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Organizational Behavior (Sukkur Institute of Business Administration)
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DATA DRIVEN DECISIONS FOR
BUSINESS
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TABLE OF CONTENTS
Introduction and project plan..........................................................................................................................................1
Data quality issues and remedies....................................................................................................................................1
Data analysis and commentary.......................................................................................................................................3
Issue 2 Solution:.........................................................................................................................................................6
Issue 1 Solution:.........................................................................................................................................................8
Data charting and commentary.......................................................................................................................................9
Issue 3 Solution:.......................................................................................................................................................10
Conclusions and recommendations...............................................................................................................................11
References.....................................................................................................................................................................12
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INTRODUCTION AND PROJECT PLAN
This section will present a comprehensive outline of the research project and rationalize the
proposed approach for its dissemination to the Corporate Strategy Manager at Café on the Sea
(COTS). The present study aims to elucidate the potential of data analytics in enhancing the
business performance of COTS's coffee shops by referring to a pertinent data analytics
framework.
The aim of this report is to conduct an analysis of the present business performance of COTS's
coffee shops and offer insights and recommendations for strategic decision-making. The
utilization of data analytics can provide significant insights to facilitate evidence-based decisionmaking and enhance performance.
Data quality issues were observed in the dataset, including the presence of missing values and
inconsistent entries. We have proposed the utilization of data cleansing techniques through the
utilization of Excel's tools to tackle these issues. The study involved conducting targeted
analyses aimed at identifying underperforming products and selecting the optimal coffee shop for
expansion. The impact of a home delivery service on sales performance was also evaluated.
The project will be executed in adherence to a data analytics framework, thereby guaranteeing a
thorough and dependable analysis. The utilization of data analytics possesses the capability to
enhance business performance through the revelation of valuable insights, recognition of
prospects, and augmentation of customer experiences.
Our report will provide comprehensive insights and practical suggestions to the Corporate
Strategy Manager, enabling them to make well-informed choices and enhance the expansion and
profitability of COTS's coffee shops.
DATA QUALITY ISSUES AND REMEDIES
When gathering, integrating, and cleansing data, analysts frequently face a few common
problems. Because of these obstacles, the data may not be as high-quality, reliable, or helpful as
it could be for analysis. Common problems are listed below:
1. Data Availability and Accessibility: Data analysts may face difficulties in obtaining
access to relevant data sources. Some data may be proprietary or confidential, making it
challenging to acquire. Additionally, data may be scattered across multiple systems or
departments, requiring coordination and cooperation to gather the necessary data (Singh
& Dwivedi, 2020)
2. Data Quality and Accuracy: Data can suffer from quality issues such as missing values,
inconsistencies, duplication, and outliers. Incomplete or inaccurate data can lead to biased
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3.
4.
5.
6.
7.
8.
or incorrect analysis. Data analysts must identify and address these issues through data
cleaning techniques to ensure the reliability of their analysis (Singh & Dwivedi, 2020).
Data Integration and Data Silos: Integrating data from different sources can be complex,
especially when dealing with incompatible formats, data structures, or naming
conventions. Data silos, where data is stored in isolated systems or departments, can
hinder data integration efforts. Data analysts need to consolidate and transform data into a
unified format to enable meaningful analysis (Jagadish et al., 2014).
Data Relevance and Context: Data analysts must ensure that the collected data is relevant
to the analysis objectives. They need to understand the context of the data and its
limitations. Without considering the relevance and context, the analysis may produce
misleading or incomplete insights (Jagadish et al., 2014)..
Data Privacy and Security: Data analysts must adhere to data privacy regulations and
ensure the protection of sensitive information. Anonymization or pseudonymization
techniques may be required to safeguard personal or confidential data. Data security
measures must also be in place to prevent unauthorized access or data breaches.
Data Bias and Preprocessing Biases: Bias can be inherent in the data collection process or
introduced during data preprocessing. For example, sampling bias may occur if the data
collection process is skewed towards specific demographics or locations. Preprocessing
steps such as data filtering, transformation, or feature selection can also introduce bias if
not carefully managed.
Data Volume and Scalability: Handling large volumes of data can pose challenges in
terms of storage, processing power, and computational resources. Data analysts may need
to employ techniques such as data sampling, aggregation, or parallel processing to
manage and analyze large datasets efficiently.
Data Governance and Documentation: Establishing proper data governance practices is
crucial to maintain data quality, traceability, and transparency. Data analysts should
document the data collection, integration, and cleaning processes to ensure
reproducibility and facilitate collaboration with other team members or stakeholders
(Singh & Dwivedi, 2020.
Data analysts apply a wide variety of approaches and tools, including data cleansing algorithms,
data integration frameworks, statistical methods, and data governance frameworks, to address
these difficulties. To further evaluate data accuracy, address specific data concerns, and
guarantee the data's relevance and trustworthiness for analysis, they work closely with subject
experts and data stakeholders.
The quality and usefulness of the data, as well as the reliability and significance of the insights it
yields, can be enhanced by data analysts' being cognizant of these overarching problems and
employing best practices in data collection, integration, and cleansing.
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Based on the provided dataset, here are the data problems that are identified:
1. Missing values: In the dataset, some entries have missing values. For example, in the row
"Southampton 2020 2 Hot drinks - £0," the sales volume is missing. This can cause issues
in data analysis and calculations.
2. Typos and inconsistent values: There are variations in the naming conventions used for
product groups, such as "Cold drinks" and "Colddrinks," "Pastry" and "Pazztry," and
"Sandwiches" and "Sandwich." This inconsistency can lead to confusion and inaccuracies
in data analysis. To address this, a data cleansing process can be implemented to
standardize the naming conventions and ensure consistency throughout the dataset.
3. Incorrect column headers: The column header "Product category" is misspelled as
"Product category" This inconsistency can lead to confusion and affect data
interpretation.
4. There is an inconsistency in the year entry, where "2032" is used instead of "2023,"
Here are the identified data problems and possible solutions using Excel's data validation and
other tools:
1. Inconsistent product category names: In the dataset, the product category names have
variations like "Coffee," "Hot drinks," "Cold drinks," etc. To address this, you can create
a list of valid product categories in a separate sheet or column and use Excel's data
validation feature to create a drop-down list for the product category column. This
ensures consistent and accurate category entries.
2. Missing sales values: In some instances, the sales value is missing or represented as "-".
To address this, you can use Excel's data validation to ensure that the sales value column
only accepts numeric values. You can set up a rule to flag or highlight any invalid or
missing values, allowing you to manually input or correct them.
3. Misspelled product category: There is a misspelled category name "Pazztry" in one of the
entries. To address this, you can use Excel's Find and Replace feature to search for and
correct the misspelled category name. Alternatively, you can create a lookup table with
correct category names and use Excel's VLOOKUP or INDEX-MATCH functions to
replace the incorrect entries.
4. By following these steps, we can quickly correct the inconsistency in the year entry
throughout the dataset. Make sure to double-check the entries after replacing to ensure all
the incorrect years have been successfully updated to "2023."
 Select the column containing the year entries that need to be corrected.
 Go to the "Home" tab in Excel.
 Click on the "Replace" button in the "Editing" group, or press Ctrl + H on your keyboard.
The Find and Replace dialog box will appear.
 In the "Find what" field, enter "2032" (the incorrect year).
 In the "Replace with" field, enter "2023" (the correct year).
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
Click on the "Replace All" button to replace all instances of "2032" with "2023" in the
selected column (Devi & Kalia, 2015).
DATA ANALYSIS AND COMMENTARY
Table A Sales volume and value by month, by year and across the 3 years period
Year and Months
Sum of Sales Value
Sum of Sales Volume
58742.25
18629.5
1
4022.25
1229.5
2
3263.5
1330.5
3
4877.75
1530
4
5456.5
1735.5
5
4869.75
1513
6
5103.75
1591.5
7
5772.5
1755
8
5846.25
1819.5
9
4838
1508
10
4915.25
1522
11
4651.25
1480
12
5125.5
1615
62502.25
19677.5
1
4219.25
1307
2
4486.25
1395.5
3
5015.25
1541
4
6127
1914
5
4355.25
1459
6
5487.75
1723.5
7
5717
1766
8
6246.5
1935
9
5180.25
1588
10
5373
1659.5
11
5333
1671.5
12
4961.75
1717.5
68967.11
21609.192
4189.75
1300
2020
2021
2022
1
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2
4717.25
1456
3
5293.75
1629.5
4
6398.75
2013.5
5
5313.25
1675
6
5663.194
1804.456
7
6780.642
2119.458
8
7746.294
2408.606
9
5940.85
1881.81
10
5936.346
1882.732
11
6229.862
1936.802
12
4757.172
1501.328
2023
1180.5
360
12
1180.5
360
191392.11
60276.192
Grand Total
Table B Benchmark comparisons of product group’s performance covering sales volume
and value by quarter, by year and across the 3 years period
Sum of Sales
Volume
Row Labels
Column
Labels
1-3
4-6
7-9
10-12
Grand Total
1181.5
1336.992
1477.098
1360.4
5355.99
2020
376
446
475
421.5
1718.5
2021
400.5
390
510
429.5
1730
2022
405
500.992
492.098
474.4
1872.49
35
35
Cakes
2023
Coffee
3923.5
4687.2
5071.976
4580.016
18262.692
2020
1288.5
1525.5
1567.5
1338
5719.5
2021
1272.5
1605.5
1591
1576.5
6045.5
2022
1362.5
1556.2
1913.476
1545.516
6377.692
120
120
2032
Cold drinks
2384
2946.532
3215.848
3001.212
11547.592
2020
721.5
893
967.5
903
3485
2021
819
915
1079.5
983.5
3797
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2022
843.5
1138.532
1168.848
2023
Colddrinks
171
105
2020
91.5
105
2021
79.5
4202.592
63
63
187
463
196.5
79.5
2022
Hot drinks
1051.712
187
187
1833.5
2328.512
2441.492
2268.408
8871.912
2020
562
722
767
687
2738
2021
677.5
764.5
787
712
2941
2022
594
842.012
887.492
820.408
3143.912
49
49
2023
Kakes
45
2021
45
2022
49.5
94.5
45
49.5
49.5
Pastry
2402
2992.56
3318.368
3173.934
11886.862
2020
812.5
847.5
993.5
1001
3654.5
2021
676
1080
993
1054.5
3803.5
2022
913.5
1065.06
1331.868
1118.434
4428.862
Pazztry
59
89
72
220
2021
59
2022
59
89
89
2023
Sandwich
39
2021
19
2022
20
Sandwiches
72
72
34.5
73.5
19
34.5
54.5
764.5
859.66
1020.092
855.892
3500.144
2020
238
301
312
266.5
1117.5
2021
259.5
277.5
328.5
292.5
1158
2022
267
281.16
379.592
275.892
1203.644
21
21
2023
Grand Total
12719
15429.45
6
16781.374 15346.362
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ISSUE 2 SOLUTION:
Products with the worst sales performance which are the candidates to be removed from
the shops’ menu.
Producct Category
Sum of Sales Value
Sum of Sales Volume
Cakes
26779.95
5355.99
Coffee
71970.768
18262.692
Cold drinks
27953.98
11547.592
Colddrinks
1157.5
463
Hot drinks
17743.824
8871.912
Kakes
472.5
94.5
Pastry
23431.724
11886.862
Pazztry
440
220
Sandwich
441
73.5
Sandwiches
21000.864
3500.144
Grand Total
191392.11
60276.192
products with the worst sales performance which are the candidates to be removed from the shops’ menu
80000
70000
60000
50000
40000
Sum of Sales Value
Sum of Sales Volume
30000
20000
10000
0
The sales data provided for each category can be analysed to see which products performed the
worst. The data allows us to examine the "Sum of Sales Volume" column and identify the lowvolume products that could be dropped from the menu.
The numbers show that "Colddrinks," "Kakes," "Pazztry," and "Sandwich" sell at much lower
rates than the rest of the store's offerings. The poor sales performance of these items makes them
possible candidates for removal from the menu.
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We can safely disregard the "Colddrinks" entry because it looks to be a duplicate category.
If the Procurement Manager gets rid of these underperforming items, they can streamline the
menu, cut down on overall ingredient costs, and maybe boost profits.
Sum of
Sales
Volume
Sum of
Sales
Value
Row Labels
2020
2021
2022
Blackpool
8315
8753
1-3
1844
4-6
Total
Sum
Sales
Value
of
2020
2021
2022
10443.692
26564
27831
33385.36
27511.692
87780.36
2014
1890
5883
6428
6191
5748
18502
2077
2189
2485.456
6587
6781
7912.694
6751.456
21280.694
7-9
2324
2320
3194.874
7486
7462
10165.036
7838.874
25113.036
10-12
2070
2230
2873.362
6608
7160
9116.63
7173.362
22884.63
6076.5
6490.5
6859.5
18483.7
5
20298.75
21876.75
19426.5
60659.25
1-3
1371
1231.5
1453.5
3409.5
4101.75
4655.25
4056
12166.5
4-6
1659
1735.5
1762.5
5290.5
5439
5581.5
5157
16311
7-9
1597.5
1818
1944
5204.25
5865.75
6204.75
5359.5
17274.75
1449
1705.5
1699.5
4579.5
4892.25
5435.25
4854
14907
216
536
684
1609.5
773
2419.5
46
92
463
1463.5
Portsmouth
10-12
Southam
1-3
202
3
Total
Sum
of
Sales
Volume
21
46
4-6
1463.5
170
73
21
4238
4218
3770
339
1-3
875
952
4-6
1104
7-9
10-12
Southampton
Grand Total
126
92
463
10-12
2023
592
146
126
264
864
13694.5
13688.5
12095.5
1054.5
12565
40533
1042
2871
3099
3354.5
2869
9324.5
1172
782
3552.5
3750
2417.5
3058
9720
1161
1151
1271
3766.5
3816
4098
3583
11680.5
1098
943
675
339
3504.5
3023.5
2225.5
1054.5
3055
9808
18629.5
19677.5
21609.192
360
58742.2
5
62502.25
68967.11
1180.5
60276.192
191392.11
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Table C Benchmark comparisons of sales volume and value between coffee shops by
quarter, by year and across the 3 years period
ISSUE 1 SOLUTION:
Three coffee shops to identify the best shop to invest in expanding its floor area.
Coffee Shop
Sum of Sales Value
Sum of Sales Volume
Blackpool
87780.36
27511.692
Portsmouth
60659.25
19426.5
Southampton
40533
12565
Grand Total
188972.61
59503.192
Three coffee shops to identify the best shop to invest in expanding its floor area.
Southampton
Sum of Sales Value
Sum of Sales Volume
Portsmouth
Blackpool
0
10000 20000 30000 40000 50000 60000 70000 80000 90000100000
To assess the performance and potential for expansion, we can consider both sales value and
sales volume.
Sales Value Analysis:



Blackpool: £87,780.36
Portsmouth: £60,659.25
Southampton: £40,533
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Based on sales value alone, Blackpool has the highest revenue, indicating a stronger financial
performance compared to the other coffee shops.
Sales Volume Analysis:
1. Blackpool: 27,511.692
2. Portsmouth: 19,426.5
3. Southampton: 12,565
Blackpool also performs better than the other coffee shops in terms of sales volume, suggesting a
higher frequency of client transactions.
Blackpool is the most promising coffee shop for growth, both financially and in terms of
potential customer base. When compared to Portsmouth and Southampton, it outperforms both in
terms of sales revenue and client base.
Because of its success and high demand, the Corporate Strategy Manager should think about
increasing the coffee shop's footprint in Blackpool.
DATA CHARTING AND COMMENTARY
Comparison of sales value trends across coffee shops over time
40000
35000
30000
25000
Total
20000
15000
10000
5000
0
Chart A Comparison of sales value trends across coffee shops over time
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Product category performance comparisons between coffee shops
9000
Cakes
Coffee
Cold drinks
Colddrinks
Hot drinks
Kakes
Pastry
Pazztry
Sandwich
Sandwiches
8000
7000
6000
5000
4000
3000
2000
1000
0
Blackpool
Portsmouth
Southam
Southampton
Chart B Product category performance comparisons between coffee shops
ISSUE 3 SOLUTION:
The home delivery service offered in Blackpool have a positive impact on the sales
performance of the shop?
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Impact of the home-delivery service offered in the Blackpool area, and in comparison with the other two cities.
Southampton; 21.18%
Southam; 1.26%
Blackpool; 45.86%
Blackpool
Portsmouth
Southam
Southampton
Portsmouth; 31.69%
Chart C Impact of the home-delivery service offered in the Blackpool area, and in
comparison with the other two cities.
Given the available data, it seems reasonable to conclude that the home delivery service has
contributed to the growth of the Blackpool store's revenue. When compared to the other locations
in the chain, Blackpool's sales (£87,780.36) far outpace those of Portsmouth (£60,659.25),
Southam (£2,419.50), and Southampton (£40,533.00).
Since Blackpool's sales value is much greater than the sales values of other shops in the chain,
this may indicate that the Blackpool store's sales performance has improved as a result of the
launch of the home delivery service.
In light of this information, it seems reasonable to infer that the home delivery service introduced
in the Blackpool store was a significant factor in its better sales performance compared to the
other stores in the company. To prove a definitive causal relationship between the home delivery
service and the increased sales, a more extensive analysis is needed, including full sales data and
comparison before and after the introduction of the service.
CONCLUSIONS AND RECOMMENDATIONS
In conclusion, the data provided by the customer allowed for insightful conclusions to be drawn
about the sales performance of product categories and coffee shops. We fixed the data issues with
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data cleansing methods, making the data set reliable and consistent. More educated choices about
menu optimization, cost reduction, and possible profit rise might be made after underperforming
products were exposed. We also found that the Blackpool coffee shop had the most potential for
growth by comparing its sales volume and value to those of other coffee shops. Last but not least,
the Blackpool store's sales increased after it began offering home delivery.
Based on the findings, it is recommended to take the following actions:
1. Implement data cleansing techniques to address missing values, typos, inconsistent
values, and incorrect column headers in the dataset. This will ensure the accuracy and
reliability of future analyses.
2. Consider removing low-performing products, such as "Colddrinks," "Kakes," "Pazztry,"
and "Sandwich," from the menu. This will streamline the menu, reduce ingredient costs,
and potentially increase profitability.
3. Focus on expanding the floor area of the Blackpool coffee shop, as it has shown the
highest sales value and volume among the analyzed coffee shops. This expansion will
accommodate the growing customer base and capitalize on the shop's strong
performance.
4. Continue offering the home delivery service in the Blackpool shop, as it has shown a
positive impact on sales performance. Monitor the service's effectiveness closely and
make further improvements based on customer feedback and market trends to sustain its
success.
Through the implementation of these suggestions, the coffee chain can effectively optimize its
menu offerings, strategically allocate resources towards shop expansion initiatives, and leverage
successful services to bolster sales and improve overall business performance.
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REFERENCES
Singh, S. K., & Dwivedi, R. K. (2020). Data Mining: Dirty Data and Data Cleaning. Available at
SSRN: https://ssrn.com/abstract=3610772 or http://dx.doi.org/10.2139/ssrn.3610772
Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R.,
& Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7),
86-94. doi:10.1145/2611567
Devi, S., & Kalia, A. (2015). Study of Data Cleaning and Comparison of Data Cleaning Tools.
International Journal of Computer Science and Mobile Computing, 4(3), 360-370.
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