2011 q4 McKinsey quarterly - Big data, you have it, now use it

Big data
You have it, now use it.
2011 Number 4

2011 Number 4
This Quarter
Quietly, the volume of data that companies generate
and collect has soared in recent years. While the only
outward sign may be growth in the number of
servers needed to process and store data, the business
implications are profound.
This issue of McKinsey Quarterly provides a state-of-the-art CEO’s
guide to navigating the era of “big data.” Building on McKinsey
Global Institute research released earlier this year, Brad Brown,
Michael Chui, and James Manyika present a series of questions
and thought-provoking examples intended to concentrate busy leaders’
minds on the implications of big data. Several intriguing thinkers
and practitioners—Massachusetts Institute of Technology professor
Erik Brynjolfsson, Cloudera cofounder Jeff Hammerbacher,
AstraZeneca senior executive Mark Lelinski, and Butler University
men’s basketball coach Brad Stevens—offer their perspectives on
competing through data. And for executives ready to start creating a
big data strategy, McKinsey’s Jacques Bughin, John Livingston, and
Sam Marwaha present a road map for action based on the experiences
of companies on the cutting edge.

Several other quiet but potent forces run through this issue. Santa Fe
Institute professor W. Brian Arthur describes how a “second
economy” of machine-to-machine interactions is imperceptibly taking
root beneath the surface of the physical world, potentially overtak-
ing it in economic importance within the next 20 years. McKinsey’s
Joanna Barsh and Lareina Yee present research about the silent
killer of women’s careers—rarely acknowledged but widely held mind-
sets that often block the path to the C-suite—and suggest some
robust antidotes for companies that are serious about boosting the
number of women in their senior ranks. At a personal level, we all
know that honest feedback from colleagues can help us stay in touch.
But a cone of silence surrounds many CEOs and their top teams,
says Harvard Business School professor Robert S. Kaplan, who has some
pointed advice for turning up the volume.
Finally, a coalition of experts from McKinsey’s oil and gas, automotive,
strategy, and operations practices explores the implications of
another quiet trend: the historic rise of oil consumption in emerging
markets. While good news in that it reflects economic improvement for
millions, steadily rising demand could strain global supply capacity
in the years ahead. Well-coordinated regulatory and behavioral
changes throughout the world may get us through the crunch, say Scott
Nyquist and his colleagues. But an unexpected oil price spike is
also possible. Presented here are some no-regrets moves companies
can make now to prepare strategically and operationally.
Easy as it is for issues like these to get drowned out by the din of daily
battle, staying ahead of them may well make all the difference in
the years ahead. We hope this issue of the Quarterly helps you keep your
organization focused today on what will matter most tomorrow.
Allen P. Webb
Editor-in-Chief

On the cover
Big data
You have it, now use it
Are you ready
for the era
of ‘big data’?
Competing
through data:
Three experts offer
their game plans
Brad Brown, Michael Chui, and
James Manyika
Radical customization, constant
experimentation, and novel business
models will be new hallmarks
of competition as companies capture
and analyze huge volumes of
data. Here’s what you should know.
MIT professor Erik Brynjolfsson,
Cloudera cofounder Jeff Hammerbacher,
and Butler University men’s basketball
coach Brad Stevens reflect on the power
of data.
24
36
Features
48
60
Changing
companies’ minds
about women
Top executives
need feedback—
here’s how they
can get it
Joanna Barsh and Lareina Yee
Robert S. Kaplan
Leaders who are serious about getting
more women into senior management need
a hard-edged approach to overcome
the invisible barriers holding them back.
As executives become more senior,
they are less likely to receive constructive
feedback on their performance or their
strategy. To get it, they should call on their
junior colleagues.

90
100
The second
economy
The changing
shape of
US recessions
Byron Auguste, Susan Lund,
and James Manyika
Digitization is creating a second
economy that’s vast, automatic, and
invisible—thereby bringing the
biggest change since the Industrial
Revolution.
Recovery time for US employment
after recessions has increased
dramatically over the last two decades.
Feature
72 Oil’s uncertain
future
What you need to know
It’s possible, though far from certain,
that oil prices will spike in the
years ahead. Here’s why—and how
you can prepare.
Another oil shock?
Tom Janssens, Scott Nyquist, and
Occo Roelofsen
The automotive sector’s road
to greater fuel efficiency
Russell Hensley and Andreas Zielke
Anticipating economic
headwinds
Jonathan Ablett, Lowell Bryan,
and Sven Smit
Building a supply chain that
can withstand high oil prices
Knut Alicke and Tobias Meyer
Special report
74
78
84
87
Extra Point
Big data for the CEO
Idea Exchange
Readers mix it up
with authors of articles
from McKinsey Quarterly
2011 Number 3
Departments
McKinsey on
the Web
Highlights from
our digital offerings
7 1208
Picture This
W. Brian Arthur

Cybersecurity: A senior
executive’s guide
A new era for commodities
Seizing the potential
of ‘big data’
Executive perspective
AstraZeneca’s ‘big data’
partnership
Freeing up the sales force
for selling
How strategic is
our technology agenda?
James Kaplan, Shantnu Sharma, and
Allen Weinberg
Richard Dobbs, Jeremy Oppenheim,
and Fraser Thompson
Jacques Bughin, John Livingston,
and Sam Marwaha
Olivia Nottebohm, Tom Stephenson,
and Jennifer Wickland
A changing corporate-technology
landscape and more aggressive
hackers make safeguarding valuable
corporate data a top-management
issue, not just an IT problem.
Cheap resources underpinned economic
growth for much of the 20th century.
The 21st will be different.
Companies are learning to use large-
scale data gathering and analytics
to shape strategy. Their experiences
highlight the principles—and
potential—of big data.
Mark Lelinski, an executive at the
global drugmaker, explains
how the company is using data to
build customer relationships
that focus on the total cost of care.
Most sales reps spend less than half
of their time actually selling. By
reshaping sales operations, companies
can help them focus on their real job.
CEOs should shake up the technology
debate to ensure that they capture
the upside of technology-driven threats.
Here’s how.
10
13
103
110
115
Leading Edge Applied Insight
A quick chat with the world’s
biggest baker
Grupo Bimbo CEO Daniel Servitje
reflects on his company’s growth
in developed and emerging markets.
16
Sizing the Internet’s
economic impact
Eric Hazan, James Manyika, and
Matthieu Pelissie du Rausas
New McKinsey research underscores the
magnitude of the Net’s impact on
global growth and corporate performance.
18
Brad Brown and Johnson Sikes

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Spinning off businesses can have
real advantages in creating value—
if executives understand how.
Finding the courage
to shrink
McKinsey analyzed the potential impact
on 33 industries. Two dimensions
stood out: the plan’s effect on profit pools
and on the competitive landscape.
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Readers mix it up with authors of articles from McKinsey Quarterly
2011 Number 3
Idea Exchange
8
We’re all marketers now
The cover package of our previous issue focused on the transform-
ational changes under way in marketing, including an explosion of
digital media, increasingly rich data, and organizational flux as companies
seek to engage customers more effectively. Authors Tom French,
Laura LaBerge, and Paul Magill of McKinsey continued exploring
these issues with readers on mckinseyquarterly.com:
Cy Heidari
President and CEO, ValueTelligence, New York, New York
“While the approach is sensible for large-capitalization companies, it
may not apply to small- and midcapitalization companies due to the added
operational costs, even if they outsource their marketing activities.”
Thinking small
McKinsey’s Laura LaBerge responds:
“You’re right to recognize the compressed challenge this environment presents to smaller
companies, yet these firms also have unique opportunities. With less hierarchy to
stifle cross-functional coordination, it’s easier for employees at smaller companies to
wear several hats and embed marketing thinking across the organization. It’s also
easier for employees to share experiences with customers, gain clearer insights, and
create a shared view of customer-engagement requirements. The need to prioritize
more means these companies pick their battlegrounds carefully and leverage close
customer relationships to better focus their efforts.”
Cross-functional challenges
Jo Moffatt
Managing director, Woodreed, United Kingdom
“The trouble with internal engagement—which wasn’t mentioned, even
though brands help develop employees who can ensure a consistent consumer
experience—is that HR and marketing tend to work in silos. They
should harness each other’s strengths, fusing HR’s people knowledge with
marketing’s brand and customer expertise.”
McKinsey’s Paul Magill responds:
“The HR–marketing disconnect is a tragedy at many companies, since the brand is central
to both, but we are seeing several create ‘pervasive’ strategies that bridge the internal–
external divide through ‘planned’ and ‘unplanned’ customer interactions. ‘Planned’ inter-
actions help identify top-priority touch points, and we increasingly see marketing
working with HR to find frontline employees who aren’t always marketers but can still
deliver a better customer experience. ‘Unplanned’ interactions lead to the employee
branding efforts you describe. A great example is when the two functions design, build,
and deploy the brand internally, while marketing embeds the execution in HR. Here, the
marketing function takes the kind of organization-wide, multistakeholder view of engage-
ment we recommend, then divides up responsibility for executing the strategy.”

To centralize or not to centralize?
In our previous issue, McKinsey alumnus Andrew Campbell (now director
of the London-based Ashridge Strategic Management Centre), along with
Sven Kunisch and Günter Müller-Stewens of the University of St. Gallen
Institute of Management, suggested that executives use three questions to
focus internal debate about centralization proposals: (1) Is centralization
mandated? (2) Does it add 10 percent to market capitalization? (3) Are the
risks low? Below, McKinsey’s Suzanne Heywood suggests some
additional considerations, to which Campbell responds:
Suzanne Heywood
Principal, McKinsey & Company, London
“In our experience, companies need to first determine—based on their sector,
strategy, and growth history—whether they have an ingoing bias for or
against centralization. Companies should then weigh the potential benefits
and drawbacks that might arise from it. With a bias against centralization,
some functional activities will still need to be centralized, but the benefits
would have to outweigh the risks substantially; the opposite would be true
if the bias were for centralization.
“Second, it’s important to recognize that centralization may yield improve-
ments—such as enhancing knowledge sharing or minimizing operating
risk—that are difficult to quantify in terms of a market-capitalization bench-
mark. Finally, if companies do decide to centralize a function, they should
also consider alternatives to structural change. In many cases, making ‘softer’
changes (for example, standardizing processes, creating functional
networks to bring people together) can also result in centralization-related
benefits. It is wise to consider these mechanisms first and only implement
structural change if they will clearly not be effective.”
Andrew Campbell responds:
“You are right that benefits and drawbacks vary by business model and that many
are qualitative. But it’s because so much of this assessment is qualitative
that companies need to use a quantitative hurdle (such as the 10 percent market-
capitalization rule) and be confident in the potential gains from centralization
before assuming the risks. In my experience, qualitative assessments are too easily
unbalanced by subjective arguments, so there is real value in the quantitative
nature of question two.
“With the ‘softer’ changes, such as standardizing processes or bringing people
together, it’s implied that these actions are not ‘centralization’ and do not need to
be judged against the same criteria. However, these actions do involve some
degree of centralization. Who decides what the standard process should look like?
Who decides whom to bring together, how often, and when? We should still
bear in mind the three centralization questions and the hurdles this decision should
cross when implementing less structural changes.”
9

10 2011 Number 4
A rash of highly publicized IT
security breaches that have
struck sophisticated companies in
recent months has led many senior
executives to worry about how
safe their own corporate environ-
ments really are. Despite these
concerns, executives often
lack a clear sense of how to combat
the growing threats. As a result,
they are placing more pressure on
CIOs and IT security executives
to raise their companies’ technology
ramparts. But from our experience
and interviews with IT security exec-
utives at 25 top global companies,
we believe that technology tactics
alone are insufficient. To gain
ground against the hackers
in protecting information assets
such as business plans and
intellectual property—without
constraining business growth and
flexibility—companies must
adopt cybersecurity approaches
that require much more engagement
from the CEO and other senior
executives.
Why IT environments are
harder to protect
Greater volumes of online
transactions are creating enormous
incentives for cybercriminals.
Companies that mine transaction
data and customer information,
James Kaplan, Shantnu Sharma, and Allen Weinberg
A changing corporate-technology landscape and more aggressive hackers make safeguarding
valuable corporate data a top-management issue, not just an IT problem.
Cybersecurity: A senior
executive’s guide
Leading Edge
Research, trends, and emerging thinking
10 13
16 18
A new era
for commodities
Cybersecurity:
A senior
executive’s guide
A quick
chat with the
world’s biggest
baker
Sizing
the Internet’s
economic
impact

11
from outside the IT organization.
They will be vital to help identify and
then champion business practice
changes that create intelligent con-
straints for employees, customers,
and partners. Senior leaders also
may need to arbitrate competing
demands: some business
units naturally might favor lighter
safeguards that raise the risk
of critical-data loss, while overly
stringent controls advocated
by IT leaders will get in the way of
doing business.
At one company we surveyed,
the CEO is now directly involved with
senior security executives in
making key decisions. Elsewhere,
security officers are embedded
in business units to facilitate
dialogue at the most meaningful
level. Some security leaders
now report to the risk committees
of company boards.
Address cybersecurity ‘business
back,’ not technology forward
Many companies need to reverse
conventional thinking about security.
Rather than focus on vulnerable
technologies at the back end of
processes, they should first decide
which business assets must be
protected. Some large institutions
have launched multiyear programs
to classify their data troves and
better focus such efforts. Before
enhancing plans to collaborate,
other companies are scanning the
full value chain to clarify the
expectations of vendors about how
information will be exchanged.
Still others are starting with
customers—thinking through, for
example, how to collect enough
information to verify their identity
for example, create new and
valuable stores of intellectual
property that are attractive targets.
Moreover, employees are demanding
access to corporate networks
from the same mobile devices they
use in their personal lives, creating
new crevices for hackers to exploit.
Another challenge: companies are
eager to optimize supply chains
by inducing vendors and customers
to join their corporate networks.
But in this way, they may be rendering
their own defenses more porous
and only as secure as those
of their weakest partner. One large
company, for example, barred its
employees from using peer-to-peer
software to share sensitive company
documents over the Web, only
to discover that on-site contractors
routinely used this software
to review the same documents.
Approaching security
differently
The threats will only rise in com-
plexity and virulence, painful as that
may be for leaders to contemplate.
Professional cybercrime organi-
zations, political “hacktivists,” and
state-sponsored groups are ever
more technologically advanced, in
some cases outstripping the skills
and resources of corporate security
teams. (One hacker group provides
“cybercrime as a service,” receiving
payment for each end-user
device it infects with malware.) To
make business-led strategies
work, companies must undertake
the following steps.
Engage at the top
Meeting these challenges requires
the involvement of senior executives

12 2011 Number 4
without forcing them to spend too
much time signing on. Getting
this balance right, a critical element
of meeting the cybersecurity
challenge, can serve as a competi-
tive differentiator.
Protect the data, not the perimeter
Motivated attackers will always find
ways to penetrate the most
sophisticated corporate defenses.
Some companies are embracing
this reality by redesigning how they
house and regulate access to
data. If customer credit card infor-
mation, for example, resides in
a single database, a cybercriminal
must breach security only once
to profit. Separating credit card
numbers and expiration dates vastly
complicates a hacker’s task. At
some companies, plugging a laptop
into the system allows employees
to access only publicly available data;
viewing customer files or working
with corporate applications requires
a more rigorous, multistage
authentication of identity. Since
malicious insiders often pose
the greatest threat of all, some com-
panies limit, by specific roles
and functions, the number of people
who can access core production
systems and data.
Refresh strategies to address
evolving business needs and threats
CEOs fervently want to “solve” the
security problem, but it would
be more fruitful to acknowledge that
it’s an ongoing battle, so security
tactics must change constantly.
Advanced companies conduct sim-
ulated cyberattacks to identify
unexpected vulnerabilities and to
build the muscles needed to
manage breaches. Some have built
massive analytic capabilities
to sift though data such as e-mail
headers or to identify unusual IP
traffic patterns that could be warning
signs of emerging threats. Finally,
to ensure that cybersecurity is
sustainable, leaders need to make
it part of their business case
for entering new regions or investing
in new products and other major
initiatives.
Clearly, we are in the early days of
what will be a long war between
cybercriminals and global institutions
of all shapes and sizes. As in any
prolonged struggle, the combatants
will continually react and adapt.
No matter how an organization’s
tactics evolve, leaders can boost the
odds of prevailing by developing
approaches that cut across commer-
cial strategy, operations, risk
management, and the legal and
technology functions—supported by
a mandate and active engagement
from the most senior level of
executives.
Allen Weinberg is a director in
McKinsey’s New York office, where
James Kaplan is a principal;
Shantnu Sharma is a consultant
in the Boston office.
Copyright © 2011 McKinsey & Company.
All rights reserved. We welcome your
comments on this article. Please send them
to quarterly_comments@mckinsey.com.

13Leading Edge
Has the global economy
entered an era of persistently high,
volatile commodity prices? Our
research shows that during the past
eight years alone, they have
undone the decline of the previous
century, rising to levels not seen
since the early 1900s (exhibit).
In addition, volatility is now greater
than at any time since the oil-
shocked 1970s because commodity
prices increasingly move in lock-
step. Our analysis suggests that they
will remain high and volatile for
at least the next 20 years if current
trends hold—barring a major
macroeconomic shock—as global
resource markets oscillate in
response to surging global demand
and inelastic supplies.
Demand for energy, food, metals,
and water should rise inexorably as
three billion new middle-class
consumers emerge in the next two
decades.1
The global car fleet,
for example, is expected almost to
double, to 1.7 billion, by 2030.
In India, we expect calorie intake per
person to rise by 20 percent during
that period, while per capita meat
consumption in China could
increase by 60 percent, to 80 kilo-
grams (176 pounds) a year. Demand
for urban infrastructure also
will soar. China, for example, could
annually add floor space totaling
2.5 times the entire residential and
commercial square footage of
the city of Chicago, while India could
add floor space equal to another
Chicago every year.
Such dramatic growth in demand
for commodities actually isn’t
unusual. Similar factors were at play
throughout the 20th century as
the planet’s population tripled
and demand for various resources
jumped anywhere from 600 to
2,000 percent. Had supply remained
constant, commodity prices
would have soared. Yet dramatic
improvements in exploration,
extraction, and cultivation techniques
kept supply ahead of ever-
increasing global needs, cutting the
real price of an equally weighted
index of key commodities by
almost half. This ability to access
progressively cheaper resources
underpinned a 20-fold expansion of
the world economy.
There are three differences today.
First, we are now aware of the
Richard Dobbs, Jeremy Oppenheim, and Fraser Thompson
Cheap resources underpinned economic growth for much of the 20th century.
The 21st will be different.
A new era
for commodities

14 2011 Number 4
potential climatic impact of carbon
emissions associated with surging
resource use. Without major
changes, global carbon emissions
will remain significantly above
the level required to keep increases
in the global temperature below
2 degrees Celsius—the threshold
identified as potentially catastrophic.2
Second, it’s becoming increasingly
difficult to expand the supply
of commodities, especially in the
short run. While there may not
be absolute resource shortages—
the perceived risk of one has his-
torically spurred efficiency-
enhancing innovations—we are
at a point where supply is
increasingly inelastic. Long-term
marginal costs are increasing
for many resources as depletion
rates accelerate and new invest-
ments are made in more complex,
less productive locations.
Third, the linkages among resources
are becoming increasingly
important. Consider, for example,
the potential ripple effects
of water shortfalls at a time when
roughly 70 percent of all water
is consumed by agriculture and
12 percent by energy production. In
In little more than a decade, commodity prices have
soared from historic lows to new highs.
Q4 2011
MGI commodities
Exhibit 1 of 1
McKinsey Global Institute commodity price index (average of 1999–2001 = 100)1
World War I
World War II 1970s oil shock
Postwar
depression
Great
Depression
260
1900 1910 19301920 1940 1950 1960 1970 1980 1990 2000 2010 20112
240
220
200
180
160
140
120
100
80
60
0
1 Based on arithmetic average of 4 commodity indexes: food, agricultural raw materials, metals, and energy. Each index was
weighted by total world export volumes from 1999 to 2001 at indexed prices (in real terms) over same time period. Energy index
excludes gas prices prior to 1922, for which data are unavailable.
2Based on average of ﬁrst 4 months of 2011.
Source: FAOSTAT (Food and Agriculture Organization of the United Nations); Grilli and Yang commodity price index, 1988;
International Monetary Fund (IMF) primary commodity prices; Organisation for Economic Co-operation and Development; Stephan
Pfaffenzeller et al., “A short note on updating the Grilli and Yang commodity price index,” World Bank Economic Review,
2007, Volume 21, Number 1, pp. 151–63; World Bank commodity price data; UN Comtrade; McKinsey Global Institute analysis
In little more than a decade, soaring commodity prices have
erased a century of steady declines.

15Leading Edge
Uganda, water shortages have
led to escalating energy prices, which
led to the use of more wood
fuels, which led to deforestation and
soil degradation that threatened
the food supply.
Higher commodity prices are one
way of bringing supply and demand
nearer to balance—but not a
desirable means for most policy
makers and business leaders,
since lofty prices can drag down
profits and growth (for more,
see “Anticipating economic head-
winds,” on page 84). Another
approach is to squeeze greater
“productivity” from natural resources
by, for example, improving mining
recovery rates, making households
more energy efficient, and
capturing and reusing wastewater.
Our research—summarized
in a forthcoming McKinsey Global
Institute report on the world’s
natural-resource needs in the 21st
century—suggests that better
resource productivity could single-
handedly meet more than 20 per-
cent of forecast 2030 demand for
energy, steel, water, and land. In
addition, higher long-term resource
prices might create the necessary
incentive for breakthroughs,
especially around energy-related
technologies that could reduce
carbon emissions (for more on this
topic, see “Another oil shock?” on
page 74). More will need to be done,
of course, and we’re not suggesting
that it’s easy; major policy, behavioral,
and institutional barriers must
be addressed. Yet as we enter a new
era for commodities, there’s little
choice but to act.
Richard Dobbs is a director of
the McKinsey Global Institute
(MGI) and a director in McKinsey’s
Seoul office; Jeremy Oppenheim
is a director in the London office;
Fraser Thompson is a senior
fellow at MGI and is based in the
London office.
Copyright © 2011 McKinsey & Company.
1
See David Court and Laxman Narasimhan,
“Capturing the world’s emerging middle
class,” mckinseyquarterly.com, July 2010.
2
The Stern Review on the Economics
of Climate Change, released in 2006, and
the International Panel on Climate
Change (IPCC) claim that an increase in
temperatures of more than 2 degrees
Celsius (3.6 degrees Fahrenheit) above those
of preindustrial times could cut GDP by
up to 20 percent, force more than a billion
people to migrate, make many species
extinct, threaten major cities as a result of
rising seas, and decrease agricultural yields,
putting pressure on food (and fuel) supplies.
Major changes in energy production and
usage could lower carbon emissions to keep
temperatures below that threshold.

Grupo Bimbo CEO Daniel Servitje reflects
on his company’s growth in developed and
emerging markets.
A quick
chat with the
world’s
biggest baker
What is the world’s largest
baker? Guess again if you didn’t
say Grupo Bimbo, the Mexican
packaged-goods company that has
become a global player in the
food marketplace. Grupo Bimbo has
its highest sales in Mexico and the
United States (penetrated primarily
through a few big acquisitions,
most recently of Sara Lee’s fresh-
baked-goods unit, for which it is
awaiting regulatory approval). It also
operates throughout Latin America
and in China.
Daniel Servitje, the 52-year-old
son of the company’s founder, has
served as CEO since 1997.
During that time, sales have more
than quintupled and profitability
has improved. In this excerpt
from an interview with McKinsey’s
Alejandro Diaz, Mr. Servitje
discussed Grupo Bimbo’s geo-
graphical expansion, the challenges
that engendered, and how
the company’s origins as a family
business aided its growth.
The Quarterly: How do
the company’s emerging-market
roots differentiate you from
other multinational companies?
Daniel Servitje: We’re probably
looking at things from a different
perspective, from the ground up—a
little bit more humble and more
focused on economic uncertainties.
We suffered a lot during the
devaluations and economic crises
in the 1980s and 1990s. That’s
still part of our baggage when we
analyze the situation in other
countries. Also, we have always
tried to understand the market
on a local and regional basis; it might
be just one or two cities. Bread
cannot travel for long distances,
which forces us to have a
very localized, fine-tuned view
of markets.
The Quarterly: Grupo Bimbo
is testing the waters in China. How
is it going?
Daniel Servitje: I’ve been
surprised. I thought it was going to
be much more complicated
for a Latin American company to
develop its business—to replicate
our business model—in China.
Surprisingly, it has not been
as difficult as I had expected, and
even less difficult than what we
would find in other Latin American
countries. The challenge in China
is to develop the bread market as a
category. That’s where we are
still doing a lot of work.
The Quarterly: Can you say a little
more about the challenges you
have experienced in Latin America?
16 2011 Number 4

Daniel Servitje: When we entered
the market in Latin America,
we thought that there were a lot of
similarities to our culture and
our business system. Some things
worked out very well, and many
things were disasters. A few years
ago, we did an acquisition in
Central America that we thought was
a simple one. But it ended up
being very complex; we did not really
achieve the benefits that we had
hoped for. Even though we speak
Spanish and we understand
the culture, the labor rules and the
complexities of each market can
get us to a very different place. We
learned our lessons the hard way in
that case. From that circumstance,
we’ve tried to develop a more
thorough approach to our MA due
diligence and to be more sensitive
to the complexities of labor.
Despite the hurdles, we have
become the leading baking player in
Brazil. But our challenge there is
to find the right model to penetrate
the traditional mom-and-pop
segment, which is quite different
from what we find in other Latin
American countries.
The Quarterly: Grupo Bimbo
has been a public company
for 30 years but still has a family
business orientation. How is
that an advantage?
Daniel Servitje: We see things
with a longer time perspective and
base our decisions on a larger
time horizon. That allows us to view
things with a perspective very
different from the ones that we see
in many multinationals. For
example, we lost money for more
than ten years in our Mexican
snack business. We kept on building
our base, gaining more knowledge
of the business, and scaling up our
company until we turned it around.
Now it’s a very viable business.
Our company has been growing
by about 10 percent compounded
annually for many years—four
to five times the GDP growth of
Mexico and the United States. We
have been blessed because
we’ve focused on a strict number
of categories and built a very
strong distribution network in many
countries. We also had a high aim
of becoming an international player
and the commitment of our
board to sustain this strategy for
many years. If we had not been
willing to reinvest in the businesses
in difficult times, certainly
this strategy wouldn’t have been
successful.
Alejandro Diaz is a director in
McKinsey’s Dallas office.
Copyright © 2011 McKinsey Company.
17Leading Edge
View the full
interview, “The
making of an
emerging-market
champion,” on
mckinseyquarterly
.com.
Daniel Servitje
CEO of Grupo Bimbo

18 2011 Number 4
Eric Hazan, James Manyika, and Matthieu Pelissie du Rausas
New McKinsey research underscores the magnitude of the Net’s impact on global growth
and corporate performance.
Sizing the Internet’s
economic impact
The Internet has profoundly
changed the workings of
the global economy. Yet a precise
measure of the magnitude of the
Internet’s impact, whether at
the level of national economies or
of individual firms, has remained
elusive. In an effort to quantify
the Internet’s effect on economic
activity, McKinsey examined
national-account data of 13 nations
that account for 70 percent of
global GDP. We also surveyed 4,800
small and medium-sized enter-
prises in 12 countries on their use
of the Internet and its effect on
their performance. Econometric
analyses of both macro- and micro-
economic data provided rein-
forcing evidence of the Internet’s
sizable and expanding influence on
global and corporate growth.
The growth dividend for
countries . . .
At the highest level, we found that
the Internet now accounts for 3.4 per-
cent of GDP across the economies
we studied, ranging from highs of
5 to more than 6 percent in Sweden
and the United Kingdom, where
consumers and corporations alike
are heavy users, to less than
1.5 percent in Brazil and Russia,
where adoption is weaker.1
The sources of this activity include
private consumption (from
online commerce to smartphone
purchases); investment by
companies in software, servers,
and communications gear; and
public investments in areas such as
Internet infrastructure. If measured
as a global industry unto itself,
the Internet now contributes more to
GDP than education, agriculture,
or utilities do.
The magnitude of its impact is likely
to increase, because the Internet’s
contribution to global economic
growth is accelerating: from 2005 to
2009, it accounted for 21 percent
of the combined GDP growth of nine
developed economies we studied.
That was more than double the
growth contribution over a longer
(14-year) period from 1995 to 2009.
The Net is also propelling growth
in less mature economies, such as
China and India, though so far
at a more moderate pace (Exhibit 1).

19Leading Edge
The Internet’s contribution to global economic growth
is accelerating.
Q4 2011
Internet growth
Exhibit 1 of 2
Internet’s contribution to
global GDP growth, %
Nominal GDP growth,1
1995–2009, %
Mature
countries
High-growth
countries
1In local currencies.
2Negative growth due to inﬂation.
Source: Organisation for Economic Co-operation and Development national accounts; McKinsey Global Institute analysis
The Internet’s contribution to global economic
growth is accelerating.
Sweden 3.9
15
33
Germany 1.9
14
24
United Kingdom 4.7
11
23
France 3.4
10
18
South Korea 7.0
7
16
United States 4.7
8
15
Italy 3.4
4
12
Canada 4.6
6
10
Japan N/A2 –0.3
India 13.1
4
5
China 9.5
3
3
Brazil 10.7
2
2
Russia 26.7
1
1
Over 14 years, 1995–2009
Over more recent 5 years, 2004–09
Moreover, our study indicates that
the Internet’s net impact on jobs
has been positive: for every position
eliminated through productivity
gains associated with it, 2.6 are
created. This finding is confirmed in
a detailed study of France, as
well as a survey of 4,800 small and
medium-sized enterprises
we conducted in 12 countries.
Finally, our study also shows that
Internet maturity—measured
by a variety of factors characterizing
a country’s Internet use, infra-
structure, online expenditures, and

20 2011 Number 4
Small and medium-sized enterprises that use Web
technologies extensively are growing more quickly and
exporting more widely.
Q4 2011
Internet growth
Exhibit 2 of 2
Small and medium-sized enterprises
Annual growth over
last 3 years, %
Enterprises grouped by degree of
Web-technology utilization1
Export revenues as
% of total sales
1 Based on number of technologies possessed by companies and number of employees, customers, and suppliers with access
to those technologies.
Source: May 2011 McKinsey survey of 4,800 small and medium-sized enterprises in 12 countries; McKinsey Global
Institute analysis
Small and medium-sized enterprises that use Web
technologies extensively are growing more quickly
and exporting more widely.
Low
(42% of respondents)
6.2 2.5
Medium
7.4 2.7
High
13.0 5.3
e-commerce—correlates with
standard-of-living improvements,
measured in terms of GDP per
capita. We also found higher growth
rates for labor productivity in
nations such as the United States,
where Internet usage and infra-
structure were more mature, and
a correlation between highly
developed Internet ecosystems and
higher GDP growth rates.
. . . and for companies
Our global research on small and
medium-sized enterprises also
indicates that companies with two
characteristics—employing larger
numbers of Internet technologies
(such as blogs, social networks,
and e-commerce sites) and
enjoying high rates of adoption
among employees, customers, and
suppliers—recorded revenue
growth of 13 percent over the last
three years, twice the rate of
companies with lower levels of
Internet adoption. Furthermore, the
profit levels of these Internet-
intense companies were 10 percent
higher than those of less intense
Web users, and the rate at which
they added workers was twice as
high. While it’s impossible
to say definitively which way the
causation runs, our research
does suggest greater efficiency
at the more Internet-intense
companies (their cost of goods sold
and administrative costs were
lower) and, as Exhibit 2 shows,
a stronger ability to expand market
reach (export revenues were
markedly higher).
The 10 percent profitability advan-
tage enjoyed by heavy Internet
users represents a significant global
profit pool. Companies that supply

21Leading Edge
infrastructure, a business environ-
ment that combines moderate
regulation with protections for intel-
lectual capital, and strong
educational and training programs
that foster technical skills—have
more companies among the ranks
of global Internet suppliers.
Each of these four policy areas
offers plenty of space for committed
executives and policy makers to
collaborate for improvements.
The authors would like to thank
Jacques Bughin, Michael Chui,
Vincent Luciani, and Remi Said for
their contributions to this research.
Eric Hazan is a principal in
McKinsey’s Paris office, where
Matthieu Pelissie du Rausas
is a director; James Manyika
is a director of the McKinsey Global
Institute and a director in the
San Francisco office.
Copyright © 2011 McKinsey Company.
1
The size of the Internet economy represents
the sum of Internet consumption (of
which service, access, and e-commerce are
key elements), private investment, public
expenditure, and the trade balance in Web-
related goods and services.
2
For more see Michael Chui, Markus Löffler,
and Roger Roberts, “The Internet of Things,”
mckinseyquarterly.com, March 2010.
hardware, software, and services to
the global Internet ecosystem claim
one-quarter of that pool. Leading
this Internet supply network of
250 firms are companies in Japan,
Sweden, and the United States.
Rising in influence are China and
India: as they develop more potent
export players, their Internet
supply sectors are growing at four
and five times, respectively, the
rate of the US industry.
Stoking future growth
As business leaders establish
strategic priorities amid rapid, Web-
related change, they should look
for opportunities to:
• reinvent business models
to capture productivity and perfor-
mance improvements unlocked
by the Internet
• exploit emerging Web trends,
such as the Internet of Things,2
in
which chips create highly
efficient networks from almost
any physical product
• embrace new, flexible organi-
zational structures enabled by
Web networks that link
employees, customers, and
business partners
Leaders also should focus on
emerging opportunities in countries
that are still getting up the Internet
growth curve. Those countries
can make big strides toward tapping
into more of the Internet economy’s
benefits, our research suggests.
We found that countries with four
characteristics—easy access to
start-up capital, a solid broadband

Big data
You have it, now use it.
22
24
Are you ready for the
era of ‘big data’?
Brad Brown, Michael Chui,
and James Manyika
36
Competing through
data: Three experts offer
their game plans
With data flooding into your company
as never before, information is no
longer just an IT issue; it’s yours as a
senior leader. Maybe your company
is sitting on powerful data assets that
could strengthen its ability to compete,
or perhaps there’s a competitor that’s
suddenly aiming a “big data” strategy
right at you. In our first story, find out
why mastering data and analytics is now
mission critical, and ask yourself five
questions that will help you understand
looming competitive challenges. Then
turn to a leading academic expert, a
data entrepreneur, and a top college
basketball coach who zero in on how you
can use data to compete.

24
The top marketing executive at a sizable US retailer recently
found herself perplexed by the sales reports she was getting. A major
competitor was steadily gaining market share across a range of
profitable segments. Despite a counterpunch that combined online
promotions with merchandizing improvements, her company kept
losing ground.
When the executive convened a group of senior leaders to dig into the
competitor’s practices, they found that the challenge ran deeper than
they had imagined. The competitor had made massive investments in
its ability to collect, integrate, and analyze data from each store
and every sales unit and had used this ability to run myriad real-world
experiments. At the same time, it had linked this information to
suppliers’ databases, making it possible to adjust prices in real time, to
reorder hot-selling items automatically, and to shift items from
store to store easily. By constantly testing, bundling, synthesizing, and
making information instantly available across the organization—
from the store floor to the CFO’s office—the rival company had become
a different, far nimbler type of business.
What this executive team had witnessed first hand was the game-
changing effects of big data. Of course, data characterized the information
age from the start. It underpins processes that manage employees;
it helps to track purchases and sales; and it offers clues about how
customers will behave.
Radical customization, constant experimentation,
and novel business models will be new
hallmarks of competition as companies capture
and analyze huge volumes of data. Here’s
what you should know.
Are you ready for the
era of ‘big data’?
Brad Brown, Michael Chui, and James Manyika

25
But over the last few years, the volume of data has exploded. In 15 of
the US economy’s 17 sectors, companies with more than 1,000
employees store, on average, over 235 terabytes of data—more data
than is contained in the US Library of Congress. Reams of data
still flow from financial transactions and customer interactions but
also cascade in at unparalleled rates from new devices and multiple
points along the value chain. Just think about what could be hap-
pening at your own company right now: sensors embedded in process
machinery may be collecting operations data, while marketers scan
social media or use location data from smartphones to understand teens’
buying quirks. Data exchanges may be networking your supply
chain partners, and employees could be swapping best practices on
corporate wikis.
All of this new information is laden with implications for leaders and
their enterprises.1
Emerging academic research suggests that com-
panies that use data and business analytics to guide decision making
are more productive and experience higher returns on equity than
competitors that don’t.2
That’s consistent with research we’ve conducted
showing that “networked organizations” can gain an edge by opening
information conduits internally and by engaging customers and sup-
pliers strategically through Web-based exchanges of information.3
Over time, we believe big data may well become a new type of corporate
asset that will cut across business units and function much as a
powerful brand does, representing a key basis for competition. If that’s
right, companies need to start thinking in earnest about whether
they are organized to exploit big data’s potential and to manage the
threats it can pose. Success will demand not only new skills but also
new perspectives on how the era of big data could evolve—the widening
circle of management practices it may affect and the foundation it
represents for new, potentially disruptive business models.
1
For more, see the McKinsey Global Institute report Big data: The next frontier
for innovation, competition, and productivity, available free of charge online at
mckinsey.com/mgi.
2
See Erik Brynjolfsson, Lorin M. Hitt, and Heekyung Hellen Kim, “Strength in numbers:
How does data-driven decisionmaking affect firm performance?” Social Science
Research Network (SSRN), April 2011. In this study, the authors found that effective use
of data and analytics correlated with a 5 to 6 percent improvement in productivity,
as well as higher profitability and market value. For more, see the forthcoming e-book by
Brynjolfsson and coauthor Andrew McAfee, Race Against the Machine: How the
digital revolution accelerates innovation, drives productivity, and irreversibly transforms
employment and the economy (Digital Frontier Press, October 2011).
3
See Jacques Bughin and Michael Chui, “The rise of the networked enterprise: Web 2.0
finds its payday,” mckinseyquarterly.com, December 2010.

26 2011 Number 4
Five big questions about big data
In the remainder of this article, we outline important ways big data
could change competition: by transforming processes, altering
corporate ecosystems, and facilitating innovation. We’ve organized the
discussion around five questions we think all senior executives
should be asking themselves today.
At the outset, we’ll acknowledge that these are still early days for big
data, which is evolving as a business concept in tandem with the under-
lying technologies. Nonetheless, we can identify big data’s key ele-
ments. First, companies can now collect data across business units and,
increasingly, even from partners and customers (some of this is truly
big, some more granular and complex). Second, a flexible infrastructure
can integrate information and scale up effectively to meet the surge.
Finally, experiments, algorithms, and analytics can make sense of all
this information. We also can identify organizations that are making
data a core element of strategy. In the discussion that follows and else-
where in this issue, we have assembled case studies of early movers in
the big data realm (see “Seizing the potential of ‘big data’,” on page 103,
and the accompanying sidebar, “AstraZeneca’s ‘big data’ partnership,”
on page 104).
In addition, we’d suggest that executives look to history for clues about
what’s coming next. Earlier waves of technology adoption, for example,
show that productivity surges not only because companies adopt new
technologies but also, more critically, because they can adapt their man-
agement practices and change their organizations to maximize the
potential. We examined the possible impact of big data across a number
of industries and found that while it will be important in every sector
and function, some industries will realize benefits sooner because they
are more ready to capitalize on data or have strong market incentives
to do so (see sidebar, “Parsing the benefits: Not all industries are
created equal”).
The era of big data also could yield new management principles. In
the early days of professionalized corporate management, leaders dis-
covered that minimum efficient scale was a key determinant of
competitive success. Likewise, future competitive benefits may accrue
to companies that can not only capture more and better data but
also use that data effectively at scale. We hope that by reflecting on
such issues and the five questions that follow, executives will be

27Are you ready for the era of ‘big data’?
better able to recognize how big data could upend assumptions
behind their strategies, as well as the speed and scope of the change
that’s now under way.
What happens in a world of radical transparency,
with data widely available?
As information becomes more readily accessible across sectors, it can
threaten companies that have relied on proprietary data as a com-
petitive asset. The real-estate industry, for example, trades on infor-
mation asymmetries such as privileged access to transaction data
and tightly held knowledge of the bid and ask behavior of buyers. Both
require significant expense and effort to acquire. In recent years,
however, online specialists in real-estate data and analytics have started
to bypass agents, permitting buyers and sellers to exchange perspec-
tives on the value of properties and creating parallel sources for real-
estate data.
Beyond real estate, cost and pricing data are becoming more accessible
across a spectrum of industries. Another swipe at proprietary infor-
mation is the assembly by some companies of readily available satellite
imagery that, when processed and analyzed, contains clues about
competitors’ physical facilities. These satellite sleuths glean insights
into expansion plans or business constraints as revealed by facility
capacity, shipping movements, and the like.
One big challenge is the fact that the mountains of data many companies
are amassing often lurk in departmental “silos,” such as RD, engi-
neering, manufacturing, or service operations—impeding timely exploi-
tation. Information hoarding within business units also can be a
problem: many financial institutions, for example, suffer from their own
failure to share data among diverse lines of business, such as financial
markets, money management, and lending. Often, that prevents these
companies from forming a coherent view of individual customers or
understanding links among financial markets.
Some manufacturers are attempting to pry open these departmental
enclaves: they are integrating data from multiple systems, inviting
collaboration among formerly walled-off functional units, and even
seeking information from external suppliers and customers to
cocreate products. In advanced-manufacturing sectors such as auto-
motive, for example, suppliers from around the world make thou-
1
(continued on page 30)

Parsing the
benefits: Not all
industries are created
equal
Even as big data changes the
game for virtually every sector, it
also tilts the playing field, favoring
some companies and industries,
particularly in the early stages of
adoption. To understand those
dynamics, we examined 20 sectors
in the US economy, sized their
contributions to GDP, and devel-
oped two indexes that estimate
each sector’s potential for value
creation using big data, as well
as the ease of capturing that value.1
As the accompanying sector map
shows (exhibit), financial players
get the highest marks for value crea-
tion opportunities. Many of these
companies have invested deeply in
IT and have large data pools to
exploit. Information industries, not
surprisingly, are also in this league.
They are data intensive by nature,
and they use that data innovatively
to compete by adopting sophis-
ticated analytic techniques.
The public sector is the most fertile
terrain for change. Governments
collect huge amounts of data, trans-
act business with millions of
citizens, and, more often than not,
suffer from highly variable perform-
ance. While potential benefits are
large, governments face steep bar-
riers to making use of this trove:
few managers are pushed to exploit
the data they have, and govern-
ment departments often keep data
in siloes.
Fragmented industry structures
complicate the value creation poten-
tial of sectors such as health
care, manufacturing, and retailing.
The average company in them is
relatively small and can access only
limited amounts of data. Larger
players, however, usually swim in
bigger pools of data, which they
can more readily use to create value.
The US health care sector, for
example, is dotted by many small
companies and individual physi-
cians’ practices. Large hospital
chains, national insurers, and
drug manufacturers, by contrast,
stand to gain substantially through
the pooling and more effective
analysis of data. We expect this
trend to intensify with changing
regulatory and market conditions.
In manufacturing, too, larger
companies with access to much
internal and market data will be
able to mine new reservoirs of value.
Smaller players are likely to benefit
only if they discover innovative
ways to share data or grow through
industry consolidation. The same
goes for retailing, where—despite
a healthy strata of data-rich
chains and big-box stores on
the cutting edge of big data—
28 2011 Number 4

Are you ready for the era of ‘big data’?
most players are smaller, local
businesses with a limited ability to
gather and analyze information.
A final note: this analysis is a snap-
shot in time for one large country.
As companies and organizations
sharpen their data skills, even
low-ranking sectors (by our gauges
of value potential and data capture),
such as construction and education,
could see their fortunes change.
Q4 2011
Big data sidebar on sector productivity
Exhibit 1 of 1
Bigdata:ease-of-captureindex1
Big data: value potential index1
The ease of capturing big data’s value, and the magnitude
of its potential, vary across sectors.
Utilities
Manufacturing
Health care
providersNatural resources
Example: US economy
Professional services
Construction
Administrative
services
Management of companies
Computers and other electronic products
Transportation and warehousing
Real estate
Wholesale trade
Finance and
insurance
Information
Other services
Retail trade
Accommodation and food
Educational
services
Arts and
entertainment
Government
High
Low High
1 For detailed explication of metrics, see appendix in McKinsey Global Institute full report Big data: The next frontier for
innovation, competition, and productivity, available free of charge online at mckinsey.com/mgi.
Source: US Bureau of Labor Statistics; McKinsey Global Institute analysis
Size of bubble indicates relative
contribution to GDP
29
1
The big data value potential index takes into
account a sector’s competitive conditions,
such as market turbulence and performance
variability; structural factors, such as
transaction intensity and the number of
potential customers and business partners;
and the quantity of data available. The ease-
of-capture index takes stock of the number
of employees with deep analytical talent
in an industry, baseline investments in IT,
the accessibility of data sources, and the
degree to which managers make data-driven
decisions.

30 2011 Number 4
sands of components. More integrated data platforms now allow com-
panies and their supply chain partners to collaborate during the
design phase—a crucial determinant of final manufacturing costs.
If you could test all of your decisions, how would
that change the way you compete?
Big data ushers in the possibility of a fundamentally different type
of decision making. Using controlled experiments, companies can
test hypotheses and analyze results to guide investment decisions
and operational changes. In effect, experimentation can help managers
distinguish causation from mere correlation, thus reducing the var-
iability of outcomes while improving financial and product performance.
Robust experimentation can take many forms. Leading online companies,
for example, are continuous testers. In some cases, they allocate a set
portion of their Web page views to conduct experiments that reveal what
factors drive higher user engagement or promote sales. Companies
selling physical goods also use experiments to aid decisions, but big data
can push this approach to a new level. McDonald’s, for example, has
equipped some stores with devices that gather operational data as they
track customer interactions, traffic in stores, and ordering patterns.
Researchers can model the impact of variations in menus, restaurant
designs, and training, among other things, on productivity and sales.
Where such controlled experiments aren’t feasible, companies can use
“natural” experiments to identify the sources of variability in perfor-
mance. One government organization, for instance, collected data on
multiple groups of employees doing similar work at different sites.
Simply making the data available spurred lagging workers to improve
their performance.
A next-generation retailer will be able
to track the behavior of individual
customers from Internet click streams,
update their preferences, and
model their likely behavior in real time.
2

Leading retailers, meanwhile, are monitoring the in-store movements
of customers, as well as how they interact with products. These retailers
combine such rich data feeds with transaction records and conduct
experiments to guide choices about which products to carry, where to
place them, and how and when to adjust prices. Methods such as
these helped one leading retailer to reduce the number of items it stocked
by 17 percent, while raising the mix of higher-margin private-label
goods—with no loss of market share.
How would your business change if you used big
data for widespread, real-time customization?
Customer-facing companies have long used data to segment and target
customers. Big data permits a major step beyond what until recently
was considered state of the art, by making real-time personalization pos-
sible. A next-generation retailer will be able to track the behavior of
individual customers from Internet click streams, update their preferences,
and model their likely behavior in real time. They will then be able
to recognize when customers are nearing a purchase decision and nudge
the transaction to completion by bundling preferred products, offered
with reward program savings. This real-time targeting, which would also
leverage data from the retailer’s multitier membership rewards pro-
gram, will increase purchases of higher-margin products by its most
valuable customers.
Retailing is an obvious place for data-driven customization because
the volume and quality of data available from Internet purchases,
social-network conversations, and, more recently, location-specific
smartphone interactions have mushroomed. But other sectors,
too, can benefit from new applications of data, along with the growing
sophistication of analytical tools for dividing customers into more
revealing microsegments.
One personal-line insurer, for example, tailors insurance policies for
each customer, using fine-grained, constantly updated profiles of
customer risk, changes in wealth, home asset value, and other data inputs.
Utilities that harvest and analyze data on customer segments can
markedly change patterns of power usage. Finally, HR departments
that more finely segment employees by task and performance are
beginning to change work conditions and implement incentives that
improve both satisfaction and productivity.4
3
4
See Nora Gardner, Devin McGranahan, and William Wolf, “Question for your HR chief:
Are we using our ‘people data’ to create value?” mckinseyquarterly.com, March 2011.

32 2011 Number 4
How can big data augment or even replace
management?
Big data expands the operational space for algorithms and machine-
mediated analysis. At some manufacturers, for example, algorithms
analyze sensor data from production lines, creating self-regulating
processes that cut waste, avoid costly (and sometimes dangerous) human
interventions, and ultimately lift output. In advanced, “digital” oil
fields, instruments constantly read data on wellhead conditions, pipe-
lines, and mechanical systems. That information is analyzed by clus-
ters of computers, which feed their results to real-time operations centers
that adjust oil flows to optimize production and minimize downtimes.
One major oil company has cut operating and staffing costs by 10 to
25 percent while increasing production by 5 percent.
Products ranging from copiers to jet engines can now generate data
streams that track their usage. Manufacturers can analyze the incoming
data and, in some cases, automatically remedy software glitches or
dispatch service representatives for repairs. Some enterprise computer
hardware vendors are gathering and analyzing such data to schedule
preemptive repairs before failures disrupt customers’ operations. The
data can also be used to implement product changes that prevent
future problems or to provide customer use inputs that inform next-
generation offerings.
Some retailers are also at the forefront of using automated big data
analysis: they use “sentiment analysis” techniques to mine the huge
streams of data now generated by consumers using various types
of social media, gauge responses to new marketing campaigns in real
time, and adjust strategies accordingly. Sometimes these methods
cut weeks from the normal feedback and modification cycle.
But retailers aren’t alone. One global beverage company integrates
daily weather forecast data from an outside partner into its demand
and inventory-planning processes. By analyzing three data points—
temperatures, rainfall levels, and the number of hours of sunshine on
a given day—the company cut its inventory levels while improving
its forecasting accuracy by about 5 percent in a key European market.
The bottom line is improved performance, better risk management,
and the ability to unearth insights that would otherwise remain hidden.
As the price of sensors, communications devices, and analytic soft-
ware continues to fall, more and more companies will be joining this
managerial revolution.
4

Could you create a new business model based
on data?
Big data is spawning new categories of companies that embrace
information-driven business models. Many of these businesses play
intermediary roles in value chains where they find themselves
generating valuable “exhaust data” produced by business transactions.
One transport company, for example, recognized that in the course
of doing business, it was collecting vast amounts of information on
global product shipments. Sensing opportunity, it created a unit
that sells the data to supplement business and economic forecasts.
Another global company learned so much from analyzing its own
data as part of a manufacturing turnaround that it decided to create
a business to do similar work for other firms. Now the company
aggregates shop floor and supply chain data for a number of manu-
facturing customers and sells software tools to improve their
performance. This service business now outperforms the company’s
manufacturing one.
Big data also is turbocharging the ranks of data aggregators, which
combine and analyze information from multiple sources to generate
insights for clients. In health care, for example, a number of new
entrants are integrating clinical, payment, public-health, and behavioral
data to develop more robust illness profiles that help clients manage
costs and improve treatments.
And with pricing data proliferating on the Web and elsewhere, entre-
preneurs are offering price comparison services that automatically
compile information across millions of products. Such comparisons
can be a disruptive force from a retailer’s perspective but have created
substantial value for consumers. Studies show that those who use the
services save an average of 10 percent—a sizable shift in value.
Confronting complications
Up to this point, we have emphasized the strategic opportunities big
data presents, but leaders must also consider a set of complications.
Talent is one of them. In the United States alone, our research shows,
the demand for people with the deep analytical skills in big data
(including machine learning and advanced statistical analysis) could
outstrip current projections of supply by 50 to 60 percent. By 2018,
as many as 140,000 to 190,000 additional specialists may be required.
5

34 2011 Number 4
Also needed: an additional 1.5 million managers and analysts with a
sharp understanding of how big data can be applied. Companies must
step up their recruitment and retention programs, while making sub-
stantial investments in the education and training of key data personnel.
The greater access to personal information that big data often demands
will place a spotlight on another tension, between privacy and con-
venience. Our research, for example, shows that consumers capture a
large part of the economic surplus that big data generates: lower
prices, a better alignment of products with consumer needs, and life-
style improvements that range from better health to more fluid social
interactions.5
As a larger amount of data on the buying preferences,
health, and finances of individuals is collected, however, privacy
concerns will grow.
That’s true for data security as well. The trends we’ve described often
go hand in hand with more open access to information, new devices
for gathering it, and cloud computing to support big data’s weighty
storage and analytical needs. The implication is that IT architectures
will become more integrated and outward facing and will pose greater
risks to data security and intellectual property. For some ideas on how
leaders should respond, see “Cybersecurity: A senior executive’s guide,”
on page 10.
Although corporate leaders will focus most of their attention on big
data’s implications for their own organizations, the mosaic of company-
level opportunities we have surveyed also has broader economic
5
See Jacques Bughin, “The Web’s €100 billion surplus,” mckinseyquarterly.com, January 2011.

implications. In health care, government services, retailing, and manu-
facturing, our research suggests, big data could improve productivity
by 0.5 to 1 percent annually. In these sectors globally, it could produce
hundreds of billions of dollars and euros in new value.
In fact, big data may ultimately be a key factor in how nations, not just
companies, compete and prosper. Certainly, these techniques offer
glimmers of hope to a global economy struggling to find a path toward
more rapid growth. Through investments and forward-looking poli-
cies, company leaders and their counterparts in government can capi-
talize on big data instead of being blindsided by it.
Copyright © 2011 McKinsey Company. All rights reserved.
We welcome your comments on this article. Please send them to
quarterly_comments@mckinsey.com.
Brad Brown is a director in McKinsey’s New York Office; Michael Chui
is a senior fellow with the McKinsey Global Institute (MGI) and is
based in the San Francisco office; James Manyika is a director of MGI
and a director in the San Francisco office.

36
Competing through
data: Three experts offer
their game plans
As big data creates new opportunities
and threats, it also demands new mind-sets from
senior executives about the role of information
in business and even the nature of competitive
advantage. The perspectives that follow may
help shake up your thinking and forge that new
frame of mind.
Massachusetts Institute of Technology (MIT)
professor Erik Brynjolfsson explores the implica-
tions of intriguing new research about the
relationship among data, analytics, productivity,
and profitability. Jeff Hammerbacher, co-
founder of the data-oriented start-up Cloudera,
provides a view from the front lines about
what it takes to harness the flood of data now at
companies’ collective fingertips. Finally, basketball
coach Brad Stevens describes how, on a tight
budget, he uses data that’s powerful (even if not
extraordinarily “big”) to help his Butler University
squad punch above its weight. Presented here are
edited versions of interviews with each, conducted
by McKinsey’s Michael Chui and Frank Comes.
Erik Brynjolfsson
Professor of management
science at the Massachusetts
Institute of Technology’s
Sloan School of Management
Jeff Hammerbacher
Cofounder and chief scientist
of Cloudera
Brad Stevens
Butler University men’s
basketball coach

37
The data advantage
Most great revolutions in science are preceded by revolutions in measure-
ment. We have had a revolution in measurement, over the past few
years, that has allowed businesses to understand in much more detail
what their customers are doing, what their processes are doing, what
their employees are doing. That tremendous improvement in measure-
ment is creating new opportunities to manage things differently.
Erik Brynjolfsson is the Schussel Family
Professor of Management Science
at the Massachusetts Institute of Technology’s
Sloan School of Management, director of
the MIT Center for Digital Business, and one
of the world’s leading researchers on
how IT affects productivity.
“Too many managers are not opening
their eyes to this opportunity and
understanding what big data can do to
change the way they compete.”
The professor
Erik Brynjolfsson
©SteveDunwell

38 2011 Number 4
Our research has found a shift from using intuition toward using data
and analytics in making decisions. This change has been accompanied
by measurable improvement in productivity and other performance
measures. Specifically, a one-standard-deviation increase toward data
and analytics was correlated with about a 5 to 6 percent improvement
in productivity and a slightly larger increase in profitability in those same
firms. The implication for companies is that by changing the way
they make decisions, they’re likely to be able to outperform competitors.
Becoming data driven
The prerequisite, of course, is the technological infrastructure: the ability
to measure things in more detail than you could before. The harder
thing is to get the set of skills. That includes not just some analytical
skills but also a set of attitudes and an understanding of the business.
Then the third thing, which is the subtlest but perhaps the most important,
is cultural change about how to use data. A lot of companies think
they’re using data, and you often see bar charts and pie charts and num-
bers in management presentations. But, historically, that kind of data
was used more to confirm and support decisions that had already been
made, rather than to learn new things and to discover the right answer.
The cultural change is for managers to be willing to say, “You know, that’s
an interesting problem, an interesting question. Let’s set up an
experiment to discover the answer.”
“I think this revolution in measurement,
starting with the switch from analog
to digital data, is as profound as, say, the
development of the microscope and
what it did for biology and medicine.”
Too many managers are not opening their eyes to this opportunity and
understanding what big data can do to change the way they compete.
They have to be ready to show some vulnerability and say, “Look, we’re
open to the data” and not go in there saying, “Hey, I’m gonna manage
from the gut. I have years of experience and I know the answers to this
going in.” I think, historically, a lot of managers have been implicitly
or explicitly rewarded for that kind of confidence. You have to have a
different kind of confidence to be willing to let the data speak.

39Competing through data: Three experts offer their game plans
One CEO told me that when he pushed this attitude, he had to change
over 50 percent of his senior-management team because they just
didn’t get it. Obviously, that was a painful thing to have to do. But the
results have been very successful. And they require that level of
aggressiveness by top management, if it really wants to end up in that
group of leaders as opposed to the laggards.
Required skills
Having enough data to get a statistically significant result is not a prob-
lem. There’s plenty of data. So the skills often have more to do with
sampling methodologies, designing experiments, and working these
very, very large data sets without becoming overwhelmed. If you look
inside companies, you also see a transformation in the functions that
are using data. CIOs are discovering that, more and more, it’s the
marketing people and the people working with customers—customer
relationship management—who have the biggest data needs. These
are the people CIOs are working with most closely. This is part of a
broader revolution as we move from just financial numerical data
toward all sorts of nonfinancial metrics.
Often, the nonfinancial metrics give a quicker and more accurate
measure of what’s happening in the business. I was talking to Gary
Loveman—the CEO of Caesar’s Entertainment, formerly Harrah’s,
and a PhD graduate of MIT. He’s used some of these techniques to
revolutionize what’s happening in that industry. But, interestingly,
increasingly what he measures is customer satisfaction and a lot of
other intermediate metrics. He said that customer satisfaction met-
rics were much quicker and more precise metrics of what was happening
in response to some of the policy changes that he put in place.
Think of it this way. If customers end up satisfied or dissatisfied, that
will affect the probability of their coming back next year. Now, next
year’s financial results will be affected as a result. And you could, in
principle, try to match up the experience the customer had this
year with future years’ return rates. But a much quicker way of getting
feedback on which processes are working is to look at customer
satisfaction when you put process changes in place.
The new landscape
I think this revolution in measurement, starting with the switch from
analog to digital data, is as profound as, say, the development of the
microscope and what it did for biology and medicine. It’s not just big
data in the sense that we have lots of data. You can also think of it as

40 2011 Number 4
“nano” data, in the sense that we have very, very fine-grained data—an
ability to measure things much more precisely than in the past. You
can learn about the preferences of an individual customer and person-
alize your offerings for that particular customer.
One of the biggest revolutions has involved enterprise information
systems, like ERP, enterprise resource planning; CRM, customer
relationship management; or SCM, supply chain management—those
large enterprise systems that companies have spent hundreds of
millions of dollars on. You can use the data from them not just to manage
operations but to gain business intelligence and learn how they could
be managed differently. A common pattern that we’re seeing is that three
to five years after installing one of these big enterprise systems, com-
panies start saying, “Hey, we need some business intelligence tools to
take advantage of all this data.” It’s up to managers now to seize that
opportunity and take advantage of this very fine-grained data that just
didn’t exist previously.
The path ahead
There’s some good news and there’s some not-so-good news. The good
news is that technology’s not slowing down, and the pie is getting
bigger. Productivity is accelerating. And that should make us all better
off. However, it’s not making us all better off. Over the past 20 years
or so, median wages in the United States have stagnated because a lot
of people don’t have the skills to take full advantage of this technology.
And, unfortunately, I don’t see that changing any time soon unless
we have a much bigger effort to change the kinds of skills that are avail-
able in the workforce and have a set of technologies that people can
tap into more readily.
This flood of data and analytical opportunities creates more value for
people who can be creative in seeing patterns and for people who
can be entrepreneurial in creating new business opportunities that take
advantage of these patterns. My hope is that the technology will
create a platform that people can tap into to create new entrepreneurial
ventures—some of them, perhaps, huge hits like Facebook or Zynga
or Google. But also, perhaps equally important for the economy, hun-
dreds of thousands or millions of small entrepreneurial ventures,
eBay based or app based, would mean millions of ordinary people can
be creative in using technology and their entrepreneurial energies
to create value. That would be an economy where not only does the pie
get bigger but each part of the pie—each of the individuals—benefits
as well.

The open-source advantage
I was Facebook’s first research scientist. The initial goal for that
position was to understand how changes to the site were impacting
user behavior. We had built our own infrastructure to allow us to do
some terabyte analytics, but we were going to have to scale it to up to
petabytes.1
We realized that instead of continuing to invest in infra-
“ If you can understand consumer behavior
and get your hands around as much
behavioral data as possible to better guide
product decision making, then every
penny you can eke out is increasing your
margins and allowing you to invest more.”
1
Under the International System of Units, a terabyte equals one trillion bytes, or 1,000
gigabytes. A petabyte is equal to 1,000 terabytes.
Before cofounding Silicon Valley
software start-up Cloudera in 2009, at the
age of 26, Jeff Hammerbacher was a
quantitative analyst on Wall Street and one
of Facebook’s first employees.
The data
entrepreneur
Jeff Hammerbacher

42 2011 Number 4
structure, we could build a more powerful shared resource to facilitate
business analysis by working with the open-source community.
In founding Cloudera, I saw a path to a complete infrastructure for
doing analytical data management. It would be made up of existing
open-source projects as well as open-source versions of a lot of the
technologies that we had built out internally at Facebook. Cloudera
would be a corporate entity for pursuing those goals and ensuring
that it wasn’t just Facebook that would be able to use this technology
but, really, any enterprise.
Data leaders
When we started Cloudera, we didn’t have a core thesis around where
the technology would be adopted or what the market was going to
look like. Early adopters were clearly in the Web and digital-media
spaces. But in terms of traditional industries, the federal govern-
ment surprised me. They really are the leaders in multimedia data
analysis—working with text, images, video. In the intelligence
agencies, I’ve seen more sophistication than in commercial domains.
I was also surprised to see the retail space. Retailers had very large
volumes of data, and because many were branching out into e-commerce,
they had a lot of Web logs and Web data as well. There is an arms race
going on right now in retail. If you can understand consumer behavior
and get your hands around as much behavioral data as possible to
better guide product decision making, then every penny you can eke
out is increasing your margins and allowing you to invest more.
Financial services was one sector that I had hoped would be an early
adopter, but these companies tend not to look at their businesses as a
whole in the same way that retail does. Data management is thought
of as project specific, even to the point where individual trading desks
could have their own chief technology officers. Our technology tends
to work best as a shared infrastructure for multiple lines of business.
Where this is headed is learning how to point this new infrastructure
for storing and analyzing data at real business problems, as well
as growing the imagination of businesspeople about what they can do
when a variety of experts analyze the data. If you can digitize reality,
then you can move your world faster than before.

Building a big data function
You need to make a commitment to conceiving of data as a competitive
advantage. The next step is to build out a low-cost, reliable infra-
structure for data collection and storage for whichever line of business
you perceive to be most critical to your company. If you don’t have
that digital asset, then you’re not even going to be able to play the game.
And then you can start layering on the complex analytics. Most com-
panies go wrong when they start with the complex analytics.
When deciding how to incorporate analytics expertise into an organi-
zation, you have to be honest about what your organization looks
like—your capacity to hire and your long-term vision for what that
organization is going to be. There isn’t one right answer. Yahoo!
built a centralized group called Strategic Data Solutions to run the
entire gamut. Rather than just building a small group of people
primarily focused on marketing analytics, the company took an end-to-
end view, extending from data storage to the actual PL. In our
group at Facebook, because we were a very fast-moving organization,
we were much more of a platform—a service organization for the
rest of the company.
The rise of the ‘data scientist’
I tried to articulate this title of data scientist in a book I put together
with O’Reilly Media.2
I now actually see people describing themselves
as data scientists in their job titles on LinkedIn and scientists talking
about themselves as data scientists. So it’s evolving. People realize that
there is a gap between the current role of statistician or data analyst
or business analyst and what they actually want. They are grappling
with the set of tools and the set of skills that they need. Across the
whole research cycle, it’s a combination of skills that social scientists
understand, plus additional programming skills, plus the ability to
do aggressive prioritization. And, of course, a good grounding in statis-
tics and machine learning.3
That collection of skills is difficult to find.
2
Jeff Hammerbacher and Toby Segaran, eds., Beautiful Data: The Stories Behind Elegant
Data Solutions, Sebastopol, CA: O’Reilly, 2009.
3
Machine learning is a form of artificial intelligence in which algorithms allow computers to
make decisions based on data streams.

44 2011 Number 4
Coach Stevens holds the National Collegiate Athletic Association
(NCAA) record for most games won in the first four years as a
Division I head basketball coach. Among those wins was a series of
thrilling NCAA tournament games that brought his Butler University
team to the championship final in 2010 and 2011.
Before joining Butler, which is located in Indianapolis, Indiana,
and has just 4,500 students, he was a marketing associate at the
global pharmaceutical group Eli Lilly. In the following interview,
Stevens explains how focusing on the numbers has helped improve
his team’s game.
“ I can have all the data I want to have—
but I still have to communicate it
to our players. It has to get into their
minds. And they have to utilize it. ”
Brad Stevens is head coach of the Butler
University men’s basketball team.
The coach
Brad Stevens
©CourtesyofButler
University

The Quarterly: How have things changed in basketball with regard
to the use of data and analytics?
Brad Stevens: You know, I’m a bad person to ask about that because
I’m 34. The data’s always been an important part of my job. I’ve
always looked at it through that lens, even when I was a young assistant.
This is how I work best. For me, it’s incredibly interesting. There are
complexities that you can really study using numbers. We don’t have
access to the highest end—we’re not sitting here with NBA4
money to
invest in a numbers-and-research department. But I think you can speak
to your team with numbers and give your players pretty clear-cut
and defined examples of what they need to do to get better.
The Quarterly: If you had an infinite budget, what sorts of things
would you do?
Brad Stevens: The first thing is that I’d have one of the positions on
our staff, or maybe a whole group on our staff, working on statistics.
They would look at game planning and how players are most effective:
what they’re doing when they’re most effective, where they are on the
court—really show players the exact way that they are most effective in
different areas of the game. That’s an incredibly useful teaching tool.
The Quarterly: In the absence of those resources, that staff, what
do you do?
Brad Stevens: I first break down all of the statistics that I can on
opponents to try to get my mind wrapped around what their trends are.
I’ll look for how many three-point attempts per field goal attempt5
—
that tells you what kind of team they are right away. You can look at
offensive-rebound percentages. Defensive- and offensive-turnover
percentages. How teams shoot against them. What they defend well.
What they try to defend well.
Then there’s the ability to cut film on computers and to do so quickly.
We can watch all of somebody’s moves off of a ball screen. All of a
person’s moves going left. All of the post moves, going to the middle or
going to the baseline. Whatever the case may be. And we can really
4
The US National Basketball Association.
5
For an explanation of basketball terminology, visit www.fiba.com/pages/eng/fc/baskBasi/
glos.asp.

46 2011 Number 4
determine their effectiveness from that. We obviously hope that the
film validates the statistics and we can figure out what’s unique about
what players do.
One thing that you have to be careful of is not getting caught up in just
season statistics. Teams change. And as we get to the latter part of
the season, I’ll spend a lot more time asking, “What’s happened in the
past five games? What are they doing differently from a statistical
standpoint? What have they improved on? What have they regressed in?”
Of course, I can have all the data I want to have—but I still have to
communicate it to our players. It has to get into their minds. And they
have to utilize it. So you can’t inundate them. You can’t take three
seconds to make a decision in basketball. It’s a game that moves too
quickly for that. There’s no huddle in between plays; there’s not a
moment in between every pitch. You’ve got to have thoughts in your
mind about what the people that you’re playing against like to do, and
what you do best, and at the same time you can’t be inundated with
those thoughts or it’ll affect the way you play. That makes commu-
nicating data and simplifying it for the players incredibly important.
The Quarterly: Can you say more about how you simplify data,
how you engage your players?
Brad Stevens: You’ve got to figure out how they react, how they best
comprehend, how they best learn in a team setting, how they best
learn in an individual setting, and go from there. Each team’s different,
each player’s different. And, you know, it may mean bringing in a guy
who has a mind for numbers and saying, “The bottom line is that, right
now, you’re shooting 43 percent. You’re a better shooter than that. If
“ As we get to the latter part of the season,
I’ll spend a lot more time asking, ‘What’s
happened in the past five games? What are
they doing differently from a statistical
standpoint? What have they improved on?
What have they regressed in?’”

47
you make one more shot a game, you’re probably at 48 or 49 percent.
How can we make it so you’re one more shot effective for a game?”
The Quarterly: Was there one game or a couple of games where this
really played out and made a difference?
Brad Stevens: Every game we play in. There’s not a game when
this wouldn’t have played a major role. We’re not the most talented,
so we have to be good in these little areas. Sometimes, you know, the
numbers hurt you. You believe one thing, and then the other team has
a night that’s unique. But more times than not, the score takes care
of itself, as Bill Walsh6
says.
Competing through data: Three experts offer their game plans
Copyright © 2011 McKinsey Company. All rights reserved.
We welcome your comments on this article. Please send them to
quarterly_comments@mckinsey.com.
Michael Chui is a senior fellow at the McKinsey Global Institute and is
based in McKinsey’s San Francisco office; Frank Comes is a member of
McKinsey Publishing and is based in the New Jersey office.
6
Bill Walsh coached the US National Football League’s San Francisco 49ers to three Super
Bowl titles (1982, 1985, and 1989). His book The Score Takes Care of Itself: My Philosophy of
Leadership (Portfolio, August 2009), published two years after his death, was coauthored
with his son, Craig, and with Steve Jamison.

Changing
companies’ minds
about women
Joanna Barsh and Lareina Yee
Leaders who are serious about getting more women
into senior management need a hard-edged approach to
overcome the invisible barriers holding them back.
The problem
Your company has trouble retaining
promising women or promoting them
into top jobs. Structural changes,
such as “flextime,” aren’t helping enough;
they do little to address the invisible
but powerful beliefs, held by many man-
agers, that subtly, and unintentionally,
hamper women’s careers.
Why it matters
A bevy of research highlights strong
statistical correlations among large num-
bers of senior women, financial per-
formance, and organizational health. The
bottom line: companies gain hard
business benefits from a more diverse
senior team.
What to do about it
There are no sure answers yet. But
the experience of companies making
progress suggests that injecting greater
rigor into people processes—more
data, thoughtful targets that push
women into the consideration set for
key roles, a company-specific business
case for women, better sponsorship
approaches—can make a difference.
ArtworkbyGwendaKaczor

50 2011 Number 4
Despite significant corporate commitment to the advance-
ment of women’s careers, progress appears to have stalled. The
percentage of women on boards and senior-executive teams remains
stuck at around 15 percent in many countries, and just 3 percent
of Fortune 500 CEOs are women.
The last generation of workplace innovations—policies to support
women with young children, networks to help women navigate
their careers, formal sponsorship programs to ensure professional
development—broke down structural barriers holding women back.
The next frontier is toppling invisible barriers: mind-sets widely held
by managers, men and women alike, that are rarely acknowledged
but block the way.
When senior leaders commit themselves to gender diversity, they really
mean it—but in the heat of the moment, deeply entrenched beliefs
cause old forms of behavior to resurface. All too often in our experience,
executives perceive women as a greater risk for senior positions, fail
to give women tough feedback that would help them grow, or hesitate
to offer working mothers opportunities that come with more travel
and stress. Not surprisingly, a survey we conducted earlier this year indi-
cated that although a majority of women who make it to senior roles
have a real desire to lead, few think they have meaningful support to
do so, and even fewer think they’re in line to move up.
Our ideas for breaking this cycle are directional, not definitive. They
rest on our experience in the trenches with senior executives, on
discussions with 30 diversity experts, and on the reflections of leaders
we’ve interviewed at companies that have been on this journey for
years. These companies include Pitney Bowes, 38 percent of whose vice
presidents are women; Shell, where more than a quarter of all
supervisors and professional staff worldwide are women; and Time
Warner, where more than 40 percent of the senior executives in its
operating divisions are women and where the share of women in senior
roles has jumped 30 percent in the past six years. Great progress,
but even these three companies are the first to admit how much further
they have to go.
Their collective experience suggests to us that real progress requires
systemwide change driven by a hard-edged approach, including targets
ensuring that women are at least considered for advancement, the
rigorous application of data in performance dialogues to overcome prob-

51Changing companies’ minds about women
lematic mind-sets, and genuine sponsorship. Committed senior
leaders are of course central to such efforts, which can take many
years. We hope our suggestions, and the real-life examples that illus-
trate them, will stir up your thinking about how to confront the
silent but potent beliefs that probably are undermining women in your
organization right now.
Invisible, unconscious, and in the way
For evidence of the problem, look no further than the blocked, leaky
corporate-talent pipeline: women account for roughly 53 percent
of entry-level professional employees in the largest US industrial corpo-
rations, our research shows.1
But according to Catalyst, a leading advo-
cacy group for women, they hold only 37 percent of middle-management
positions, 28 percent of vice-president and senior-managerial roles,
and 14 percent of seats on executive committees. McKinsey research
shows similar numbers for women on executive committees outside
the United States—from a high of 17 percent in Sweden to just 2 percent
in Germany and India.2
Our analysis further reveals that at every
step along the US pipeline, the odds of advancement for men are about
twice those for women. And nearly four times as many men as women
at large companies make the jump from the executive committee
to CEO.3
To understand what’s going on, look to the words that appeared
most frequently in open-ended responses to our recent survey as expla-
nations for poor retention and promotion of women: “politics,”
“management,” “the company,” “people,” and “the organization.” These
forces manifest themselves in myriad ways. We’ve all heard endless
variations on the mind-sets that set women up for failure:
“She’s too aggressive” (or “too passive”). Whether a woman is perceived
as aggressive or passive, that’s different from the judgment a man
1
The entry-level figure is from our April 2011 report, Unlocking the full potential of women
in the US economy. Read an executive summary or download the full report on the
McKinsey Company Web site.
2
The full report, Women at the top of corporations: Making it happen, part of McKinsey
Company’s Women Matter 2010 series, is available on the McKinsey Company
Web site. The differences among countries reflect significant variance in their starting
points and cultural norms—which, for example, can make it difficult for a woman to
outearn her husband.
3
Part of the reason is that almost twice as many executive-level women as men (60 percent
versus 35 percent) occupy staff roles that are less likely to lead to the top job.

2011 q4 McKinsey quarterly - Big data, you have it, now use it

2011 q4 McKinsey quarterly - Big data, you have it, now use it

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2011 q4 McKinsey quarterly - Big data, you have it, now use it