Despite the tremendous growth and opportunities in big data today, women still play a small role in this arena. There are several reasons why women are underrepresented in tech, especially in big data. One major reason is that of retention of women technologists within the workforce. This is also known as the leaky pipeline issue, where studies indicate a 50% decline in representation of women from entry to executive levels in tech roles today. Speaker: Kavitha Mariappan This talk was originally presented at Spark Summit East 2017.

1. The Leaky Pipeline Problem:
Making your Mark as a Woman in Big Data
Kavitha Mariappan
VP of Marketing, Databricks
February 8th, 2017

2. About Me

3. Women are Still Under-Represented in
Tech (and Big Data)
SOURCES: 1. U.S. Bureau of Labor Statistics 2. McKinsey and LeanIn.Org Women in the Workplace Report 3. Harvey Nash CIO Survey 2015 4. www/anitaborg.org/
With this rate of progress, it will take until 2133 to close the gender gap,
says the World Economic Forum.
23% of technical jobs in the
US are held by women1
17% of women make it
to the C-Suite2
8% of CIOs in the
US are women 3
7% US tech start ups
are women-owned 4
Women entrepreneurs begin with
of the funding
of male-owned ventures1/8 5

4. Why Does the Gender Gap Still
Persist?
The
“Network Effect”
Retention due to work-life
integration & cultural issues:
the ‘Leaky Pipeline’
Fewer girls are
entering STEM
—still!—

5. The ‘Leaky Pipeline’ Issue
Why?
• Lack of female role models
• Lack of mentoring opportunities
• Work/life challenges
• Lack of clear career path
• Lack understanding of politics
• Perceived lack of skills/experience
• Feeling isolated/not supported
• Gender stereotyping
Anita Borg Institute 2015 Impact Report
There still is a 50% decline in representation of women
from entry to exec levels in tech jobs.

6. Why are Women in our Field Invisible?
10%
of Spark Summit East
Speakers
are Women
14%
of Spark Summit East
Attendees
are Women
at Strata Hadoop San Jose
74 of 394
Speakers
were Women

7. Thoughts from Claudia Perlich
one of the nation’s top Data Scientists
Ten years ago, having an
advanced degree in the
equivalent of data science
was not exactly sought after
in industry, and few of us
ventured in that direction
Many women in data science
are simply not in the right
places to be seen
Most of her female data
science friends have
chosen to stay in academia
Why are Women in our Field Invisible?

8. The Network Effect
• The best way to hire is through referrals!
• When it comes to referrals and recommendations,
people tend to recommend others much like themselves,
which reproduces the status quo
Sources: 1. Federal Reserve Bank of New York 2. Fernandez & Campero, 2012
64%
of employees
recommend candidates
of the same gender1
for exec high-tech jobs,
referred candidates are
much more likely to be
men than women
2

9. The Unconsciously Biased Address Book – The 20% Problem
by Rick Klau, Partner at Google Ventures
The Network Effect
Over 1,900 contacts
in his address book
399
were women
The previous year 79.7% of people he followed on Twitter were men;
today his address book is 79.9% men

10. Examples of Innate Behavior
• We feel uncomfortable to ask for a pay raise or a bonus
• We are less likely to self-promote
• We opt out even before we throw our hat in the ring
• We sit at the table but often don’t ‘take a seat at the table’
• We often don’t put our hand up for the high-visibility
projects or promotions

11. Embrace Adversity with Diversity
• In general, I find that playing the gender card is not fair to
either men or women – so let’s be constructive.
• Be proactive – ask for what you deserve
• You can’t win if you don’t play – seek opportunities and make
them yours
• Seek out mentors and networks
• Don’t just be the utility technical player
• Embrace your ‘inner girl’
• Stay hungry!
As Margaret Thatcher once said,
“plan your work for today and every day, then work your plan.”

12. Thank You.
kavitha@databricks.com
@kmariappan on Twitter