1- Introduction & project plan
a) Purpose/ objective of this report
Data-driven processes are often divided into three stages: data collecting, evaluating patterns and facts from raw data, and making business choices based on the extracted information. The goal of this research is to provide a report that will offer COTS senior management with a solid evidence-based platform for strategic business decisions (Grover et al., 2018). Based on data-driven research, this report assesses the efficacy of the marketing effort. The primary goal of using a data-driven strategy is to determine if a marketing effort has a beneficial influence on sales performance in the United Kingdom. The important points are as follows:
- The corporate strategy manager is interested in understanding the options available for local expansion strategy to increase of the shop. For this purpose it is required to conduct a sales value and volume study of the three coffee shops to find the optimum locations for future growth(Grover et al., 2018). The marketing manager wishes to direct COT’s advertising campaign toward market segments with the greatest sales potential in order to enhance revenue per client. It will cover sales value and volume trends by month and year for the past three years, from 2020 to 2022.
- Conduct a customer study to determine which market categories generate the most income. The sales director wanted to know whether two new food items on the menu could be spread to other locations in the network.
- What are the outcomes of the new goods introduced to the Plymouth menu that have a beneficial influence on the shop’s sales performance?
b) Overall plan of project
The methodologies of data analytics are used to the process of analyzing the COT’s sales data. The trend chart is used in order to do an analysis on the overall sales volume as well as sales of the items during the course of the year. A comparison is made between the sales volumes of the individual items throughout the course of each year. This enables a determination to be made as to whether or not there has been an increase in the total sales volume of items after the introduction of the new products.
c) Data analytics and frameworks
The descriptive and visualization components of data analytics have been used in the preparation of this report (Khade, 2016). Excel’s Data Analysis is used as an analytical environment for the purpose of chart capturing as well as the determination of descriptive tables. The sales performance of the data is shown visually and evaluated, and the whole sales dataset is assessed by utilizing descriptive statistics. This is done for each market segment.
d) COTs business key Performance Indicators
The diversity of the markets served and the breadth of goods offered are the primary measures of success for the BIJ. The sales volume and sales value are the key performance indicators (KPIs) for the organization’s sales department (Gupta et al., 2016).
2- Data quality issues and remedies
a) Generic issues related to Data and its resolution
Inferences drawn from data analytics may help businesses make choices that are both highly lucrative and very effective. The notion of data analytics is being used by a growing number of companies when making decisions about company activity. The raw data that are obtained by an analyst have a great deal of room for interpretation (Bachechi et al., 2022). The low quality of the data leads to enormous financial losses as well as the targeting of clients who are not suitable. Throughout the process of data analysis, an analyst will encounter a variety of challenges. These problems may be broken down into two categories: 1. general issues, and 2. specific difficulties. Some general issues includes
- Collection of Data
The procedure is defined as the techniques used to collect information about relevant characteristics. If you want reliable analysis findings, choose a relevant characteristic to your investigation. The information is gathered from reliable sources to eliminate the possibility of inaccuracy (Zhang et al., 2022).
- Cleansing and preparation of data.
This is necessary before moving on to the data analysis phase. Cleansing and data preparation methods remove uncertainties from the dataset. After using the data cleaning method, the quality of the dataset as a whole improves dramatically (Lyan et al., 2021).
b) COT Data issues and its resolution
- There were 6 entries found years for the year 2032 instead of 2022. In order to further use the data, we are considered the said entries for the year 2022.
- Further there were 9 market segments provide in the scenario. However in order to streamline the data, we have adjust the 9 segments in 6 segments. Details are mentioned as follow.
9 Market segments | 6 Market segments (Revised) |
Young people | Young People (e.g. Students) |
Single professional people | Single professional/ Working people |
Married young couples | Young Married Couples |
Families with children | Married Couples with Children (e.g. Families) |
Tourists | Tourists |
Retired people | Retired people |
Married Y.P. | Young Married Couples |
Turistas | Tourists |
Retired | Retired people |
Fam. children | Married Couples with Children (e.g Families) |
- There were 11 entries found in the column A coffee city Pooleham instead of Poole. In order to further use the data, we are considered the said entries on Poole City instead of Pooleham.
- There were 4 entries which contained negative sales which is practically not possible. Therefore these instances will be converted as positive sales.
City shop | Year | Month | Market segment | Sales Volume | Sales Value |
Newquay | 2020 | 2 | Young people | 138.00 | -£545.00 |
Plymouth | 2020 | 4 | Retired people | (1.00) | -£354.00 |
Plymouth | 2021 | 5 | Married young couples | (1.00) | -£397.00 |
Newquay | 2021 | 12 | Married young couples | 108.50 | -£271.25 |
3- Data analysis and commentary
Table-A- Trends in sales values and volume by month & year
The following table breaks the annual sales by month during a three-year period, broken down by city. According to the table during the past 3 year starting from 2020 to 2022, the Plymouth City shop has the highest sales volume & sales value whereas Newquay as on runner-up position and Poole is on the last place. The below table contain shop wise sales in volume and value terms covering for the last 3 years.
Month | Newquay Sales Volume | Plymouth Sales Volume | Poole sales Volume | Newquay Sales Value £ | Plymouth Sales Value £ | Poole sales Value £ |
Jan-20 | 411 | 521 | 287 | 1,363.25 | 1,709.00 | 920.00 |
Feb-20 | 458 | 609 | 256 | 1,501.00 | 1,954.00 | 904.50 |
Mar-20 | 505 | 718 | 314 | 1,642.25 | 2,228.00 | 1,045.50 |
Apr-20 | 627 | 696 | 406 | 2,011.00 | 2,099.00 | 1,363.50 |
May-20 | 482 | 685 | 331 | 1,574.75 | 2,161.00 | 1,108.00 |
Jun-20 | 538 | 703 | 350 | 1,730.75 | 2,328.00 | 1,062.00 |
Jul-20 | 583 | 808 | 372 | 1,927.00 | 2,650.00 | 1,188.50 |
Aug-20 | 562 | 822 | 440 | 1,847.75 | 2,614.00 | 1,444.50 |
Sep-20 | 458 | 685 | 359 | 1,459.50 | 2,174.00 | 1,149.50 |
Oct-20 | 475 | 682 | 362 | 1,504.25 | 2,210.00 | 1,215.00 |
Nov-20 | 476 | 664 | 342 | 1,515.25 | 2,084.00 | 1,039.00 |
Dec-20 | 501 | 731 | 388 | 1,592.00 | 2,342.00 | 1,259.50 |
Jan-21 | 410 | 600 | 303 | 1,381.25 | 1,867.00 | 933.00 |
Feb-21 | 428 | 643 | 331 | 1,355.75 | 2,022.00 | 1,106.50 |
Mar-21 | 391 | 778 | 368 | 1,339.75 | 2,513.00 | 1,157.50 |
Apr-21 | 658 | 804 | 453 | 2,054.50 | 2,593.00 | 1,479.50 |
May-21 | 509 | 587 | 362 | 1,573.75 | 1,558.00 | 1,104.50 |
Jun-21 | 567 | 795 | 349 | 1,758.75 | 2,556.00 | 1,144.00 |
Jul-21 | 655 | 720 | 396 | 2,060.50 | 2,290.00 | 1,357.50 |
Aug-21 | 638 | 892 | 405 | 2,086.50 | 2,908.00 | 1,281.00 |
Sep-21 | 531 | 706 | 354 | 1,699.75 | 2,271.00 | 1,169.50 |
Oct-21 | 547 | 721 | 383 | 1,732.00 | 2,364.00 | 1,268.00 |
Nov-21 | 581 | 737 | 354 | 1,852.00 | 2,320.00 | 1,194.00 |
Dec-21 | 571 | 770 | 366 | 1,276.25 | 2,410.00 | 1,174.50 |
Jan-22 | 404 | 581 | 315 | 1,299.75 | 1,917.00 | 1,028.00 |
Feb-22 | 492 | 635 | 329 | 1,601.25 | 2,042.00 | 985.00 |
Mar-22 | 549 | 687 | 394 | 1,784.25 | 2,226.00 | 1,310.50 |
Apr-22 | 648 | 913 | 463 | 2,107.25 | 2,812.00 | 1,451.50 |
May-22 | 549 | 714 | 390 | 1,768.75 | 2,369.00 | 1,204.50 |
Jun-22 | 566 | 863 | 390 | 1,746.50 | 2,664.69 | 1,147.00 |
Jul-22 | 658 | 1,059 | 414 | 2,078.50 | 3,359.14 | 1,287.00 |
Aug-22 | 713 | 1,209 | 489 | 2,334.00 | 3,899.79 | 1,599.50 |
Sep-22 | 580 | 935 | 357 | 1,830.25 | 2,896.10 | 1,220.50 |
Oct-22 | 577 | 928 | 376 | 1,847.00 | 2,929.85 | 1,186.50 |
Nov-22 | 586 | 976 | 387 | 1,828.25 | 3,221.61 | 1,190.00 |
Dec-22 | 540 | 955 | 368 | 1,748.00 | 2,978.17 | 1,153.50 |
The below data shows the mean, median, standard deviations, minimum , maximum, range & total value for each coffee shops.
| Newquay Sales Volume | Newquay Sales Value £ | Plymouth Sales Volume | Plymouth Sales Value £ | Poole Sales Volume | Poole Sales Value £ |
Mean | 539.38 | 1,717.03 | 764.80 | 2,431.68 | 369.53 | 1,189.79 |
Median | 547.50 | 1,739.25 | 720.50 | 2,335.00 | 367.00 | 1,180.50 |
Standard Deviation | 79.84 | 258.46 | 143.60 | 470.35 | 47.07 | 155.12 |
Minimum | 391.00 | 1,276.25 | 521.00 | 1,558.00 | 256.00 | 904.50 |
Maximum | 713.00 | 2,334.00 | 1,208.61 | 3,899.79 | 489.00 | 1,599.50 |
Range | 322.00 | 1,057.75 | 687.61 | 2,341.79 | 233.00 | 695.00 |
Total for 3 Years | 19,417.50 | 61,813.25 | 27,532.69 | 87,540.36 | 13,303.00 | 42,832.50 |
Table B- Benchmark comparison of market Segments performance covering Sales volume, and value by Quarter across 3 years
The below table shows that Market Segments performance covering Sales volume, and value by Quarter, by year and across 3 years. The quarterly data shows the sales volume based performance of 6 segments which are major contributor for sales value.
Quarter | Married Couples with Children | Retired people | Single professional / Working people | Tourists | Young Married Couples | Young People (e.g Students) |
1st Quarter 20 | 370.00 | 809.50 | 560.00 | 238.00 | 809.00 | 1,291.50 |
2nd Quarter 20 | 447.00 | 847.50 | 715.00 | 298.00 | 992.00 | 1,517.50 |
3rd Quarter 20 | 486.00 | 1,007.50 | 764.00 | 317.00 | 974.50 | 1,563.50 |
4th Quarter 20 | 424.50 | 1,006.00 | 688.00 | 277.50 | 897.00 | 1,328.00 |
1st Quarter 21 | 405.50 | 715.00 | 679.50 | 263.50 | 870.00 | 1,275.50 |
2nd Quarter 21 | 429.00 | 1,082.00 | 764.50 | 282.50 | 922.00 | 1,603.50 |
3rd Quarter 21 | 510.00 | 995.00 | 788.00 | 335.50 | 1,082.50 | 1,586.00 |
4th Quarter 21 | 435.50 | 1,048.50 | 709.00 | 280.50 | 983.50 | 1,571.50 |
1st Quarter 22 | 409.00 | 920.50 | 589.00 | 265.00 | 846.50 | 1,355.50 |
2nd Quarter 22 | 504.99 | 1,156.06 | 844.01 | 299.16 | 1,129.53 | 1,562.20 |
3rd Quarter 22 | 553.60 | 1,319.87 | 893.49 | 378.59 | 1,353.85 | 1,914.48 |
4th Quarter 22 | 506.40 | 1,193.43 | 866.41 | 332.39 | 1,123.71 | 1,670.52 |
5,481.49 | 12,100.86 | 8,860.91 | 3,567.64 | 11,984.09 | 18,239.69 |
The quarterly data shows the sales values based performance of 6 segments which are major contributor for sales values
Quarter | Married Couples with Children(£) | Retired people (£) | Single professional/ Working people (£) | Tourists (£) | Young Married Couples (£) | Young People (e.g Students) (£) |
1st Quarter 20 | 1,862.00 | 1,578.00 | 1,160.00 | 1,413.00 | 2,013.50 | 5,241.00 |
2nd Quarter 20 | 2,260.00 | 2,096.00 | 1,456.00 | 1,775.00 | 2,450.00 | 6,109.00 |
3rd Quarter 20 | 2,453.00 | 2,006.00 | 1,565.00 | 1,849.00 | 2,428.75 | 6,273.00 |
4th Quarter 20 | 2,158.50 | 2,021.00 | 1,333.00 | 1,584.00 | 2,246.50 | 5,418.00 |
1st Quarter 21 | 1,990.50 | 1,508.00 | 1,387.00 | 1,497.00 | 2,138.50 | 5,146.00 |
2nd Quarter 21 | 2,143.00 | 2,108.00 | 1,524.00 | 1,759.00 | 2,687.00 | 6,395.00 |
3rd Quarter 21 | 2,537.00 | 1,974.00 | 1,561.00 | 1,966.00 | 2,730.75 | 6,355.00 |
4th Quarter 21 | 2,139.50 | 2,100.00 | 1,389.00 | 1,787.00 | 2,452.75 | 6,265.00 |
1st Quarter 22 | 2,011.00 | 1,893.00 | 1,155.00 | 1,617.00 | 2,094.75 | 5,423.00 |
2nd Quarter 22 | 2,482.96 | 2,303.12 | 1,705.02 | 1,762.96 | 2,819.33 | 6,197.80 |
3rd Quarter 22 | 2,686.99 | 2,684.74 | 1,805.98 | 2,252.55 | 3,408.62 | 7,665.90 |
4th Quarter 22 | 2,519.00 | 2,365.87 | 1,720.82 | 2,068.35 | 2,714.78 | 6,694.06 |
Total | 27,243.45 | 24,637.72 | 17,761.82 | 21,330.86 | 30,185.23 | 73,182.77 |
The below data shows the mean, median, standard deviations, minimum , maximum, range & total sales volume and value of each segment performed and subdivided on quarterly basis.
Married Couples with Children | Retired people | Single professional | ||||
Volume | Value (in GBP) | Volume | Value (in GBP) | Volume | Value (in GBP) | |
Mean | 456.79 | 2,270.29 | 1,008.41 | 2,053.14 | 738.41 | 1,480.15 |
Median | 441.25 | 2,209.25 | 1,006.75 | 2,058.50 | 739.50 | 1,490.00 |
Standard Deviation | 52.16 | 248.89 | 163.18 | 306.10 | 98.72 | 199.53 |
Minimum | 370.00 | 1,862.00 | 715.00 | 1,508.00 | 560.00 | 1,155.00 |
Maximum | 553.60 | 2,686.99 | 1,319.87 | 2,684.74 | 893.49 | 1,805.98 |
Range | 183.60 | 824.99 | 604.87 | 1,176.74 | 333.49 | 650.98 |
Total | 5,481.49 | 27,243.45 | 12,100.86 | 24,637.72 | 8,860.91 | 17,761.82 |
Tourists | Young Married Couples | Young People | ||||
Volume | Value (in GBP) | Volume | Value (in GBP) | Volume | Value (in GBP) | |
Mean | 297.30 | 1,777.57 | 998.67 | 2,515.44 | 1,519.97 | 6,098.56 |
Median | 290.25 | 1,768.98 | 979.00 | 2,451.38 | 1,562.85 | 6,231.40 |
Standard Deviation | 36.84 | 228.46 | 146.83 | 372.46 | 176.03 | 682.84 |
Minimum | 238.00 | 1,413.00 | 809.00 | 2,013.50 | 1,275.50 | 5,146.00 |
Maximum | 378.59 | 2,252.55 | 1,353.85 | 3,408.62 | 1,914.48 | 7,665.90 |
Range | 140.59 | 839.55 | 544.85 | 1,395.12 | 638.98 | 2,519.90 |
Total | 3,567.64 | 21,330.86 | 11,984.09 | 30,185.23 | 18,239.69 | 73,182.77 |
Table C- Benchmarking comparison of sales volume & value between coffee shops by quarter , by year and across the 3 years
The below data shows the performance of each coffee shop based on quarterly basis. The sales volume and sales value is mentioned on quarterly basis to identify the lowest, highest sales volume and sales value based on quarterly and yearly basis depends upon the requirements of management of organization.
Month | Newquay Sales Volume | Plymouth Sales Volume | Poole Sales Volume | Newquay Sales Value | Plymouth Sales Value | Poole Sales Value |
1st Quarter 20 | 1,373 | 1,848 | 857 | 4,506.50 | 5,891.00 | 2,870.00 |
2nd Quarter 20 | 1,646 | 2,084 | 1,087 | 5,316.50 | 6,588.00 | 3,533.50 |
3rd Quarter 20 | 1,603 | 2,315 | 1,171 | 5,234.25 | 7,438.00 | 3,782.50 |
4th Quarter 20 | 1,452 | 2,077 | 1,092 | 4,611.50 | 6,636.00 | 3,513.50 |
1st Quarter 21 | 1,229 | 2,021 | 1,002 | 4,076.75 | 6,402.00 | 3,197.00 |
2nd Quarter 21 | 1,734 | 2,186 | 1,164 | 5,387.00 | 6,707.00 | 3,728.00 |
3rd Quarter 21 | 1,824 | 2,318 | 1,155 | 5,846.75 | 7,469.00 | 3,808.00 |
4th Quarter 21 | 1,698 | 2,228 | 1,103 | 4,860.25 | 7,094.00 | 3,636.50 |
1st Quarter 22 | 1,445 | 1,903 | 1,038 | 4,685.25 | 6,185.00 | 3,323.50 |
2nd Quarter 22 | 1,763 | 2,490 | 1,243 | 5,622.50 | 7,845.69 | 3,803.00 |
3rd Quarter 22 | 1,951 | 3,203 | 1,260 | 6,242.75 | 10,155.03 | 4,107.00 |
4th Quarter 22 | 1,703 | 2,859 | 1,131 | 5,423.25 | 9,129.63 | 3,530.00 |
The below data shows the mean, median, standard deviations, minimum , maximum, range & total sales volume and value of each segment performed and subdivided on quarterly basis for each coffee shop
| Newquay | Plymouth | Poole | |||
| Volume | Value (in GBP) | Volume | Value (in GBP) | Volume | Value (in GBP) |
Mean | 1,618 | 5,151 | 2,294 | 7,295 | 1,109 | 3,569 |
Median | 1,672 | 5,275 | 2,207 | 6,901 | 1,117 | 3,585 |
Standard Deviation | 198 | 593 | 378 | 1,198 | 104 | 312 |
Minimum | 1,229 | 4,077 | 1,848 | 5,891 | 857 | 2,870 |
Maximum | 1,951 | 6,243 | 3,203 | 10,155 | 1,260 | 4,107 |
Range | 723 | 2,166 | 1,355 | 4,264 | 403 | 1,237 |
Total for 3 Years | 19,418 | 61,813 | 27,533 | 87,540 | 13,303 | 42,833 |
4- Data visualization & Commentary
Table A-Sales Volume of 3 coffee shop findings
Sales Volume
- From the graphical presentation, it is analysis the Plymouth Sales volume remains on 1st position, whereas Newguay and Poole shop remains on 2nd and 3rd position while performing the sales volume figures on month on month basis in last 3 years i.e from Jan 2020 to Dec-2022.
Sales Value
- From the graphical presentation, it is analysis the Plymouth Sales value remains on 1st position, whereas Newguay and Poole shop remains on 2nd and 3rd position while performing the sales value figures on month on month basis in last 3 years i.e from Jan 2020 to Dec-2022.
Table B- Benchmark comparison of Market Segments performance covering Sales volume, and value by Quarter, by year and across 3 years
Sales Volume
- With reference to graphical presentation of Sales volume of 6 market segments for each quarter of last 3 years, it is clear that the young people (students) remain in the 1st position in performing sales volume in each quarter. 2nd and 3rd positions goes to young married couple and retired people. From this analysis it is concluded that organization is covering the above mentioned segment and didn’t focused on other segments and specially tourists.
Sales Volume
With reference to graphical presentation of Sales value of 6 market segments for each quarter of last 3 years, it is clear that the young people (students) remain in the 1st position in performing sales volume in each quarter. The lowest sales figure observed from young people segments is £5,000 where as the highest sales figure observed from other segment is £3,500. There is a big gap (approx. £1500) of sales values between the young people sales figures and sales figure of other segments.
Table C- Sales Volume-Impact of Additional 2 New products
Sales Volume
From the graphical presentation of Sales volume of 3 coffee shops based on 3 years Quarterly basis, it is identified that 3rd Quarter-2022 remains the best performing session among all the 12 Quarters as all 3 shops hit the highest sales volume across the 3 years performance.
According to the COTs strategy new products were introduced on trial basis at Plymouth Coffee shop. The results seem graph and table is crystal clear. The sales volume of Plymouth is on higher sider as compare to Poole and Newquay during the last 3 quarters 2022. Plymouth sales volume is 8553 whereas Poole and Newquay sales volume shows 3634 & 5416 respectively
Sales value
From the graphical presentation, the maximum sales value marked in the 3rd Quarter-2022 whereas the 2nd & 3rd position marked for the 4th Quarter-2022 & 2nd Quarter-2022. It seems that overall growth was remain at the higher side in the year 2022.
According to the COTs strategy new products were introduced on trial basis at Plymouth Coffee shop. The sales value of Plymouth is on higher sider as compare to Poole and Newquay during the last 3 quarters 2022, Plymouth sales value is £27,130 whereas Poole and Newquay sales volume shows £11,440 & £17,288 respectively
5- Conclusion and recommendations
Conclusion on Sales performance and Operations of coffee shops based on 3 issues.
Following are some observation based on below data
- The Highest sales volume of 713 and sales value of £2334 was performed by Newquay in Aug-22
- The Highest sales volume of 1208 and sales value of £3900 was performed by Plymouth in Aug-22
- The Highest sales volume of 489 and sales value of £1600 was performed by Poole in Aug-22
- The lowest sales volume of 391 in Mar-21 and lowest sales value of £1276 in Dec-21 was performed by Newquay
- The lowest sales volume of 521 in Jan-20 and lowest sales value of £1276 in Mar-21 was performed by Plymouth
- The lowest sales volume of 256 and sales value of £904.50 was performed by Poole in Feb-22
- The Average sales volume & sales value of Newquay for the period of 3 years is 539 & £1717
- The Average sales volume & sales value of Plymouth for the period of 3 years is 764.80 & £2431.68
- The Average sales volume & sales value of Poole for the period of 3 years is 369.5. & £1189.79
- The Highest sales volume and sales value of all segments was performed in the 3rd Quarter 2022
- The lowest sales volume and sales value of all segments was performed in the 1st Quarter 2020 & 1st Quarter 2021
- The largest sales volume and sales value generate by all 3 shop is in the 3rd Quarter 2022
- The lowest sales volume and sales value generate by Newquay was in the 1st Quarter-2021 whereas Plymouth and Poole generated lowest sales volume and sales value in the 1st Quarter-2020
Business recommendations to COTS s CEO and top management
- While monitoring performance of 3 years, it is recommended to management of COTs to focus on sales volume and sales value performance of Pool and Newquay coffee. The focus could on additional marketing, discount offering, preferential rates to corporate clients and better customer services.
- In order to increase overall sales of 3 coffee shops, organizational management shall focus on market segments which includes married couples, single professionals and tourist. These segments shall be offer different reduced price offers, deal packs, membership and preferential services to encourage them to bring more sales volume and sales value.
- In order to bring sales value across the all segments excluding young people and reduce the sales value gap, prices shall be set according to the age, market segment, time of visiting and other variable factors
- As the organization has test 2 new products at Plymouth, the organization shall also test the same products to other coffee shops to ascertain the results based on entire chain of COTs.
Suggests related to data analytics & it’s better usage across the organizations
- While dealing with the case study, a data set was provided which contained several error. Those errors were removed through data cleansing exercising and then graphical presentation and decision were make. The errors included were typo errors of year 2032, spelling mistakes in market segments which leads to 9 segments rather than 6 segments & negative sales values. Therefore COTs shall work on automatic MIS system to capture the data on real time basis which shall be address these matters.
- Further, the cost of manufacturing the products and running cost of coffee shops need to be mentioned as they are required to reduce cost of business.
- Apply methods of statistical analysis to the data in order to find patterns, outliers, and correlations. Data-driven decision-making is bolstered by methods like regression analysis, hypothesis testing, and clustering (Cao et al., 2021).
- Use predictive analytics to make predictions about the future based on current information. As a result, businesses may be better prepared for future market shifts, allocate resources more efficiently, and boost their overall performance (Youssef et al., 2022).
- Encourage workers to make choices based on facts and insights rather than gut feelings, which will help foster a data-driven culture inside the company. Give your staff the tools and education they need to become more data literate and start making better use of data analytics (Fathi et al., 2021).
6- References
- Bachechi, C., Po, L. and Rollo, F. (2022) ‘Big Data Analytics and visualization in traffic monitoring’, Big Data Research, 27, p. 100292. doi:10.1016/j.bdr.2021.100292.
- Cao, T. et al. (2021) ‘Enhancing auditors’ reliance on data analytics under inspection risk using fixed and growth mindsets’, The Accounting Review, 97(3), pp. 131–153. doi:10.2308/tar-2020-0457.
- Fathi, M. et al. (2021) ‘Big Data Analytics in weather forecasting: A systematic review’, Archives of Computational Methods in Engineering, 29(2), pp. 1247–1275. doi:10.1007/s11831-021-09616-4.
- Grover, V. et al. (2018) ‘Creating strategic business value from Big Data Analytics: A research framework’, Journal of Management Information Systems, 35(2), pp. 388–423. doi:10.1080/07421222.2018.1451951.
- Gupta, S. et al. (2016) ‘ESP health monitoring KPI: A real-time predictive analytics application’, All Days . doi:10.2118/181009-ms.
- Khade, A.A. (2016) ‘Performing customer behavior analysis using big data analytics’, Procedia Computer Science, 79, pp. 986–992. doi:10.1016/j.procs.2016.03.125.
- Lyan, G. et al. (2021) ‘Impact of data cleansing for Urban Bus Commercial Speed Prediction’, SN Computer Science, 3(1). doi:10.1007/s42979-021-00966-1.
- Youssef, M.A., Eid, R. and Agag, G. (2022) ‘Cross-national differences in big data analytics adoption in the retail industry’, Journal of Retailing and Consumer Services, 64, p. 102827. doi:10.1016/j.jretconser.2021.102827.
- Zhang, D. et al. (2022) ‘Big Data Analytics, resource orchestration, and Digital Sustainability: A case study of smart city development’, Government Information Quarterly, 39(1), p. 101626. doi:10.1016/j.giq.2021.101626.
