An
executive’s guide to machine learning
It’s no longer the
preserve of artificial-intelligence researchers and born-digital companies like
Amazon, Google, and Netflix.
Machine learning is based on algorithms that can learn from
data without relying on rules-based programming. It came into its own as a
scientific discipline in the late 1990s as steady advances in digitization and
cheap computing power enabled data scientists to stop building finished models
and instead train computers to do so. The unmanageable volume and complexity of
the big data that the world is now swimming in have increased the potential of
machine learning—and the need for it.
In 2007 Fei-Fei Li,
the head of Stanford’s Artificial Intelligence Lab, gave up trying to program
computers to recognize objects and began labeling the millions of raw images
that a child might encounter by age three and feeding them to computers. By
being shown thousands and thousands of labeled data sets with instances of,
say, a cat, the machine could shape its own rules for deciding whether a
particular set of digital pixels was, in fact, a cat. Last November, Li’s
team unveiled a program that identifies the visual elements of any picture with
a high degree of accuracy. IBM’s Watson machine relied on a similar
self-generated scoring system among hundreds of potential answers to crush the
world’s best Jeopardy! players in 2011.
Dazzling as such feats
are, machine learning is nothing like learning in the human sense (yet). But
what it already does extraordinarily well—and will get better at—is
relentlessly chewing through any amount of data and every combination of
variables. Because machine learning’s emergence as a mainstream management tool
is relatively recent, it often raises questions. In this article, we’ve posed
some that we often hear and answered them in a way we hope will be useful for
any executive. Now is the time to grapple with these issues, because the
competitive significance of business models turbocharged by machine learning is
poised to surge. Indeed, management author Ram Charan suggests that “any
organization that is not a math house now or is unable to become one soon is
already a legacy company.
1. How are traditional industries using
machine learning to gather fresh business insights?
Well, let’s start with sports. This past
spring, contenders for the US National Basketball Association championship
relied on the analytics of Second Spectrum, a California machine-learning
start-up. By digitizing the past few seasons’ games, it has created predictive
models that allow a coach to distinguish between, as CEO Rajiv Maheswaran puts
it, “a bad shooter who takes good shots and a good shooter who takes bad
shots”—and to adjust his decisions accordingly.
You can’t get more venerable or traditional
than General Electric, the only member of the original Dow Jones Industrial
Average still around after 119 years. GE already makes hundreds of millions of
dollars by crunching the data it collects from deep-sea oil wells or jet engines
to optimize performance, anticipate breakdowns, and streamline maintenance. But
Colin Parris, who joined GE Software from IBM late last year as vice president
of software research, believes that continued advances in data-processing
power, sensors, and predictive algorithms will soon give his company the same
sharpness of insight into the individual vagaries of a jet engine that Google
has into the online behavior of a 24-year-old netizen from West Hollywood.
2. What about outside North America?
In Europe, more than a dozen banks have
replaced older statistical-modeling approaches with machine-learning techniques
and, in some cases, experienced 10 percent increases in sales of new products,
20 percent savings in capital expenditures, 20 percent increases in cash
collections, and 20 percent declines in churn. The banks have achieved these
gains by devising new recommendation engines for clients in retailing and in
small and medium-sized companies. They have also built microtargeted models
that more accurately forecast who will cancel service or default on their
loans, and how best to intervene.
Closer to home, as a
recent article in McKinsey Quarterly notes,3 our colleagues have been applying hard analytics to the
soft stuff of talent management. Last fall, they tested the ability of three
algorithms developed by external vendors and one built internally to forecast,
solely by examining scanned résumés, which of more than 10,000 potential
recruits the firm would have accepted. The predictions strongly correlated with
the real-world results. Interestingly, the machines accepted a slightly higher
percentage of female candidates, which holds promise for using analytics to
unlock a more diverse range of profiles and counter hidden human bias.
As ever more of the
analog world gets digitized, our ability to learn from data by developing and
testing algorithms will only become more important for what are now seen as
traditional businesses. Google chief economist Hal Varian calls this “computerkaizen.”
For “just as mass production changed the way products were assembled and
continuous improvement changed how manufacturing was done,” he says, “so
continuous [and often automatic] experimentation will improve the way we
optimize business processes in our organizations.”
3. What were the early foundations of machine
learning?
Machine learning is based on a number of
earlier building blocks, starting with classical statistics. Statistical
inference does form an important foundation for the current implementations of
artificial intelligence. But it’s important to recognize that classical
statistical techniques were developed between the 18th and early 20th centuries
for much smaller data sets than the ones we now have at our disposal. Machine
learning is unconstrained by the preset assumptions of statistics. As a result,
it can yield insights that human analysts do not see on their own and make
predictions with ever-higher degrees of accuracy.
More recently, in the 1930s and 1940s, the
pioneers of computing (such as Alan Turing, who had a deep and abiding interest
in artificial intelligence) began formulating and tinkering with the basic
techniques such as neural networks that make today’s machine learning possible.
But those techniques stayed in the laboratory longer than many technologies did
and, for the most part, had to await the development and infrastructure of
powerful computers, in the late 1970s and early 1980s. That’s probably the
starting point for the machine-learning adoption curve. New technologies
introduced into modern economies—the steam engine, electricity, the electric
motor, and computers, for example—seem to take about 80 years to transition
from the laboratory to what you might call cultural invisibility. The computer
hasn’t faded from sight just yet, but it’s likely to by 2040. And it probably
won’t take much longer for machine learning to recede into the background.
4. What does it take to get started?
C-level executives will best exploit machine
learning if they see it as a tool to craft and implement a strategic vision.
But that means putting strategy first. Without strategy as a starting point,
machine learning risks becoming a tool buried inside a company’s routine
operations: it will provide a useful service, but its long-term value will
probably be limited to an endless repetition of “cookie cutter” applications
such as models for acquiring, stimulating, and retaining customers.
We find the parallels with M&A
instructive. That, after all, is a means to a well-defined end. No sensible
business rushes into a flurry of acquisitions or mergers and then just sits
back to see what happens. Companies embarking on machine learning should make
the same three commitments companies make before embracing M&A. Those
commitments are, first, to investigate all feasible alternatives; second, to
pursue the strategy wholeheartedly at the C-suite level; and, third, to use (or
if necessary acquire) existing expertise and knowledge in the C-suite to guide
the application of that strategy.
The people charged with creating the strategic
vision may well be (or have been) data scientists. But as they define the
problem and the desired outcome of the strategy, they will need guidance from
C-level colleagues overseeing other crucial strategic initiatives. More
broadly, companies must have two types of people to unleash the potential of
machine learning. “Quants” are schooled in its language and methods.
“Translators” can bridge the disciplines of data, machine learning, and
decision making by reframing the quants’ complex results as actionable insights
that generalist managers can execute.
Access to troves of useful and reliable data
is required for effective machine learning, such as Watson’s ability, in tests,
to predict oncological outcomes better than physicians or Facebook’s recent
success teaching computers to identify specific human faces nearly as
accurately as humans do. A true data strategy starts with identifying gaps in
the data, determining the time and money required to fill those gaps, and
breaking down silos. Too often, departments hoard information and politicize
access to it—one reason some companies have created the new role of chief data
officer to pull together what’s required. Other elements include putting
responsibility for generating data in the hands of frontline managers.
Start small—look for low-hanging fruit and trumpet
any early success. This will help recruit grassroots support and reinforce the
changes in individual behavior and the employee buy-in that ultimately
determine whether an organization can apply machine learning effectively.
Finally, evaluate the results in the light of clearly identified criteria for
success.
5. What’s the role of top management?
Behavioral change will be critical, and one of
top management’s key roles will be to influence and encourage it. Traditional
managers, for example, will have to get comfortable with their own variations
on A/B testing, the technique digital companies use to see what will and will
not appeal to online consumers. Frontline managers, armed with insights from
increasingly powerful computers, must learn to make more decisions on their
own, with top management setting the overall direction and zeroing in only when
exceptions surface. Democratizing the use of analytics—providing the front line
with the necessary skills and setting appropriate incentives to encourage data
sharing—will require time.
C-level officers should think about applied
machine learning in three stages: machine learning 1.0, 2.0, and 3.0—or, as we
prefer to say, description, prediction, and prescription. They probably don’t
need to worry much about the description stage, which most companies have
already been through. That was all about collecting data in databases (which
had to be invented for the purpose), a development that gave managers new
insights into the past. OLAP—online analytical processing—is now pretty routine
and well established in most large organizations.
There’s a much more urgent need to embrace the
prediction stage, which is happening right now. Today’s cutting-edge technology
already allows businesses not only to look at their historical data but also to
predict behavior or outcomes in the future—for example, by helping credit-risk
officers at banks to assess which customers are most likely to default or by
enabling telcos to anticipate which customers are especially prone to “churn”
in the near term.
A frequent concern for the C-suite when it
embarks on the prediction stage is the quality of the data. That concern often
paralyzes executives. In our experience, though, the last decade’s IT
investments have equipped most companies with sufficient information to obtain
new insights even from incomplete, messy data sets, provided of course that
those companies choose the right algorithm. Adding exotic new data sources may
be of only marginal benefit compared with what can be mined from existing data
warehouses. Confronting that challenge is the task of the “chief data
scientist.”
Prescription—the third
and most advanced stage of machine learning—is the opportunity of the future
and must therefore command strong C-suite attention. It is, after all, not
enough just to predict what customers are going to do; only by
understanding why they are going to do it can companies
encourage or deter that behavior in the future. Technically, today’s
machine-learning algorithms, aided by human translators, can already do this.
For example, an international bank concerned about the scale of defaults in its
retail business recently identified a group of customers who had suddenly
switched from using credit cards during the day to using them in the middle of
the night. That pattern was accompanied by a steep decrease in their savings
rate. After consulting branch managers, the bank further discovered that the
people behaving in this way were also coping with some recent stressful event.
As a result, all customers tagged by the algorithm as members of that
microsegment were automatically given a new limit on their credit cards and
offered financial advice.
The prescription stage of machine learning,
ushering in a new era of man–machine collaboration, will require the biggest
change in the way we work. While the machine identifies patterns, the human
translator’s responsibility will be to interpret them for different
microsegments and to recommend a course of action. Here the C-suite must be
directly involved in the crafting and formulation of the objectives that such
algorithms attempt to optimize.
6. This sounds awfully like automation
replacing humans in the long run. Are we any nearer to knowing whether machines
will replace managers?
It’s true that change is coming (and data are
generated) so quickly that human-in-the-loop involvement in all decision making
is rapidly becoming impractical. Looking three to five years out, we expect to
see far higher levels of artificial intelligence, as well as the development of
distributed autonomous corporations. These self-motivating, self-contained
agents, formed as corporations, will be able to carry out set objectives
autonomously, without any direct human supervision. Some DACs will certainly
become self-programming.
One current of opinion sees distributed
autonomous corporations as threatening and inimical to our culture. But by the
time they fully evolve, machine learning will have become culturally invisible
in the same way technological inventions of the 20th century disappeared into
the background. The role of humans will be to direct and guide the algorithms
as they attempt to achieve the objectives that they are given. That is one
lesson of the automatic-trading algorithms which wreaked such damage during the
financial crisis of 2008.
No matter what fresh insights computers
unearth, only human managers can decide the essential questions, such as which
critical business problems a company is really trying to solve. Just as human
colleagues need regular reviews and assessments, so these “brilliant machines”
and their works will also need to be regularly evaluated, refined—and, who
knows, perhaps even fired or told to pursue entirely different paths—by
executives with experience, judgment, and domain expertise.
The winners will be neither machines alone,
nor humans alone, but the two working together effectively.
7. So in the long term there’s no need to
worry?
It’s hard to be sure, but distributed autonomous corporations
and machine learning should be high on the C-suite agenda. We anticipate a time
when the philosophical discussion of what intelligence, artificial or
otherwise, might be will end because there will be no such thing as
intelligence—just processes. If distributed autonomous corporations act
intelligently, perform intelligently, and respond intelligently, we will cease
to debate whether high-level intelligence other than the human variety exists.
In the meantime, we must all think about what we want these entities to do, the
way we want them to behave, and how we are going to work with them.
June 2015 |
byDorian Pyle and Cristina San Jose
http://www.mckinsey.com/Insights/High_Tech_Telecoms_Internet/An_executives_guide_to_machine_learning?cid=Digital-eml-alt-mkq-mck-oth-1507
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