Data Handling & Business Intelligence 2 - Desklib

Data Handling &Business
Intelligence 2

TABLE OF CONTENTS
INTRODUCTION......................................................................................................................3
Part 1......................................................................................................................................3
TASK 2......................................................................................................................................7
Presenting screen shorts and explaining the interpretation...................................................7
Explaining data mining methods..........................................................................................14
Explaining the advantages & disadvantages of SPSS over excel.......................................15
CONCLUSION........................................................................................................................16
REFERENCES.........................................................................................................................17

INTRODUCTION
Data handling mainly accounts for the process which helps in ensuring that the
research data is being stored in a secure manner. It also involves complying with the policies
and procedures pertaining to managing the large data sets. On the other side, the business
intelligence accounts for the procedural and technical infrastructure which involves
collection, storage and then analysing the collected data through the application of analytical
tools and techniques. This report provides an insight into the usage of the large data and the
application of excel as a tool for the purpose of analysing the data along with practical
example of the same. In addition to this, it involves analysing the usage of SPSS which is a
data mining software in the data analysis.
Part 1
Evaluating the use of Excel for pre-processing, analysing and visualising the data
Excel is having a number of options or features which can be utilized by the
individuals and the firms for the purpose of effectively analysing the large data sets. Excel
helps in displaying data analysis report in a number of ways which helps in getting better
insights into the data collected and in deriving meaningful information from the same. In
excel, data can be sort out using the filter option helps in only viewing the information which
is necessary. In excel data visualization can be carried out using the charts which supports in
graphical representation of the data (Raubenheimer, 2017). This is depicted in the form of
bar, pie or line charts and many others. It offers the user with the number of chart types which
one can choose from and customise their charts as per requirement and better understanding.
In addition to this, the most widely used function of excel is Pivot charts which shows data
series and axes in the same way as the normal standards charts but it provides additional
filtering options and controls on the charts. In addition to this, it involves effectively making
use of the Pivot table as it can incorporate huge data along with various complex worksheet
data which also involves numbers and text. This helps in better analysing the data. Along
with this, Pivot chart can be created with the filter options which results into effectively
accomplishing the desired results and undertaking various business and strategic decisions.
Excel is having bulk of formulas and functions which results into making it easy for
the user to effectively analyse the data as per the requirement. In this, various types of charts
can be created with the help of wide range such as the clustered chart, stacked chart, gauge
chart, pie chart, Venn diagram, scatter chart, bullet chart, funnel chart etc. In excel in simple
terms, large data set can be easily visualized through effective classification and

categorization of it. Finding out the relationship between the two, understanding the
composition, distribution and overlapping of the data (Prodromou, 2017). It also assists in
determining any patterns, trends within the given data. In addition to this, it also supports in
detecting outliers along with other anomalies within the given data. Through this, the user can
also carry out the prediction about the future trends which results into providing menacing
full and engaging insights about the data which consequently leads to undertaking a better
decision about the future.
Practical Application
The below Pivot table shows the decline in profits and sales over the year from 2009 to 2012.
The table underneath provides the detail about the profits and sales with respect to region
across all the 4 years.
Row Labels
Sum of
Profit
Sum of
Sales
2009 434096.02
4209896.84
6
2010 364917.33
3560087.04
5
2011 380310.5
3429944.98
1
2012 342444.13
3715671.95
3
Grand
Total 1521767.98
14915600.8
2
Row Labels
Sum of
Profit
Sum of
Sales
Atlantic 238960.66
2014248.20
4
2009 81948.62 668393.28
2010 55233.32
508428.088
5
2011 47012.14 357271.574
2012 54766.58 480155.261
North Carolina 2841.11
116376.483
5
2009 -1282.19 20337.03
2010 3188.56 42098.744
2011 347.42 32028.2
2012 587.32 21912.5095
Northwest
Territories 8307.05 83817.746
2009 4510.35 22145.2305
2010 1616.28 16484.4905
2011 3625 26332.5095