Data Handling and Business Intelligence

DATA HANDLING AND
BUSINESS INTELLIGENCE

Contents
INTRODUCTION...........................................................................................................................1
Determining the decline in sales/profits over the years, and evaluating the use of Excel for pre-
processing the data, analysing the data and visualising the data.................................................1
PART 2............................................................................................................................................7
2.1 Weka workings......................................................................................................................7
2.2 Explaining the most common data mining methods that can be used in business..............10
2.3 Discussing the advantages/disadvantages of Weka over Excel...........................................12
CONCLUSION..............................................................................................................................13
REFERENCES..............................................................................................................................14

INTRODUCTION
Data handling is the procedure of storing and securing the data which is collected through
research (Beyer, 2019). This process is based upon the concept of business intelligence for which
data acts as an asset. The term business intelligence refers to the technologies which helps a
business to effectively operate and attain competitive advantage. The main aim of this report is to
build an understanding regarding the data warehousing and the tools by which data can be
handled and mined.
This report is divided into two parts. In the first part, the software application of Microsoft
Excel is used to pre process, analyse and visualise the data using Superstore data along with
current trends in data warehousing, business intelligence and data mining are also analysed. In
the second part of this report, the software application of Weka is used to present the clustering
using “audidealership” data. In this part, most common data mining methods are also analysed
which a business organisation can use in their operations. Along with which, benefits and
limitations of Weka over Excel are also discussed.
PART 1
Determining the decline in sales/profits over the years, and evaluating the use of Excel for pre-
processing the data, analysing the data and visualising the data
Microsoft excel is a software programmed which allows users to develop spread sheets and
then analyse it with various tools such as data analysis and graphical tools (Cao, Ewing and
Thompson, 2012). This software application is used for the data of Superstore. This data set has
21 variables including Row ID, Order Date, Order Priority, Order Quantity, Sales, Discount
, Ship Mode and many more. This data has information of total 8399 orders. By using
Excel formulas and tools, this data of Superstore is pre processed, analysed and visualised.
Pre processing the data:
The procedure of pre processing the data is quite complex and has various techniques to do
it. A standard procedure of pre processing of data involves five stages which are data cleaning,
data integration, data transformation, data reduction and lastly data discretisation (Jolliffe and
Stephenson, 2012). For this process, the tool of Microsoft Excel which is used is Pivot table.
Pivot table is a tool of summarising, classifying and processing the data so that it can be
further used for evaluation and analysis. Using this tool, the data set of Superstore is pre
1