Conding Like a Girl slides from a youtube live presentation. How teams with women gain with diversity.

1. C O D I N G L I K E
A G I R L
How teams with women gain with diversity

2. WHO A M I ?
Gabriela

3. SÃO LUÍS
TERESINA
PALMAS
BRASÍLIA
ACRE
RIO BRANCO
FORTALEZA
RIO GRANDE DO NORTE
MACEIÓALAGOAS
AMAPÁMACAPÁ
AMAZONAS
BOA
VISTA
RORAIMA
MANAUS
BAHIA
CEARÁ
SALVADOR
ESPÍRITO SANTO
VITÓRIA
GOIÂNIA
GOIÁS
MARANHÃO
MATO GROSSO
CUIABÁ
PORTO
VELHO
CAMPO GRANDE
MATO GROSSO DO SUL
MINAS GERAIS
BELO HORIZONTE
PARÁ
BELÉM
JOÃO PESSOA PARAÍBA
SÃO PAULO
PARANÁ
CURITIBA
PIAUÍ RECIFE PERNAMBUCO
RIO DE JANEIRO
RIO DE JANEIRO
NATAL
RIO GRANDE DO SUL
PORTO ALEGRE
RONDÔNIA
SÃO PAULO
SANTA CATARINA
FLORIANÓPOLIS
ARACAJU SERGIPE
TOCANTINS
Where I live
Where I am from
Gabriela D'Ávila

4. ABOUT ME
• Major in Digital Game
Development
• Senior Software Engineer
with 7 years in the market
• Web Development
• Women in Technology
Advocate
• Lego fanatic =)
LEGO
HUGE
FAN
Developer

5. R E A L LY

7. WHAT TO E X P E C T
• What are the concepts?
• What is the current scenario?
• Why diversity matters?

8. D I V E R S I T Y
• Gender
• Racial
• Ethnic
• Sexual Orientation

9. B I A S

10. B I A S
• Project Implicit® from Harvard University
• Preferences, attitudes and memory

11. P RO J E C T
I M P L I C I T®

12. P RO J E C T I M P L I C I T ®
Strong automatic association of Male with 26%
Science and Female with Liberal Arts
Moderate automatic association of Male with
3%
1%
6%
18%
18%
28%
0% 7.5% 15% 22.5% 30%
Science and Female with Liberal Arts
Slight automatic association of Male with
Science and Female with Liberal Arts
Little to no association between
genders and academic domains
Slight automatic association of Male with
Liberal Arts and Female with Science
Moderate automatic association of Male with
Liberal Arts and Female with Science
Strong automatic association of Male with
Liberal Arts and Female with Science
Source: Project Implicit

13. I AT S C O R E S
Source: Project Implicit
Male with Science
Female with Liberal Arts
Female with Science
Male with Liberal Arts

14. L I K E A G I R L

15. “You know, you are really intelligent, for a girl.”
–Undisclosed friend

16. L I K E A G I R L
• You drive like a girl
• You punch like a girl
• You fight like a girl
• You [verb] like a girl

17. D I V E R S I T Y
R E P O RT S

18. A L L A R E A S
100%
80%
60%
40%
20%
0%
1%
30% 31% 30% 30% 37%
62%
70% 69% 70% 70%
Apple Facebook Google Twitter Yahoo
Male Female Non Disclosed*
Source: Companies Diversity Reports

19. 100%
80%
60%
40%
20%
0%
1%
TECH
20% 15% 17% 10% 15%
85%
90%
85% 83%
80%
Apple Facebook Google Twitter Yahoo
Male Female Non Disclosed*
Source: Companies Diversity Reports

20. NON-TECH
100%
80%
60%
40%
20%
0%
1%
35% 47% 48% 50% 52%
47%
52% 50% 53%
65%
Apple Facebook Google Twitter Yahoo
Male Female Non Disclosed*
Source: Companies Diversity Reports

21. H I G H L E V E L
100%
80%
60%
40%
20%
0%
1%
28% 23% 21% 21% 23%
77% 79% 79% 77%
72%
Apple Facebook Google Twitter Yahoo
Male Female Non Disclosed*
Source: Companies Diversity Reports

22. TECH
100%
80%
60%
40%
20%
0%
1%
20% 15% 17% 10% 15%
85%
90%
85% 83%
80%
Apple Facebook Google Twitter Yahoo
Male Female Non Disclosed*
30% 31% 30% 30% 37%
70% 69% 70% 70%
NON-TECH
100%
80%
60%
40%
20%
100%
80%
60%
40%
20%
0%
1%
62%
1%
35% 47% 48% 50% 52%
47%
52% 50% 53%
65%
Apple Facebook Google Twitter Yahoo
Male Female Non Disclosed*
SENIOR LEVEL
100%
80%
60%
40%
20%
0%
1%
28% 23% 21% 21% 23%
77% 79% 79% 77%
72%
Apple Facebook Google Twitter Yahoo
Male Female Non Disclosed*
ALL AREAS
0%
Apple Facebook Google Twitter Yahoo
Male Female Non Disclosed*
Source: Companies Diversity Reports

23. C U R R E N T S C E N A R I O O F
T H E T E C H WORKFORCE
• Men 2.7x more chance of leading positions
• Women gravitates towards other women
• Lack of role models
• Women values flexibility more than men
Source: Anita Borg Institute, Climbing the technical ladder

24. R A N K L E V E L S
Entry Mid High
10.9% 20.2%
55.2%
56%
24.6%
33.1%
Source: Anita Borg Institute, Climbing the technical ladder
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Women Men

25. W H Y D I V E R S I T Y
MAT T E R S ?

26. D I V E R S I T Y
I N C R E A S E S G RO U P
PERFORMANCE

27. GROUP PERFORMANCE
• Collective Intelligence is increased
• Diverse teams are more efficient
• Better problem solving
• More innovative solutions
Source: Ernest & Young, Prof. Anita Williams Woolley, Anita Borg Institute

28. GROUP PERFORMANCE
• Three factors:
1 - Social sensitivity
2 - Numbers of speaking members
3 - Proportion of females on the group
Source: Ernest & Young, Prof. Anita Williams Woolley, Anita Borg Institute

29. D I V E R S I T Y P OW E R S
INNOVAT I O N

30. INNOVAT I O N
• Competitive advantage
• Diverse groups outstanding performance
• Patents with mixed gender cited more often
Source: Ernest & Young, Prof. Anita Williams Woolley, Anita Borg Institute

31. "If people think alike, then no matter how smart they are they most likely will get stuck at
the same locally optimal solutions. Innovating, requires thinking differently. That's why
diversity powers innovation."
–Scott Page, University of Michigan
Source: London Business School, Anita Borg Institute

32. N E I L D E G R A S S E
TYSON

33. N E I L D E G R A S S E TYSON
Astrophysicist

34. “Before we start talking about genetic differences, you
got to come up with a system that is equal
opportunity. Then we can have that conversation.”
–Neil deGrasse Tyson, at 2009 New York Conference - Link

35. T H A N K YOU
• Twitter: @gabidavila
• Web: http://gabriela.io
• Email: gabidavila@gmail.com

36. R E F E R E N C E S
• ANITA BORG INSTITUTE - Climbing the Technical Ladder
• ANITA BORG INSTITUTE - The Case for Investing on Women
• ANITA BORG INSTITUTE - Women Technologists Count
• ANITA WOLLEY - Evidence for a Collective Intelligence Factor in the
Performance of Human Groups
• CATALYST, Why Diversity Matters?
• ERNST & YOUNG - Groundbreakers
• ILLUMINATE VENTURES - High Performance Entrepreneurs