Big Data Analytics: What it is and why it matters -

ITECH1103- BIG DATA AND ANALYTICS

Table of Contents
Executive Summary:.................................................................................................. 1
Background information............................................................................................. 1
Discussion of findings............................................................................................... 14
CONCLUSION............................................................................................................ 15
References:.............................................................................................................. 15

Executive Summary:
In this era of big data, visualization has emerged as a critical component of analytics. The size or
volume of Big data is an obvious issue present in front of many software tools, that tries to
process and analyze them. Visual analytics is a new domain which focuses on the both analytics
and visualization and their firm integration. With the technological advancement in storage and
computing capabilities merged with state of art analytics are ensuring quick and better way of
data analysis. From visual analytics organizations can transform raw data into meaningful
information. The dataset chosen for performing analytics shows the complete customer
information and purchasing details. This data can be used to derive important insights and
patterns that can help the organization in increasing their business in certain areas and domains
with respect to customer’s preferences. The analysis performed on the data shows important
visuals of the interpreted information such as countries where customers purchase in high
quantity, customers that has aided in most revenue generation, highest and lowest number of
orders placed by the customers and so on. To perform the analysis on the data we have utilized
SAS Visual Analytics tool. SAS Visual Analytics is a Web-based environment for analytics
visualization that enables the analyst to find relationships and patterns in data that could not be
initially recognized. SAS Visual Analytics can help provide the best, fastest visualizations
possible and thereby help in overcoming the challenges of Big data processing. The major
challenge with the use of SAS Viya is that visual interpretation and representation of data at
multiple levels of abstraction and at very large scale can affect its reliability and usefulness.
Background information
In recent years, the storage and computing capability of computers has seen an enormous hike.
Data is generated every second at a pace that cannot be imagined and along with this our
capacity to store and process this data has also increased tremendously. However, we are
currently unable to utilize this huge amount of data (Big Data) as our ability to analyze these data
is insignificant and has not increased proportionally (Rohrer, Paul & Nebesh, 2014). This data
being inconsistent, massive and of huge volume is very useful for many business and other
applications when extracted in meaningful results and information. Many software tools for data
analysis available today lacks the capabilities to interact with the huge amount of data within the
databases. This is lack of capabilities is often devoted to the characteristic of Big data which is
high volume, high velocity and complex variety (Chawla, Bamal & Khatana, 2018). The size or
volume of Big data is an obvious issue present in front of many software tools, that tries to
process and analyze them. Also increasing velocity of data makes it an urgent need to analyze
and process them dynamically. Similarly complexity or variety of Big data poses another issues
for analysis. As the process of data- gathering and producing is becoming more sophisticated and
problem domains becomes increasingly complex, more detailed, multidimensional and complex
dataset are being produced and collected. Visual analytics is a new domain which focuses on the
both analytics and visualization and their firm integration. Visual analytics are considered by
most of the analyst and researchers in attempt to improve analysis capabilities. To define, visual
analytics is the use of interactive visual interfaces to perform analytical reasoning (Caban &
Gotz, 2015). Analytical reasoning is the factor that makes visual analytics different from other
information visualization fields.

We have chosen the dataset that shows the complete customer information for example;
customer ID, name of the customer, country, postal code as well as purchasing information
corresponding to each customer such as unit cost, days to deliver, discount percent, profit,
quantity ordered and retail price and so on. For every organization customers are the most
important entity, and it is critical for the successful business that they should retain their
customer and focus their strategies in such a manner that both the customers and the organization
gets maximum benefits (Raj, 2015). This require to extract useful insights from customer’s data
and purchase history which can be done using visual analytics on the given dataset. To perform
the analysis on the data we would utilize SAS Visual Analytics tool. SAS Visual Analytics is a
Web-based environment for analytics visualization that enables the analyst to find relationships
and patterns in data that could not be initially recognized. We have used SAS Viya because of its
advanced analytical capabilities combined with self-service and interactive BI and reporting to
discover meaningful insights from any type and size of data. The characteristics of big data that
is three Vs can be dealt efficiently with SAS Viya. SAS viya have visualization techniques for
example network diagram and word clouds to visualize unstructured and semi structured data. It
enables the combination of big data and fast response time with the help of correlation matrix to
identify relationship of millions and billions of variables. Additionally, intelligent autocharts
provides incredible visuals for quick view of the data (SAS helps organizations unlock the
unrealized potential of their analytics investment, 2019).
Insights, justifications and visuals:
Below are the reports and visuals for the following questions:
There are total 68,300 customers in the database, as shown in the below snapshot.