Data Driven Decisions for Business

This article discusses the importance of data analysis in businesses and how it helps in making smarter decisions. It also provides insights into the sales data of Bangles and identifies the issues from data analysis. The article includes tables and charts to present the data in a summarized form.

DATA DRIVEN DECISIONS FOR
BUSINESS

TABLE OF CONTENTS
MAIN BODY...................................................................................................................................3
Summarizing the key changes and trend that increased significance of data analysis...............3
Producing a summary data which describe the data and business function of Bangles.............3
Presenting the issues from data...................................................................................................8
Statistics......................................................................................................................................8
Visual images through descriptive statistics.............................................................................14
A short commentary for each chart...........................................................................................18
REFERENCES..............................................................................................................................20

MAIN BODY
Summarizing the key changes and trend that increased significance of data analysis
In the current era, data analysis is highly used in large as well as small companies. The importance of data analysis in each
field increases due to problems faced by the companies and it is only facts and figures that helps to identify any issues faced by
business. Also, each businesses seeking to increased their productivity and improve financial outcomes which can be attained by
implementation of data analysis (Todd, 2017). Apart from this, to enhance sales, business seeking towards better customer relationship
where data analysis also plays an important role. It is so because with the help of data analysis, company evaluate their ad campaigns,
personalized content and then develop products. This in turn assist to improve the performance of a company and that is why, to
comply with changing era, large businesses use data analysis which increases the importance of the same.
In addition to this, it becomes very difficult for business to analyse the frequent changes of market and to predict the future,
data analysis is more useful. Thus, marketing data analysis assist to provide an idea pertaining to future which in turn helps in
improving the sales. That is why, it has been analysed that with the help of machine learning’s companies are able to recognize the
data pattern and forecast accordingly. This in turn leads to smarter business moves and improve the operations by enhancing profit
margin as well.
Producing a summary data which describe the data and business function of Bangles
Product wise total of every year

Month wise total of all the product

Quarter wise sales volume and sales value
Quarter 1

Quarter 2

Quarter 3
Quarter 4

With the help of the above data and table it is clear that the sales value and sales volume of all the product category is different.
In addition to this, it is also clear that quarter wise as well the data is different for all the categories of products. With the analysis of
the data it is clear that the with respect to the sales volume 2018 was the year which had maximum sales out of all the three years. The
reason behind this might be because of the reason that in 2018 the product and services of company would have been better
(Amekedzi, 2021). Along with this it can also be implied that different product range provides a good range of idea that all the product
provided by Bangles are good and sales is increasing. Also, with the help of the data it is clear that bracelet is the maximum selling
product for the company. further with the evaluation it was analysed that ankle bracelet and hairband is the least selling product for the
company. hence, with this it can be evaluated that company need to work on managing and maintaining these products so that
effective decisions can be taken.
Presenting the issues from data
The major issue being identified within the data is relating to the data fact that data is too much. This caused an issue because
of the reason that when the data it too much then it becomes problematic in managing the data. Hence, this created a problem and
because of this it took a lot of time in condensing and summarising the data (Çanakoğlu, Erzurumlu and Erzurumlu, 2018). The
problem was resolved with help of the use of excel and in that pivot table was used. The pivot table is used in order to manage the data
in much easier and effective manner. This assisted in effective evaluation of the data.
Statistics
A
Row Labels
Sum of Sales
Volume
Sum of Sales
Value
1 970 1091621.96
2 1135 1329471.54
3 862 942370.8
4 661 754331.24

5 985 1139144.28
6 1281 1504904.76
7 1060 1188059.98
8 905 1017553.01
9 1070 1180023.51
10 700 746992.69
11 814 889228.16
12 948 1074000.46
Grand Total 11391 12857702.39
B
Average of
Price
Column
Labels
Row Labels Accessory
Ankle
bracelet Bracelet
Hair
band Hairband Necklace Ring
Grand
Total
1 2082.20375 979.56707 1191.3658 1335.1834 940.98607 1354.3826
2 2220.933103 1000.0328 1236.8553 1443.3518 933.01046 1305.8298
3 1425.221667 970.33261 1111.1591 1300.7014 1084.3997 1160.7301
4 1311.664667 250 993.56588 1158.7966 1596.0389 1063.6198 1153.9595
5 1842.786154 1015.8233 1171.7678 1386.1185 1099.9919 1220.2993
6 1922.013333 1000.5993 1234.3802 1404.6406 1058.2568 1281.2612
7 1794.200263 948.5078 1169.8093 1376.1719 1042.9965 1266.3372
8 1871.011773 974.88257 1104.6473 961.964 1561.7415 1059.7044 1264.4477
9 1791.341798 1006.3387 1124.6164 937.82679 1241.9037 1043.0527 1228.998
10 1989.3615 986.37918 1078.2005 994.37167 1205.2017 945.94488 1181.5766
11 1818.542222 943.90413 1148.5011 997.65121 1353.2481 1014.938 1245.7701
12 2187.509359 946.42883 1181.5234 1065.2148 1774.6108 1038.2085 1417.9023
Grand Total 1876.786006 250 980.53018 1164.4316 991.4057 1414.9094 1027.0925 1258.2894

C
Column
Labels
Row Labels 2018 2019 2020
Grand
Total
Sum of Sales Volume
Accessory 12 52 242 306
Ankle bracelet 10 10
Bracelet 1270 1735 2364 5369
Hair band 1189 999 285 2473
Hairband 228 228
Necklace 942 548 583 2073
Ring 431 369 132 932
Sum of Sales Value
Accessory 16192.1 101566.33 453543.5 571301.93
Ankle bracelet 2500 2500
Bracelet 1232579.2 1634832.4 2447356.5 5314768.1
Hair band 1457780.02 1135085.9 313036.3 2905902.2
Hairband 223355 223355
Necklace 1263897.93 747051.72 866578.76 2877528.4
Ring 446457.47 374254.35 141634.87 962346.69
Total Sum of Sales
Volume 3854 3703 3834 11391
Total Sum of Sales Value 4419406.72 3992790.7 4445505 12857702
Quarter
Quarter 1
Column Labels
Row Labels Accessory Bracelet Hair Necklace Ring Grand

band Total
Sum of Sales Value
1 6122.33 410211.75 284250 301015.15 90022.73 1091621.96
2 62665.24 502853.6 324860.29 333205.27 105887.14 1329471.54
3 32092.94 414381.46 216910.07 221421.96 57564.37 942370.8
Sum of Sales Volume
1 3 416 238 225 88 970
2 30 504 261 232 108 1135
3 20 422 194 168 58 862
Total Sum of Sales Value 100880.51 1327446.81 826020.36 855642.38 253474.24 3363464.3
Total Sum of Sales
Volume 53 1342 693 625 254 2967
Quarter 2
Column Labels
Row Labels Accessory
Ankle
bracelet Bracelet
Hair
band Necklace Ring
Grand
Total
Sum of Sales Value
4 26594.68 2500 335726.7 167105.49 163155.93 59248.44 754331.24
5 47912.44 420252.34 328052.59 251077.64 91849.27 1139144.28
6 85289.47 599339.07 348994.08 375455.05 95827.09 1504904.76
Sum of Sales Volume
4 16 10 328 143 107 57 661
5 26 417 277 180 85 985
6 44 603 275 264 95 1281
Total Sum of Sales Value 159796.59 2500 1355318.11 844152.16 789688.62 246924.8 3398380.28
Total Sum of Sales
Volume 86 10 1348 695 551 237 2927

Quarter 3
Column Labels
Row Labels Accessory Bracelet
Hair
band Necklace Ring
Grand
Total
Sum of Sales Value
7 13003.92 248055.05 173690.78 215356.31 77362.77 727468.83
8 19917.79 228955.78 221599.89 165475.52 57730 693678.98
9 39586.18 263669.26 167406.78 150417.13 84574.26 705653.61
Sum of Sales Volume
7 8 272 140 156 74 650
8 10 249 200 107 54 620
9 21 277 147 126 79 650
Total Sum of Sales Value 72507.89 740680.09 562697.45 531248.96 219667.03 2126801.42
Total Sum of Sales
Volume 39 798 487 389 207 1920
Quarter 4
Column Labels
Row Labels Accessory Bracelet
Hair
band Necklace Ring
Grand
Total
Sum of Sales Value
10 11302.04 186040.7 183371.07 70500.12 32969.26 484183.19
11 146.34 200103.24 184379 149990.96 49862.45 584481.99
12 7480.74 295754.04 273347.45 183799.94 77539.67 837921.84
Sum of Sales Volume
10 5 197 170 58 34 464
11 0 222 161 114 48 545
12 3 314 236 132 75 760
Total Sum of Sales Value 18929.12 681897.98 641097.52 404291.02 160371.38 1906587.02

Total Sum of Sales
Volume 8 733 567 304 157 1769
D
Column
Labels
Row
Labels Accessory
Ankle
bracelet Bracelet Hair band Hairband Necklace Ring
Grand
Total
2018
Sum
of Sales
Value 16192.1 2500 1232579.2 1457780.02 1263897.93 446457.47 4419406.72
Sum
of Sales
Volume 12 10 1270 1189 942 431 3854
Avera
ge of
Price 1730.705417 250 963.0671101 1233.171364 1331.714601 1026.285926 1206.079146
2019
Sum
of Sales
Value 101566.33 1634832.39 1135085.91 747051.72 374254.35 3992790.7
Sum
of Sales
Volume 52 1735 999 548 369 3703
Avera
ge of
Price 2019.188917 943.7096372 1133.911208 1396.385729 990.0338564 1258.617288
2020
Sum 453543.5 2447356.54 313036.3 223355 866578.76 141634.87 4445504.97

of Sales
Value
Sum
of Sales
Volume 242 2364 285 228 583 132 3834
Avera
ge of
Price 1867.370882 1034.813806 1098.912707 991.4056977 1516.627759 1064.957669 1307.577647
Total
Sum of
Sales
Value 571301.93 2500 5314768.13 2905902.23 223355 2877528.41 962346.69 12857702.39
Total
Sum of
Sales
Volume 306 10 5369 2473 228 2073 932 11391
Total
Average
of Price 1876.786006 250 980.5301844 1164.431607 991.4056977 1414.909363 1027.092484 1258.2894
Visual images through descriptive statistics
Chart 1

Chart 2

Chart 3

Chart 4

A short commentary for each chart
With the help of the above tables and charts it is clearly visible that company need to work on effective management of the
business. the reason pertaining to this fact is that the graphs outlines many different types of the figures and facts that are of relevance
to the company. the first chart is outlining the total sum of value and volume of all the product combined on basis of every month
(Berndtsson and et.al., 2020). The reason pertaining to this fact is that this will assist the company in identifying that which product is
selling the most and which is not. Hence, as a result of this, company will be in position to manage and maintain the business
strategies in the same manner that sales of good products are increased more and product which are not having good position ned to be
improved. Further with help of another data, it is clearly visible that average price is being highlighted in this graph. Hence, with the
chart it is clearly visible that accessory is the product which is having the highest range of price.
Thus, it is advisable to the company that they must try to increase the sales of accessory so that good amount of profit can be
attained by the company. in addition to this, with the help of the sum of values and volume it is clear that on an average bracelet is the

maximum selling product and company must make strategies to increase the sales of bracelet within the market. Further in the last
chart it was evident that sum of sales volume year wise was presented (Hunke and et.al., 2017). This simply reflects the fact that in the
year 2020 the sales were maximum in the 6 month. However, in the year 2019, the maximum sales volume was in the month of 6
only.

REFERENCES
Books and Journals
Amekedzi, K., 2021. Using Business Intelligence Tools to Make Data-Driven Decisions.
Berndtsson, M., and et.al., 2020. 13 Organizations' Attempts to Become Data-Driven. International Journal of Business Intelligence
Research (IJBIR), 11(1), pp.1-21.
Çanakoğlu, E., Erzurumlu, S.S. and Erzurumlu, Y.O., 2018. How data-driven entrepreneur analyzes imperfect information for
business opportunity evaluation. IEEE Transactions on Engineering Management, 65(4), pp.604-617.
Hunke, F., and et.al., 2017, July. Towards a process model for data-driven business model innovation. In 2017 IEEE 19th Conference
on Business Informatics (CBI) (Vol. 1, pp. 150-157). IEEE.
Todd, J., 2017. Data-Driven Decisions: Business Intelligence (BI) Training Skills.