are resulting in a rapid rise in data volumes
(Practically) No Growth
in CPU speed
Expectation Gap:
factor of 10
Expectation Gap:
factor of 1000
Expectation Gap:
factor of 100

19. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
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Moore’s Law is broken: 2 Solutions
• Low-Level Assembler &
C code
• Mathematical “tricks”
• Algorithmic
It’s the “seventies solution”
• High-Level (i.e. slow) Language (i.e. Java & Spark)
• Computing farm
Supported by:
• “Legacy”
• Cloud services (Warning: Linear price-grid!)
• Consulting Companies
• Coders without any Assembler/C or algorithmic knowledge
This is a possible solution if your wallet can grow
exponentially with the size of your data. This will
ultimately lead to high cost linked to legacy software.

20. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
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OPEN

21. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Infrastructure
Complexity
Implementation (Time)
Hadoop, Exadata and others cost hundreds of thousands of dollars
Data mining requires PhD’s with years of experience in the field
Implementing predictive models or complex KPI can take months or more to
be integrated in the business processes
Storage
Data can be stored in a highly compressed file format that allows to store (and process) all the
data from a 10 millions telecom for 3 years on one simple laptop
Barriers to entry for “Big Data”

22. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Complexity
Do you need Phd in stats to
answer analytical questions?
(See also the concept of “Citizen Developer here:
http://www.gartner.com/it-glossary/citizen-
developer/ )

23. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Complexity

24. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
Deal with Big Data
on a laptop: Live
demo!
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Scale of Solution

25. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
On my laptop:
• Compute a aggregate on a table with 17 columns and 1.3 billion rows: 70 seconds
(finest granularity for 2 years of ticket data from a larger retailer with 1 million customer)
• Sort a (CDR) table with 8 columns and 1 billion rows: 120 seconds.
(finest granularity for 2 month of CDR data from a telecom with 5 millions subscribers)
• Create 3 KDD-Grade-Predictive models (for churn, cross-sell, up-sell) on a learning
dataset (from the KDD2009) with 50K rows and 15K columns (and than apply this
model on the same scoring dataset): 20 minutes (about 7 minutes per model)
The laptop has: Core i7 CPU at 2.4 GHz and a simple SSD drive.
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Storage and computational Speed Live Demo!
Conclusion: A simple laptop is enough to store & process all your data,
whatever the size, the origin, the granularity, the type or the format of the data.

26. © 2016 Timi Americas S.A.S. – TIMi: Faster predictions, better decisions.
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… to boldly go where no dataminer has bone before!