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.

1. Armando Vieira @lidinwise
Good Data Science Bad Data Science
Armando Vieira
Data Science Consultant
London, Sep 2016

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. 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. 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. Armando Vieira @lidinwise
The magic triangle?

6. Armando Vieira @lidinwise
The Data Science strategy
Culture
Talent
Strategy

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. 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. Armando Vieira @lidinwise
Paradigm
Shift

10. Armando Vieira @lidinwise
It’s all about talent
Algorithms are a
commodity.
Talent is not.

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. Armando Vieira @lidinwise
Good Data Science
• From chats to CLTV
• Automate CS
• Networks effect
• Explore, test and learn
• Feedback loop

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. 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. 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. Armando Vieira @lidinwise
The age of the machines

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. 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. Armando Vieira @lidinwise

20. Armando Vieira @lidinwise

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.