BA Business Management: Data Handling and Business Intelligence Report

This report provides a comprehensive analysis of data handling and business intelligence. Part 1 focuses on identifying sales and profit trends over the years, with an evaluation of Excel for data preprocessing, demonstrating the use of Excel functions such as pivot tables, lookup, graphs, and charts. Part 2 delves into the application of Weka software for clustering audileadership data and explores various data mining methods applicable within a business context. The report also provides a comparative analysis of Weka and Excel, outlining their respective advantages and disadvantages. The report concludes with a discussion of the key findings and insights derived from the analysis, along with references to the sources used.

Data Handling and Business
Intelligence
1

Contents
INTRODUCTION...........................................................................................................................3
PART 1............................................................................................................................................3
Identify the sales and profit over years, evaluate the use of Excel for pre-processing the data or
information..................................................................................................................................3
Demonstrate that how can practical ways to perform operation by using Excel function such as
Pivot table, if, Lookup, graph and chart......................................................................................6
PART 2..........................................................................................................................................11
Audileadership data in the conjunction by using Weka software and perform clustering........11
Describe about the data mining methods that can be used within a business............................14
Advantage and disadvantage of Weka over Excel....................................................................15
CONCLUSION..............................................................................................................................17
REFERENCES..............................................................................................................................18
2

INTRODUCTION
Data Mining is consider as a process of identifying the different patterns, interrelation
between large volume data or information. this process is mainly used the large organization
where every day collecting large information within system. It will support for filtering data on
the basis of categorising. Marketing assistant will participate in the business expansion so that
they will use data mining software to gather relevant information or data. In order to cut the
cost / price while improving the customer relationship. Moreover, it will minimise the various
type of risk, threat in the organization. Data mining is an important factor for exploring and
analysing the large amount of data. It provide the facilities to discover the meaningful pattern,
facts, and figures. The documentation will describe about the sales information and also
calculating the profit, sales over years. The primary vision is to predict the future outcome or
result through data mining concept. In additional, data mining is a type of appropriate technique
which help for building the machine learning model in term of artificial intelligence.
PART 1
Identify the sales and profit over years, evaluate the use of Excel for pre-processing the data or
information.
Row Labels Average of Profit Sum of Sales
Furniture 68.11660673 5178590.542
2009 140.1369955 1469508.194
2010 20.65391403 1250043.046
2011 115.326226 1258336.514
2012 -5.173357143 1200702.788
Office Supplies 112.3690738 3752762.1
2009 153.4285381 1031244.56
2010 97.14263473 885095.79
2011 80.42802855 816902.13
2012 117.6447423 1019519.62
Technology 429.2075157 5984248.182
2009 337.0125974 1668572.052
2010 474.5130402 1416503.546
2011 518.2162105 1380213.417
2012 398.3725568 1518959.168
3

Grand Total 181.1844243 14915600.82
Table:1
Figure 1
Figure 2
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Figure 3
Calculate the sum of profit and sum of sales through Excel
Row Labels
Sum of
Sales Sum of Profit
Furniture 5178590.542 117433.03
Atlantic 708726.782 15345.65
North Carolina 43545.614 3478.88
Northwest Territories 31451.192 5057.79
Ontario 1361004.216 22280.36
Prarie 919191.126 30551.22
Quebec 605784.144 -760.77
West 1172989.394 30924.64
Yukon 335898.074 10555.26
Office Supplies 3752762.1 518021.43
Atlantic 478464.42 66970.47
North Carolina 38615.47 -3124.02
Northwest Territories 21955.03 1317.97
Ontario 1122325.15 188888.85
Prarie 720090.43 83259.7
Quebec 351822.68 42982.17
West 797510.76 116666.85
Yukon 221978.16 21059.44
Technology 5984248.182 886313.52
Atlantic 827057.0015 156644.54
North Carolina 34215.3995 2486.25
Northwest Territories 30411.524 1931.29
Ontario 1296912.697 228045.36
Prarie 1198023.046 207349.2
5

Quebec 552588.256 98205.25
West 1627049.122 149417.12
Yukon 417991.137 42234.51
Grand Total 14915600.82 1521767.98
Table: 2
Figure 4
Demonstrate that how can practical ways to perform operation by using Excel function such as
Pivot table, if, Lookup, graph and chart.
Pivot table: it is based on the statistics that mainly summarised large amount of data which
become more extensive table. It may include averages, sums and other type of statistical
information. Pivot table is consider as technique which mainly used for data processing. There
are large number of statistical data used to draw attention towards useful information (Aufaure
and et.al., 2016). A pivot table summarised the data by using tool and processing to reorganise,
count, group and average data stored within database. It allows for user transform column into
rows.
6

Lookup: this function is basically used to categorise under excel and reference functions. It
can be performed the rough match lookup either in a one column range and return the
corresponding values.
Calculate the Sum of shipping cost, sum or product base margin and sum of sales for Furniture.
Row Labels
Sum of Shipping
Cost
Sum of Product Base
Margin
Sum of
Sales
Furniture 53243.69 1006.77 5178590.542
Bookcases 8646.07 122.09 822652.04
Chairs & Chairmats 15512.69 228.46 1761836.55
Office Furnishings 8402.72 414.31 698093.81
Tables 20682.21 241.91 1896008.142
Figure 5
Estimate the actual Sum of shipping cost, sum or product base margin and sum of sales for office
supplies.
Office Supplies 36095.51 2116.77
Appliances 6854.11 240.64
Binders and Binder Accessories 6633.52 342.42
Envelopes 1682.77 91.97
Labels 288.66 108.61
Paper 7914.41 458.85
Pens & Art Supplies 2041.81 337.76
7

Rubber Bands 225.21 95.14
Scissors, Rulers and Trimmers 670.51 92.23
Storage & Organization 9784.51 349.15
Figure 6
Calculate the Sum of shipping cost, sum or product base margin and sum of sales for office
supplies.
Technology 18491.84 1148.77 5984248.182
Computer Peripherals 4067.34 449.67 795875.94
Copiers and Fax [phone] 1130361.3
Office Machines 7135.91 149.75 2168697.14
Telephones and Communication 4841.71 512.98 1889313.802
Figure 7
8

Calculate Sum of region By Profit
Row Labels
Sum of
Profit
Atlantic 238960.66
North Carolina 2841.11
Northwest
Territories 8307.05
Ontario 439214.57
Prarie 321160.12
Quebec 140426.65
West 297008.61
Yukon 73849.21
Grand Total 1521767.98
Figure 8
Date wise count customer segments
Row
Labels
Count of Customer
Segment
13/01/2009 4
13/01/2010 8
13/01/2011 8
13/01/2012 12
13/02/2009 6
13/02/2010 6
13/02/2011 5
9

13/02/2012 8
13/03/2009 6
13/03/2010 4
13/03/2011 3
13/03/2012 8
13/04/2009 5
13/04/2010 5
13/04/2011 9
13/04/2012 6
13/05/2009 8
13/05/2010 13
13/05/2011 5
13/05/2012 10
13/06/2009 5
13/06/2010 4
13/06/2011 4
10

Figure 9
PART 2
Audileadership data in the conjunction by using Weka software and perform clustering.
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