Exe cu tive EDGE
telephonic conversations, e-mail communications, videos and the like. This
creates a maze of data that cannot be
easily handled with traditional models,
resulting in a waste of time and human
effort. So what can tame big data and put
it to good use?
Machine Learning
Applying machine learning algorithms on big data is the art of putting all
fragmented and often disconnected data
sources together to generate actionable
insights for the enterprise. To gain that
360-degree view of the customer, organizations need to be able to leverage
internal and external sources of information to assess customer sentiment. As
more and more organizations are stepping out of the traditional boundaries of
the enterprise to understand the impact
of the environment on their business, the
number of data sources keeps multiplying. Social media channels, websites,
automatic censors at the workplace and
robotics, for instance, are producing a
plethora of structured, unstructured and
semi-structured data.
Machine learning weaves together
the two budding trends of 2014 – realtime data collection and automation
of business processes. Bringing in the
computational power, machine learning
runs on the machine scale. The number
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of variables and factors that are taken
into consideration by this methodology
is unlimited. Machine learning brings in
the capability to cover data from varied
channels, such as social media, websites, automatic censors at the workplace and robotics. The job of data
scientists here becomes to oversee what
type of variables enter the models, adjust model parameters to get better fits
and finally interpret the content of models for decision-makers.
How and When to Introduce
Machine Learning
Machine learning is ideally suited to
the complexity of dealing with disparate
data sources and the huge variety of
variables and amounts of data involved.
The more data fed to an ML system, the
more it can learn, resulting in higher quality insights.
Keep in mind that big data can only
unfold incremental insights. The Pareto
80-20 rule applies here as well, as 80
percent of the details one would need
for business come from the internal and
transactional data. Using big data is only
viable for organizations that have matured in the data utilization curve. Once
the business intelligence bit and predictive analytics have been achieved, only
then does it makes sense to move toward big data.
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