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ARTWORK Tamar Cohen
Happy Motoring, 2010, silk screen
on vintage road map, 26″ x 18″
Spotlight
SPOTLIGHT ON BIG DATA
Big Data:
The Management Revolution
Exploiting vast new fl ows of information can radically
improve your company’s performance. But fi rst you’ll
have to change your decision-making culture.
by Andrew McAfee and Erik Brynjolfsson
HBR.ORG
October 2012 Harvard Business Review 61
SPOTLIGHT ON BIG DATA
the analytics that were used in the past. We can mea-
sure and therefore manage more precisely than ever
before. We can make better predictions and smarter
decisions. We can target more-effective interven-
tions, and can do so in areas that so far have been
dominated by gut and intuition rather than by data
and rigor.
As the tools and philosophies of big data spread,
they will change long-standing ideas about the value
of experience, the nature of expertise, and the prac-
tice of management. Smart leaders across industries
will see using big data for what it is: a management
revolution. But as with any other major change in
business, the challenges of becoming a big data–
enabled organization can be enormous and require
hands-on—or in some cases hands-o� —leadership.
Nevertheless, it’s a transition that executives need to
engage with today.
What’s New Here?
Business executives sometimes ask us, “Isn’t ‘big
data’ just another way of saying ‘analytics’?” It’s true
that they’re related: The big data movement, like an-
alytics before it, seeks to glean intelligence from data
and translate that into business advantage. However,
there are three key di� erences:
Volume. As of 2012, about 2.5 exabytes of data
are created each day, and that number is doubling
every 40 months or so. More data cross the internet
every second than were stored in the entire internet
just 20 years ago. This gives companies an opportu-
nity to work with many petabyes of data in a single
data set—and not just from the internet. For instance,
it is estimated that Walmart collects more than 2.5
petabytes of data every hour from its customer
transactions. A petabyte is one quadrillion bytes, or
the equivalent of about 20 million filing cabinets’
worth of text. An exabyte is 1,000 times that amount,
or one billion gigabytes.
“You can’t manage what you don’t measure.”
There’s much wisdom in that saying, which has been
attributed to both W. Edwards Deming and Peter
Drucker, and it explains why the recent explosion
of digital data is so important. Simply put, because
of big data, managers can measure, and hence know,
radically more about their businesses, and directly
translate that knowledge into improved decision
making and performance.
Consider retailing. Booksellers in physical stores
could always track which books sold and which did
not. If they had a loyalty program, they could tie
some of those purchases to individual customers.
And that was about it. Once shopping moved online,
though, the understanding of customers increased
dramatically. Online retailers could track not only
what customers bought, but also what else they
looked at; how they navigated through the site; how
much they were in� uenced by promotions, reviews,
and page layouts; and similarities across individuals
and groups. Before long, they developed algorithms
to predict what books individual customers would
like to read next—algorithms that performed better
every time the customer responded to or ignored
a recommendation. Traditional retailers simply
couldn’t access this kind of information, let alone act
on it in a timely manner. It’s no wonder that Amazon
has put so many brick-and-mortar bookstores out of
business.
The familiarity of the Amazon story almost
masks its power. We expect companies that were
born digital to accomplish things that business ex-
ecutives could only dream of a generation ago. But
in fact the use of big data has the potential to trans-
form traditional businesses as well. It may offer
them even greater opportunities for competitive
advantage (online businesses have always known
that they were competing on how well they under-
stood their data). As we’ll discuss in more detail, the
big data of this revolution is far more powerful than
“You can’t manage what you don’t measure.”
There’s much wisdom in that saying, which has been
attributed to both W. Edwards Deming and Peter
Drucker, and it explains why the recent explosion
of digital data is so important. Simply put, because
of big data, managers can measure, and hence know,
radically more about their businesses, and directly
translate that knowledge into improved decision
making and performance.
Consider retailing. Booksellers in physical stores
could always track which books sold and which did
62 Harvard Business Review October 2012
Big Data: the ManageMent revolution
Velocity. For many applications, the speed of
data creation is even more important than the vol-
ume. Real-time or nearly real-time information
makes it possible for a company to be much more ag-
ile than its competitors. For instance, our colleague
Alex “Sandy” Pentland and his group at the MIT Me-
dia Lab used location data from mobile phones to
infer how many people were in Macy’s parking lots
on Black Friday—the start of the Christmas shopping
season in the United States. This made it possible to
estimate the retailer’s sales on that critical day even
before Macy’s itself had recorded those sales. Rapid
insights like that can provide an obvious competitive
advantage to Wall Street analysts and Main Street
managers.
Variety. Big data takes the form of messages, up-
dates, and images posted to social networks; read-
ings from sensors; GPS signals from cell phones,
and more. Many of the most important sources of
big data are relatively new. The huge amounts of
information from social networks, for example, are
only as old as the networks themselves; Facebook
was launched in 2004, Twitter in 2006. The same
holds for smartphones and the other mobile devices
that now provide enormous streams of data tied
to people, activities, and locations. Because these
devices are ubiquitous, it’s easy to forget that the
iPhone was unveiled only five years ago, and the
iPad in 2010. Thus the structured databases that
stored most corporate information until recently are
ill suited to storing and processing big data. At the
same time, the steadily declining costs of all the ele-
ments of computing—storage, memory, processing,
bandwidth, and so on—mean that previously expen-
sive data-intensive approaches are quickly becoming
economical.
As more and more business activity is digitized,
new sources of information and ever-cheaper equip-
ment combine to bring us into a new era: one in
which large amounts of digital information exist on
virtually any topic of interest to a business. Mobile
phones, online shopping, social networks, electronic
communication, GPS, and instrumented machinery
all produce torrents of data as a by-product of their
ordinary operations. Each of us is now a walking
data generator. The data available are often unstruc-
tured—not organized in a database—and unwieldy,
but there’s a huge amount of signal in the noise,
simply waiting to be released. Analytics brought rig-
orous techniques to decision making; big data is at
once simpler and more powerful. As Google’s direc-
tor of research, Peter Norvig, puts it: “We don’t have
better algorithms. We just have more data.”
how Data-Driven
Companies Perform
The second question skeptics might pose is this:
“Where’s the evidence that using big data intelligently
will improve business performance?” The business
press is rife with anecdotes and case studies that sup-
posedly demonstrate the value of being data-driven.
But the truth, we realized recently, is that nobody
was tackling that question rigorously. To address this
embarrassing gap, we led a team at the MIT Center
for Digital Business, working in partnership with
McKinsey’s business technology office and with our
colleague Lorin Hitt at Wharton and the MIT doctoral
student Heekyung Kim. We set out to test the hypoth-
esis that data-driven companies would be better per-
formers. We conducted structured interviews with
executives at 330 public North American companies
about their organizational and technology manage-
ment practices, and gathered performance data from
their annual reports and independent sources.
Not everyone was embracing data-driven deci-
sion making. In fact, we found a broad spectrum
of attitudes and approaches in every industry. But
across all the analyses we conducted, one relation-
idea in Brief
Data-driven decisions are better
decisions—it’s as simple as that. Using
big data enables managers to decide
on the basis of evidence rather than
intuition. For that reason it has the
potential to revolutionize management.
Companies that were born digital,
such as Google and Amazon, are
already masters of big data. But the
potential to gain competitive advan-
tage from it may be even greater for
other companies.
The managerial challenges, however,
are very real. Senior decision makers
have to embrace evidence-based
decision making. Their companies
need to hire scientists who can find
patterns in data and translate them
into useful business information. And
whole organizations need to redefine
their understanding of “judgment.”
About the
Spotlight Artist
Each month we illustrate
our Spotlight package with
a series of works from an
accomplished artist. We
hope that the lively and
cerebral creations of these
photographers, painters,
and installation artists
will infuse our pages with
additional energy and intel-
ligence to amplify what are
often complex and abstract
concepts.
This month we spotlight
the work of tamar Cohen, a
New York native, whose col-
lages explore the relation-
ship between the abstract
and the everyday with polka
dots and vintage paper
ephemera. She is drawn to
the simplicity and power of
printed images produced
in the 1950s and 1960s and
uses polka dots “to focus,
reveal, subsume, and re-
contextualize my abstracted
paper narratives.”
View more of the artist’s
work at tamarcohen.com.Ph
oT
o
G
rA
Ph
Y:
M
Ar
k
rA
N
DA
ll
hBr.orG
october 2012 harvard Business review 63
Spotlight on Big Data
ship stood out: The more companies character-
ized themselves as data-driven, the better they
performed on objective measures of financial and
operational results. In particular, companies in the
top third of their industry in the use of data-driven
decision making were, on average, 5% more produc-
tive and 6% more profitable than their competitors.
This performance difference remained robust after
accounting for the contributions of labor, capital,
purchased services, and traditional IT investment.
It was statistically significant and economically im-
portant and was reflected in measurable increases in
stock market valuations.
So how are managers using big data? Let’s look
in detail at two companies that are far from Silicon
Valley upstarts. One uses big data to create new busi-
nesses, the other to drive more sales.
improved Airline EtAs
Minutes matter in airports. So does accurate informa-
tion about flight arrival times: If a plane lands before
the ground staff is ready for it, the passengers and
crew are effectively trapped, and if it shows up later
than expected, the staff sits idle, driving up costs. So
when a major U.S. airline learned from an internal
study that about 10% of the flights into its major hub
had at least a 10-minute gap between the estimated
time of arrival and the actual arrival time—and 30%
had a gap of at least five minutes—it decided to take
action.
At the time, the airline was relying on the aviation
industry’s long-standing practice of using the ETAs
provided by pilots. The pilots made these estimates
during their final approach to the airport, when they
had many other demands on their time and atten-
tion. In search of a better solution, the airline turned
to PASSUR Aerospace, a provider of decision-support
technologies for the aviation industry. In 2001 PAS-
SUR began offering its own arrival estimates as a
service called RightETA. It calculated these times
by combining publicly available data about weather,
flight schedules, and other factors with proprietary
data the company itself collected, including feeds
from a network of passive radar stations it had in-
stalled near airports to gather data about every plane
in the local sky.
PASSUR started with just a few of these instal-
lations, but by 2012 it had more than 155. Every 4.6
seconds it collects a wide range of information about
every plane that it “sees.” This yields a huge and con-
stant flood of digital data. What’s more, the company
keeps all the data it has gathered over time, so it has
an immense body of multidimensional information
spanning more than a decade. This allows sophisti-
cated analysis and pattern matching. RightETA es-
sentially works by asking itself “What happened all
the previous times a plane approached this airport
under these conditions? When did it actually land?”
After switching to RightETA, the airline virtually
eliminated gaps between estimated and actual ar-
rival times. PASSUR believes that enabling an airline
to know when its planes are going to land and plan
accordingly is worth several million dollars a year
at each airport. It’s a simple formula: Using big data
leads to better predictions, and better predictions
yield better decisions.
Speedier, More
personalized promotions
A couple of years ago, Sears Holdings came to the con-
clusion that it needed to generate greater value from
the huge amounts of customer, product, and promo-
tion data it collected from its Sears, Craftsman, and
Lands’ End brands. Obviously, it would be valuable
to combine and make use of all these data to tailor
promotions and other offerings to customers, and to
personalize the offers to take advantage of local con-
ditions. Valuable, but difficult: Sears required about
64 Harvard Business Review october 2012
Expertise from Surprising Sources
often someone coming from outside an
industry can spot a better way to use big
data than an insider, just because so many
new, unexpected sources of data are avail-
able. one of us, Erik, demonstrated this
in research he conducted with Lynn Wu,
now an assistant professor at Wharton.
they used publicly available web search
data to predict housing-price changes
in metropolitan areas across the United
States. they had no special knowledge of
the housing market when they began their
study, but they reasoned that virtually
real-time search data would enable good
near-term forecasts about the housing
market—and they were right. in fact, their
prediction proved more accurate than the
official one from the national association
of Realtors, which had developed a far
more complex model but relied on rela-
tively slow-changing historical data.
this is hardly the only case in which
simple models and big data trump
more-elaborate analytics approaches.
Researchers at the Johns Hopkins School
of Medicine, for example, found that they
could use data from google Flu trends
(a free, publicly available aggregator of
relevant search terms) to predict surges in
flu-related emergency room visits a week
before warnings came from the Centers
for Disease Control. Similarly, twitter up-
dates were as accurate as official reports
at tracking the spread of cholera in Haiti
after the January 2010 earthquake; they
were also two weeks earlier.
BIG DATA: THE MANAGEMENT REVOLUTION
eight weeks to generate personalized promotions, at
which point many of them were no longer optimal for
the company. It took so long mainly because the data
required for these large-scale analyses were both vo-
luminous and highly fragmented—housed in many
databases and “data warehouses” maintained by the
various brands.
In search of a faster, cheaper way to do its analytic
work, Sears Holdings turned to the technologies and
practices of big data. As one of its � rst steps, it set up a
Hadoop cluster. This is simply a group of inexpensive
commodity servers whose activities are coordinated
by an emerging software framework called Hadoop
(named after a toy elephant in the household of Doug
Cutting, one of its developers).
Sears started using the cluster to store incoming
data from all its brands and to hold data from exist-
ing data warehouses. It then conducted analyses on
the cluster directly, avoiding the time-consuming
complexities of pulling data from various sources
and combining them so that they can be analyzed.
This change allowed the company to be much faster
and more precise with its promotions. According to
the company’s CTO, Phil Shelley, the time needed
to generate a comprehensive set of promotions
dropped from eight weeks to one, and is still drop-
ping. And these promotions are of higher quality, be-
cause they’re more timely, more granular, and more
personalized. Sears’s Hadoop cluster stores and pro-
cesses several petabytes of data at a fraction of the
cost of a comparable standard data warehouse.
Shelley says he’s surprised at how easy it has been
to transition from old to new approaches to data
management and high-performance analytics. Be-
cause skills and knowledge related to new data tech-
nologies were so rare in 2010, when Sears started the
transition, it contracted some of the work to a com-
pany called Cloudera. But over time its old guard of
IT and analytics professionals have become comfort-
able with the new tools and approaches.
The PASSUR and Sears Holding examples il-
lustrate the power of big data, which allows more-
accurate predictions, better decisions, and precise
interventions, and can enable these things at seem-
ingly limitless scale. We’ve seen big data used in sup-
ply chain management to understand why a carmak-
er’s defect rates in the � eld suddenly increased, in
customer service to continually scan and intervene
in the health care practices of millions of people, in
planning and forecasting to better anticipate online
sales on the basis of a data set of product character-
istics, and so on. We’ve seen similar payo� s in many
other industries and functions, from � nance to mar-
keting to hotels and gaming, and from human re-
source management to machine repair.
Our statistical analysis tells us that what we’re
seeing is not just a few � ashy examples but a more
fundamental transformation of the economy. We’ve
become convinced that almost no sphere of business
activity will remain untouched by this movement.
A New Culture of Decision Making
The technical challenges of using big data are very
real. But the managerial challenges are even greater—
starting with the role of the senior executive team.
Muting the HiPPOs.
One of the most critical
aspects of big data is its impact on how decisions
are made and who gets to make them. When data
are scarce, expensive to obtain, or not available in
digital form, it makes sense to let well-placed people
make decisions, which they do on the basis of ex-
perience they’ve built up and patterns and relation-
ships they’ve observed and internalized. “Intuition”
is the label given to this style of inference and deci-
sion making. People state their opinions about what
the future holds—what’s going to happen, how well
something will work, and so on—and then plan ac-
cordingly. (See “The True Measures of Success,” by
Michael J. Mauboussin, in this issue.)
For particularly important decisions, these
people are typically high up in the organization, or
they’re expensive outsiders brought in because of
their expertise and track records. Many in the big
data community maintain that companies often
make most of their important decisions by relying
on “HiPPO”—the highest-paid person’s opinion.
To be sure, a number of senior executives are
genuinely data-driven and willing to override their
own intuition when the data don’t agree with it. But
we believe that throughout the business world today,
One of the most critical
aspects of big data is its impact on how decisions
are made and who gets to make them. When data
are scarce, expensive to obtain, or not available in
digital form, it makes sense to let well-placed people
make decisions, which they do on the basis of ex-
perience they’ve built up and patterns and relation-
ships they’ve observed and internalized. “Intuition”
HBR.ORG
October 2012 Harvard Business Review 65
Big data’s power does not
erase the need for vision
or human insight.
Spotlight on Big Data
people rely too much on experience and intuition
and not enough on data. For our research we con-
structed a 5-point composite scale that captured the
overall extent to which a company was data-driven.
Fully 32% of our respondents rated their companies
at or below 3 on this scale.
New roles. Executives interested in leading a big
data transition can start with two simple techniques.
First, they can get in the habit of asking “What do the
data say?” when faced with an important decision
and following up with more-specific questions such
as “Where did the data come from?,” “What kinds
of analyses were conducted?,” and “How confident
are we in the results?” (People will get the message
quickly if executives develop this discipline.) Sec-
ond, they can allow themselves to be overruled by
the data; few things are more powerful for changing
a decision-making culture than seeing a senior ex-
ecutive concede when data have disproved a hunch.
When it comes to knowing which problems to
tackle, of course, domain expertise remains critical.
Traditional domain experts—those deeply familiar
with an area—are the ones who know where the
biggest opportunities and challenges lie. PASSUR,
for one, is trying to hire as many people as possible
who have extensive knowledge of operations at
America’s major airports. They will be invaluable in
helping the company figure out what offerings and
markets it should go after next.
As the big data movement advances, the role of
domain experts will shift. They’ll be valued not for
their HiPPO-style answers but because they know
what questions to ask. Pablo Picasso might have
been thinking of domain experts when he said,
“Computers are useless. They can only give you
answers.”
Five Management Challenges
Companies won’t reap the full benefits of a transi-
tion to using big data unless they’re able to manage
change effectively. Five areas are particularly impor-
tant in that process.
Leadership. Companies succeed in the big data
era not simply because they have more or better
data, but because they have leadership teams that
set clear goals, define what success looks like, and
ask the right questions. Big data’s power does not
erase the need for vision or human insight. On the
contrary, we still must have business leaders who
can spot a great opportunity, understand how a mar-
ket is developing, think creatively and propose truly
novel offerings, articulate a compelling vision, per-
suade people to embrace it and work hard to realize
it, and deal effectively with customers, employees,
stockholders, and other stakeholders. The success-
ful companies of the next decade will be the ones
whose leaders can do all that while changing the way
their organizations make many decisions.
Talent management. As data become cheaper,
the complements to data become more valuable.
Some of the most crucial of these are data scientists
and other professionals skilled at working with large
quantities of information. Statistics are important,
but many of the key techniques for using big data
are rarely taught in traditional statistics courses.
Perhaps even more important are skills in cleaning
and organizing large data sets; the new kinds of data
rarely come in structured formats. Visualization
tools and techniques are also increasing in value.
Along with the data scientists, a new generation of
computer scientists are bringing to bear techniques
for working with very large data sets. Expertise in
the design of experiments can help cross the gap be-
tween correlation and causation. The best data sci-
entists are also comfortable speaking the language of
business and helping leaders reformulate their chal-
lenges in ways that big data can tackle. Not surpris-
ingly, people with these skills are hard to find and in
great demand. (See “Data Scientist: The Sexiest Job
of the 21st Century,” by Thomas H. Davenport and
D.J. Patil, in this issue.)
Technology. The tools available to handle the
volume, velocity, and variety of big data have im-
proved greatly in recent years. In general, these tech-
nologies are not prohibitively expensive, and much
of the software is open source. Hadoop, the most
commonly used framework, combines commodity
hardware with open-source software. It takes in-
coming streams of data and distributes them onto
cheap disks; it also provides tools for analyzing the
data. However, these technologies do require a skill
set that is new to most IT departments, which will
need to work hard to integrate all the relevant inter-
nal and external sources of data. Although attention
to technology isn’t sufficient, it is always a necessary
component of a big data strategy.
Decision making. An effective organization
puts information and the relevant decision rights in
the same location. In the big data era, information
is created and transferred, and expertise is often not
where it used to be. The artful leader will create an
organization flexible enough to minimize the “not
getting Started
You don’t need to make
enormous up-front
investments in IT to
use big data (unlike
earlier generations of
IT-enabled change).
Here’s one approach
to building a capability
from the ground up.
1
Pick a business unit to
be the testing ground.
it should have a quant-
friendly leader backed
up by a team of data
scientists.
2
Challenge each key
function to identify five
business opportunities
based on big data, each
of which could be proto-
typed within five weeks
by a team of no more
than five people.
3
implement a process
for innovation that
includes four steps:
experimentation,
measurement, sharing,
and replication.
4
Keep in mind Joy’s Law:
“Most of the smartest
people work for someone
else.” open up some
of your data sets and
analytic challenges to
interested parties across
the internet and around
the world.
ContinueD on Page 6866 Harvard Business Review october 2012
HBR.oRg
Spotlight on Big Data
invented here” syndrome and maximize cross-
functional cooperation. People who understand the
problems need to be brought together with the right
data, but also with the people who have problem-
solving techniques that can effectively exploit them.
Company culture. The first question a data-
driven organization asks itself is not “What do we
think?” but “What do we know?” This requires a
move away from acting solely on hunches and in-
stinct. It also requires breaking a bad habit we’ve no-
ticed in many organizations: pretending to be more
data-driven than they actually are. Too often, we
saw executives who spiced up their reports with lots
of data that supported decisions they had already
made using the traditional HiPPO approach. Only
afterward were underlings dispatched to find the
numbers that would justify the decision.
Without queStion, many barriers to success remain.
There are too few data scientists to go around. The
technologies are new and in some cases exotic. It’s
too easy to mistake correlation for causation and to
find misleading patterns in the data. The cultural
challenges are enormous, and, of course, privacy
concerns are only going to become more significant.
But the underlying trends, both in the technology
and in the business payoff, are unmistakable.
The evidence is clear: Data-driven decisions tend
to be better decisions. Leaders will either embrace
this fact or be replaced by others who do. In sector af-
ter sector, companies that figure out how to combine
domain expertise with data science will pull away
from their rivals. We can’t say that all the winners
will be harnessing big data to transform decision
making. But the data tell us that’s the surest bet.
hBR Reprint R1210C
Andrew McAfee is a principal research scientist at
Mit’s Center for Digital Business and the author of
Enterprise 2.0 (Harvard Business School Press, 2009).
erik Brynjolfsson is the Schussel Family Professor at Mit’s
Sloan School of Management and the director of its Center
for Digital Business. they are the coauthors of Race Against
the Machine (Digital Frontier Press, 2012).
“We’re still hammering out the details with the treasury Department.”
Ca
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o
o
n
: D
av
e
Ca
RP
en
te
R
68 Harvard Business Review october 2012
HBR.oRg
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