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Some companies have built their very
businesses on their ability to collect, analyze, and act on
data. Every company can learn from what these firms do. |
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by Thomas H. Davenport |
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Thomas H. Davenport (tdavenport@babson.edu)
is the President’s Distinguished Professor of Information
Technology and Management at Babson College in Babson Park,
Massachusetts, the director of research at Babson Executive
Education, and a fellow at Accenture. He is the author of
Thinking for a Living (Harvard Business School Press,
2005). |
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We all know the power of the killer app.
Over the years, groundbreaking systems from companies such as
American Airlines (electronic reservations), Otis Elevator
(predictive maintenance), and American Hospital Supply (online
ordering) have dramatically boosted their creators’ revenues
and reputations. These heralded—and coveted—applications
amassed and applied data in ways that upended customer
expectations and optimized operations to unprecedented
degrees. They transformed technology from a supporting tool
into a strategic weapon.
Companies questing for killer
apps generally focus all their firepower on the one area that
promises to create the greatest competitive advantage. But a
new breed of company is upping the stakes. Organizations such
as Amazon, Harrah’s, Capital One, and the Boston Red Sox have
dominated their fields by deploying industrial-strength
analytics across a wide variety of activities. In essence,
they are transforming their organizations into armies of
killer apps and crunching their way to victory.
Organizations are competing on analytics not just
because they can—business today is awash in data and data
crunchers—but also because they should. At a time when firms
in many industries offer similar products and use comparable
technologies, business processes are among the last remaining
points of differentiation. And analytics competitors wring
every last drop of value from those processes. So, like other
companies, they know what products their customers want, but
they also know what prices those customers will pay, how many
items each will buy in a lifetime, and what triggers will make
people buy more. Like other companies, they know compensation
costs and turnover rates, but they can also calculate how much
personnel contribute to or detract from the bottom line and
how salary levels relate to individuals’ performance. Like
other companies, they know when inventories are running low,
but they can also predict problems with demand and supply
chains, to achieve low rates of inventory and high rates of
perfect orders.
And analytics competitors do all those
things in a coordinated way, as part of an overarching
strategy championed by top leadership and pushed down to
decision makers at every level. Employees hired for their
expertise with numbers or trained to recognize their
importance are armed with the best evidence and the best
quantitative tools. As a result, they make the best decisions:
big and small, every day, over and over and over.
Employees
hired for their expertise with numbers or trained to
recognize their importance are armed with the best
evidence and the best quantitative tools. As a result,
they make the best decisions.
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Although numerous
organizations are embracing analytics, only a handful have
achieved this level of proficiency. But analytics competitors
are the leaders in their varied fields—consumer products,
finance, retail, and travel and entertainment among them.
Analytics has been instrumental to Capital One, which has
exceeded 20% growth in earnings per share every year since it
became a public company. It has allowed Amazon to dominate
online retailing and turn a profit despite enormous
investments in growth and infrastructure. In sports, the real
secret weapon isn’t steroids, but stats, as dramatic victories
by the Boston Red Sox, the New England Patriots, and the
Oakland A’s attest.
At such organizations, virtuosity
with data is often part of the brand. Progressive makes
advertising hay from its detailed parsing of individual
insurance rates. Amazon customers can watch the company
learning about them as its service grows more targeted with
frequent purchases. Thanks to Michael Lewis’s best-selling
book Moneyball, which demonstrated the power of
statistics in professional baseball, the Oakland A’s are
almost as famous for their geeky number crunching as they are
for their athletic prowess.
To identify
characteristics shared by analytics competitors, I and two of
my colleagues at Babson College’s Working Knowledge Research
Center studied 32 organizations that have made a commitment to
quantitative, fact-based analysis. Eleven of those
organizations we classified as full-bore analytics
competitors, meaning top management had announced that
analytics was key to their strategies; they had multiple
initiatives under way involving complex data and statistical
analysis, and they managed analytical activity at the
enterprise (not departmental) level.
This article lays
out the characteristics and practices of these statistical
masters and describes some of the very substantial changes
other companies must undergo in order to compete on
quantitative turf. As one would expect, the transformation
requires a significant investment in technology, the
accumulation of massive stores of data, and the formulation of
companywide strategies for managing the data. But at least as
important, it requires executives’ vocal, unswerving
commitment and willingness to change the way employees think,
work, and are treated. As Gary Loveman, CEO of analytics
competitor Harrah’s, frequently puts it, “Do we think this is
true? Or do we know?”
Anatomy of an Analytics Competitor
One analytics competitor that’s at the top
of its game is Marriott International. Over the past 20 years,
the corporation has honed to a science its system for
establishing the optimal price for guest rooms (the key
analytics process in hotels, known as revenue management).
Today, its ambitions are far grander. Through its Total Hotel
Optimization program, Marriott has expanded its quantitative
expertise to areas such as conference facilities and catering,
and made related tools available over the Internet to property
revenue managers and hotel owners. It has developed systems to
optimize offerings to frequent customers and assess the
likelihood of those customers’ defecting to competitors. It
has given local revenue managers the power to override the
system’s recommendations when certain local factors can’t be
predicted (like the large number of Hurricane Katrina evacuees
arriving in Houston). The company has even created a revenue
opportunity model, which computes actual revenues as a
percentage of the optimal rates that could have been charged.
That figure has grown from 83% to 91% as Marriott’s
revenue-management analytics has taken root throughout the
enterprise. The word is out among property owners and
franchisees: If you want to squeeze the most revenue from your
inventory, Marriott’s approach is the ticket.
Clearly,
organizations such as Marriott don’t behave like traditional
companies. Customers notice the difference in every
interaction; employees and vendors live the difference every
day. Our study found three key attributes among analytics
competitors:
Widespread use of modeling and
optimization. Any company can generate simple
descriptive statistics about aspects of its business—average
revenue per employee, for example, or average order size. But
analytics competitors look well beyond basic statistics. These
companies use predictive modeling to identify the most
profitable customers—plus those with the greatest profit
potential and the ones most likely to cancel their accounts.
They pool data generated in-house and data acquired from
outside sources (which they analyze more deeply than do their
less statistically savvy competitors) for a comprehensive
understanding of their customers. They optimize their supply
chains and can thus determine the impact of an unexpected
constraint, simulate alternatives, and route shipments around
problems. They establish prices in real time to get the
highest yield possible from each of their customer
transactions. They create complex models of how their
operational costs relate to their financial performance.
Leaders in analytics also use sophisticated
experiments to measure the overall impact or “lift” of
intervention strategies and then apply the results to
continuously improve subsequent analyses. Capital One, for
example, conducts more than 30,000 experiments a year, with
different interest rates, incentives, direct-mail packaging,
and other variables. Its goal is to maximize the likelihood
both that potential customers will sign up for credit cards
and that they will pay back Capital One.
Progressive
employs similar experiments using widely available insurance
industry data. The company defines narrow groups, or cells, of
customers: for example, motorcycle riders ages 30 and above,
with college educations, credit scores over a certain level,
and no accidents. For each cell, the company performs a
regression analysis to identify factors that most closely
correlate with the losses that group engenders. It then sets
prices for the cells, which should enable the company to earn
a profit across a portfolio of customer groups, and uses
simulation software to test the financial implications of
those hypotheses. With this approach, Progressive can
profitably insure customers in traditionally high-risk
categories. Other insurers reject high-risk customers out of
hand, without bothering to delve more deeply into the data
(although even traditional competitors, such as Allstate, are
starting to embrace analytics as a strategy).
An enterprise approach. Analytics
competitors understand that most business functions—even
those, like marketing, that have historically depended on art
rather than science—can be improved with sophisticated
quantitative techniques. These organizations don’t gain
advantage from one killer app, but rather from multiple
applications supporting many parts of the business—and, in a
few cases, being rolled out for use by customers and
suppliers.
UPS embodies the evolution from targeted
analytics user to comprehensive analytics competitor. Although
the company is among the world’s most rigorous practitioners
of operations research and industrial engineering, its
capabilities were, until fairly recently, narrowly focused.
Today, UPS is wielding its statistical skill to track the
movement of packages and to anticipate and influence the
actions of people—assessing the likelihood of customer
attrition and identifying sources of problems. The UPS
Customer Intelligence Group, for example, is able to
accurately predict customer defections by examining usage
patterns and complaints. When the data point to a potential
defector, a salesperson contacts that customer to review and
resolve the problem, dramatically reducing the loss of
accounts. UPS still lacks the breadth of initiatives of a
full-bore analytics competitor, but it is heading in that
direction.
Analytics competitors treat all such
activities from all provenances as a single, coherent
initiative, often massed under one rubric, such as
“information-based strategy” at Capital One or
“information-based customer management” at Barclays Bank.
These programs operate not just under a common label but also
under common leadership and with common technology and tools.
In traditional companies, “business intelligence” (the term IT
people use for analytics and reporting processes and software)
is generally managed by departments; number-crunching
functions select their own tools, control their own data
warehouses, and train their own people. But that way, chaos
lies. For one thing, the proliferation of user-developed
spreadsheets and databases inevitably leads to multiple
versions of key indicators within an organization.
Furthermore, research has shown that between 20% and 40% of
spreadsheets contain errors; the more spreadsheets floating
around a company, therefore, the more fecund the breeding
ground for mistakes. Analytics competitors, by contrast, field
centralized groups to ensure that critical data and other
resources are well managed and that different parts of the
organization can share data easily, without the impediments of
inconsistent formats, definitions, and standards.
In
traditional companies, departments manage analytics
—number-crunching functions select their own tools and
train their own people. But that way, chaos lies.
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Some analytics
competitors apply the same enterprise approach to people as to
technology. Procter & Gamble, for example, recently
created a kind of überanalytics group consisting of more than
100 analysts from such functions as operations, supply chain,
sales, consumer research, and marketing. Although most of the
analysts are embedded in business operating units, the group
is centrally managed. As a result of this consolidation,
P&G can apply a critical mass of expertise to its most
pressing issues. So, for example, sales and marketing analysts
supply data on opportunities for growth in existing markets to
analysts who design corporate supply networks. The supply
chain analysts, in turn, apply their expertise in certain
decision-analysis techniques to such new areas as competitive
intelligence.
The group at P&G also raises the
visibility of analytical and data-based decision making within
the company. Previously, P&G’s crack analysts had improved
business processes and saved the firm money; but because they
were squirreled away in dispersed domains, many executives
didn’t know what services they offered or how effective they
could be. Now those executives are more likely to tap the
company’s deep pool of expertise for their projects.
Meanwhile, masterful number crunching has become part of the
story P&G tells to investors, the press, and the public.
Senior executive
advocates. A companywide embrace of analytics
impels changes in culture, processes, behavior, and skills for
many employees. And so, like any major transition, it requires
leadership from executives at the very top who have a passion
for the quantitative approach. Ideally, the principal advocate
is the CEO. Indeed, we found several chief executives who have
driven the shift to analytics at their companies over the past
few years, including Loveman of Harrah’s, Jeff Bezos of
Amazon, and Rich Fairbank of Capital One. Before he retired
from the Sara Lee Bakery Group, former CEO Barry Beracha kept
a sign on his desk that summed up his personal and
organizational philosophy: “In God we trust. All others bring
data.” We did come across some companies in which a single
functional or business unit leader was trying to push
analytics throughout the organization, and a few were making
some progress. But we found that these lower-level people
lacked the clout, the perspective, and the cross-functional
scope to change the culture in any meaningful way.
CEOs leading the analytics charge require both an
appreciation of and a familiarity with the subject. A
background in statistics isn’t necessary, but those leaders
must understand the theory behind various quantitative methods
so that they recognize those methods’ limitations—which
factors are being weighed and which ones aren’t. When the CEOs
need help grasping quantitative techniques, they turn to
experts who understand the business and how analytics can be
applied to it. We interviewed several leaders who had retained
such advisers, and these executives stressed the need to find
someone who can explain things in plain language and be
trusted not to spin the numbers. A few CEOs we spoke with had
surrounded themselves with very analytical people—professors,
consultants, MIT graduates, and the like. But that was a
personal preference rather than a necessary practice.
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Going to
Bat for Stats
Sidebar
R0601H_A (Located at the end of this
article) |
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Of course, not all
decisions should be grounded in analytics—at least not wholly
so. Personnel matters, in particular, are often well and
appropriately informed by instinct and anecdote. More
organizations are subjecting recruiting and hiring decisions
to statistical analysis (see the sidebar “Going to Bat for
Stats”). But research shows that human beings can make quick,
surprisingly accurate assessments of personality and character
based on simple observations. For analytics-minded leaders,
then, the challenge boils down to knowing when to run with the
numbers and when to run with their guts.
Their Sources of Strength
Analytics competitors are more than simple
number-crunching factories. Certainly, they apply
technology—with a mixture of brute force and finesse—to
multiple business problems. But they also direct their
energies toward finding the right focus, building the right
culture, and hiring the right people to make optimal use of
the data they constantly churn. In the end, people and
strategy, as much as information technology, give such
organizations strength.
The right focus. Although analytics
competitors encourage universal fact-based decisions, they
must choose where to direct resource-intensive efforts.
Generally, they pick several functions or initiatives that
together serve an overarching strategy. Harrah’s, for example,
has aimed much of its analytical activity at increasing
customer loyalty, customer service, and related areas like
pricing and promotions. UPS has broadened its focus from
logistics to customers, in the interest of providing superior
service. While such multipronged strategies define analytics
competitors, executives we interviewed warned companies
against becoming too diffuse in their initiatives or losing
clear sight of the business purpose behind each.
Another consideration when allocating resources is how
amenable certain functions are to deep analysis. There are at
least seven common targets for analytical activity, and
specific industries may present their own (see “Things You Can
Count On”). Statistical models and algorithms that dangle the
possibility of performance breakthroughs make some prospects
especially tempting. Marketing, for example, has always been
tough to quantify because it is rooted in psychology. But now
consumer products companies can hone their market research
using multiattribute utility theory—a tool for understanding
and predicting consumer behaviors and decisions. Similarly,
the advertising industry is adopting econometrics—statistical
techniques for measuring the lift provided by different ads
and promotions over time.
The most proficient
analytics practitioners don’t just measure their own
navels—they also help customers and vendors measure theirs.
Wal-Mart, for example, insists that suppliers use its Retail
Link system to monitor product movement by store, to plan
promotions and layouts within stores, and to reduce
stock-outs. E.&J. Gallo provides distributors with data
and analysis on retailers’ costs and pricing so they can
calculate the per-bottle profitability for each of Gallo’s 95
wines. The distributors, in turn, use that information to help
retailers optimize their mixes while persuading them to add
shelf space for Gallo products. Procter & Gamble offers
data and analysis to its retail customers, as part of a
program called Joint Value Creation, and to its suppliers to
help improve responsiveness and reduce costs. Hospital
supplier Owens & Minor furnishes similar services,
enabling customers and suppliers to access and analyze their
buying and selling data, track ordering patterns in search of
consolidation opportunities, and move off-contract purchases
to group contracts that include products distributed by Owens
& Minor and its competitors. For example, Owens &
Minor might show a hospital chain’s executives how much money
they could save by consolidating purchases across multiple
locations or help them see the trade-offs between increasing
delivery frequency and carrying inventory.
The most
proficient analytics practitioners don’t just measure
their own navels—they also help customers and vendors
measure theirs.
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The right culture. Culture is a soft
concept; analytics is a hard discipline. Nonetheless,
analytics competitors must instill a companywide respect for
measuring, testing, and evaluating quantitative evidence.
Employees are urged to base decisions on hard facts. And they
know that their performance is gauged the same way. Human
resource organizations within analytics competitors are
rigorous about applying metrics to compensation and rewards.
Harrah’s, for example, has made a dramatic change from a
rewards culture based on paternalism and tenure to one based
on such meticulously collected performance measurements as
financial and customer service results. Senior executives also
set a consistent example with their own behavior, exhibiting a
hunger for and confidence in fact and analysis. One exemplar
of such leadership was Beracha of the Sara Lee Bakery Group,
known to his employees as a “data dog” because he hounded them
for data to support any assertion or hypothesis.
Not
surprisingly, in an analytics culture, there’s sometimes
tension between innovative or entrepreneurial impulses and the
requirement for evidence. Some companies place less emphasis
on blue-sky development, in which designers or engineers chase
after a gleam in someone’s eye. In these organizations,
R&D, like other functions, is rigorously metric-driven. At
Yahoo, Progressive, and Capital One, process and product
changes are tested on a small scale and implemented as they
are validated. That approach, well established within various
academic and business disciplines (including engineering,
quality management, and psychology), can be applied to most
corporate processes—even to not-so-obvious candidates, like
human resources and customer service. HR, for example, might
create profiles of managers’ personality traits and leadership
styles and then test those managers in different situations.
It could then compare data on individuals’ performance with
data about personalities to determine what traits are most
important to managing a project that is behind schedule, say,
or helping a new group to assimilate.
There are,
however, instances when a decision to change something or try
something new must be made too quickly for extensive analysis,
or when it’s not possible to gather data beforehand. For
example, even though Amazon’s Jeff Bezos greatly prefers to
rigorously quantify users’ reactions before rolling out new
features, he couldn’t test the company’s
search-inside-the-book offering without applying it to a
critical mass of books (120,000, to begin with). It was also
expensive to develop, and that increased the risk. In this
case, Bezos trusted his instincts and took a flier. And the
feature did prove popular when introduced.
The right people. Analytical firms
hire analytical people—and like all companies that compete on
talent, they pursue the best. When Amazon needed a new head
for its global supply chain, for example, it recruited Gang
Yu, a professor of management science and software
entrepreneur who is one of the world’s leading authorities on
optimization analytics. Amazon’s business model requires the
company to manage a constant flow of new products, suppliers,
customers, and promotions, as well as deliver orders by
promised dates. Since his arrival, Yu and his team have been
designing and building sophisticated supply chain systems to
optimize those processes. And while he tosses around phrases
like “nonstationary stochastic processes,” he’s also good at
explaining the new approaches to Amazon’s executives in clear
business terms.
Established analytics competitors such
as Capital One employ squadrons of analysts to conduct
quantitative experiments and, with the results in hand, design
credit card and other financial offers. These efforts call for
a specialized skill set, as you can see from this job
description (typical for a Capital One analyst):
High conceptual problem-solving and quantitative
analytical aptitudes…Engineering, financial, consulting,
and/or other analytical quantitative educational/work
background. Ability to quickly learn how to use software
applications. Experience with Excel models. Some graduate work
preferred but not required (e.g., MBA). Some experience with
project management methodology, process improvement tools
(Lean, Six Sigma), or statistics preferred.
Other
firms hire similar kinds of people, but analytics competitors
have them in much greater numbers. Capital One is currently
seeking three times as many analysts as operations
people—hardly the common practice for a bank. “We are really a
company of analysts,” one executive there noted. “It’s the
primary job in this place.”
Good analysts must also
have the ability to express complex ideas in simple terms and
have the relationship skills to interact well with decision
makers. One consumer products company with a 30-person
analytics group looks for what it calls “PhDs with
personality”—people with expertise in math, statistics, and
data analysis who can also speak the language of business and
help market their work internally and sometimes externally.
The head of a customer analytics group at Wachovia Bank
describes the rapport with others his group seeks: “We are
trying to build our people as part of the business team,” he
explains. “We want them sitting at the business table,
participating in a discussion of what the key issues are,
determining what information needs the businesspeople have,
and recommending actions to the business partners. We want
this [analytics group] to be not just a general utility, but
rather an active and critical part of the business unit’s
success.”
Of course, a combination of analytical,
business, and relationship skills may be difficult to find.
When the software company SAS (a sponsor of this research,
along with Intel) knows it will need an expert in
state-of-the-art business applications such as predictive
modeling or recursive partitioning (a form of decision tree
analysis applied to very complex data sets), it begins
recruiting up to 18 months before it expects to fill the
position.
In fact, analytical talent may be to the
early 2000s what programming talent was to the late 1990s.
Unfortunately, the U.S. and European labor markets aren’t
exactly teeming with analytically sophisticated job
candidates. Some organizations cope by contracting work to
countries such as India, home to many statistical experts.
That strategy may succeed when offshore analysts work on
stand-alone problems. But if an iterative discussion with
business decision makers is required, the distance can become
a major barrier.
The right technology. Competing on
analytics means competing on technology. And while the most
serious competitors investigate the latest statistical
algorithms and decision science approaches, they also
constantly monitor and push the IT frontier. The analytics
group at one consumer products company went so far as to build
its own supercomputer because it felt that commercially
available models were inadequate for its demands. Such heroic
feats usually aren’t necessary, but serious analytics does
require the following:
A data strategy. Companies have
invested many millions of dollars in systems that snatch data
from every conceivable source. Enterprise resource planning,
customer relationship management, point-of-sale, and other
systems ensure that no transaction or other significant
exchange occurs without leaving a mark. But to compete on that
information, companies must present it in standard formats,
integrate it, store it in a data warehouse, and make it easily
accessible to anyone and everyone. And they will need a
lot of it. For example, a company may spend several years
accumulating data on different marketing approaches before it
has gathered enough to reliably analyze the effectiveness of
an advertising campaign. Dell employed DDB Matrix, a unit of
the advertising agency DDB Worldwide, to create (over a period
of seven years) a database that includes 1.5 million records
on all the computer maker’s print, radio, network TV, and
cable ads, coupled with data on Dell sales for each region in
which the ads appeared (before and after their appearance).
That information allows Dell to fine-tune its promotions for
every medium in every region.
Business intelligence software. The
term “business intelligence,” which first popped up in the
late 1980s, encompasses a wide array of processes and software
used to collect, analyze, and disseminate data, all in the
interests of better decision making. Business intelligence
tools allow employees to extract, transform, and load (or ETL,
as people in the industry would say) data for analysis and
then make those analyses available in reports, alerts, and
scorecards. The popularity of analytics competition is partly
a response to the emergence of integrated packages of these
tools.
Computing hardware. The volumes of
data required for analytics applications may strain the
capacity of low-end computers and servers. Many analytics
competitors are converting their hardware to 64-bit processors
that churn large amounts of data quickly.
The Long Road Ahead
Most
companies in most industries have excellent reasons to pursue
strategies shaped by analytics. Virtually all the
organizations we identified as aggressive analytics
competitors are clear leaders in their fields, and they
attribute much of their success to the masterful exploitation
of data. Rising global competition intensifies the need for
this sort of proficiency. Western companies unable to beat
their Indian or Chinese competitors on product cost, for
example, can seek the upper hand through optimized business
processes.
Companies just now embracing such
strategies, however, will find that they take several years to
come to fruition. The organizations in our study described a
long, sometimes arduous journey. The UK Consumer Cards and
Loans business within Barclays Bank, for example, spent five
years executing its plan to apply analytics to the marketing
of credit cards and other financial products. The company had
to make process changes in virtually every aspect of its
consumer business: underwriting risk, setting credit limits,
servicing accounts, controlling fraud, cross selling, and so
on. On the technical side, it had to integrate data on 10
million Barclaycard customers, improve the quality of the
data, and build systems to step up data collection and
analysis. In addition, the company embarked on a long series
of small tests to begin learning how to attract and retain the
best customers at the lowest price. And it had to hire new
people with top-drawer quantitative skills.
Much of
the time—and corresponding expense—that any company takes to
become an analytics competitor will be devoted to
technological tasks: refining the systems that produce
transaction data, making data available in warehouses,
selecting and implementing analytic software, and assembling
the hardware and communications environment. And because those
who don’t record history are doomed not to learn from it,
companies that have collected little information—or the wrong
kind—will need to amass a sufficient body of data to support
reliable forecasting. “We’ve been collecting data for six or
seven years, but it’s only become usable in the last two or
three, because we needed time and experience to validate
conclusions based on the data,” remarked a manager of customer
data analytics at UPS.
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You Know
You Compete on Analytics When...
Sidebar R0601H_B (Located at the end of this
article) |
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And, of course, new
analytics competitors will have to stock their personnel
larders with fresh people. (When Gary Loveman became COO, and
then CEO, of Harrah’s, he brought in a group of statistical
experts who could design and implement quantitatively based
marketing campaigns and loyalty programs.) Existing employees,
meanwhile, will require extensive training. They need to know
what data are available and all the ways the information can
be analyzed; and they must learn to recognize such
peculiarities and shortcomings as missing data, duplication,
and quality problems. An analytics-minded executive at Procter
& Gamble suggested to me that firms should begin to keep
managers in their jobs for longer periods because of the time
required to master quantitative approaches to their
businesses.
The German pathologist Rudolph Virchow
famously called the task of science “to stake out the limits
of the knowable.” Analytics competitors pursue a similar goal,
although the universe they seek to know is a more
circumscribed one of customer behavior, product movement,
employee performance, and financial reactions. Every day,
advances in technology and techniques give companies a better
and better handle on the critical minutiae of their
operations.
The Oakland A’s aren’t the only ones
playing moneyball. Companies of every stripe want to be part
of the game.
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Reprint Number R0601H |
Harvard Business Review OnPoint edition 3005 |
Harvard Business Review OnPoint collection 3048 |
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The analysis-versus-instinct debate, a
favorite of political commentators during the last two U.S.
presidential elections, is raging in professional sports,
thanks to several popular books and high-profile victories.
For now, analysis seems to hold the lead.
Most
notably, statistics are a major part of the selection and
deployment of players. Moneyball, by Michael Lewis,
focuses on the use of analytics in player selection for the
Oakland A’s—a team that wins on a shoestring. The New England
Patriots, a team that devotes an enormous amount of attention
to statistics, won three of the last four Super Bowls, and
their payroll is currently ranked 24th in the league. The
Boston Red Sox have embraced “sabermetrics” (the application
of analysis to baseball), even going so far as to hire Bill
James, the famous baseball statistician who popularized that
term. Analytic HR strategies are taking hold in European
soccer as well. One leading team, Italy’s A.C. Milan, uses
predictive models from its Milan Lab research center to
prevent injuries by analyzing physiological, orthopedic, and
psychological data from a variety of sources. A fast-rising
English soccer team, the Bolton Wanderers, is known for its
manager’s use of extensive data to evaluate players’
performance.
Still, sports managers—like business
leaders—are rarely fact-or-feeling purists. St. Louis
Cardinals manager Tony La Russa, for example, brilliantly
combines analytics with intuition to decide when to substitute
a charged-up player in the batting lineup or whether to hire a
spark-plug personality to improve morale. In his recent book,
Three Nights in August, Buzz Bissinger describes that
balance: “La Russa appreciated the information generated by
computers. He studied the rows and the columns. But he also
knew they could take you only so far in baseball, maybe even
confuse you with a fog of overanalysis. As far as he knew,
there was no way to quantify desire. And those numbers told
him exactly what he needed to know when added to twenty-four
years of managing experience.”
That final sentence is
the key. Whether scrutinizing someone’s performance record or
observing the expression flitting across an employee’s face,
leaders consult their own experience to understand the
“evidence” in all its forms.
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1. You apply sophisticated
information systems and rigorous analysis not only to your
core capability but also to a range of functions as varied as
marketing and human resources.
2. Your senior
executive team not only recognizes the importance of analytics
capabilities but also makes their development and maintenance
a primary focus.
3. You treat fact-based
decision making not only as a best practice but also as a part
of the culture that’s constantly emphasized and communicated
by senior executives.
4. You hire not only
people with analytical skills but a lot of people with the
very best analytical skills—and consider them a key to
your success.
5. You not only employ analytics
in almost every function and department but also consider it
so strategically important that you manage it at the
enterprise level.
6. You not only are expert at
number crunching but also invent proprietary metrics for use
in key business processes.
7. You not only use
copious data and in-house analysis but also share them with
customers and suppliers.
8. You not only avidly
consume data but also seize every opportunity to generate
information, creating a “test and learn” culture based on
numerous small experiments.
9. You not only
have committed to competing on analytics but also have been
building your capabilities for several years.
10. You not only emphasize the importance of
analytics internally but also make quantitative capabilities
part of your company’s story, to be shared in the annual
report and in discussions with financial analysts.
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Going to Bat for Stats