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Data Science Strategy

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Deep Neural Networks (DNN), or simply Deep Learning (DL), took Artificial Intelligence (AI) by storm and have infiltrated into business at an unprecedented rate. Access to vast amounts of data, recently made available by the Big Data revolution, extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges.
The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. However, incorporating this technology in established business is far from obvious: cultural inertia in organizations, lack of transparency in most DL models and the complexity in training these models are some of the issues that will be addressed.

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Data Science Strategy

  1. 1. Armando Vieira @lidinwise Good Data Science Bad Data Science Armando Vieira Data Science Consultant London, Sep 2016
  2. 2. Armando Vieira @lidinwise The Scenario • Today 1% software use AI - in 2018 it will be 50% • AI is achieving human level accuracy in image, video, voice recognition and text • 90% data was generated last 2 years • Smart devices are connecting everything However only a few organizations are taking advantage of these forces. Why?
  3. 3. Armando Vieira @lidinwise Data Science - the fluffy side “We want to extract value from our 10 Tb of Data” “We want to become data centric organization” “Data Science will transform our business”
  4. 4. Armando Vieira @lidinwise What’s not Data Science • It is not Science • It is not Data • It is not IT • It is not about unicorns • It is not about money
  5. 5. Armando Vieira @lidinwise The magic triangle?
  6. 6. Armando Vieira @lidinwise The Data Science strategy Culture Talent Strategy
  7. 7. Armando Vieira @lidinwise How to design a Data Science strategy? DS strategy should be designed to take advantage of the forces unleashed by AI and data available to refocus the business through careful redesign and integration on data driven processes. As in a business strategy, it requires a deep understanding of the business and the technology. There is no template
  8. 8. Armando Vieira @lidinwise How? • Requires a long term vision • Backed by highest level decision makers • It requires careful engineer of business processes • Need an experienced data scientist advisor • It is normally painful • Can only be partially outsourced
  9. 9. Armando Vieira @lidinwise Paradigm Shift
  10. 10. Armando Vieira @lidinwise It’s all about talent Algorithms are a commodity. Talent is not.
  11. 11. Armando Vieira @lidinwise Wrong data science Department Diagnostic Why it failed? Digital Marketing Not cost effective Outsourced Operations Non integrated CLV Fragmentation Sales Too many products “Not a DS problem” Fraud Hard rules - easy to trick Incomplete data Pricing Use more parameters Too complex to integrate Short-term thinking , not prepared for secondary uses data, legacy data, no team, Underestimate effort, lack of management support and budget
  12. 12. Armando Vieira @lidinwise Good Data Science • From chats to CLTV • Automate CS • Networks effect • Explore, test and learn • Feedback loop
  13. 13. Armando Vieira @lidinwise DS Check List • Cross functional teams? • Openly discuss failure? How many failures in DS? • Data is ready and consistent? • Open source friendly? Use Github? • Where do you store data: DW, Data Lake, Cloud? • Does any manager understand what are you doing? • Are they ready to learn or unlearn on wrong Assumptions? • Do DS have a seat in the decision room?
  14. 14. Armando Vieira @lidinwise How to make it happen? • Start at the highest level • Have your long-term strategy ready • Recruit a small, but smart team • Don’t underestimate the effort. DS is painful • Start proxy deliveries and long-term goals • Communicate your vision
  15. 15. Armando Vieira @lidinwise Problems What data to consider? How to formulate the problem as a DS problem? How to sell the outcomes? How to implement it? Simple vs complex – gains in productivity Maintenance, cost, scalability Stability – stationary
  16. 16. Armando Vieira @lidinwise The age of the machines
  17. 17. Armando Vieira @lidinwise The AI revolution AI is contributing to a transformation of society “happening ten times faster and at 300 times the scale, or roughly 3,000 times the impact of the Industrial Revolution”.
  18. 18. Armando Vieira @lidinwise “I was a skeptic for a long time, but the progress now is real. The results are real. It works!” - Marc Andreessen
  19. 19. Armando Vieira @lidinwise
  20. 20. Armando Vieira @lidinwise
  21. 21. Armando Vieira @lidinwise Conclusions • DS is about changing the culture of your organization • Its not magic & should not be cosmetic • DS is a two side sword: it can potentiate your business or become a money sink • Put the buzz aside and build a strategy • If you don’t have culture, start to build it • Read my book “Business Applications of Deep Learning” – 2017.

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