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.
[...]
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,
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 “computer
kaizen.” 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
[...]
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 (exhibit).
Continua
aqui
Postagens
