Organizational Behavior and Leadership In the 21st Century sept 20 2024

Case Analysis/Discussions Content

1. Please find attached case study 1, “DATA SCIENCE AND ITS RELATIONSHIP TO BIG DATA AND DATA DRIVEN DECISION MAKING.” and the requirements for the report.

Women in the
Workplace

2

023

About the study

Women in the Workplace is the largest study on the state of women in
corporate America.

1

In 2015, McKinsey & Company and LeanIn.Org
launched the study to give companies insights and tools to advance
gender diversity in the workplace. Between 2015 and 2023, over 900
companies participated in the study, and more than 450,000 people
were surveyed on their workplace experiences. This year, we collected
information from 276 participating organizations employing over 10
million people, surveyed more than 27,000 employees, and conducted
interviews with people of diverse identities, including women of color,
LGBTQ+ women, and women with disabilities.2

Sign up to participate in the 2024 study at womenintheworkplace.com.

2 | WOMEN IN THE WORKPLACE: ABOUT THE STUDY

http://womenintheworkplace.com

9

1

3

16

2

2

PART 1

State of the pipeline5

Table of Contents

Introduction 4

3 | WOMEN IN THE WORKPLACE: TABLE OF CONTENTS

PART 2

Debunking four myths on the state of women

On women’s ambition

On women’s career progression

On women’s everyday experiences

On flexibility and the future of work

8

PART 3

Recommendations for companies29

Acknowledgments

Report authors

Corporate pipeline by industry

Methodology

Endnotes

42

43

44

46

48

Debunking Four Myths
That Hold Women Back

For the ninth year of the Women in the Workplace report, we start with the corporate pipeline
because it offers a bird’s-eye view of the state of women in corporate America. The story is
both encouraging and frustrating. Over the last several years, there have been sizable gains in
senior leadership.3 This is an important step in the right direction and shows what companies
can accomplish when they focus their efforts on a well-understood problem. However, with
lagging progress in the middle of the pipeline—and a persistent underrepresentation of
women of color—true parity remains painfully out of reach.4

This year’s report debunks four myths about women’s workplace experiences and career
advancement. A few of these myths cover old ground, but given the notable lack of progress,
they warrant repeating. A few have re-emerged and intensified with the shift to flexible work.5
We hope highlighting them will help companies find a path forward that casts aside outdated
thinking once and for all and accelerates progress for women. The future of work for women
depends on getting this right.

INTRODUCTION

4 | WOMEN IN THE WORKPLACE: INTRODUCTION

State of the
Pipeline

PART

1

ENTRY LEVEL MANAGER SR. MANAGER/
DIRECTOR VP SVP C-SUITE

2023
TOTAL WOMEN 48% 40% 36% 33% 27% 28%

% CHANGE
FROM 2015–2023 7% 8% 13% 22% 17% 65%

% POINT CHANGE
FROM 2015–2023 +3pp +3pp +4pp +6pp +4pp +11pp

% of employees by level at the start of

2023

REPRESENTATION IN THE CORPORATE PIPELINE BY GENDER AND RACE7

Despite gains at the top, women
remain underrepresented
Over the past nine years, women—and especially women of
color—remain underrepresented across the corporate pipeline.6

However, we see a growing bright spot in senior leadership. Since
2015, the number of women in the C-suite has increased from 17 to
28 percent, and the representation of women at the VP and SVP
levels has also improved significantly.

6 | WOMEN IN THE WORKPLACE: PIPELINE

WO

MEN OF COLOR

WHITE

WOMEN

MEN OF COLOR

WHITE MEN 34%

18%

2

9%

18%

42%

18%

27%

13%

48%

16%

27%

9% 7% 7% 6%

26% 21%

22%

14% 15%

15%

53% 58%

56%

WOMEN MEN

Women represent roughly 1 in 4 C-suite leaders,
and women of color just 1 in 16.

These hard-earned gains
are encouraging yet fragile
Progress remains slow for women at the manager and director levels,
creating a weak middle in the pipeline and impacting the majority of
women in corporate America. And the “Great Breakup” continues for
women at the director level, the group next in line for senior leadership
positions.8 Similar to last year, women directors are leaving at a higher
rate than in past years—and at a notably higher rate than men at the
same level. As a result of these two dynamics, there are strikingly fewer

women in line for top positions.9

Moreover, progress for women of color is lagging behind. At nearly
every step in the pipeline, the representation of women of color falls
relative to white women and men of the same race and ethnicity. Until
companies address this inequity head on, women of color will remain
severely underrepresented in leadership positions—and mostly absent
from the C-suite.

7 | WOMEN IN THE WORKPLACE: PIPELINE

“People need to see leaders who look like
themselves to understand that it’s possible
for them.”

BLACK WOMAN
DIRECTOR, WORKS HYBRID

Women of color face the steepest drop-off
in representation from entry-level to C-suite
positions. As they move up the pipeline, their
representation drops by two-thirds.

LATINAS OFTEN DON’T SEE THEMSELVES IN LEADERSHIP

Latinas stand out as being the least likely of any group of
women to receive a raise in the last year and also face the
steepest climb up the corporate ladder: only 1 percent of
C-suite executives are Latina. “It’s disheartening to be part of
an organization for many years and still not see a person like
me in senior leadership,” explains one Latina professional.
“Until I see somebody like me in the C-suite, I’m never going to
really feel like I belong.”

Four Myths on
the State of
Women at Work

PART 2

REALITY

Women are becoming less
ambitious

MYTH

Women are more ambitious than
before the pandemic—and flexibility

is fueling that ambition

Recent headlines suggest that women’s ambition is diminishing. Our data
tell a different story. Women remain highly ambitious, and flexible work is
helping them pursue their ambitions.

Women are equally
as ambitious as men
At every stage of the pipeline, women are as committed to their
careers and as interested in being promoted as men. Women and
men at the director level—when the C-suite is in closer view—are also
equally interested in senior leadership roles. And young women are
especially ambitious.10 Nine in 10 want to be promoted to the next
level, and 3 in 4 aspire to become senior leaders.

Moreover, the pandemic and increased flexibility did not dampen
women’s ambitions. Roughly 8 in 10 women want to be promoted to
the next level this year, compared to 7 in 10 in 2019.11 And the same
holds true for men.

% of women and men and those 30 and under who say their career is important to them and they are interested in being promoted to the next level

WOMEN ARE JUST AS COMMITTED TO THEIR CAREERS AND ADVANCING AS MEN

96%

97%

MEN WOMEN

81% 81%

MEN WOMEN

94% 93%

MEN WOMEN

96%

MEN WOMEN

96%

View career as important

Interested in getting promoted to the next level

All employees Age 30 and under All employees Age 30 and under

“In my next role, I hope to be a director. I like
my current role, but I would like to see myself
moving up.”

LATINA MOTHER
SENIOR MANAGER, WORKS ON-SITE

10 | WOMEN IN THE WORKPLACE: AMBITION

Women of color are even more ambitious
than white women: 96% say that their career
is important to them, and 88% want to be
promoted to the next level.

Women who work hybrid or remotely are no
more likely than women who work on-site to
consider reducing their hours or switching to a
less demanding job.

Workplace flexibility helps
unlock women’s ambitions
Women who work hybrid or remotely are as ambitious as women and
men who work on-site. Also, women who work flexibly are just as
ambitious as women who don’t work flexibly. In fact, flexibility is
allowing women to pursue their ambitions. One in 5 women say
flexibility has helped them stay at their organization or avoid reducing
their hours. A large number of women who work hybrid or remotely
point to feeling less fatigued and burned out as a primary benefit. And
a majority of women report having more focused time to get their
work done when they work remotely.

% of women and men who are interested in being promoted to the next level

WOMEN WHO WORK HYBRID OR REMOTELY ARE AS AMBITIOUS AS WOMEN AND MEN WHO WORK ON-SITE

11 | WOMEN IN THE WORKPLACE: AMBITION

“Flexible work has made me more productive
because I can build work around whatever
I’ve got going on with my personal life. If I
wake up early in the morning, I can jump
online and go through emails real quick.”

WHITE WOMAN, MOTHER
DIRECTOR, WORKS REMOTELY

WOMEN MEN

On-site

Hybrid

Remote

On-site

Hybrid

Remote

79%

85%

80%

79%
80%

83%

Interested in getting promoted to the next level

Women’s ambition remains high
even as they prioritize their
personal lives more
The pandemic showed women that a new model of balancing work and
life was possible. Now, few want to return to the way things were. Most
women are taking more steps to prioritize their personal lives, but at no
cost to their ambition—they remain just as committed to their careers,
and just as interested in advancing, as women who aren’t. These women
are defying the outdated notion that work and life are incompatible—and
that one comes at the expense of the other.

“The house is crazy. A dog, our four kids, a
wife. Being able to juggle all that is going on
in the personal life by having flexibility at
work is extremely important. It leads to a
healthy balance from my perspective
between work and personal life.”

WHITE MAN, FATHER
DIRECTOR, WORKS HYBRID

Men are also prioritizing both life and career:
Roughly 60% of men are taking more steps to
prioritize their personal lives, and like women,
they are just as ambitious as men who aren’t.

12 | WOMEN IN THE WORKPLACE: AMBITION

% of women and men who are and aren’t taking more steps to prioritize personal lives who see career as important and want to be promoted 12

WOMEN WHO ARE INVESTING MORE IN THEIR PERSONAL LIVES ARE JUST AS AMBITIOUS

MEN WOMEN

Taking more steps to
prioritize their lives

View career as important

Interested in getting
promoted to the next level

97%

82%

83%

97%

Not taking
more steps

View career as important

Interested in getting
promoted to the next level

80%

80%

96%

96%

The biggest barrier to women’s
advancement is the “glass ceiling”

The “broken rung” is the greatest
obstacle women face on the path

to senior leadership

MYTH

REALITY

The glass ceiling—a term coined over 40 years ago to describe an invisible barrier
preventing women from reaching senior leadership—is often cited as the primary
reason more women don’t rise to the top. Our data point to a bigger problem much
earlier in the pipeline.

For every 100 men promoted to manager,
far fewer women are promoted

100

ALL
MEN

ALL
WOMEN

WHITE
WOMEN

ASIAN
WOMEN

BLACK
WOMEN

LATINAS

87
91 89

54

76

Ratio of promotions to manager for men vs. women

WOMEN LOSE THE MOST GROUND AT THE FIRST STEP UP TO MANAGER

14 | WOMEN IN THE WORKPLACE: BROKEN RUNG

The broken rung remains the biggest
barrier women face
For the ninth consecutive year, women face their biggest hurdle at the first critical
step up to manager. This year, for every 100 men promoted from entry level to
manager, 87 women were promoted. And this gap is trending the wrong way for
women of color: this year, 73 women of color were promoted to manager for every
100 men, down from 82 women of color last year. As a result of this broken rung,
women fall behind and can’t catch up.13

2018 2019 2020 2021 2022

58 58 82 96 54

Progress for early career Black women remains
the farthest out of reach.14 After rising in 2020 and
2021, likely in response to heightened focus on
their advancement, the number of Black women
promoted to manager for every 100 men has fallen
back to 2019 levels.15

100%

50%

“I’ve always done every task, every
project ahead of schedule and
under budget, and I still couldn’t
get the promotions I saw my white
colleagues getting.”

BLACK WOMAN
C-SUITE, WORKS HYBRID

Here are three things every
company should know about
the broken rung:

15 | WOMEN IN THE WORKPLACE: BROKEN RUNG

Women are not responsible for it

Outdated thinking often points to two explanations for the broken rung:
women are not asking for promotions, and they’re more likely to step
away from work. Neither is true. Women at the entry and manager levels
ask for promotions as often as men do, and they are no more likely to
leave their company—this year, 17 percent of entry-level men chose to
leave, compared to 16 percent of women at the same level.

Bias is a strong driver of the broken rung

If women’s career choices don’t explain the broken rung, what does?
Women are often hired and promoted based on past accomplishments,
while men are hired and promoted based on future potential. This unfair
thinking—rooted in what social scientists refer to as “performance
bias”—can be particularly challenging.16 Because women early in their
careers have shorter track records and similar work experiences relative
to their men peers, performance bias can especially disadvantage them at
the first promotion to manager.17

Until the broken rung is fixed, gender parity in
senior leadership remains out of reach

While companies are increasing women’s representation at the top, doing
so without addressing the broken rung offers only a temporary stopgap.
Because of the gender disparity in early promotions, men end up holding
60 percent of manager-level positions in a typical company, while women
occupy 40 percent. Since men significantly outnumber women, there are
fewer women to promote to director, and the number of women decreases
at every subsequent level.

“A president of a tech company said
something that stuck with me. She said,
‘Women are hired for what they have done.
Men are hired for what they can become.’
Women have to have a proven record, but
men do not.”

SOUTHEAST ASIAN WOMAN
VICE PRESIDENT, WORKS HYBRID

Almost a quarter of women 30
and under say that their age has
contributed to them missing out
on a raise, promotion, or chance
to get ahead.

Microaggressions have a
large and lasting impact on women

REALITY

Microaggressions
have a “micro” impact

MYTH

The term microaggressions implies they’re insignificant. This is simply not true. In reality,
microaggressions take a heavy toll on women and inhibit their career progression.

17 | WOMEN IN THE WORKPLACE: MICROAGGRESSIONS

Microaggressions are demeaning or dismissive
comments and actions— rooted in bias—directed
at a person because of their gender, race, or other
aspects of their identity.

Self-shielding, also known as self-monitoring,
refers to efforts to avoid or protect oneself from
mistreatment by continuously modifying one’s
behaviors. This includes code-switching,
restricting self-expression, or hiding aspects
of one’s identity.

Why the “micro” in microaggressions?

The term microaggressions was coined in 1970 by researchers to refer
to the prejudiced and exclusionary acts that may be more subtle than
overt discrimination, but nonetheless have a big impact on well-being.23

The popular misunderstanding that microaggressions are minor or
insignificant minimizes the real harm they cause.

Despite the “micro” in their
name, microaggressions have
a macro impact
Microaggressions signal disrespect, cause acute stress, and can negatively
impact women’s careers and health.18 Years of data show that women
experience microaggressions at a significantly higher rate than men: they are
twice as likely to be interrupted and hear comments on their emotional state.
For women with traditionally marginalized identities, these slights happen
more often and are even more demeaning. As just one example, Asian and
Black women are seven times more likely than white women to be confused
with someone of the same race and ethnicity.19

As a result, the workplace is a mental minefield for many women, particularly
those with traditionally marginalized identities. Women who experience
microaggressions are much less likely to feel psychologically safe, which
makes it harder to take risks, propose new ideas, or raise concerns.20 The
stakes just feel too high. On top of this, 78 percent of women who face
microaggressions—so the vast majority—self-shield at work, or adjust the
way they look or act in an effort to protect themselves.21 For example, many
women choose not to speak up or share an opinion to avoid seeming difficult
or aggressive to their colleagues. The stress caused by these dynamics cuts
deep. Women who experience microaggressions—and self-shield to deflect
them—are three times more likely to think about quitting their jobs and four
times more likely to almost always be burned out.22 By leaving
microaggressions unchecked, companies miss out on everything women
have to offer and risk losing talented employees.

Women with traditionally marginalized
identities face more microaggressions at work

ALL MEN ALL WOMEN
LGBTQ+
WOMEN

WOMEN
WITH

DISABILITIES
WHITE

WOMEN
ASIAN

WOMEN LATINAS
BLACK

WOMEN

MICROAGGRESSIONS 24

Challenges to competence

14% 21% 26% 32% Others get credit for their ideas 21% 17% 15% 22%

17% 23% 33% 39% Their judgment is questioned 24% 16% 17% 27%

5% 9% 11% 14% They’ve been mistaken for
someone more junior

9% 8% 6% 9%

10% 22% 30% 35% They’re interrupted or spoken
over more than others

22% 19% 19%

24%

Demeaning and “othering”

2% 5% 13% 12% Others comment on their
appearance

5% 3% 5% 6%

6% 12% 21% 25% Others comment on their emotional
state

12% 7% 10% 13%

10% 14% 23% 25% They’re criticized for
their demeanor 25 15% 9% 14% 18%

2% 4% 6% 6% They’re confused with someone
else of the same race/ethnicity

2% 14% 6% 15%

3% 4% 5% 5% They feel judged because
of their accent

2% 7% 10% 8%

5% 7% 8% 9% Others make assumptions
about their culture 26 2% 17% 16% 13%

BETTER EXPERIENCE WORSE EXPERIENCE

18 | WOMEN IN THE WORKPLACE: MICROAGGRESSIONS

ASIAN WOMEN ARE OVERLOOKED AT WORK

Asian women are significantly more likely than women overall to be mistaken
for other colleagues of the same race or ethnicity. This experience, which is all
too common for Black women as well, is not only disrespectful, but it means
their contributions at work may go unnoticed. In addition, assumptions about
their culture signal a lack of attention and respect. “I’ve gotten mistaken for
Chinese,” explains one Filipino manager. “People will ask me about some kind
of Chinese delicacy assuming that all Asian backgrounds are the same.”

As microaggressions harm women and
threaten their psychological safety, they
self-shield to protect themselves

BETTER EXPERIENCE WORSE EXPERIENCE

ALL MEN ALL WOMEN
LGBTQ+
WOMEN

WOMEN
WITH

DISABILITIES
WHITE

WOMEN
ASIAN

WOMEN LATINAS
BLACK

WOMEN

SELF-SHIELDING BEHAVIORS 27

4% 8% 20%

17%

They feel pressure to change
their appearance to look more

professional
9% 7% 7% 9%

23% 32% 47% 49% They tone down what they say
to avoid being unlikable

32% 28% 26%

37%

4% 6% 35% 29% They hide important aspects
of their identity to fit in

6% 3% 6% 5%

9% 15% 28% 29% They have to code-switch to
blend in with others

12% 15% 15%

36%

22% 31% 42% 48% They don’t speak up or share an
opinion to avoid seeming difficult

31% 25% 27% 39%

15% 25% 33%

41%

They feel like they have to perform

perfectly to avoid scrutiny or
judgment

24% 24% 20% 33%

PSYCHOLOGICAL SAFETY

57% 56% 61% 56% They don’t worry they’ll be
penalized for mistakes 28 61% 51% 44% 45%

62% 54% 59% 52% They feel comfortable disagreeing
with coworkers 29 57% 51% 45%

44%

52% 48% 54% 49% They rarely feel excluded 30 52% 44% 39% 37%

19 | WOMEN IN THE WORKPLACE: MICROAGGRESSIONS

BLACK WOMEN ARE OFTEN FORCED TO CODE-SWITCH

Black women are more than twice as likely as women
overall to code-switch at work by changing their
mannerisms, tone, or speaking style. They are also more
likely than women of other races and ethnicities not to
speak up or share an opinion to avoid appearing difficult or
aggressive. “I speak very differently at home than I do at
work,” explains one Black woman. “I feel like I have to be
careful about the way I say things.”

20 | WOMEN IN THE WORKPLACE: MICROAGGRESSIONS

Microaggressions lead to negative
outcomes for women

LGBTQ+ WOMEN FEEL PRESSURE TO HIDE THEIR
FULL IDENTITIES AT WORK

More than any other group of women, LGBTQ+ women
feel the need to hide important parts of their identities
to fit in at work.34 They are also 2.5 times more likely to
feel pressure to change their appearance to be perceived
as more professional. Such self-shielding behaviors make
it harder for them to bring their authentic selves to work.
“I had an experience where I think I got turned down for
a promotion because of my hair. I wasn’t as girly as the
others going for that role,” explains one director who
identifies as bisexual. “And when I looked at myself
compared to the other s—I didn’t wear makeup and I didn’t
wear jewelry—I didn’t have an executive presence.”

“It’s like I have to act extra happy so I’m not looked at as bitter
because I’m a Black woman. And a disabled Black woman at
that. If someone says something offensive to me, I have to
think about how to respond in a way that does not make me
seem like an angry Black woman.”

BLACK WOMAN WITH A PHYSICAL DISABILITY
ENTRY LEVEL, WORKS REMOTELY

Roughly 1 in 3 women with disabilities and
1 in 4 LGBTQ+ and Black women have felt
invisible or like their accomplishments
didn’t get noticed at work.

Women who experience
microaggressions and

self-shield 31 are …

4.2x
more likely

to almost
always feel
burned out

3.3x
more likely

to consider
leaving their

company

2.6x
more likely

to say they
wouldn’t recommend

their company 32

3.8x
more likely

to feel they
don’t have an equal

opportunity to advance 33

“When I was climbing the ladder to executive
director, I felt that the only way that I could be
successful was to do everything I possibly could
to assimilate. I would watch how the white
female leaders would dress, how they would
communicate, how they would interact. I felt I
needed to look like that, sound like that, and
model that.”

LATINA
MANAGER, WORKS HYBRID

IN THEIR WORDS

“There’s not as many people of color or even women, so I
do feel like you need to present yourself a certain way in
order to be taken seriously or even considered.”

SOUTH ASIAN WOMAN
ENTRY LEVEL, WORKS HYBRID

“I had an experience with a boss … being painted as sassy,
feisty, or rude. I feel like I have to be so careful about how
I’m doing here —about what I’m doing here—because I just
feel like I’m really going to get mischaracterized if I’m not
careful with my words.”

LATINA, TRANS WOMAN
ENTRY LEVEL, WORKS ON-SITE

“I can’t change the color of my skin, and that is what makes
everyone afraid when I walk into a room. It’s the color of my
skin. I can’t erase it. At the end of the day, if I did not have
this color skin, I wouldn’t have to work as hard as I do to
maintain my seat and protect my name.”

AFRO-LATINA WOMAN WITH A PHYSICAL DISABILITY
C-SUITE, WORKS ON-SITE

“We experience [a] sense of un-belonging in
many spaces and constant microaggressions
based on our identities as indigenous people.”

INDIGENOUS WOMAN
DIRECTOR, WORKS HYBRID

“Being born female, and I present very
feminine, people assume that I’m straight and
that I’m cisgender. [When on-site] I’m mentally
preparing myself for how much I want to
disclose about my gender.”

WHITE NONBINARY PERSON
ENTRY LEVEL, WORKS HYBRID

21 | WOMEN IN THE WORKPLACE: MICROAGGRESSIONS

It’s mostly women who want—
and benefit from—flexible work

Men and women see flexibility as
a “top 3” employee benefit and

critical to their company’s success

MYTH

REALITY

Healthcare benefits

Opportunities to work remotely

Control over when you work

Mental health benefits

Bereavement leave

Parental leave

Childcare and caregiver benefits

Opportunities to work

on-site

23 | WOMEN IN THE WORKPLACE: FLEXIBILITY

% of women and men saying these benefits are most important to them

EMPLOYEES HIGHLY VALUE OPPORTUNITIES TO WORK FLEXIBLY

83%
79%

60%

78%

54%

24%

18%

17%

15%

14%

68%

38%

25%

22%

22%

12%

MEN WOMEN

Stereotypes about women suggest they are the only workers who care about
flexibility. In reality, a majority of men and women place a high premium on flexible
work and point to it as a key benefit.

Employees view flexibility as vital
today—and for the future of work
A vast majority of employees say that opportunities to work remotely and
have control over their schedules are top company benefits, second only to
healthcare. Workplace flexibility even ranks above tried-and-true benefits such
as parental leave and childcare.

Flexibility is also core to how employees view the future of work. Half of women
and a third of men point to “offering significant flexibility in when and where
employees work” as a top-three factor in their company’s future success.

As workplace flexibility transforms from a nice-to-have for some employees to a
crucial benefit for most, women continue to value it more. This is likely because
they still do a disproportionate amount of childcare and household work.35

Flexibility refers to remote or hybrid work, as
well as flexible work options such as the ability
to set your own hours.

For mothers, flexibility is not just about
where—but also when—they work.
Mothers with young children are especially
likely to rank flexible scheduling as a top
employee benefit.36 And without flexibility,
38% say that they would have had to
otherwise leave their company or reduce
their work hours.

% of employees who say they feel this way when working flexibly

WOMEN ARE FAR MORE CONFIDENT WORKING FLEXIBLY THAN THEY WERE 2 YEARS AGO 37

A CLOSER LOOK

Employees are increasingly
comfortable working flexibly

A majority of women and men work more flexibly than
they did before the pandemic, and relatively few feel
judged or worry it will negatively impact their careers.
Most notably, women are far more likely to feel set up to
succeed when they work this way than they did two
years ago.

24 | WOMEN IN THE WORKPLACE: FLEXIBILITY

“Working from home you’re going to be
more comfortable, and you’re going to
get more done in the process.”

WHITE WOMAN
ENTRY LEVEL, WORKS HYBRID

2021

10%
12%

Set up to succeed

2023

MEN WOMEN

32%

27%

MEN WOMEN

2021

18%

13%

Worried that it will hurt my career

2023

MEN WOMEN

11%9%

MEN WOMEN

25 | WOMEN IN THE WORKPLACE: FLEXIBILITY

Women and men cite stress-reducing
upsides with remote work.
Twenty-nine percent of women and
25% of men who work remotely say
one of the biggest benefits is having
fewer unpleasant interactions with
coworkers. Even more—53% of women
and 36% of men—point to reduced
pressure around managing their
personal style or appearance.

When women work remotely, they face fewer
microaggressions and have higher levels of
psychological safety.39

The ability to work remotely delivers
benefits to most employees
Hybrid and remote work are delivering important benefits to employees.
Most women and men point to better work-life balance as a primary
benefit of remote work, and a majority mention less fatigue and burnout.
And research shows that good work-life balance and low burnout are
key to organizational success.38

Moreover, 83 percent of employees say the ability to work more efficiently
and productively is a primary benefit of working remotely. However, it’s
worth noting that companies see this differently: only half of HR leaders say
employee productivity is a primary benefit of working remotely.

% of employees working hybrid or remotely who say this is one of the biggest benefits of their work arrangement

THE TOP 5 BENEFITS OF HYBRID AND REMOTE WORK MEN WOMEN

86% 79%

80%

54%

36%

32%

86%

60%

53%

41%

83% 79%

67%

58%

29%

26%

72%

62%

43%

30%

Hybrid Remotely

1 You have an easier time balancing
work/life

2 You are more efficient and productive

3 You experience less work burnout or
fatigue

4 You feel less pressure to manage your
personal style or appearance

5 You are better able to coordinate and
communicate cross-functionally

WOMEN MEN

1 You feel more personally connected to your coworkers

2 You have better access to work resources and equipment

3 You have an easier time collaborating with coworkers

4 You are better able to coordinate and communicate cross-functionally

5 You have more opportunities to hear from and/or interact with senior leaders

% of employees working on-site who say this is one of the biggest benefits of their work arrangement

THE TOP 5 BENEFITS OF ON-SITE WORK

26 | WOMEN IN THE WORKPLACE: FLEXIBILITY

On-site work delivers benefits
to employees—but with room
for improvement
Employees who work in person also see tangible benefits. A majority
point to an easier time collaborating and a stronger personal connection
to coworkers as the biggest benefits of working on-site—two factors
central to employee well-being and effectiveness.

However, the culture of office work may be falling short. While 77
percent of companies believe a strong organizational culture is a key
benefit of on-site work, most employees disagree: only 39 percent of
men and 34 percent of women who work on-site say a key benefit is
feeling more connected to their organization’s culture. On top of this,
men are more likely to benefit from working on-site.

Gen Z wants to spend some time in the
office. Just 18 percent of the youngest
employees want to work fully remotely.40

61% 62%

62%

61%

49%

37%

60%

56%

41%

37%

27 | WOMEN IN THE WORKPLACE: FLEXIBILITY

A CLOSER LOOK

Men are benefiting disproportionately
from on-site work
Compared to women, men are more likely to be “in the know,”
receive the mentorship and sponsorship they need, and have their
accomplishments noticed and rewarded when they work on-site.

You feel more connected to your
organization’s mission and your

work when on-site

20%

29%

% of women and men who say these things are more true on-site than remote or are benefits of on-site work41

You’re more “in the know” about
decisions that impact you and your

work when on-site

MEN WOMEN

20%

27%

You get more of the mentorship
and sponsorship you need when

on-site

16%
23%

A major benefit of working
on-site is getting useful
feedback more often

31%

22%

MEN REPORT GREATER BENEFITS OF WORKING ON-SITE THAN WOMEN

28 | WOMEN IN THE WORKPLACE: FLEXIBILITY

% of companies with policy or guidelines regarding remote and hybrid work

A BREAKDOWN OF COMPANIES’ HYBRID, REMOTE, AND ON-SITE POLICIES

No formal policy

57%
3 days

Policy for teams/business unitsEnterprise-wide policy

24% 19%57%

22%
2 days

6%
4 days

3%
5 days

6%
1 day

2%
Less than

once a week

5%
Never

A majority of companies with enterprise-wide policies require 2–3 days in the office 42

Industry 43 Policy breakdown by industry
Average number of
days required on-site

Public and social sector

Finance

Energy and basic materials

Food and restaurants

Tech

Transportation, logistics,
and infrastructure

Retail

Engineering, automotive
and industrial manufacturing

Healthcare

2.2

ACROSS DIFFERENT INDUSTRIES, COMPANY POLICIES VARY WIDELY

100%

66%

63%

47%

23% 11%

22% 15%

32% 21%

2.8

2.7

2.5

3.0

1.9

BY THE NUMBERS

Enterprise-level policy or guidelines

No enterprise-level policy or guidelines; individual teams/business units have their own policies

No specific guidelines on the frequency that employees should be in office

2.8

2.8

2.3

58% 31% 12%

54% 8% 38%

53% 33% 13%

37% 11%53%

52% 28% 20%

Recommendations
for companies

PART 3

Measure employees’ outcomes and experiences—
and use the data to fix trouble spots

Companies should track outcomes for drivers of women’s advancement: hiring,
promotions, and attrition. Visibility into other metrics that influence career
progression is also important, such as participation in career development
programs, performance ratings, and employee sentiments on job satisfaction
and inclusion. Then it’s critical that companies mine their data for insights that
will improve women’s experiences and create equal opportunities for
advancement. Ultimately, data tracking is only valuable if it leads to
organizational change.

1

Track outcomes to improve women’s
experience and progression
Tracking outcomes is critical to any successful business initiative. Most companies
do this consistently when it comes to achieving their financial objectives, but few
apply the same rigor to women’s advancement. Here are three steps to get started:

30 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

As companies work to support and advance women, they should focus on five core areas:

● Tracking outcomes for women’s representation

● Empowering managers to be effective people leaders

● Addressing microaggressions head on

● Unlocking the full potential of flexible work

● Fixing the broken rung, once and for all

Take an intersectional approach to
outcome tracking

Tracking metrics by race and gender combined should be table
stakes. Yet even now, fewer than half of companies do this, and far
fewer track data by other self-reported identifiers, such as LGBTQ+
identity.44 Without this level of visibility, the experiences and career
progression of women with traditionally marginalized identities can
go overlooked.

Share internal goals and metrics with employees

Awareness is a valuable tool for driving change—when employees are able to
see opportunities and challenges, they’re more invested in being part of the
solution.45 In addition, transparency with diversity, equity, and inclusion (DEI)
goals and metrics can send a powerful signal to employees with traditionally
marginalized identities that they are supported within the organization.

31 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

3

2

Clarify managers’ priorities and
reward results

Companies need to explicitly communicate to managers what is
core to their roles and incentivize them to take action. The most
effective way to do this is to include responsibilities like career
development, DEI, and employee well-being in managers’ job
descriptions and performance reviews. Yet relatively few
companies evaluate managers on metrics linked to employee
career progression and satisfaction. For example, although 61
percent of companies point to DEI as a top manager capability,
only 28 percent of people managers say their company
recognizes DEI in performance reviews. This discrepancy may
partially explain why relatively few employees say their
manager treats DEI as a priority.

Equip managers with the skills they need
to be successful

To effectively manage the new demands being placed on them,
managers need ongoing education. This includes repeated,
relevant, and high-quality training that emphasizes specific
examples of core concepts and includes concrete actions that
managers can incorporate into their daily practices. And topics
should be carefully selected to give managers the tools they need to
be successful—for example, a focused training on how to manage a
dispersed team may be far more valuable than a broader training on
management essentials. Finally, companies should adopt an “often
and varied” approach to training and upskilling and create regular
opportunities for coaching so that managers can continue to build
the awareness and capabilities they need to be effective.

Support and reward managers as key
drivers of organizational change
Managers are on the front lines of employees’ experiences and central to
driving organizational change. As companies more deeply invest in the culture
of work, managers play an increasingly critical role in fostering DEI, ensuring
employee well-being, and navigating the shift to flexible work. These are all
important business priorities, but managers do not always get the direction and
support they need to deliver on them. Here are three steps to get started:

32 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

1

2

According to companies, the three
most critical mindsets and abilities
for managers are:

1. Treating DEI as a top
business priority

2. A strong growth mindset and
willingness to evolve as a leader

3. The ability to build trusting
relationships with teams

Make sure managers have the time and
support to get it right

It requires significant intentionality and follow-through to be a good
people and culture leader, and this is particularly true when it comes
to fostering DEI. Companies need to make sure their managers have
the time and resources to do these aspects of their job well. On top
of this, companies should put policies and systems in place to make
it easier for managers. As a few examples, many managers would
benefit from sample scripts for challenging conversations and
standardized questions to gauge their team members’ well-being and
job satisfaction in one-on-one meetings.46

33 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

% of women and men who say that their manager consistently takes these actions

A SNAPSHOT OF MANAGER ACTIONS MEN WOMEN

3

48%
46%

63%
64%

57%
58%

38%
34%

54%
54%

50%
51%

64%
66%

50% 100%

Mentors and guides you

Builds a trusting relationship with
you and other team members

Treats employee well-being
as a top priority

Treats diversity, equity, and
inclusion as a top priority

Focuses on your results instead of
where and when work gets done

Shows a growth mindset and
willingness to evolve as a leader

Communicates with you and your team
in a proactive and intentional way

A CLOSER LOOK

Most companies say DEI is a top and growing
priority—and critical to their success
Most companies have increased or maintained their financial and staffing
investments in diversity, equity and inclusion over the past year. And nearly three in
four say DEI is critical to their future success.

% of companies that say staff and budget for DEI work has increased, decreased, or stayed the same

THE MAJORITY OF COMPANIES HAVE DEEPENED THEIR INVESTMENTS IN DEI OVER THE PAST YEAR 47

% of companies selecting this as one of the top 3 most important things to focus on for the future success of their organization

COMPANIES POINT TO DEI AS A TOP 3 DRIVER OF THEIR FUTURE SUCCESS

34 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

60%
Increased
investment

4%
Decreased
investment

34%
Maintained
investment

1 Fostering diversity, equity, and inclusion 73%

2 Effective people management 59%

3 Fostering a culture of innovation 45%

Make clear that microaggressions are
not acceptable

Many people don’t realize the harm microaggressions cause.
To raise employee awareness and set the right tone, it’s crucial
that senior leaders communicate that microaggressions and
disrespectful behavior of any kind are not welcome. Companies
can help with this by developing a code of conduct that
articulates what supportive and respectful behavior looks
like—as well as what’s unacceptable and uncivil behavior.

Teach employees to avoid and
challenge microaggressions

Most people don’t fully understand how microaggressions
work, so they end up saying and doing things that are hurtful.48
Similarly, employees often don’t recognize microaggressions,
let alone know what to say or do to be helpful.49 That’s why it’s
so important that companies have employees participate in
high-quality bias and allyship training and receive periodic
refreshers to keep key learnings top of mind.

Take steps to put an end to
microaggressions
Microaggressions are pervasive, harmful to the employees who
experience them, and result in missed ideas and lost talent. Companies
need to address microaggressions head on. Here are three steps to get
started:

Create a culture where it’s normal to
surface microaggressions

It’s important for companies to foster a culture that encourages
employees to speak up when they see microaggressions or
other disrespectful behavior. Although these conversations can
be difficult, they often lead to valuable learning and growth.
Senior leaders can play an important role in modeling that it is
safe to surface and discuss these behaviors.50

35 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

2

3

1

Establish clear expectations and norms
around working flexibly

Without this clarity, employees may have very different and
conflicting interpretations of what’s expected of them, which
can create confusion and make coordination on and across
teams difficult. As part of this process, companies need to find
the right balance between setting organization-wide guidelines
and allowing managers to work with their teams to determine
an approach that works best for them.

Measure the impact of new initiatives to
support flexibility and adjust as needed

The last thing companies want to do is fly in the dark as they
navigate the transition to flexible work. As organizations roll out
new practices and programs to support flexibility, they should
carefully track what’s working, and what’s not, and adjust their
approach accordingly—a test-and-learn mentality and a spirit of
co-creation with employees are critical to getting these
changes right.

36 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

Invest in tracking and optimizing flexibility
The past few years have seen a transformation in how we work. Flexibility is now the norm in
most companies; the next step is unlocking its full potential and bringing out the best of what
different work arrangements have to offer. Here are three steps to get started:

Women at companies with supportive flexible
policies are more likely to report having equal
opportunities to advance—and are less likely to
consider leaving.

1

2

Most employers now offer opportunities to
work flexibly and remotely—and the vast
majority have maintained or increased
their flexible work options in the last year.
Yet less than 10% of HR leaders point to
“offering significant flexibility in where and
when employees work” as a top-three
driver of their company’s future success,
compared to 42% of employees. This
misalignment may signal that companies
have not yet internalized how beneficial
flexibility is to their employees—and by

extension, to their business.

Trained managers on how to work with
remote and flex employees

Provided networking opportunities
across work arrangements

Communicated that employees should
not be penalized for working flexibly

Redesigned performance evaluations to
emphasize results, not where and when

employees work

Tracked promotions and other
outcomes by work arrangement

Put safeguards in place to ensure a level
playing field across work arrangements

Companies should take steps to ensure that employees aren’t
penalized for working flexibly. This includes putting systems in
place to make sure that employees are evaluated fairly, such as
redesigning performance reviews to focus on results, rather than
when and where work gets done. Managers should also be
equipped to be part of the solution. This requires educating
managers on flexibility stigma and the importance of making sure
their team members get equal recognition for their contributions
and equal opportunities to advance. In addition, to ensure the
playing field is leveled, companies should track outcomes by work
arrangement to see if employees are getting the same
opportunities and advancing at similar rates.

Few companies currently track outcomes
across work arrangements. For example,
only 30% have tracked the impact of their
return-to-office policies on key DEI outcomes.

37 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

52%

44%

29%

17%

9%

100%

ACTIONS COMPANIES HAVE TAKEN TO ENSURE EQUAL OPPORTUNITY ACROSS WORK ARRANGEMENTS

% of companies that have done the following to ensure equal opportunities for career development and progression regardless of working model 52

50%

3

Flexibility stigma is the unfair judgment that
employees often face when they work flexible hours
or work from home. Research shows that employees
who work flexibly face more doubts about their
productivity and commitment, even when they
produce the same results as their colleagues.51

Track inputs and outcomes

To uncover inequities in the promotions process, companies
need to track who is put up for and who receives promotions, by
race and gender combined. Tracking with this intersectional lens
enables employers to identify and address the obstacles faced
by women of color, and companies can use these data to identify
otherwise invisible gaps and refine their promotions processes.

Work to de-bias performance reviews
and promotions

Leaders should put safeguards in place to ensure that evaluation
criteria are applied fairly and bias doesn’t creep into
decision-making. Companies can take these actions:

● Send “bias” reminders before performance evaluations
and promotion cycles explaining how common biases
can impact reviewers’ assessments. Research shows
that anti-bias training can wear off over time, so seasonal
refreshers like this can make a big difference.53

● Appoint a “bias monitor” to keep performance evaluations
and promotions discussions focused on the core criteria for
the job and surface potentially biased decision-making.
Research shows that this has a large impact by shutting
down conversations that are likely influenced by bias.54

● Have reviewers explain the rationale behind their
performance evaluations and promotion recommendations.
When individuals have to justify their decisions, they are
less likely to make snap judgments or rely on gut feelings,
which are prone to bias.

38 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

Fix the broken rung for women,
with a focus on women of color
Fixing the broken rung is a tangible, achievable goal and will set off a
positive chain reaction across the pipeline. After nine years of very little
progress, there is no excuse for companies failing to take action.
Here are three steps to get started:

Companies need clear evaluation
criteria to stop bias from entering
hiring and reviews. Evaluation tools
should be easy to use and designed
to gather measurable, objective input
that can be compared across
candidates.

1

2

Invest in career advancement for women
of color

Companies should make sure their career development programs
address the distinct biases and barriers that women of color
experience. Yet only a fraction of companies tailor career program
content for women of color. And given that women of color tend to
get less career advice and have less access to senior leaders,
formal mentorship and sponsorship programs can be particularly
impactful.55 It’s also important that companies track the outcomes
of their career development programs with an intersectional lens
to ensure they are having the intended impact and not
inadvertently perpetuating inequitable outcomes.

39 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

There are clear and consistent criteria
for evaluating performance

Performance criteria are consistently
applied for all employees

There are quantifiable measures for
performance (e.g., ratings of one to five)

Evaluators are given timely guidance on
how to avoid bias (e.g., unconscious
bias) as part of evaluator instructions

There is a method in place for surfacing
biased comments or evaluations

COMPANIES CAN DO MORE TO DE-BIAS THEIR PERFORMANCE REVIEW PROCESSES

% of companies and % of employees saying this is true for performance reviews at their company

75%47%

58%19%

29%10%

62%35%

83%42%

What employees say What companies say

100%

3

Less than half of companies track the outcomes
of career development programs by gender and
race combined, and fewer than 1 in 5 do so for
sponsorship and mentorship programs.

50%

DEI tracking and metrics

Regularly audit DEI policies and programs

Conduct root cause analysis of DEI
challenges to design targeted solutions

Career advancement initiatives

Career development programs with
tailored content for women

Career development programs with
tailored content for women of color

ERGs with tailored content for women

ERGs with tailored content
for women of color

System for tracking promotion outcomes
of those participating in career programs

Support for flexible working models

Measure the use and impact of
flexibility policies

Gather feedback from employees who
work flexibly (e.g., satisfaction)

Put policies in place to ensure equal
opportunities for career development and
progression across work arrangements

Train managers so they’re better
equipped to manage employees working
remotely and/or at flexible times

40 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

PRACTICES OF TOP PERFORMING COMPANIES

% of top performing companies vs. % of all other companies who report having this policy, practice, or program

Practices of top performing companies
Companies with strong women’s representation across the pipeline are more likely to have certain
practices in place. The following data are based on an analysis of top performers—companies that have a
higher representation of women and women of color than their industry peers.

ALL OTHER COMPANIES TOP PERFORMING COMPANIES

100%50%

70%54%

79%43%

95%76%

80%67%

61%44%

45%24%

67%48%

93%83%

64%48%

80%61%

64%47%

41 | WOMEN IN THE WORKPLACE: RECOMMENDATIONS

PRACTICES OF TOP PERFORMING COMPANIES

% of top performing companies vs. % of all other organizations who report having this policy, practice, or program

Manager trainings on core topics

Cultivating a growth mindset and
willingness to evolve as a leader

Effectively supporting employee
well-being

Fostering diversity, equity, and inclusion

Building trusting relationships with teams

Proactively and intentionally
communicating with teams
across different work models

Mentoring and guiding employees
across different working models

Performance reviews

Evaluate employees for contributing
to a positive culture

Evaluate employees for fostering DEI

Employee benefits and support

Childcare reimbursements

Supports for employees caring
for sick or elderly adults

43%29%

84%67%

100%50%

ALL OTHER COMPANIES TOP PERFORMING COMPANIES

75%50%

78%60%

87%68%

72%57%

99%85%

91%78%

81%67%

65%51%

Acknowledgments
McKinsey & Company and LeanIn.Org would like to thank the 276
companies and more than 27,000 employees who participated in this
year’s study. By sharing their information and insights, they’ve given us
new visibility into the state of women in the workplace and the steps
companies can take to achieve gender equality.

We appreciate the continued help of Defined Contribution Institutional
Investment Association (DCIIA), The Equity Collaborative, Expanding
Equity, International Dairy Foods Association (IDFA), Massachusetts High
Technology Council (MHTC), PayTech Women, The Press Forward, and
Women’s Foodservice Forum (WFF) in convening participants in their

respective industries.

We would like to thank Qualtrics and IntelliSurvey for their help in
conducting the surveys for this study and Getty Images for providing the
photography from the Lean In Collection used in this report and website.

ADDITIONAL RESOURCES FOR COMPANIES

Lean In runs programs to support women and improve the culture of work—and they’re available at no cost,
because every company should have the tools to build an equitable workplace. Our 50 Ways to Fight Bias
training takes the guesswork out of identifying and challenging bias with specific research-backed
recommendations for what to say and do. Allyship at Work focuses on practical steps managers and
employees can take to practice allyship. And our Lean In Circles program brings women together for peer
mentorship and skill building, and pairs well with our new Women at Work Collection—a leadership
curriculum designed specifically for women and customizable for women of color, LGBTQ+ women, and
women with disabilities. Find out why thousands of organizations like Adidas, Sony Music Group, and
Walmart are using our programs and how you can bring them to your company at leanin.org/partner.

McKinsey & Company has made a commitment to researching and building diverse leadership, as well as
inclusive and equitable work environments. We have a track record of client service to institutions working
to modernize their talent and business processes as well as cultures to support these aims. McKinsey offers
award-winning programs to equip leaders with the network, capabilities, and mindsets needed to achieve
their goals. Our Connected Leaders Academy has enrolled 67,000 leaders. This program—offered at no
cost and which includes customized content relevant to Black, Hispanic and Latino, and Asian
leaders—focuses on early professionals, midcareer managers, and senior executives. Our DEI Maturity
Assessment has provided 250+ clients with a comprehensive framework to assess and drive their DEI
strategy. Inclusion assessment has been used by 100+ clients to assess employee perspectives on how
effectively leaders, peers, and systems support inclusion in the workplace. We also offer an Inclusion
Incubator program aimed at fostering meaningful inclusive leadership behaviors. Visit
https://www.mckinsey.com/featured-insights/diversity-and-inclusion to explore McKinsey’s client service,
research, and insights on DEI.

42 | WOMEN IN THE WORKPLACE: ACKNOWLEDGMENTS

https://leanin.org/50-ways-to-fight-gender-bias

https://leanin.org/allyship-at-work

https://leanin.org/circles

http://leanin.org/partner

https://www.mckinsey.com/capabilities/people-and-organizational-performance/how-we-help-clients/mckinsey-academy/connected-leaders-academy/leadership-essentials

https://www.mckinsey.com/featured-insights/diversity-and-inclusion

Report authors
RACHEL THOMAS is cofounder and CEO of LeanIn.Org and
cofounded the Women in the Workplace study in 2015. Under
her leadership, Lean In has become a go-to resource for
original research and educational programs to advance women
and foster diversity, equity, and inclusion in the workplace.
Rachel regularly speaks and writes on issues at the intersection
of women and work.

CAROLINE FAIRCHILD is editor in chief and VP of education at
LeanIn.Org. Before Lean In, Caroline worked in journalism, most
recently at LinkedIn News, where she led coverage of women
in the workplace, and Fortune, where she founded The
Broadsheet, a popular newsletter on women leaders.

GINA CARDAZONE, Ph.D., is the research principal at LeanIn.Org.
She is a community and cultural psychologist specializing in
mixed-methods research. Prior to Lean In, she was a research
consultant working with NGOs, universities, and government
agencies.

MARIANNE COOPER, Ph.D., is a sociologist at the VMware
Women’s Leadership Innovation Lab at Stanford University, where
she conducts research on gender, women’s leadership, and
diversity and inclusion. She has written on these topics for The
Atlantic, The New York Times, and Harvard Business Review.

PRIYA FIELDING-SINGH, Ph.D., is a senior manager of research
and education at LeanIn.Org. She previously worked in academia
as an applied social scientist, mixed-methods researcher, and book
author focused on gender and health equity.

MARY NOBLE-TOLLA, Ph.D., is a senior manager of research and
content at LeanIn.Org. Mary worked in journalism and wrote on
politics and social justice. She also taught English and politics at
Oxford and Princeton.

AMBER BURTON is a manager of research and education at
LeanIn.Org. Prior to Lean In, she worked in journalism covering HR,
DEI, and the future of work for Fortune magazine, Protocol, and The
Wall Street Journal.

Additional writers and analysts:
Briana Edwards
Thamara Jean
Lizbeth Kim, Ph.D.
Jemma York

ALEXIS KRIVKOVICH is the managing partner for McKinsey’s
Bay Area office and oversees FinTech efforts in North America.
She serves financial services and technology companies as they
seek to align their organizations for growth and productivity.
Alexis cofounded the Women in the Workplace research, is
passionate about supporting executive teams to execute on
their diversity strategies, and invests deeply in sponsoring
younger women to build thriving careers.

LAREINA YEE is a senior partner in McKinsey’s Bay Area office.
She is the chair of McKinsey’s Global Technology Council and
focuses on helping clients grow and sustain results. Lareina
cofounded the Women in the Workplace research, served as
McKinsey’s first chief diversity and inclusion officer, and is a
leading expert on advancing diversity in business, championing
best practices that companies can use to change results. She
previously authored Race in the Workplace and Diversity
Matters.

EMILY FIELD is a partner in McKinsey’s Seattle office in the
People and Organizational Performance practice. She advises
organizations globally across industries to deliver on their
performance goals and people aspirations. She has written about
the importance of managers in Harvard Business Review and is
the author of Power to the Middle: Why Managers Hold the Keys
to the Future of Work (Boston: Harvard Business Review Press,
July 2023).

NICOLE ROBINSON, Ph.D., is an associate partner in McKinsey’s
Bay Area office and a leader of diversity, equity, and inclusion work
across the firm. She is dedicated to helping clients through
transformational change that meaningfully improves equity within
organizations. For more than fifteen years, Nicole has researched
and published articles on the impact of gender issues on cultures,
language, and careers.

SANDRA KUEGELE is an engagement manager in McKinsey’s
Washington, D.C., office. She helps clients across sectors to
ignite strategic and organizational change with a focus on
education, culture, leadership, and workforce development.
In previous academic work, she researched bias in decision-
making and tools for emotional well-being of frontline workers
in education.

43 | WOMEN IN THE WORKPLACE: REPORT AUTHORS

44 | WOMEN IN THE WORKPLACE: CORPORATE PIPELINE BY INDUSTRY

Although women are broadly underrepresented in corporate America, the talent pipeline varies
by industry. Some industries struggle to attract entry-level women (e.g., Technology: Hardware; IT
and Telecom; Engineering and Industrial Manufacturing), while others fail to advance women into
middle management (Energy, Utilities, and Basic Materials) or senior leadership (Oil and Gas).

Industries have different talent pipelines
CORPORATE PIPELINE BY INDUSTRY

ASSET MANAGEMENT AND
INSTITUTIONAL INVESTORS

BANKING AND
CONSUMER FINANCE

CONSUMER PACKAGED
GOODS

ENERGY, UTILITIES, AND
BASIC MATERIALS

ENGINEERING AND INDUSTRIAL
MANUFACTURING

IT SERVICES AND
TELECOM

INSURANCE

HEALTHCARE SYSTEMS
AND SERVICES

FOOD AND BEVERAGE
DISTRIBUTION

ENTRY LEVEL MANAGER SR. MANAGER VP SVP C-SUITE

45% 41% 41% 28% 22% 22%

53% 44% 38% 32% 30% 34%

53% 49% 44% 44% 31% 35%

36% 24% 24% 25% 21% 30%

33% 26% 25% 23% 24% 22%

54% 44% 42% 35% 16% 15%

49% 39% 37% 33% 28% 23%

76% 70% 61% 50% 46% 32%

42% 35% 29% 28% 30% 28%

63% 55% 41% 37% 31% 30%

FOOD AND BEVERAGE
MANUFACTURING

MEDIA AND
ENTERTAINMENT

OIL AND GAS

PHARMACEUTICALS AND
MEDICAL PRODUCTS

PROFESSIONAL AND
INFORMATION SERVICES

PUBLIC AND
SOCIAL SECTOR

RETAIL

TECHNOLOGY: HARDWARE

RESTAURANTS

TECHNOLOGY: SOFTWARE

TRANSPORTATION, LOGISTICS,
AND INFRASTRUCTURE

ENTRY LEVEL MANAGER SR. MANAGER VP SVP C-SUITE

50% 44% 43% 45% 34% 39%

39% 25% 23% 23% 13% 15%

55% 51% 44% 39% 33% 38%

52% 43% 33% 33% 24% 26%

49% 46% 43% 41% 36% 32%

58% 48% 43% 41% 40% 32%

58% 50% 43% 39% 37% 36%

32% 27% 23% 22% 17% 24%

43% 38% 37% 36% 30% 30%

48% 37% 33% 35% 28% 25%

45 | WOMEN IN THE WORKPLACE: CORPORATE PIPELINE BY INDUSTRY

Methodology
RESEARCH PARTICIPATION

This report is based on research from 276 companies across the United
States and Canada, building on similar research conducted annually by
McKinsey & Company and LeanIn.Org since 2015, as well as research from
McKinsey & Company in 2012.

A total of 276 participating companies from the private, public, and social
sectors submitted talent pipeline data, and 274 of them also submitted HR
policies and programs data. In addition, more than 27,000 employees from 33
companies were surveyed on their workplace experiences. We also
interviewed 39 women and nonbinary individuals, including people of different
races and ethnicities, LGBTQ+ individuals, and people with disabilities at all
levels in their organizations, working remotely, hybrid, or on-site.

We grouped companies by industry to create benchmarks that provide peer
comparisons. The number of companies from each industry is as follows:

● Asset Management and Institutional Investors—28

● Banking and Consumer Finance—22

● Consumer Packaged Goods—9

● Energy, Utilities, and Basic Materials—17

● Engineering and Industrial Manufacturing—25

● Food and Beverage Distribution—7

● Food and Beverage Manufacturing—7

● Healthcare Systems and Services—23

● Insurance—12

● IT Services and Telecom—6

● Media and Entertainment—5

● Oil and Gas—9

● Pharmaceutical and Medical Products—18

● Professional and Information Services—8

● Public and Social Sector—7

● Restaurants—12

● Retail—10

● Tech: Hardware—13

● Tech: Software—20

● Transportation, Logistics, and Infrastructure—15

● Other—3

Companies opted in to the study in response to invitations from McKinsey
& Company and LeanIn.Org or by indicating interest through our public
website. Participation in the Employee Experience Survey was encouraged, but
optional.

All talent pipeline data collection occurred between May and July 2023. Talent
pipeline data reflect representation of men and women as of December 31,
2022, as well as personnel changes (e.g., due to promotion, hiring, attrition)
during 2022. Therefore, all talent pipeline data do not represent any changes
that occurred during 2023. In July 2023, 52 companies also submitted
optional “H1” data on their talent pipeline representation and personnel
changes for the first half of 2023.

Human resource leaders and professionals provided information on policies,
programs, and priorities on behalf of their company between June and
September 2023. Additionally, employees were surveyed on their workplace
experiences between June and August 2023. These datasets represent
point-in-time snapshots and reflect companies’ responses and employees’
experiences at the time the survey was taken.

Where appropriate, some statements describing women’s experiences in
the workplace were taken from past survey data that have been published
in prior Women in the Workplace reports.

PIPELINE DATA AND ANALYTICS

Overall Metrics

All pipeline metrics (e.g., representation, promotion rates, hiring shares,
attrition rates) were initially calculated for each participating company.
Company results were then averaged for each industry and each industry’s
data were weighted by the composition of the Fortune 500 in 2022. This
enabled us to avoid overemphasizing or underemphasizing particular
industries and better estimate trends over time based on each year’s
sample of companies.

The industry breakdown of the Fortune 500 used for our weighting was:

● Energy and Basic Materials—19%

● Engineering and Automotive and Industrial Manufacturing—10%

● Finance—19%

● Food and Restaurants—7%

● Healthcare—8%

● Media and Entertainment—2%

● Professional and Information Services—4%

● Retail—18%

● Tech—11%

● Transportation, Logistics, and Infrastructure—3%

Definition of Job Levels

Companies categorized their employees into six levels based on the
following standard definitions, taking into account reporting structure and
salaries. The levels and definitions provided were:

● L1—Executives: CEO and direct reports to CEO, responsible for
company operations and profitability (board members are not
included in our primary analyses unless they are also employees)

● L2—Senior vice presidents and other similar roles: senior leaders of
the organization with significant business unit or functional oversight

● L3—Vice presidents and other similar roles: leaders within the
organization, responsible for activities/initiatives within a subunit of a
business unit or function, or who report directly to senior vice
presidents

● L4—Directors: seasoned managers and contributors, with
responsibility for multiple teams and discrete functions or operating
units

● L5—Managers: junior managers and contributors, responsible for
small teams and/or functional units or operations

● L6—Entry level: employees responsible for carrying out discrete tasks
and participating on teams, typically in an office or corporate setting
(field employees like cashiers or customer service representatives are
not included in our primary talent pipeline analyses)

46 | WOMEN IN THE WORKPLACE: METHODOLOGY

TALENT PIPELINE

Metrics and Analytics

Talent pipeline data included the representation of men and women
(overall, in line versus staff roles, by race/ethnicity, and optionally for
functional roles like marketing, sales, and engineering). In addition,
companies reported the number of men and women who were hired,
promoted, and who left the company (overall, by race/ethnicity, and
optionally for functional roles like marketing, sales, and engineering
roles, as well as optionally for voluntarily versus involuntarily leaving).

Promotion and attrition rates were calculated for women and men,
overall and by race/ethnicity, at each level. Promotion rates were
calculated by dividing the number of promotions of that gender into a
level by the number of employees of that gender in the level below at
the start of the year. Attrition rates were calculated by dividing the
number of each gender who left the company at a given level by the
number of employees of that gender in that level at the start of the year.
Submitted data were checked for consistency and inconsistent data
were excluded as needed.

EMPLOYEE EXPERIENCE SURVEY AND ANALYTICS

Survey Participation

More than 27,000 employees from 33 organizations elected to
participate in the Employee Experience Survey. The survey questions
covered multiple themes (e.g., overall satisfaction, flexibility and
remote/hybrid workplaces, advancement, employee well-being, equity,
mentorship, sponsorship) as well as demographic questions (e.g.,
gender, gender of primary manager, race/ethnicity, age, disability, sexual
orientation, role, family status, household characteristics, and
responsibilities).

Bivariate and Multivariate Statistical Reporting

Survey results were reported as an unweighted polled average of
responses across companies. Many of the questions offered a five-point
labeled response scale (e.g., “Strongly disagree” to “Strongly agree”).
Unless otherwise specified, analyses aggregated the top two and
bottom two boxes of the response scale (e.g., combining “Somewhat
agree” and “Strongly agree”).

Where we highlight differences between genders or other groups, we
highlight only those differences that are substantial and reliable. To that
end, all differences noted in this report are statistically significant to a 95
percent confidence level and/or reflect a difference of at least five
percentage points between two groups unless otherwise indicated.

● Definition of Remote Work Status Composite Variable

Participants were asked how often they currently work on-site
and were given interval-level responses as options. During the
data analysis, the five options were transformed into the following
variables:

○ Remote: Never, or less than one day a week on-site

○ Hybrid: 1 day, 2 days, 3 days, or 4 days a week on-site

○ On-site: 5 days a week on-site

HR PROGRAMS AND POLICIES

Human resource professionals from two hundred seventy-four
organizations provided information on gender diversity policies and
programs on behalf of their organization. We report the percentage of
organizations that have a program, policy, priority, or position out of the
total number of companies that submitted HR program/policy data.

QUALITATIVE INTERVIEWS

We conducted individual interviews with 38 women, men, and nonbinary
employees across multiple industries. Interviewees were volunteers
selected to reflect a range of levels, departments, and demographic
groups. Our interviews focused on workplace experiences to gain a
deeper understanding of the quantitative findings from the employee
survey. Individual names, company names, and any other identifying
information were kept strictly confidential, and individuals are anonymized
in this report. Within the quotes, some identifying details may have been
altered and/or withheld to protect the speaker’s anonymity. Quotes have
been edited for clarity.

HR AND DEI BEST PRACTICES

DEI best practices are based on a top performer analysis conducted with
pipeline data and HR survey data. This is supplemented by external
research, past Women in the Workplace studies, and responses from
subject matter experts about what has been most effective in improving
representation and advancement of women.

We used talent pipeline data from 271 companies that participated in both
the Talent Pipeline and HR Surveys in 2023 to identify organizations that
outperform representation of women and women of color metrics. We
compared their total women and women of color representation for L1 to
L6 to their industry’s average for these values. We then ranked the
companies by the extent to which they outperformed this year’s industry
benchmarks for total women and women of color representation from L1
to L6 in the pipeline to identify the top quartile of companies.

The key HR practices and policies that drive progress were based on the
top performer analysis and were defined as practices where there was a
statistically significant difference in the percentage of top performing
organizations (n = 69) and non–top performing organizations (n = 202)
that have adopted that practice. In cases where recommendations
included multiple individual practices (e.g., sponsorship and/or mentorship
programs for women and women of color), the recommendation was
classified as a significant practice if there was at least one statistically
significant difference between top performers and all other companies in
the analyses for any of the listed practices. To further inform solutions, we
conducted additional deep-dive analyses for theme-related top performer
groups, including assessing what practices are adopted significantly more
by organizations that have managed to promote women to the manager
level at more similar rates to men and by organizations that had lower
voluntary attrition than their industry average.

Methodology

47 | WOMEN IN THE WORKPLACE: METHODOLOGY

Endnotes

1 This report contains stock photographs for illustrative purposes only. Images do not reflect the identities of the women quoted. Within the quotes, some
identifying details may have been altered and/or withheld to protect the speaker’s anonymity.

2 In this study, “women” includes cisgender and transgender women. Due to small sample sizes for transgender women, data are reported for “women
overall” or “LGBTQ+ women” in aggregate. Women of color include Black, Latina, Asian, Native American/American Indian/Indigenous or Alaskan Native,
Native Hawaiian, Pacific Islander, Middle Eastern, or mixed-race women. Due to small sample sizes for other racial and ethnic groups, reported findings on
individual racial/ethnic groups are restricted to Black women, Latinas, and Asian women.

3 Except where otherwise noted, “senior leadership” refers to individuals at the vice president level or above (L1 to L3 in Methodology).

4 Except where otherwise noted, the “middle of the pipeline” refers to individuals at the manager and director level (L5 and L4 in Methodology).

5 Except where otherwise noted, “flexible work” or “flexibility” refers to remote or hybrid work, as well as flexible work options such as the ability to set
your own hours.

6 Total percent of women and men per level in the race and gender pipeline may not sum to overall corporate pipeline totals, as the race pipeline does
not include employees with unreported race data. Some percentages may sum to 99 percent or 101 percent due to rounding.

7 Pipeline data in this report are based on data from the end of 2022 and do not reflect changes through 2023.

8 LeanIn.Org and McKinsey & Company, Women in the Workplace 2022 (October 2022), https://womenintheworkplace.com/.

9 These trends continued throughout the first half of 2023, based on our analysis of pipeline data from a subset of 52 company participants.

10 Except where otherwise noted, “young women” and “young men” refer to employees 30 and under.

11 Full questions: [2023] How interested are you in the following? Being promoted to the next level [Selected “Very interested” or “Somewhat interested”];
[2019] Do you want to be promoted to the next level? [Selected “Yes, I would like to be promoted”].

12 Full questions: I am taking steps to prioritize my personal life more than I did before the pandemic [Respondents selected from “Strongly agree,”
“Somewhat agree,” “Neither agree nor disagree,” “Somewhat disagree,” Strongly disagree”]; My career is important to me [Respondents selected from
“Strongly agree,” “Somewhat agree,” “Neither agree nor disagree,” “Somewhat disagree,” Strongly disagree”]; How interested are you in the following?
Getting promoted to the next level: [Respondents selected from [“Very interested,” Somewhat interested,” “Neither interested nor uninterested,”
“Somewhat uninterested” “Very uninterested”].

13 In this study, numbers for the “broken rung” assume an equal number of men, women, and women of color at entry level (L6 in Methodology).

14 Entry-level workers are defined as individual contributors responsible for carrying out discrete tasks and participating on teams, typically in an office or
corporate setting (e.g., business analyst, software engineer, paralegal, operations support). Here, “early career individual” refers to entry-level employees.

15 Unpublished data, LeanIn.Org and McKinsey & Company, Women in the Workplace 2022 and Women in the Workplace 2021; published data from
Women in the Workplace 2018–2020, https://womenintheworkplace.com/.

16 LeanIn.Org, 50 Ways to Fight Bias, https://leanin.org/50-ways-to-fight-gender-bias; Joan C. Williams and Rachel Dempsey, What Works for Women at
Work (New York: NYU Press, 2014); Laurie Rudman, Corrine A. Moss-Racusin, et al., “Reactions to Vanguards: Advances in Backlash Theory,” Advances in
Experimental Social Psychology 45 (2012).

17 Ibid.

18 Monnica T. Williams, “Microaggressions: Clarification, Evidence, and Impact,” Perspectives on Psychological Science 15, no. 1 (2019),
https://journals.sagepub.com/doi/full/10.1177/1745691619827499.

19 “Asian women” refers to women of South Asian, East Asian, and Southeast Asian origin or descent. Unless otherwise stated, “Asian women” does not
include individuals of Pacific Islander, Native Hawaiian, West Asian, or Middle Eastern origin or descent.

48 | WOMEN IN THE WORKPLACE: ENDNOTES

https://womenintheworkplace.com/

https://womenintheworkplace.com/

https://leanin.org/50-ways-to-fight-gender-bias

https://journals.sagepub.com/doi/full/10.1177/1745691619827499

Endnotes

20 “Psychological safety” is the belief among employees that it’s safe to take interpersonal risks. It means employees believe they won’t be punished or
humiliated if they propose new ideas, raise concerns and issues, or admit mistakes. Amy Edmondson, “Psychological Safety and Learning Behavior in
Work Teams,” Administrative Science Quarterly 44, no. 2 (June 1999): 350–83, https://journals.sagepub.com/doi/abs/10.2307/2666999.

21 In addition to “self-shielding,” social scientists describing similar dynamics have used terms such as “impression management,” “self-monitoring,”
“stigma management,” “vigilance,” “emotional tax,” and “performative contortions.”

22 In this study, “women who experience microaggressions and self-shield” are being compared to women who do not experience either.

23 Chester M. Pierce et al., “An experiment in racism: TV commercials,” Education and Urban Society 10, no. 1 (1977).

24 In this study, respondents who experience microaggressions refers to those who selected anything other than “None of the above” from the following
list. Full question: During the normal course of business, have you experienced any of the following? Select all that apply | [Q27.1] Having others take or get
credit for your ideas; Having your judgment questioned in your area of expertise; Being mistaken for someone at a lower level; Being interrupted or
spoken over more than others; People commenting on your appearance in a way that made you uncomfortable; People commenting on your emotional
state (e.g., you’re too angry, feisty, emotional); Feeling judged because of your accent or way of speaking; People expressing doubt or disbelief at your
accomplishments; Being confused with someone else of the same race/ethnicity; Other people calling attention to your age unnecessarily; None of the
above.

25 LeanIn.Org and McKinsey & Company, Women in the Workplace 2022, https://womenintheworkplace.com/.

26 LeanIn.Org and McKinsey & Company, Women in the Workplace 2021, https://womenintheworkplace.com/.

27 In this study, self-shielding means respondent selected any response from the following list. Full question: Which of the following have you
experienced at work? Select all that apply | Respondents selected one or more of the following: You felt pressure to change your appearance to look more
professional; You toned down what you said because you didn’t want to be seen as unlikable (e.g., chose your words carefully); You hid important aspects
of your identity to fit in at work (e.g., being LGBTQ+, having a disability); You felt like you had to “code-switch” to blend in with others at work (e.g., changing
mannerisms, tone of voice, or speaking style); You chose not to speak up or share an opinion so you didn’t seem difficult or aggressive; You felt like you
had to perform perfectly to avoid scrutiny or judgment.

28 LeanIn.Org and McKinsey & Company, Women in the Workplace 2022, https://womenintheworkplace.com/.

29 Ibid.

30 Ibid.

31 Comparison of responses from women who experience microaggressions (see note 24) and engaged in self-shielding (see note 27) vs. women who do
not experience microaggressions and do not engage in self-shielding.

32 Wouldn’t recommend their company: Selected “Strongly disagree” or “Somewhat disagree” in response to question “How much do you agree with the
following statements? I would recommend this company as a great place to work.”

33 Feel they don’t have an equal opportunity to advance: Selected “Strongly disagree” or “Somewhat disagree” in response to the question “How much
do you agree with the following statements? Compared to my peers at this organization, I have an equal opportunity to advance.”

34 Due to small sample sizes, all women identifying as lesbian, bisexual, pansexual, otherwise nonheterosexual, and/or transgender were analyzed and
reported in a single category as LGBTQ+ women. This means that, throughout this report, the composite “LGBTQ+” most closely describes the experiences
of larger groups in the sample.

35 Richard Fry, Carolina Aragão, Kiley Hurst, and Kim Parker, “In a Growing Share of U.S. Marriages, Husbands and Wives Earn About the Same,” Pew
Research Center (2023),
https://www.pewresearch.org/social-trends/2023/04/13/in-a-growing-share-of-u-s-marriages-husbands-and-wives-earn-about-the-same/#:~:text=Among%2
0parents%20in%20marriages%20where,per%20week%20on%20paid%20work; Organization for Economic Cooperation and Development, “Employment:
Time spent in paid and unpaid work, by sex,” 2023, https://stats.oecd.org/index.aspx?queryid=54757#.

36 In this study, “mothers of young children” refers to women with at least one child under the age of four years old.

49 | WOMEN IN THE WORKPLACE: ENDNOTES

https://journals.sagepub.com/doi/abs/10.2307/2666999

https://womenintheworkplace.com/

https://womenintheworkplace.com/

https://womenintheworkplace.com/

https://www.pewresearch.org/social-trends/2023/04/13/in-a-growing-share-of-u-s-marriages-husbands-and-wives-earn-about-the-same/#:~:text=Among%20parents%20in%20marriages%20where,per%20week%20on%20paid%20work

https://www.pewresearch.org/social-trends/2023/04/13/in-a-growing-share-of-u-s-marriages-husbands-and-wives-earn-about-the-same/#:~:text=Among%20parents%20in%20marriages%20where,per%20week%20on%20paid%20work

https://stats.oecd.org/index.aspx?queryid=54757

Endnotes

37 [2023] Full question: When you work flexibly (e.g., work from home, work nonstandard hours), how do you feel? Select all that apply . Respondents
selected from: “Supported,” “Judged,” “Set up to succeed,” “Worried that it will hurt my career,” “Like I’m a burden to my team,” “Like it’s no big deal,” “I
don’t work flexibly,” “There are no opportunities to work flexibly at my company,” “Other.” [2021] Full question: When you request or take advantage of
opportunities to work flexibly (e.g., take time off, work from home, work non-standard hours), how do you feel? Select all that apply. Respondents selected
from: ““Supported,” “Judged,” “Set up to succeed,” “Worried that it will hurt my career,” “Like I’m a burden to my team,” “Like it’s no big deal,” “I don’t
request or take advantage of opportunities to work flexibly,” “There are no opportunities to work flexibly at my company,” “Other.”

38 Jan-Emmanuel De Neve, Micah Kaats, and George Ward, Workplace Wellbeing and Firm Performance, University of Oxford Wellbeing Research Centre
Working Paper 2304 (2023), https://wellbeing.hmc.ox.ac.uk/papers/wp-2304-workplace-wellbeing-and-firm-performance; Sabine Sonnentag, “Wellbeing
and Burnout in the Workplace: Organizational Causes and Consequences,” in James D. Wright, ed., International Encyclopedia of the Social & Behavioral
Sciences (New York: Elsevier, 2015), https://www.sciencedirect.com/science/article/abs/pii/B9780080970868730212?via%3Dihub.

39 LeanIn.Org and McKinsey & Company, Women in the Workplace 2022.

40 “Gen Z” is defined as being born in 1997 or later.

41 “Full questions: All respondents were asked: Are the following statements more true when you are working remotely, working on-site, or about the
same in both settings? You’re “in the know” about decisions that impact you and your work | You get the mentorship and sponsorship you need I You feel
more connected to your organization’s mission and your work [Respondents selected from “More true REMOTE,” “More true “ON-SITE,” “Equally true in
BOTH,” “Not true in EITHER,” “Don’t know”]; Respondents working on-site five days a week were asked: “Which of the following are the biggest benefits of
on-site work for you? Select all that apply” [Respondent selected option “You receive useful feedback more often”].

42 Some percentages may sum to 99 percent or 101 percent due to rounding.

43 Chart excludes Media and Entertainment, Professional and Information Services, and Law Firms due to small sample sizes.

44 Unpublished data, LeanIn.Org and McKinsey & Company, Women in the Workplace 2022, https://womenintheworkplace.com/.

45 Slack, “Trust, tools and teamwork: what workers want,” October 3, 2018,
https://slack.com/blog/transformation/trust-tools-and-teamwork-what-workers-want.

46 LeanIn.Org and McKinsey & Company, Women in the Workplace 2022; LeanIn.Org and McKinsey & Company, Women in the Workplace 2021
(September 2021), https://womenintheworkplace.com/.

47 Some percentages may sum to 99 percent or 101 percent due to rounding.

48 Monnica T. Williams, “Racial Microaggressions: Critical Questions, State of the Science, and New Directions,” Perspectives on Psychological Science 16,
no. 5 (2021), https://journals.sagepub.com/doi/full/10.1177/17456916211039209.

49 Monnica T. Williams, “Microaggressions: Clarification, Evidence, and Impact,” Perspectives on Psychological Sciences 15, no.1 (2019),
https://journals.sagepub.com/doi/full/10.1177/1745691619827499.

50 LeanIn.Org, 50 Ways to Fight Bias, https://leanin.org/50-ways-to-fight-gender-bias; Jennifer Kim and Alyson Meister, “How to Intervene When You
Witness a Microaggression,” Harvard Business Review, September 30, 2022,
https://hbr.org/2022/09/how-to-intervene-when-you-witness-a-microaggression.

51 Joan C. Williams, Mary Blair-Loy, and Jennifer Berdahl, “Special Issue: The Flexibility Stigma,” Journal of Social Issues 69, no. 2 (June 2013): 209–405,
https://spssi.onlinelibrary.wiley.com/toc/15404560/69/2.

52 Full question: What is your organization doing to ensure that employees have equal opportunities for career development and progression regardless
of their working model (e.g., on-site, remote/hybrid, flexible hours)? Select all that apply: “Explicitly communicated that employees should not be penalized
for working remotely and/or at flexible time,” “Redesigned performance evaluations to emphasize results, not where and when employees work,” “Track
outcomes for employees with different working models (e.g., promotions, attrition, satisfaction),” “Trained/training managers so they’re better equipped to
manage employees working remotely and/or at flexible times,” “Provide more formal networking opportunities for employees,” “Provide more informal
networking opportunities for employees,” “Put formal mechanisms in place so all employees get the same opportunities for mentorship,” “Put formal
mechanisms in place so all employees get the same opportunities for sponsorship,” “None of the above.” Provided networking opportunities is an
aggregate of informal and formal networking opportunities.

50 | WOMEN IN THE WORKPLACE: ENDNOTES

https://wellbeing.hmc.ox.ac.uk/papers/wp-2304-workplace-wellbeing-and-firm-performance

https://www.sciencedirect.com/science/article/abs/pii/B9780080970868730212?via%3Dihub

https://womenintheworkplace.com/

https://slack.com/blog/transformation/trust-tools-and-teamwork-what-workers-want

https://womenintheworkplace.com/

https://journals.sagepub.com/doi/full/10.1177/17456916211039209

https://journals.sagepub.com/doi/full/10.1177/1745691619827499

https://leanin.org/50-ways-to-fight-gender-bias

https://hbr.org/2022/09/how-to-intervene-when-you-witness-a-microaggression

https://spssi.onlinelibrary.wiley.com/toc/15404560/69/2

Endnotes

53 Shelley J. Correll, “Reducing Gender Biases in Modern Workplaces: A Small Wins Approach to Organizational Change,” Gender & Society 31, no. 6
(December 2017).

54 Joan C. Williams, Bias Interrupted: Creating Inclusion for Real and for Good (Boston: Harvard Business Review Press, 2021); Shelley J. Correll,
“Reducing Gender Biases in Modern Workplaces.”

55 LeanIn.Org and McKinsey & Company, Women in the Workplace 2022.

51 | WOMEN IN THE WORKPLACE: ENDNOTES

Upon reading the Women in the Workplace 2023 report and Chapter 2 on DEI in Organizations please complete the below assignment.

You are currently a Vice President in your organization, in charge of redesigning your organizational structure. I would like for you to develop a plan that descriptively outlines the steps on how you would increase the number of 
women of color in the workplace at the C-Suite or Director and above level. Your plan should be in essay format, not question and answer.

Please be sure to include the following:

1. Who would you recruit to be part of your planning committee, and why?

2. Briefly describe what trainings (if any) would be incorporated into your plan.

3. How would you recruit diverse employees to your organization?

4. How would you minimize the gender pay gap?

5. What policies would be included in your plan?

6. What tools and/or ways would you use to track your new organizational structure’s success?

Your plan should be a 

maximum of 3 pages in content. 

Your paper should be in APA format which includes a reference page, title page, abstract age, running header, in-text citations. 

Please use Times New Roman font, size 12. Please ensure that your similarity percentage is less than 25%, ideally less then 15%. 

You should have at least 3 references that are no earlier than 2019.

Please refer to the grading rubric for more details on how to obtain the optimal grade.

Abstract

Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-
science programs, and publications are touting data science as a hot—even ‘‘sexy’’—career choice. However, there is
confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses
into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly
what is data science. One reason is that data science is intricately intertwined with other important concepts also of
growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to
associate what a practitioner does with the definition of the practitioner’s field; this can result in overlooking the
fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost
importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve
business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to
begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better
understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be
comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close
by offering, as examples, a partial list of fundamental principles underlying

data science.

Introduction

With vast amounts of data now available, companies in

almost every industry are focused on exploiting data for

competitive advantage. The volume and variety of data have

far outstripped the capacity of manual analysis, and in some

cases have exceeded the capacity of conventional databases.

At the same time, computers have become far more powerful,

networking is ubiquitous, and algorithms have been devel-

oped that can connect datasets to enable broader and deeper

analyses than previously possible. The convergence of these

phenomena has given rise to the increasingly widespread

business application of data science.

Companies across industries have realized that they need to

hire more data scientists. Academic institutions are scram-

bling to put together programs to train data scientists. Pub-

lications are touting data science as a hot career choice and

even ‘‘sexy.’’1 However, there is confusion about what exactly

is data science, and this confusion could well lead to

1Leonard N. Stern School of Business, New York University, New York, New York.
2Data Scientists, LLC, New York, New York and Mountain View, California.

ª Foster Provost and Tom Fawcett 2013; Published by Mary Ann Liebert, Inc. This article is available under the Creative Commons License CC-BY-NC (http://creativecommons.org/

licenses/by-nc/4.0). This license permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. Permission only needs to be

obtained for commercial use and can be done via RightsLink.

DATA SCIENCE
AND ITS

RELATIONSHIP
TO BIG DATA AND

DATA-DRIVEN
DECISION MAKING

Foster Provost1 and Tom Fawcett2

ORIGINAL ARTICLE

DOI: 10.1089/big.2013.1508 � MARY ANN LIEBERT, INC. � VOL. 1 NO. 1 � MARCH 2013 BIG DATA BD51

disillusionment as the concept diffuses into meaningless buzz.

In this article, we argue that there are good reasons why it has

been hard to pin down what exactly is data science. One

reason is that data science is intricately intertwined with other

important concepts, like big data and data-driven decision

making, which are also growing in importance and attention.

Another reason is the natural tendency, in the absence of

academic programs to teach one otherwise, to associate what

a practitioner actually does with the definition of the prac-

titioner’s field; this can result in overlooking the fundamen-

tals of the field.

At the moment, trying to define the boundaries of data sci-

ence precisely is not of foremost importance. Data-science

academic programs are being developed, and in an academic

setting we can debate its boundaries. However, in order for

data science to serve business effectively, it is important (i) to

understand its relationships to these other important and

closely related concepts, and (ii) to

begin to understand what are the

fundamental principles underlying

data science. Once we embrace

(ii), we can much better under-

stand and explain exactly what

data science has to offer. Further-

more, only once we embrace (ii)

should we be comfortable calling it data science.

In this article, we present a perspective that addresses all these

concepts. We first work to disentangle this set of closely in-

terrelated concepts. In the process, we highlight data science

as the connective tissue between data-processing technologies

(including those for ‘‘big data’’) and data-driven decision

making. We discuss the complicated issue of data science as a

field versus data science as a profession. Finally, we offer as

examples a list of some fundamental principles underlying

data science.

Data Science

At a high level, data science is a set of fundamental principles

that support and guide the principled extraction of infor-

mation and knowledge from data. Possibly the most closely

related concept to data science is data mining—the actual

extraction of knowledge from data via technologies that in-

corporate these principles. There are hundreds of different

data-mining algorithms, and a great deal of detail to the

methods of the field. We argue that underlying all these many

details is a much smaller and more concise set of fundamental

principles.

These principles and techniques are applied broadly across

functional areas in business. Probably the broadest business

applications are in marketing for tasks such as targeted

marketing, online advertising, and recommendations for

cross-selling. Data science also is applied for general customer

relationship management to analyze customer behavior in

order to manage attrition and maximize expected customer

value. The finance industry uses data science for credit scoring

and trading and in operations via fraud detection and work-

force management. Major retailers from Wal-Mart to Amazon

apply data science throughout their businesses, from mar-

keting to supply-chain management. Many firms have differ-

entiated themselves strategically with data science, sometimes

to the point of evolving into data-mining companies.

But data science involves much more than just data-mining

algorithms. Successful data scientists must be able to view

business problems from a data perspective. There is a fun-

damental structure to data-analytic thinking, and basic

principles that should be understood. Data science draws

from many ‘‘traditional’’ fields of study. Fundamental prin-

ciples of causal analysis must be understood. A large portion

of what has traditionally been studied within the field of

statistics is fundamental to data

science. Methods and methology

for visualizing data are vital. There

are also particular areas where

intuition, creativity, common

sense, and knowledge of a partic-

ular application must be brought

to bear. A data-science perspective

provides practitioners with structure and principles, which

give the data scientist a framework to systematically treat

problems of extracting useful knowledge from data.

Data Science in Action

For concreteness, let’s look at two brief case studies of ana-

lyzing data to extract predictive patterns. These studies il-

lustrate different sorts of applications of data science. The

first was reported in the New York Times:

Hurricane Frances was on its way, barreling across

the Caribbean, threatening a direct hit on Florida’s

Atlantic coast. Residents made for higher ground,

but far away, in Bentonville, Ark., executives at Wal-

Mart Stores decided that the situation offered a great

opportunity for one of their newest data-driven

weapons.predictive technology.

A week ahead of the storm’s landfall, Linda M.

Dillman, Wal-Mart’s chief information officer,

pressed her staff to come up with forecasts based on

what had happened when Hurricane Charley struck

several weeks earlier. Backed by the trillions of bytes’

worth of shopper history that is stored in Wal-

Mart’s data warehouse, she felt that the company

could ‘‘start predicting what’s going to happen, in-

stead of waiting for it to happen,’’ as she put it.2

Consider why data-driven prediction might be useful in this

scenario. It might be useful to predict that people in the path

‘‘PUBLICATIONS ARE TOUTING
DATA SCIENCE AS A HOT CAREER

CHOICE AND EVEN ‘SEXY.’’’

DATA SCIENCE AND BIG DATA

Provost and Fawcett

52BD BIG DATA MARCH 2013

of the hurricane would buy more bottled water. Maybe, but it

seems a bit obvious, and why do we need data science to

discover this? It might be useful to project the amount of

increase in sales due to the hurricane, to ensure that local

Wal-Marts are properly stocked. Perhaps mining the data

could reveal that a particular DVD sold out in the hurricane’s

path—but maybe it sold out that week at Wal-Marts across

the country, not just where the hurricane landing was im-

minent. The prediction could be somewhat useful, but

probably more general than Ms. Dillman was intending.

It would be more valuable to discover patterns due to the

hurricane that were not obvious. To do this, analysts might

examine the huge volume of Wal-Mart data from prior,

similar situations (such as Hurricane Charley earlier in the

same season) to identify unusual local demand for products.

From such patterns, the company might be able to anticipate

unusual demand for products and

rush stock to the stores ahead of

the hurricane’s landfall.

Indeed, that is what happened.

The New York Times reported

that: ‘‘. the experts mined the

data and found that the stores

would indeed need certain prod-

ucts—and not just the usual

flashlights. ‘We didn’t know in the

past that strawberry Pop-Tarts

increase in sales, like seven times their normal sales rate,

ahead of a hurricane,’ Ms. Dillman said in a recent interview.’

And the pre-hurricane top-selling item was beer.*’’’2

Consider a second, more typical business scenario and how it

might be treated from a data perspective. Assume you just

landed a great analytical job with MegaTelCo, one of the

largest telecommunication firms in the United States. They

are having a major problem with customer retention in their

wireless business. In the mid-Atlantic region, 20% of cell-

phone customers leave when their contracts expire, and it is

getting increasingly difficult to acquire new customers. Since

the cell-phone market is now saturated, the huge growth in

the wireless market has tapered off. Communications com-

panies are now engaged in battles to attract each other’s

customers while retaining their own. Customers switching

from one company to another is called churn, and it is ex-

pensive all around: one company must spend on incentives to

attract a customer while another company loses revenue

when the customer departs.

You have been called in to help understand the problem and

to devise a solution. Attracting new customers is much more

expensive than retaining existing ones, so a good deal of

marketing budget is allocated to prevent churn. Marketing

has already designed a special retention offer. Your task is to

devise a precise, step-by-step plan for how the data science

team should use MegaTelCo’s vast data resources to decide

which customers should be offered the special retention deal

prior to the expiration of their contracts. Specifically, how

should MegaTelCo decide on the set of customers to target to

best reduce churn for a particular incentive budget? An-

swering this question is much more complicated than it

seems initially.

Data Science and Data-Driven
Decision Making

Data science involves principles, processes, and techniques

for understanding phenomena via the (automated) analysis

of data. For the perspective of this article, the ultimate goal of

data science is improving deci-

sion making, as this generally is

of paramount interest to busi-

ness. Figure 1 places data science

in the context of other closely

related and data-related pro-

cesses in the organization. Let’s

start at the top.

Data-driven decision making

(DDD)3 refers to the practice of

basing decisions on the analysis

of data rather than purely on intuition. For example, a

marketer could select advertisements based purely on her

long experience in the field and her eye for what will work.

Or, she could base her selection on the analysis of data re-

garding how consumers react to different ads. She could also

use a combination of these approaches. DDD is not an all-or-

nothing practice, and different firms engage in DDD to

greater or lesser degrees.

The benefits of data-driven decision making have been dem-

onstrated conclusively. Economist Erik Brynjolfsson and his

colleagues from MIT and Penn’s Wharton School recently

conducted a study of how DDD affects firm performance.3

They developed a measure of DDD that rates firms as to how

strongly they use data to make decisions across the company.

They show statistically that the more data-driven a firm is, the

more productive it is—even controlling for a wide range of

possible confounding factors. And the differences are not small:

one standard deviation higher on the DDD scale is associated

with a 4–6% increase in productivity. DDD also is correlated

with higher return on assets, return on equity, asset utilization,

and market value, and the relationship seems to be causal.

Our two example case studies illustrate two different sorts of

decisions: (1) decisions for which ‘‘discoveries’’ need to be

‘‘FROM SUCH PATTERNS, THE
COMPANY MIGHT BE ABLE TO

ANTICIPATE UNUSUAL DEMAND
FOR PRODUCTS AND RUSH STOCK

TO THE STORES AHEAD OF THE
HURRICANE’S LANDFALL.’’

*Of course! What goes better with strawberry Pop-Tarts than a nice cold beer?

Provost and Fawcett

ORIGINAL ARTICLE

MARY ANN LIEBERT, INC. � VOL. 1 NO. 1 � MARCH 2013 BIG DATA BD53

made within data, and (2) decisions that repeat, especially at

massive scale, and so decision making can benefit from even

small increases in accuracy based on data analysis. The Wal-

Mart example above illustrates a type-1 problem. Linda

Dillman would like to discover knowledge that will help Wal-

Mart prepare for Hurricane Frances’s imminent arrival. Our

churn example illustrates a type-

2 DDD problem. A large tele-

communications company may

have hundreds of millions of

customers, each a candidate for

defection. Tens of millions of

customers have contracts expir-

ing each month, so each one of

them has an increased likelihood of defection in the near

future. If we can improve our ability to estimate, for a given

customer, how profitable it would be for us to focus on her,

we can potentially reap large benefits by applying this ability

to the millions of customers in the population. This same

logic applies to many of the areas where we have seen the

most intense application of data science and data mining:

direct marketing, online advertising, credit scoring, financial

trading, help-desk management, fraud detection, search

ranking, product recommendation, and so on.

The diagram in Figure 1 shows data science supporting data-

driven decision making, but also overlapping with it. This

highlights the fact that, increasingly, business decisions are

being made automatically by computer systems. Different

industries have adopted automatic decision making at dif-

ferent rates. The finance and telecommunications industries

were early adopters. In the 1990s, automated decision making

changed the banking and consumer-credit industries dra-

matically. In the 1990s, banks and telecommunications

companies also implemented massive-scale systems for

managing data-driven fraud con-

trol decisions. As retail systems

were increasingly computerized,

merchandising decisions were

automated. Famous examples in-

clude Harrah’s casinos’ reward

programs and the automated

recommendations of Amazon and

Netflix. Currently we are seeing a revolution in advertising,

due in large part to a huge increase in the amount of time

consumers are spending online and the ability online to make

(literally) split-second advertising decisions.

Data Processing and ‘‘Big Data’’

Despite the impression one might get from the media, there is

a lot to data processing that is not data science. Data engi-

neering and processing are critical to support data-science

activities, as shown in Figure 1, but they are more general and

are useful for much more. Data-processing technologies are

important for many business tasks that do not involve ex-

tracting knowledge or data-driven decision making, such as

efficient transaction processing, modern web system proces-

sing, online advertising campaign management, and others.

‘‘Big data’’ technologies, such as Hadoop, Hbase, CouchDB,

and others have received considerable media attention re-

cently. For this article, we will simply take big data to mean

datasets that are too large for traditional data-processing

systems and that therefore require new technologies. As with

the traditional technologies, big data technologies are used

for many tasks, including data engineering. Occasionally, big

data technologies are actually used for implementing data-

mining techniques, but more often the well-known big data

technologies are used for data processing in support of the

data-mining techniques and other data-science activities, as

represented in Figure 1.

Economist Prasanna Tambe of New York University’s Stern

School has examined the extent to which the utilization of big

data technologies seems to help firms.4 He finds that, after

controlling for various possible confounding factors, the use

of big data technologies correlates with significant additional

productivity growth. Specifically, one standard deviation higher

utilization of big data technologies is associated with 1–3%

higher productivity than the average firm; one standard devi-

ation lower in terms of big data utilization is associated with

1–3% lower productivity. This leads to potentially very large

productivity differences between the firms at the extremes.

FIG. 1. Data science in the context of closely related processes
in the organization.

‘‘THE BENEFITS OF DATA-DRIVEN
DECISION MAKING HAVE BEEN

DEMONSTRATED CONCLUSIVELY.’’

DATA SCIENCE AND BIG DATA
Provost and Fawcett

54BD BIG DATA MARCH 2013

http://online.liebertpub.com/action/showImage?doi=10.1089/big.2013.1508&iName=master.img-002 &w=192&h=272

From Big Data 1.0 to Big Data 2.0

One way to think about the state of big data technologies is to

draw an analogy with the business adoption of internet

technologies. In Web 1.0, businesses busied themselves with

getting the basic internet technologies in place so that they

could establish a web presence, build electronic commerce

capability, and improve operating efficiency. We can think of

ourselves as being in the era of Big Data 1.0, with firms

engaged in building capabilities to process large data. These

primarily support their current operations—for example, to

make themselves more efficient.

With Web 1.0, once firms had

incorporated basic technologies

thoroughly (and in the process had

driven down prices) they started to

look further. They began to ask

what the web could do for them,

and how it could improve upon

what they’d always done. This

ushered in the era of Web 2.0, in

which new systems and companies

started to exploit the interactive nature of the web. The

changes brought on by this shift in thinking are extensive and

pervasive; the most obvious are the incorporation of social-

networking components and the rise of the ‘‘voice’’ of the

individual consumer (and citizen).

Similarly, we should expect a Big Data 2.0 phase to follow Big

Data 1.0. Once firms have become capable of processing

massive data in a flexible fashion, they should begin asking:

What can I now do that I couldn’t do before, or do better than I

could do before? This is likely to usher in the golden era of data

science. The principles and techniques of data science will be

applied far more broadly and far more deeply than they are

today.

It is important to note that in the Web-1.0 era, some pre-

cocious companies began applying Web-2.0 ideas far ahead

of the mainstream. Amazon is a prime example, incorpo-

rating the consumer’s ‘‘voice’’ early on in the rating of

products and product reviews (and deeper, in the rating of

reviewers). Similarly, we see some companies already ap-

plying Big Data 2.0. Amazon again is a company at the

forefront, providing data-driven recommendations from

massive data. There are other examples as well. Online ad-

vertisers must process extremely large volumes of data (bil-

lions of ad impressions per day is not unusual) and maintain

a very high throughput (real-time bidding systems make

decisions in tens of milliseconds). We should look to these

and similar industries for signs of advances in big data and

data science that subsequently will be adopted by other

industries.

Data-Analytic Thinking

One of the most critical aspects of data science is the support

of data-analytic thinking. Skill at thinking data-analytically is

important not just for the data scientist but throughout the

organization. For example, managers and line employees in

other functional areas will only get the best from the com-

pany’s data-science resources if they have some basic un-

derstanding of the fundamental principles. Managers in

enterprises without substantial data-science resources should

still understand basic principles in order to engage consul-

tants on an informed basis. Investors in data-science ventures

need to understand the funda-

mental principles in order to as-

sess investment opportunities

accurately. More generally, busi-

nesses increasingly are driven by

data analytics, and there is great

professional advantage in being

able to interact competently

with and within such businesses.

Understanding the fundamental

concepts, and having frameworks

for organizing data-analytic thinking, not only will allow one

to interact competently, but will help to envision opportu-

nities for improving data-driven decision making or to see

data-oriented competitive threats.

Firms in many traditional industries are exploiting new and

existing data resources for competitive advantage. They em-

ploy data-science teams to bring advanced technologies to

bear to increase revenue and to decrease costs. In addition,

many new companies are being developed with data mining

as a key strategic component. Facebook and Twitter, along

with many other ‘‘Digital 100’’ companies,5 have high valu-

ations due primarily to data assets they are committed to

capturing or creating.{ Increasingly, managers need to man-

age data-analytics teams and data-analysis projects, marketers

have to organize and understand data-driven campaigns,

venture capitalists must be able to invest wisely in businesses

with substantial data assets, and business strategists must be

able to devise plans that exploit data.

As a few examples, if a consultant presents a proposal to

exploit a data asset to improve your business, you should be

able to assess whether the proposal makes sense. If a com-

petitor announces a new data partnership, you should rec-

ognize when it may put you at a strategic disadvantage. Or,

let’s say you take a position with a venture firm and your first

project is to assess the potential for investing in an advertising

company. The founders present a convincing argument that

they will realize significant value from a unique body of data

they will collect, and on that basis, are arguing for a sub-

stantially higher valuation. Is this reasonable? With an

‘‘SIMILARLY, WE SHOULD
EXPECT A BIG DATA 2.0 PHASE

TO FOLLOW BIG DATA 1.0 . THIS
IS LIKELY TO USHER IN THE

GOLDEN ERA OF DATA SCIENCE.’’

{Of course, this is not a new phenomenon. Amazon and Google are well-established companies that obtain tremendous value from their data assets.

Provost and Fawcett

ORIGINAL ARTICLE

MARY ANN LIEBERT, INC. � VOL. 1 NO. 1 � MARCH 2013 BIG DATA BD55

understanding of the fundamentals of data science, you

should be able to devise a few probing questions to determine

whether their valuation arguments are plausible.

On a scale less grand, but probably more common, data-

analytics projects reach into all business units. Employees

throughout these units must interact with the data-science

team. If these employees do not have a fundamental

grounding in the principles of data-analytic thinking, they

will not really understand what is happening in the business.

This lack of understanding is much more damaging in data-

science projects than in other technical projects, because the

data science supports improved

decision making. Data-science

projects require close interaction

between the scientists and the

business people responsible for the

decision making. Firms in which

the business people do not under-

stand what the data scientists are

doing are at a substantial disad-

vantage, because they waste time

and effort or, worse, because they

ultimately make wrong decisions.

A recent article in Harvard Busi-

ness Review concludes: ‘‘For all the breathless promises about

the return on investment in Big Data, however, companies

face a challenge. Investments in analytics can be useless, even

harmful, unless employees can incorporate that data into

complex decision making.’’6

Some Fundamental Concepts
of Data Science

There is a set of well-studied, fundamental concepts under-

lying the principled extraction of knowledge from data, with

both theoretical and empirical backing. These fundamental

concepts of data science are drawn from many fields that

study data analytics. Some reflect the relationship between

data science and the business problems to be solved. Some

reflect the sorts of knowledge discoveries that can be made

and are the basis for technical solutions. Others are cau-

tionary and prescriptive. We briefly discuss a few here. This

list is not intended to be exhaustive; detailed discussions even

of the handful below would fill a book.* The important thing

is that we understand these fundamental

concepts.

Fundamental concept: Extracting useful knowledge from data

to solve business problems can be treated systematically by fol-

lowing a process with reasonably well-defined stages. The Cross-

Industry Standard Process for Data Mining7 (CRISP-DM) is

one codification of this process. Keeping such a process in

mind can structure our thinking about data analytics prob-

lems. For example, in actual practice one repeatedly sees an-

alytical ‘‘solutions’’ that are not based on careful analysis of

the problem or are not carefully evaluated. Structured think-

ing about analytics emphasizes these often underappreciated

aspects of supporting decision making with data. Such

structured thinking also contrasts critical points at which

human intuition and creativity is necessary versus points at

which high-powered analytical tools can be brought to bear.

Fundamental concept: Evaluating data-science results requires

careful consideration of the context in which they will be used.

Whether knowledge extracted from data will aid in decision

making depends critically on the

application in question. For our

churn-management example, how

exactly are we going to use the

patterns that are extracted from

historical data? More generally,

does the pattern lead to better de-

cisions than some reasonable alter-

native? How well would one have

done by chance? How well would

one do with a smart ‘‘default’’ al-

ternative? Many data science eva-

luation frameworks are based on

this fundamental concept.

Fundamental concept: The relationship between the business

problem and the analytics solution often can be decomposed into

tractable subproblems via the framework of analyzing expected

value. Various tools for mining data exist, but business

problems rarely come neatly prepared for their application.

Breaking the business problem up into components corre-

sponding to estimating probabilities and computing or esti-

mating values, along with a structure for recombining the

components, is broadly useful. We have many specific tools

for estimating probabilities and values from data. For our

churn example, should the value of the customer be taken

into account in addition to the likelihood of leaving? It is

difficult to realistically assess any customer-targeting solution

without phrasing the problem as one of expected value.

Fundamental concept: Information technology can be used to

find informative data items from within a large body of data.

One of the first data-science concepts encountered in busi-

ness-analytics scenarios is the notion of finding correlations.

‘‘Correlation’’ often is used loosely to mean data items that

provide information about other data items—specifically,

known quantities that reduce our uncertainty about un-

known quantities. In our churn example, a quantity of in-

terest is the likelihood that a particular customer will leave

after her contract expires. Before the contract expires, this

would be an unknown quantity. However, there may be

known data items (usage, service history, how many friends

‘‘FACEBOOK AND TWITTER,
ALONG WITH MANY OTHER

‘DIGITAL 100’ COMPANIES, HAVE
HIGH VALUATIONS DUE

PRIMARILY TO DATA ASSETS
THEY ARE COMMITTED TO

CAPTURING OR CREATING.’’

*And they do; see http://data-science-for-biz.com.

DATA SCIENCE AND BIG DATA
Provost and Fawcett

56BD BIG DATA MARCH 2013

have canceled contracts) that correlate with our quantity of

interest. This fundamental concept underlies a vast number

of techniques for statistical analysis, predictive modeling, and

other data mining.

Fundamental concept: Entities that are similar with respect to

known features or attributes often are similar with respect to

unknown features or attributes. Computing similarity is one of

the main tools of data science. There are many ways to

compute similarity and more are invented each year.

Fundamental concept: If you look

too hard at a set of data, you will

find something—but it might not

generalize beyond the data you’re

observing. This is referred to as

‘‘overfitting’’ a dataset. Techniques

for mining data can be very pow-

erful, and the need to detect and

avoid overfitting is one of the most important concepts to

grasp when applying data-mining tools to real problems. The

concept of overfitting and its avoidance permeates data sci-

ence processes, algorithms, and evaluation methods.

Fundamental concept: To draw causal conclusions, one must

pay very close attention to the presence of confounding factors,

possibly unseen ones. Often, it is not enough simply to un-

cover correlations in data; we may want to use our models to

guide decisions on how to influence the behavior producing

the data. For our churn problem, we want to intervene and

cause customer retention. All methods for drawing causal

conclusions—from interpreting the coefficients of regression

models to randomized controlled experiments—incorporate

assumptions regarding the presence or absence of con-

founding factors. In applying such methods, it is important

to understand their assumptions clearly in order to under-

stand the scope of any causal claims.

Chemistry Is Not About Test Tubes: Data
Science vs. the Work of the Data Scientist

Two additional, related complications combine to make it

more difficult to reach a common understanding of just

what is data science and how it fits with other related

concepts.

First is the dearth of academic programs focusing on data

science. Without academic programs defining the field for

us, we need to define the field for ourselves. However, each

of us sees the field from a different perspective and thereby

forms a different conception. The dearth of academic pro-

grams is largely due to the inertia associated with academia

and the concomitant effort involved in creating new aca-

demic programs—especially ones that span traditional dis-

ciplines. Universities clearly see the need for such programs,

and it is only a matter of time before this first complication

will be resolved. For example, in New York City alone, two

top universities are creating degree programs in data sci-

ence. Columbia University is in the process of creating a

master’s degree program within its new Institute for Data

Sciences and Engineering (and has founded a center focus-

ing on the foundations of data science), and NYU will

commence a master’s degree program in data science in

fall 2013.

The second complication builds

on confusion caused by the first.

Workers tend to associate with

their field the tasks they spend

considerable time on or those

they find challenging or reward-

ing. This is in contrast to the tasks

that differentiate the field from

other fields. Forsythe described this phenomenon in an

ethnographic study of practitioners in artificial intelligence

(AI):

The AI specialists I describe view their professional

work as science (and in some cases engineer-

ing).The scientists’ work and the approach they

take to it make sense in relation to a particular view

of the world that is taken for granted in the

laboratory.Wondering what it means to ‘‘do AI,’’

I have asked many practitioners to describe their own

work. Their answers invariably focus on one or more

of the following: problem solving, writing code, and

building systems.8

Forsythe goes on to explain that the AI practitioners focus on

these three activities even when it is clear that they spend

much time doing other things (even less related specifically to

AI). Importantly, none of these three tasks differentiates AI

from other scientific and engineering fields. Clearly just being

very good at these three things does not an AI scientist make.

And as Forsythe points out, technically the latter two are not

even necessary, as the lab director, a famous AI Scientist, had

not written code or built systems for years. Nonetheless, these

are the tasks the AI scientists saw as defining their work—

they apparently did not explicitly consider the notion of what

makes doing AI different from doing other tasks that involve

problem solving, writing code, and system building. (This is

possibly due to the fact that in AI, there were academic dis-

tinctions to call on.)

Taken together, these two complications cause particular

confusion in data science, because there are few academic

distinctions to fall back on, and moreover, due to the state of

the art in data processing, data scientists tend to spend a

majority of their problem-solving time on data preparation

and processing. The goal of such preparation is either to

‘‘WITHOUT ACADEMIC
PROGRAMS DEFINING THE FIELD

FOR US, WE NEED TO DEFINE
THE FIELD FOR OURSELVES.’’

Provost and Fawcett

ORIGINAL ARTICLE

MARY ANN LIEBERT, INC. � VOL. 1 NO. 1 � MARCH 2013 BIG DATA BD57

subsequently apply data-science methods or to understand

the results. However, that does not change the fact that the

day-to-day work of a data scientist—especially an entry-level

one—may be largely data processing. This is directly analo-

gous to an entry-level chemist spending the majority of her

time doing technical lab work. If this were all she were trained

to do, she likely would not be rightly called a chemist but

rather a lab technician. Important for being a chemist is that

this work is in support of the application of the science of

chemistry, and hopefully the eventual advancement to jobs

involving more chemistry and less technical work. Similarly

for data science: a chief scientist in a data-science-oriented

company will do much less data processing and more data-

analytics design and interpretation.

At the time of this writing, discussions of data science inev-

itably mention not just the analytical skills but the popular

tools used in such analysis. For example, it is common to see

job advertisements mentioning data-mining techniques

(random forests, support vector machines), specific applica-

tion areas (recommendation systems, ad placement optimi-

zation), alongside popular software tools for processing big

data (SQL, Hadoop, MongoDB). This is natural. The partic-

ular concerns of data science in business are fairly new, and

businesses are still working to figure out how best to address

them. Continuing our analogy, the state of data science may

be likened to that of chemistry in the mid-19th century, when

theories and general principles were being formulated and the

field was largely experimental. Every good chemist had to be a

competent lab technician. Similarly, it is hard to imagine a

working data scientist who is not proficient with certain sorts

of software tools. A firm may be well served by requiring that

their data scientists have skills to access, prepare, and process

data using tools the firm has adopted.

Nevertheless, we emphasize that there is an important reason

to focus here on the general principles of data science. In ten

years’ time, the predominant technologies will likely have

changed or advanced enough that today’s choices would seem

quaint. On the other hand, the general principles of data

science are not so differerent than they were 20 years ago and

likely will change little over the coming decades.

Conclusion

Underlying the extensive collection of techniques for mining

data is a much smaller set of fundamental concepts com-

prising data science. In order for data science to flourish as a

field, rather than to drown in the flood of popular attention,

we must think beyond the algorithms, techniques, and tools

in common use. We must think about the core principles and

concepts that underlie the techniques, and also the systematic

thinking that fosters success in data-driven decision making.

These data science concepts are general and very broadly

applicable.

Success in today’s data-oriented business environment re-

quires being able to think about how these fundamental

concepts apply to particular business problems—to think

data-analytically. This is aided by conceptual frameworks that

themselves are part of data science. For example, the auto-

mated extraction of patterns from data is a process with well-

defined stages. Understanding this process and its stages helps

structure problem solving, makes it more systematic, and

thus less prone to error.

There is strong evidence that business performance can be

improved substantially via data-driven decision making,3 big

data technologies,4 and data-science techniques based on big

data.9,10 Data science supports data-driven decision mak-

ing—and sometimes allows making decisions automatically

at massive scale—and depends upon technologies for ‘‘big

data’’ storage and engineering. However, the principles of

data science are its own and should be considered and dis-

cussed explicitly in order for data science to realize its

potential.

Author Disclosure Statement

F.P. and T.F. are authors of the forthcoming book, Data

Science for Business.

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Address correspondence to:

F. Provost

Department of Information, Operations,

and Management Sciences

Leonard N. Stern School of Business

New York University

44 W. 4th Street, 8th Floor

New York, NY 10012

E-mail: fprovost@stern.nyu.edu

Provost and Fawcett

ORIGINAL ARTICLE

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Case Study report Outline

Before you begin writing, follow these guidelines to help you prepare and understand the case

study/Journal:

I. Read and Examine the Case/Journal Thoroughly

a. Take notes, highlight relevant facts, underline key problems.

II. Focus Your Analysis

a. Identify two to five key points/problems.

b. Why do they exist?

c. How do they impact the industry/organization?

d. Who is responsible for them?

III. Uncover Possible Solutions/Changes Needed

a. Review course readings, discussions, outside research, your experience.

Drafting the Report

Once you have gathered the necessary information, a draft of your case/journal report should

include these general sections, but these may differ depending on your assignment directions

or your specific case study/journal:

1. Introduction

o Identify the key points in the case

study/journal.

o Formulate and include a thesis statement, summarizing the outcome of your

analysis in 1–2 sentences.

o Demonstrate that you have researched the main topics/points in this case

study/journal.

2. Body

o Outline the various pieces of the case study/journal that you have learned.

o Evaluate these pieces by discussing the effectiveness in business practices

mentioned in the case study/journal.

o State the significance of learning this topic and give example from the case

study/journal.

3. Proposed Solution/Changes (for case studies)

o Discuss specific and realistic solution(s) or changes mentioned in the case.

o Consider strong supporting evidence, pros, and cons. Is this study/solution

realistic?

o Explain why this solution was chosen.

o Support this solution with solid evidence, such as:

▪ Concepts from class (text readings, discussions, lectures)

▪ Outside research

▪ Personal experience (anecdotes)

4. Conclusion and Recommendations

o Rephrase the main points discussed in introduction.

o Determine and discuss specific strategies for accomplishing the proposed

solution.

o If applicable, recommend further action to resolve some of the issues.

o What should be done and who should do it?