En esta sesión se expondrán las principales lecciones aprendidas en la aplicación de la ciencia de datos para la mejora de procesos internos y creación de nuevos productos en BBVA. En particular se incidirá en las principales barreras que pueden surgir para la creación de productos basados en datos, tanto en el aspecto técnico como organizativo, y se realizarán distintas propuestas para agilizar la llegada al mercado de estos productos.

1. Data Science Projects
at BBVA

2. Demystifying Big Data / 2
Diego J. Bodas Sagi
Data Scientist at BBVA Data & Analytics
PhD. AI
MBA
PMP
MSc. in Mathematic
@DiegoBodasSagi
diegobodas@yahoo.es

3. Big Data Analytics at BBVA
BBVA Data & Analytics
The Analytic Center of Excellence of
BBVA (fully owned subsidiary)
Goal: to globally drive BBVA
transformation into a digital data-driven
business
45 people from 10 countries, 33%
women, 16 PhDs
Madrid - Barcelona - México D.F.

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A Machine Learning perspective
Syllabus
01
02
03
The Practice
The Production
04
05
The Applications
The Implications

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A Machine Learning perspective
01

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Art by humans?
Why do we talk about Machine Learning today?
“The aim of art is to represent not
the outward appearance of things,
but their inward significance”
Aristotle

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How to deliver value?

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Defining objectives
DESIRABLE
NEEDS AND PROBLEMS TO BE
SOLVED
PROFITABLE
VALUE PERCEIVED BY
CUSTOMERS AND
COMPETITIVE ADVANTAGES
POSSIBLE
TECHNICAL FEASIBILITY,
CAPABILITIES, BUDGET...

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1. Consumer financial management advice
2. Retailers management advice
3. Offer the best products to our customer
4. Help public administration: mobility, tourism, public policies, etc
1. Understanding economic environment
2. Avoid fraud
3. Better risk management
4. Improving process
5. Agile development
What are we working on?
Above the glass
(income)
Above the glass
(efficiencies)

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Bad vs good questions
• What can be done with this data?
• Is this a relevant business problem
• Where can I find useful data to help me to solve
this problem?

11. Where is value created?
DATA TALENT

12. Pain point
● Finding REAL data scientists
DATA TALENT

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15. Pain point
● DATA
○ Enough data?
○ Right data?
○ Timely data?
○ ...
DATA TALENT

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Data governance is paramount

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The myths
● A Machine Learning can be “self-sufficient”.
Machine learning is a co-pilot, not an
autopilot. A person is needed to make
judgment calls on the machine's output
● The more data the better… It depends! Take
into account quality and imbalanced
datasets
● AI is replacing humans. No, IA is
“augmenting” humans

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Co – pilot…

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Be careful with this chatbot
Ref: http://www.ticbeat.com/cyborgcultura/el-chatbot-de-microsoft-que-se-volvio-nazi/

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The Practice
02
Simply applying Machine Learning algorithms to your data won’t work

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Helping frameworks: design thinking

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Helping frameworks: agile teams

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Where Design Thinking meets Data Science
Start with a question, challenge,
opportunity
Form the
hypothesis
Prototype
Iterate
Explore solutions to
similar problems
Evaluate
Design the
dataset
Model
Production Validate
Document
Visualize
EvaluateExperience
Data
Data engine
Iterate
Articulate the key questions
Build a tangible vision of the solution with
priorities, goals and scope

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Iterate and discover
Start with a question, challenge,
opportunity
Form the
hypothesis
Prototype
Iterate
Explore solutions to
similar problems
Evaluate
Design the
dataset
Model
Production Validate
Document
Visualize
EvaluateExperience
Data
Data engine
Iterate
Understand the limitations of the
algorithm, user testing
Share the insights from
quantitative exploration

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Continuous improving
Start with a question, challenge,
opportunity
Form the
hypothesis
Prototype
Iterate
Explore solutions to
similar problems
Evaluate
Design the
dataset
Model
Production Validate
Document
Visualize
EvaluateExperience
Data
Data engine
Iterate
Evaluate the impact on the experience
Reformulate the objectives

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The Production
03

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The Technology
Storage
Programming

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Stability vs Speed of Innovation
All systems are
working all the
time
All components are
changing all the
time

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The Deployment
Prototype
Deployment
Monitoring
Improving
A clear path to production is required

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The cost: machine learning is not for free
• Complex model and code
(glue code)
• Data dependencies
• Dealing with Changes in
the External World

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The Applications
04

34. Deep Learning is a subfield of
machine learning concerned
with algorithms inspired by the
structure and function of the
brain called artificial neural
networks
Deep Learning
ML
DL
Evolution

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Discussion
Ref: The Mythos of Model Interpretability by Zachary C. Lipton
https://arxiv.org/pdf/1606.03490.pdf

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Always in mind

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The Implications
05

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General Lessons
• Get to know the problem domain
• Do not be afraid to start from scratch if your assumptions
are wrong
• Monitor quality continuously
• Beware of crowdsourcing

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• Infrastructure (cost structure & Scalability)
• Learning curves change constantly and frequently
• A data science team has to be learning almost constantly
• Pay attention to motivation within the team
• Autonomy
• Competence
• Relatedness
• Bureaucracy, security, legal, norms... (work as one team)
Other key points

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The Near Futures
Standards boots business
AI
NarrowGeneral
- Driven by scientist
- Multiple task
- Understanding
- Driven by industry
- One task
- Practical

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The challenges

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The Trust Challenge

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