Big data's impact on online marketing

internetsearchinc.com
Deepak Wadhwani
Big data is so large and complex that traditional applications are not
enough; therefore, better applications are needed to manipulate it.
Big data is a data which grows and becomes available
exponentially; the data is both structured and unstructured. Big data
might be as important to business as society. More data may lead
to more accurate analysis.
Companies like Uber, Netflix, Pivotal Software Inc. and Affectiva
use and manipulate big data (applications and frameworks like
Hadoop, R, and Pivotal Big Data Suite) to accomplish their ends.
The definition of Big Data consists of five parts including the
following: Volume, Velocity, Variety, Variability, and Complexity. The
first four are called 4 V’s of big data.
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Source: HRBoss.com
The term “volume” as used in the context of big data means the
quantity of data that is generated. Many factors make data volume
increase. Data based on transactions is stored through the years.
Unstructured data is streamed in from social media. The amount of
sensor and machine-to-machine data that is being collected
continually increases. Excessive data volume was a problem in the
past, but as computing power increases we are increasingly able to
process it in meaningful ways.
The size of the data determines the following three things: value,
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potential of the data being considered, and whether it is big data or
not.
The term “velocity” as used in the context of big data means the
speed of generation of data or how fast the data is generated and
processed. Data is streaming in at a speed which is
unprecedented. Data must also be dealt with in a well-timed
manner. Most organizations face a challenge of reacting quickly
enough to deal with the data velocity.
Data comes in all types of formats. Structured numeric data comes
in traditional databases. Unstructured data formats including the
following: Documents, email, video, audio, stock ticker data and
financial transactions. Many organizations still cope with managing,
merging, and governing different formats of data.
Data is not consistent, sometimes searches or general interest in
certain topics, say social media or other types of data experienced
peaks and valleys. Data analysis techniques allow the data scientist
to mine this type of unstable data and still draw meaningful
conclusions from it.
If a large volume of data comes from multiple sources, then data
management can become a very complex process. The data has to
be linked, connected, and correlated so that the information is able
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to be comprehended — so that the analysis of the data doesn’t
become unmanageable.
This advancement in the processes of analyzing big data have only
come as a result of increasing possibility to store and manipulate it
that have arisen in the last few years. Sources of Big Data that are
collected can be separated into six broad categories: Archives,
Transactional Data, Enterprise Data, Social Media, Public Data,
and Activity Generated.
Archives:
These days, organizations just don’t want to throw away data. Even
when the data is no longer required for any foreseeable reason,
storage is just getting so affordable that many companies choose to
archive everything they can — for example, scanned documents,
scanned copies of agreements, records of
ex-employees/completed projects, banking transactions older than
the compliance regulations. They are even going as far as
collecting as much data as they can along the way. This type of
data is called Archive Data.
Transactional Data:
Most of the enterprises perform some sort of transactions, whether
they arise from web or mobile applications, CRM systems, or many
others. Supporting the transactions from these applications, there
are usually one or more relational databases as a backend
infrastructure. This type of data is mostly structured, and is referred
to as Transactional Data.
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Enterprise Data:
Enterprises store large volumes of data in various formats.
Common ones include word documents, spreadsheets,
presentations, .PDF files, XML documents, emails, flat files, and
legacy formats. Data like this which is spread across an
organization is called Enterprise Data.
Social Media:
We all know what this is. Countless interactions via networks like
Twitter, Facebook, and Pinterest accrue massive amounts of
minable data. These social networks usually involve mostly
unstructured data consisting of images, text, audio, videos, and
more. Data from these types of sources is called Social Media data.
Public Data:
This subsection of data includes data that is publicly available such
as that published by governments, data published by research
institutes, census data, Wikipedia, sample open source data feeds,
data from weather and meteorological departments, and other data
which is freely available to the public. This type of publicly
accessible data is called Public Data.
Activity Generated:
Machines create data which far surpasses the data volume created
by humans. Data from devices such as surveillance videos,
satellites, industrial machinery, cell phone towers, sensors, and
other data created by machines is called Activity Generated data.
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Hadoop:
One of the top benefits that organizations use Hadoop is that it
provides massive storage of data of any format, and enormous
processing power of data of any format. It also has the ability to
handle virtually, limitless simultaneous tasks or jobs. If data volume
and varieties keep increasing from the internet, then it is a key
consideration.
In 2008, Yahoo released Hadoop as an open-sourced project.
Today, Apache Software Foundation (ASF), which is a global
community of software developers and contributors, manages and
maintains Hadoop’s application. The distributed computing and
processing portion of Nutch became Hadoop.
Pivotal:
Pivotal has combined some open-source technologies into a
platform called Pivotal open source Suite. Pivotal Big Data Suite
allows companies to modernize their data infrastructure, discover
more insights using advanced analytics, and build analytic
applications at scale. Pivotal Suite is ready in the cloud, agile, and
open.
Netflix delivers amazing personalization to customers. Netflix can
easily aggregate data about customers, trends, viewing habits, and
more by using what movies they rate or watch. Netflix can attempt
to respond to questions that most organizations cannot or will not
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even ask. With respect to covers and colors these questions
include the following:
Which colors on covers for titles are most interesting to
customers in general?
1.
Are customers trending at a given time towards certain towards
specific types of covers? If yes, must personalized
recommendations change automatically?
2.
Should different colors on covers be used for different
customers? And many others…
3.
Please contact us today or call us now at 619.567.9322 to know
more about big data.
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Big data's impact on online marketing

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