Machine Learning: The Next Generation

 PERSPECTIVES : MACHINE LEARNING
Mac
Lea
The Next
Generation
of Insight
Artificial intelligence
comes to everyday
business, and
analytics will never
be the same.
The N
Gene
of Ins
There’s so much data out
there, it’s hard to wrap your head
around it. Luckily you don’t have
to. Technology is available to do
the work for us with massive data
sets, often in the blink of an eye.
And more importantly, computers
By Niren Sirohi, Ph.D.
can learn while doing so.
With the advent of Big Data and the interest in deriving signals
and insights from it, machine learning has emerged as a valuable technique, and in some cases an attractive alternative, to
more traditional analytic techniques. The idea behind machine
learning is to teach computers to learn from data, continuously,
and get smarter as they perform. It’s defined as the study and
construction of algorithms that can learn from and make predictions on data.
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 PERSPECTIVES : MACHINE LEARNING
Machine learning has been around for a while, and it’s being applied in some very
interesting areas. In the consumer arena, Facebook automatic image tagging is based on
a machine learning algorithm that learns from the photos you manually tag to identify
you and your friends in future pictures. And self-driving cars use image processing and
algorithms to learn where there’s a stop sign in the road or if a car is approaching, based
on what the cameras around the car see.
The growth in machine learning is accelerating. The market is predicted to reach $15.3
billion by 2019, with an average annual growth rate of 19.7 percent, according to BCC
Research. Machine learning’s growth is due in large part to three important factors:
• The dramatic increase in the availability of different types of data
• The reduction in computing power needed to run the algorithms
• The growth in inexpensive massively parallel processing architectures
The realization by businesses that they can continuously leverage the vast amounts
of data they have and act on those insights has also led to the growth and interest in
machine learning techniques.
Machine learning can be applied in almost any industry and has a few key distinctive
benefits: It self learns, gets better over time as it gets exposed to new data, and it can
be applied in real time. For example, IBM’s Watson got really good at playing chess
and won the game show Jeopardy. It is now being applied in situations like replacing
contact center representatives to handle straightforward queries, or playing the role of
personal care physician for a routine checkup. Machine learning, however, is not just for
the straightforward and the routine. It can also handle very complex tasks, such as self
driving or learning Japanese, as Watson recently did. These more complex activities are
made possible via deep learning and multilayered neural networks.
Machine learning is now beginning to move into the marketing space, so companies
can get smarter about their customers. With it, companies can be more nimble as they
improve customer experiences across the board; serving up relevant content and offers,
and/or real-time responses during customer interactions.
Imagine if you could design an algorithm that leverages all of the profiles, actions,
behaviors, and information about your customers across all channels to deliver the next
best action. This would be a machine learning algorithm that would also continuously
learn from the customer data being generated and get smarter over time.
Transparency Market Research estimates that predictive analytics software will be a
big early growth category for machine learning applications. It’s expected to reach $6.5
billion worldwide in 2019, up from $2 billion in 2012. There is a lot of potential insight
for marketers to work with.
How machine learning differs from traditional analytics
Before jumping in with investments around machine learning, it’s important to know
that machine learning is fundamentally different from traditional econometric analytic
approaches. There are a few key ideas that set it apart from other ways of using analytics:
Use all the data when feasible, not a sample. Traditional analytics relies
on gathering insight from representative samples, rather than complete
data sets, due to time, size, and computing power limitations. With the
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 PERSPECTIVES : MACHINE LEARNING
decrease in storage prices and increase in computing
power, the ability to work with all of the data available to us has significantly increased. This way more
information can be extracted, more quickly, even for
small population samples. This means that marketers
can comfortably analyze target segments that are small
without having to worry about whether they will be well
represented in samples.
It is ok to work with messy, unclean
data. Traditional analytics relies heavily
on creating a very clean data set before
developing analytical models. This is
partly driven by the scarcity of information contained in many databases. When there is a
lot of data available, it is actually better to deal with a
little messiness (e.g., words spelled differently ways in
a document) and work with large volumes of data versus
curating small amounts of clean data. Consider Google
Translate—underlying it is a machine learning algorithm
that leverages all of the Internet’s data, which can
be quite messy and unclean. Prior to this effort, most
language translation projects had worked with smaller
amounts of curated data but had not been as successful
at accurately automating translation. Of course, having
completely clean data is always preferable.
Algorithmic details may be hard to decipher, leading users to cede some control.
Certain machine learning algorithms
create their own feature sets that correlate most with the KPI of interest. Though
they may be highly predictive, these feature sets may
be unknown and have no real business meaning. For
example, a movie may have features like genre, lead
actor, and language. A machine learning algorithm on its
own may extract a new feature that is a complex combination of the three features above and this complex
combination may not have any direct business relevance
other than the fact that it is a good predictor. This point
is also closely linked to the next one around correlation
versus causation.
Correlations are ok, not everything needs
to be causal. Machine learning focuses
more on making predictions with less
interest on understanding the process by
which the data being predicted is generated. Given that,
one is likely to find patterns that are more correlations
than causations. For example, Google flu trends correlated
millions of search terms with the actual outbreak of the flu
to determine which terms were predictive. This enabled a
real-time view of where the flu was likely to happen and
gave an early warning signal that could be complementary
of other more accurate sources of information, such as the
Centers for Disease Control and Prevention.
Algorithms are self-learning and fine-tune
themselves. In traditional analytics, human
intervention is required to refit or re-estimate
models. But self-learning algorithms operate
differently. Given the right set of data, these algorithms
can learn from their own mistakes and mutate themselves to be more predictive over time. They can also
adjust to an underlying change of conditions, as long as
that is reflected in the data. Of course the change and
error correction is not instantaneous, and can take a few
cycles of data. Key to this process is continuously feeding
in data that corresponds to the outcome KPIs.
Remember, machine learning is not a panacea. Yes, it
has its advantages, but it’s not perfect. Noisy data or
misread algorithms can cause inaccuracies, However,
machine learning has real promise as the way to approach
analytics for the future. It can solve difficult problems
that are not solvable by other means. It provides the
insights and potential for taking advantage of real-time
information and updating corporate beliefs quickly,
which can provide a competitive advantage for those who
adopt it. Although machine learning is still a long way
away from nirvana, it has its place and is definitely worth
adopting for greater gains. 
Niren Sirohi, Ph.D. is vice president of predictive analytics
at iknowtion; [email protected].
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