Hiring data scientists and deploying Hadoop is not enough. Your company needs a data driven culture, based on values such as honesty, democracy, creativity and strategy. Your company also needs good data engineering and good experimentation practices.

1. Data Science &
Culture
(Or how to stop worrying and love data driven culture)
Ícaro Medeiros
Data Science Forum
São Paulo, Jun 2017

2. Inspired by
(not limited to)
refs

3. Big Data
http://www.kdnuggets.com/2017/02/origins-big-data.html
✦ Fundamental blocks: evolutions on CS e.g.
distributed systems, databases, massive AI, etc
✦ Fuzzy concept, ill-defined
✦ Popularized by Gartner
(hype-fueled consulting firm)

4. ✦ Big Data no longer considered an emerging
technology (pervasive in industry)
✦ Entered Trough of Disillusionment in 2013
https://knowledgeimmersion.wordpress.com/2016/06/22/disillusionment-of-big-data/

5. http://www.mikelnino.com/2016/03/chronology-big-data.html
Chronology of antecedents

6. Data science
✦ Statistics (late 19th century)
✦ Computer Science (1950s)
✦ Machine Learning (1950s)
✦ Data Mining (1990s)
✦ Data Science (2010s)
https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
yet another hyped term

7. Beware: controversy
✦ Data science is not all-science
✴ It’s getting more and more engineering-like, a practice
✴ Data storytelling is a creative endeavor
✦ Hyper-inflated expectations, misunderstood
concepts and hurry to get value: a dangerous
recipe

8. A new hope
machine learning
big data
https://trends.google.com/trends/explore?date=today%2012-m&geo=US&q=machine%20learning,big%20data
or hype

9. Hype: not that bad
✦ Haters gonna hate i.e. don’t fully hate the hype
✴ more practitioners = faster tech and processes evolution
✴ Highly skilled professionals and innovation
✦ Academics sometimes look for difficult unwanted
problems
✴
industry is more pragmatic, specially in tech
https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science

10. What we need…
✦ Forget about Big Data pokémons
✴ OH so in Big Data we don’t need people to think schemas?
✦ Forget about misunderstood business expectations
✴ OH in deep learning we don’t need people to train models?
✦ You need PEOPLE
✴ Collaborating with shared values
✴ Awesome in tech but more importantly: CREATIVE

11. Shared values
and practices
Culture

13. Good people
✦ People are more important than ideas
✴ A mediocre team will screw up a good idea
✴ Mediocre idea to great team: they will fix it or rethink it
✦ A good lab: different kinds of autonomous thinkers
✴ Why hire smart people if they can't fix what’s broken?
✦ Prefer a heterogeneous and complimentary team
instead of looking for unicorns

14. The mythical 10x professional
https://twitter.com/icaromedeiros/status/838968884023668737

15. Good communication
✦ Honesty, excellence, originality and self-
criticism (values)
✦ Communication structure <> organizational
✦ Be ready to hear the truth
✴ Sincerity is only valuable if people are open and willing to give
up on ideas that will not work
✦ Braintrust: Leave ego and Jobs outside the door

16. Power to the people!
✦ Product quality is everyone’s responsibility
✴ Don’t ask permission to take responsibility
✦ Passion and excellence versus autonomy
✦ Good things might shadow the bad
✴ People struggle to explore bad things to avoid being called
“complainers”

17. Rebels
http://qaspire.com/2017/05/19/sketchnote-what-rebels-want-from-their-boss/

18. Destroy data silos!
✦ Without information about data there is no science
✦ Software and data should be a collective property
within the company
✦ Knowledge management matter
✦ Communication between areas must be enforced

19. Data portals
✦ Self-service platforms to publish datasets
✴ Descriptions, schemas, samples, relations between datasets,
etc
✦ Open Data initiatives, mostly governments
✦ OSS platforms: CKAN, AirBNB’s Dataportal
✦ Examples: data.gov.uk, dados.gov.br, etc

20. “When it comes to creative
inspiration, job titles and
hierarchy are meaningless”

22. Data storytelling
✦ Explain what numbers tell in layman, clear terms
✦ Make hidden premises clear
✴ Outside data insights
✦ Convince others about actions
✴ Decreases insights-to-value interval
✦ From data to knowledge
https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs

23. What is creativity
✦ Unexpected connections of concepts and ideas
✦ It's a marathon, it needs rhythm
✦ Creativity must start somewhere and there’s power
on healthy feedback in a iterative process

24. Visual communication
✦ Clean straightforward graphs > visually appealing
✴ Choose dataviz libs wisely
✦ “Don’t make me think”
✦ The right graph for the right audience
✴ Prefer a language everyone understands

25. Visual communication 101

26. Stats are not enough
https://www.autodeskresearch.com/publications/samestats

27. Stats are not enough
https://www.autodeskresearch.com/publications/samestats

28. Strateg a

29. Avoid egotrip data science
✦ “OH my cluster has 10 Petabytes, I’m awesome”
✦ Fancy ML algorithms are not the goal
✦ The most important V in Big Data is value
https://twitter.com/amyhoy/status/847097034536554497

30. KPI versus HiPPO
✦ Tech adoption per se is meaningless
✴ Slide-driven Big Data
✴ KPIs should grow from Big Data and data insights initatives
✦ Poor defined goals -> bad decisions
✦ Define viable but ambitious goals
✦ Data beats opinion

31. Set goal, plan and GO!
✦ Business questions can't be like “OH we want to
detect things related to millennials”
✦ Clear goals must be set, with actionable metrics
✦ Balance perfect models versus time-to-market
✦ Brad Bird: “Sometimes, as a director, you’re
guiding. Sometimes you’re letting the car drive”
https://hbr.org/2017/02/how-chief-data-officers-can-get-their-companies-to-collect-clean-data

32. The process
✦ The process is not the goal
✴ It has no agenda or taste, it’s just a tool
✦ Quality is the best business plan
✦ Agile is a mindset: not only kanbans or scrum
✦ If the model will become operational, mix scientists
and engineers from start

33. Build vs Buy
✦ If you buy and your core business is not techie, you can be
illiterate in tech
✴ Benchmark before buying
✴ Accelerate results and boost internal knowledge
✦ If you build and have a good-enough techie culture, you’re
more or less good to go
✴ Assess pros and cons consciously
✦ If you surf the tech hype AND build good systems you’re
awesome

34. https://twitter.com/Doug_Laney/status/847452219641356288
When data goes to vendors…

35. http://www.louisdorard.com/machine-learning-canvas/

36. DATA
ENGINEERING

37. Big Data vs Great Data
✦ If your logical models do not make sense
✦ Most performed queries are slow
✦ If you have string-only databases
✦ If you have unused expensive data
✦ Maybe your data lake is a swamp

38. “The data is a mess”
✦ First step: accelerate human understanding of data
✴ Metadata, context, hidden assumptions
✦ Datasets might serves multiple purposes
✴ Define rationale and context
✴ Data portals and understandable datasets > Dashboards
https://hbr.org/2016/12/why-youre-not-getting-value-from-your-data-science
https://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770

39. Data lost in translation
✦ Heterogeneous and siloed databases (and people)
✦ Rethink ESB (microservices network)
✦ State-of-the-art: data workflow
✴ Luigi, Airflow (open source), almost every big tech vendor
✴ Transparency, reusability, reproducibility, traceability
✴ Automation and monitoring all the way!
https://hbr.org/2016/12/why-youre-not-getting-value-from-your-data-science

40. Beyond relational models
✦ Not all data problems fits well in traditional SQL or
DW models
✴ Key-value, columnar, graph-based, inverted index, etc
✦ Models are a framework for problem-solving
✴ Not the ultimate answer
✴ There’s no one-size-fits-all model

41. Do not forget fluency
✦ Check the company lingua franca
✦ Make it easy for critical decision-makers
✴ Adhoc SQL queries?
✴ Dashboards?
✴ Reports?

42. EXPERIMENTATION

43. Experiments
✦ Missions to discover facts towards understanding
✴ They don’t fail, any result produces new information
✴ If the initial theory was wrong: good
✴ With new facts you can reformulate the question
✦ Get more modeling questions asked more often
✦ Iterative data science

44. Product experimentation (A/B)
✦ Product experimentation should be hypothesis-
driven (not feature-driven)
✦ Define the proper exposed population
✴ No new users, no heavy users only, no early adopters
✦ Understanding effect is essential
https://medium.com/airbnb-engineering/4-principles-for-making-experimentation-count-7a5f1a5268a

45. 5 stages of A/B tests
https://www.linkedin.com/pulse/ab-testing-which-do-i-pick-sahar-heidari

46. Some other quick tips
✦ Focus on outcomes (not algorithms or methods)
✦ Design the right metric and evaluation
✦ Good experiments don't produce obvious insights
✦ Mix of data and intuition
https://twitter.com/mrdatascience/status/869957499662860288

47. Being data driven
✦ Be BAYESIAN - uncertainty is everywhere
✦ Be CURIOUS - keep learning
✦ Be AGILE - Fail fast, not too fast: evidence comes first
https://www.reaktor.com/blog/culture-eats-data-science-for-breakfast/

48. Being data driven
✦ Be TRUTHFUL - don’t torture data to please opinions
✦ Be HELPFUL - work across silos, support democracy
✦ Be WISE - know when to be analytical or intuitive
https://www.reaktor.com/blog/culture-eats-data-science-for-breakfast/

49. With the right people,
Democracy,
Creativity,
Strategy,
Big Great Data™
and Experiments
there's a good chance to do great
SCIENCE
Take-away message

50. Ícaro Medeiros
Data Scientist
icaromedeiros