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BDAS-2017 | Lesson learned from the application of data science at BBVA

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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.

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BDAS-2017 | Lesson learned from the application of data science at BBVA

  1. 1. Data Science Projects at BBVA
  2. 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. 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.
  4. 4. Data Science Projects at BBVA / 4 A Machine Learning perspective Syllabus 01 02 03 The Practice The Production 04 05 The Applications The Implications
  5. 5. Data Science Projects at BBVA / 5 A Machine Learning perspective 01
  6. 6. Data Science Projects at BBVA / 6 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
  7. 7. Data Science Projects at BBVA / 7 How to deliver value?
  8. 8. Data Science Projects at BBVA / 8 Defining objectives DESIRABLE NEEDS AND PROBLEMS TO BE SOLVED PROFITABLE VALUE PERCEIVED BY CUSTOMERS AND COMPETITIVE ADVANTAGES POSSIBLE TECHNICAL FEASIBILITY, CAPABILITIES, BUDGET...
  9. 9. Data Science Projects at BBVA / 9 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)
  10. 10. Data Science Projects at BBVA / 10 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. 11. Where is value created? DATA TALENT
  12. 12. Pain point ● Finding REAL data scientists DATA TALENT
  13. 13. Data Science Projects at BBVA / 13
  14. 14. Data Science Projects at BBVA / 14
  15. 15. Pain point ● DATA ○ Enough data? ○ Right data? ○ Timely data? ○ ... DATA TALENT
  16. 16. Data Science Projects at BBVA / 16 Data governance is paramount
  17. 17. Data Science Projects at BBVA / 17 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
  18. 18. Data Science Projects at BBVA / 18 Co – pilot…
  19. 19. Data Science Projects at BBVA / 19
  20. 20. Data Science Projects at BBVA / 20
  21. 21. Data Science Projects at BBVA / 21 Be careful with this chatbot Ref: http://www.ticbeat.com/cyborgcultura/el-chatbot-de-microsoft-que-se-volvio-nazi/
  22. 22. Data Science Projects at BBVA / 22 The Practice 02 Simply applying Machine Learning algorithms to your data won’t work
  23. 23. Data Science Projects at BBVA / 23 Helping frameworks: design thinking
  24. 24. Data Science Projects at BBVA / 24 Helping frameworks: agile teams
  25. 25. Data Science Projects at BBVA / 25 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
  26. 26. Data Science Projects at BBVA / 26 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
  27. 27. Data Science Projects at BBVA / 27 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
  28. 28. Data Science Projects at BBVA / 28 The Production 03
  29. 29. Data Science Projects at BBVA / 29 The Technology Storage Programming
  30. 30. Data Science Projects at BBVA / 30 Stability vs Speed of Innovation All systems are working all the time All components are changing all the time
  31. 31. Data Science Projects at BBVA / 31 The Deployment Prototype Deployment Monitoring Improving A clear path to production is required
  32. 32. Data Science Projects at BBVA / 32 The cost: machine learning is not for free • Complex model and code (glue code) • Data dependencies • Dealing with Changes in the External World
  33. 33. Data Science Projects at BBVA / 33 The Applications 04
  34. 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
  35. 35. Data Science Projects at BBVA / 35 Discussion Ref: The Mythos of Model Interpretability by Zachary C. Lipton https://arxiv.org/pdf/1606.03490.pdf
  36. 36. Data Science Projects at BBVA / 36 Always in mind
  37. 37. Data Science Projects at BBVA / 37 The Implications 05
  38. 38. Data Science Projects at BBVA / 38 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
  39. 39. Data Science Projects at BBVA / 39 • 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
  40. 40. Data Science Projects at BBVA / 40 The Near Futures Standards boots business AI NarrowGeneral - Driven by scientist - Multiple task - Understanding - Driven by industry - One task - Practical
  41. 41. Data Science Projects at BBVA / 41 The challenges
  42. 42. Data Science Projects at BBVA / 42 The Trust Challenge
  43. 43. Data Science Projects at BBVA / 43

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