Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life

Chapter 15: Expectations of business
intelligence
 
Big data is expected to evolve and expand rapidly. Observing the
opportunity big data offers for businesses through social media, we
understand why this incentive could become the main feature round which
companies focus their future business. Customers continuously create new
technologies by the very nature of their modes of communications and new
applications will evolve to take advantage of this. Some businesses wait for
the development of improved systems while others invest billions on big
data investigation, spending significant amounts in search of the best apps
to corner the market. Self-service platforms will be popular tools for
harnessing big data assets.
 

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Advances in technologies
 
Technological processes of business intelligence are being significantly
simplified to making it more available to the public. The development of
software for use in business intelligence projects such as programming
languages or initiatives that combine the force of algorithms, are making
these systems easier to operate. The process of big data analysis has been
streamlined to a stage that can now be managed on one personal computer
whereas, in the past it was handled by a group of supercomputers.
 
Advances in technology now enables anyone with basic computer skills to
utilize big data at home. Programs can evaluate the databases of millions of
users. Billions of social media posts can be analyzed on a single personal
computer within minutes. It once too one thousand powerful computers
over six hours to perform the same undertaking. This clearly shows that
advancements have revolutionized how data is analyzed. Business
intelligence projects can pay huge dividends, making it a perfect field for
innovators. 
 

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Hyper targeting
 
One question often asked regarding big data is about what its real its
significance is. The answer is quite obvious and simple. With hundreds of
millions of accounts on social networks such as Facebook, Twitter, and
LinkedIn, it is easy to see why companies are investing millions of dollars
in engagement with these users. Our whole world nowadays is linked
through social media. With so many users and available data online,
customer and product engagement can be turned into billions of dollars in
brand worth and sales. Hyper targeting allows marketers to access user
information and advertise to those individuals who show interest in their
product or service.
 
Hyper targeting delivers advertising content to specific segments in a
network. This super effective marketing tactic aims at targeting
advertisements on social network sites. The advantage of hyper targeting is
getting potential consumer’s personal details along with their lifestyle
preferences. This valuable, abundant source of customer information leads
to precision performance marketing. By collecting data and directly
contacting with prospective customers, operators can hyper target certain
sections of people and reach out to consumers in ways that were never
previously imagined. Benefits can go beyond delivering advertisements to
the right audience and saving on budget. Hyper targeting often helps with
audience research and can lead to partnerships, sponsorships, and other co-
branding opportunities.
   

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The possibility of big data getting out of hand
 
Some experts caution that just like customer relationship management
turned into a very costly and unsatisfactory exercise, the same may happen
with big data in the future if it is not handled correctly. They believe the
time of predicting human behavior is over. This may not be totally accurate
because big social data is now important, valuable information for
businesses to gather. Because this information takes account of all
categories of social activities online, such personal information, regions and
areas of interest, what we tweet, when we publish posts, and how many
messages we send, businesses are able to get a better picture of market
requirements.
 
Companies use such data to determine likes and dislikes and what we are
buying and want to buy, thus potentially increasing their revenues. It is up
to users to control their systems and the possibility of big data getting out of
hand is up to their efficiency or lack thereof. Whether or not big data will
translate into gold field for business or prove hazardous for organizations to
base their prospects is yet to be observed. At present however, the outlook
is positive.
 

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Making forecasts without enough information
 
According to some experts, we are, in fact, past the golden time in
predicting human behavior. Some believe this was easiest to do about five
decades ago, when consumer information was not easily accessible, but few
simple factors, such as frequency and monetary value, were able to turn
direct marketing into a huge success. Knowing how recently the last
purchase was made by a consumer was apparently pretty much the only
thing you needed to make them repeat this process by directly marketing
more products to them. 
 

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Sources of information for data management 
 
At one time only traditional business information could be tracked. With the
advancement of technology, we now have regular assess to a growing
amount of information networks. Channels from which we can collect
statistics on consumers that includes social media, search engine, sensor,
and semantic data. Information about what people are looking for online,
what they are buying and their buying habits, is readily available through
data sources  While some of these sources are company generated, others
are externally produced. This system provides a complete understanding
into the nature of data through which accurate prediction of customer and
product behavior. Attaining information from available network sources and
examining it combined with company data, delivers a complete strategy for
data management. There are five main sources of data that we can access
for valuable information.
 
Sensor data is one of our five main social sources of information. It is a
class of data that includes information collected from meters that register
utility usage. It also gathers data from supermarket cash registers, listing
products purchased and prices paid. This type of information helps to
improve practices in numerous sections of day to day transactional
outcomes.
 
Social media data is another of our five main social sources. Here data from
social media posts is collected by social listening tools. When we use
Facebook or Twitter, there is an enormous quantity of information about
ourselves we are putting out there. All of this information is being gathered
by various bodies, and is used by companies to promote business.  
 
Search engine data is also one of our five sources of data.  This information
source is used by businesses as exterior data channels, which enables them
to develop a variety of conclusions. Google permits us to make in depth

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investigations into what we are looking for on the internet, and information
recorded is utilized for search engine data aimed at specific sections of
people.
 
Enterprise application data is yet another of the five data sources and is
used to note buying habits of consumers along with additional social
behaviors. This type of information is internal. It is the property of an
organization and gathered by their own systems. This material may
comprise of the complete analytics of the business’s website, and any other
information that supports the structure of the organization’s BI system. 
 
Mobile data completes the group of five sources of information. This works
in the same way as     social media. You dish out tons of information when
you use your mobile apps is in the case when social media is used. This
data details your location, sex, and age. It is gathered and sold to businesses
to provide them with valuable consumer feedback.
 
Combining these five sources of big data analysis provides companies with
complete picture of the market they cater to.  Analytics merchants try to use
all these sources to create a data filled package that a company must
purchase to include in their information base. Organizations are going
through extreme measures to acquire and apply the information collected
from the five data sources in order to be ahead of their competitors and
boost revenues.
 

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