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Learning Center Experience
By Ahmed Banafa, Faculty
of Business and Information Technology, March 2014
The simplest definition of big data is large and complex unstructured data (images posted on
Facebook, email, text messages, GPS signals from mobile phones, tweets, and
other social media updates, etc.) that cannot be processed by traditional
database tools. To give you an example of the volume of such data, Walmart
collects over 2.5 petabytes (1015 bytes) of data every hour from customers’
talking about big data analytics, a few
terms will be explained and defined to understand this concept. Starting from
the basics, statistics is using
numbers to quantify the data. Data mining
is using statistics and programming languages to find patterns hidden in the data.
Machine learning uses data mining to
build models to predict future outcomes. Artificial
intelligence uses models built by machine learning to make machines act in an
intelligent way like playing a game or driving a car (e.g., IBM’s Watson
supercomputer and the driverless car by Google). Big data analytics is the process of studying big data to uncover
hidden patterns and correlations to make better decisions using technologies like
NoSQL databases, Hadoop, and MapReduce. The
main goal of big data analytics is to help organizations make better business decisions.
The next question: what is the difference between business intelligence
(BI) and big data analytics? BI is a reactive ad hoc analysis approach looking
at the past, while big data analytics is a proactive approach to extract the
relevant info, and analyze it to make businesses focus on the future.
As far back as 2001, industry analyst Doug Laney (currently
with Gartner) articulated the now-mainstream definition of big data as the Three
Vs: volume, velocity, and variety.
Big data analytics appeal to businesses by offering savings on
three essential levels of any business, namely: time, money, people—reduction in time of processing data translated
to saving money and the use of fewer resources to present the data for better
decisions. For example, $37,000 for a traditional relational database, $5,000
for a database appliance, and only $2,000 for a Hadoop cluster (Paul Barth at
NewVantage Partners supplied these cost figures).
Analytics 3.0 is the new wave of big data analytics,
compared to Analytics 1.0, which is BI , and Analytics 2.0, which is used by
online companies only (Google, Yahoo, Facebook, etc.). Analytics 3.0 is a new resolve to apply powerful data-gathering and analysis methods not just
to a company’s operations but also to its offerings—to embed data smartness
into the products and services customers buy.
Some of the attributes defining Analytics 3.0:
Last month Google
announced acquisition of Nest (smart home devices), a source of massive data
from homes all over the United States, confirming the direction of Analytics
3.0 by an online company at the leading edge of Analytics 2.0.
The views expressed in this article are solely those of the author(s) and do not represent the views of Kaplan University.
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