Context

Many businesses utilise data for decision-making that are referred to as data-driven business. Data-driven businesses make decisions based on the data, which guarantees that their actions can help the businesses succeed and gain a competitive advantage (Ghasemaghaei and Calic, 2019). A company like The Bangles Company, which is impeccably suitable to accomplish data driven intuitions with rapidity and efficacy can impact the remarkable growth of data. Implementing a data methodology in a coordinated and knowing manner makes a difference in a large data driven undertaking from one that lone uses information on a spontaneous premise. With the regularity of data inside the Bangles organization business, it is easier to expect that it has set up essential proficiencies in big data examination. Since data driven organisations are mounting immensely, a few patterns arise over the long haul. A portion of the patterns are accompanying toward the expanded meaning of data investigation in The Bangles Company.

Forecasting Analytics

Forecasting analytics is the technique that obtains data and foresees the value for the data for future observing at its unique trends. For instance, predicting the average annual sales of the Bangles Company centered on the data from 3 years. Predictive analysis factors in an array of inputs and foresees the future conduct and not just the number. 

Data as a service

Data as a service (DaaS) tools offer businesses with all they require to improve assimilate, manage, and store their data in the cloud. It eliminates the necessity to install large costly software packages to handle large data sets in efficiently (Marques, 2016). It additionally implies that organisation can be more adaptable with how they utilize their data and scale it as desired.

Sky-high expectations

The leaders of Bangles and IT identified that the value is masked within the data, and expectations are augmented that acquiring new instincts from this data would undo operating proficiencies and business growth (Marques, 2016). These suppositions transform into an array of business objectives that influence data investing, integrating improved decision-making, safety advancements, cost-effectiveness augmentations, and better client experiences.

Blockchain

The popularity of blockchain technology can be seen in cryptocurrency. It can enhance predictive analytics because it affirms data legitimacy, preventing false info from incorporating into analyses. It additionally permits the data analytics applications to obtain enormous data (Ridgers and Dev, 2020).

With the current trends and succession of business with the help of data-driven decisions making it can be said that data analytics assist businesses to drive efficacy, gather profound operational info and outlooks, and ultimately generate added profits. It can assist in evidence-based decision-making, examining the business-related decisions, make appropriate use of information, apply a pull on preeminent talent, and improving the aimed audience. Furthermore, data analytics explored the paramount ways for lead generation, marketing and sales, buyers' devotion and collaboration, dealing with transactions, and enhance decision-making (Marques, 2016).

Planned approaches for analytics

At present, business analytics is a leading gizmo in business fairs. It is a transformation that is impossible to evade. According to Acito and Khatri (2014), using quantitative approaches to acquire data to make informed business verdicts is called business analytics. Four analytical approaches have been explored within business analytics, including descriptive, prescriptive, predictive, and diagnostic. The analytical approach, which analyses the already existing data to explore trends and patterns and to recognise what has occurred, is called the descriptive approach. The approach that focuses on former performance to determine what and why something happens is called a diagnostic approach. The analysis usually results in an analytical dashboard. The approach in which statistics are used to foresee outcomes is referred to as the predictive approach. The approach in which analytics and several other techniques are used to determine which outcome will produce the paramount results in a given circumstance is referred to as the prescriptive approach.

(Fig 1: Business Analystics)

Picking which style to utilise depends on the scenario of business. For example, the provided scenario signifies that The Bangles Company invested in a marketing campaign in May '20 in the UK. Now what the firm is attempting to focus on is "Did the marketing campaign positively influence the UK's sales performance?"

The descriptive-analytical approach was selected utilising business analytics. The intention to select this approach is to outline the outcomes and understand what is taking place. This approach is only used for understanding the true conduct and not to generate any estimations. The descriptive approach is appropriate when businesses intend to identify features, prevalence, trends, and categorisations. Descriptive analytics represent the data in graphs (bar graphs, line charts, pie charts, etc.) (Delen and Ram, 2018). This approach was chosen as the incident (market campaign) has already taken place. The Bangles Company wants to focus on whether or not the effect of a marketing campaign is positive on sales. The descriptive approach helps to examine and observe the trends within the sales. Hence, for this module, MS Excel has been used as an analytical tool.

Analysis

DATA CLEANING

In order to analyse the data, it is of key importance to clean the data to avert inaccurateness. If data involves outliers and missing values, it can direct to biased results. Data cleaning refers to eliminating and removing inappropriate, missing, and repeating data from the dataset (Oliveira et al., 2019). There are several steps in data cleaning. Initially, the dataset must eliminate repeating values, for instance, US/USA and bracelet/ankle bracelet. The next step is to fix structural errors, for example, hairband/hairband. Step three is eradicating insignificant values. The fourth step is eliminating missing data, which was not in the case of the Bangles company dataset. Once all of these steps are carried out, now the data is ready for analysis as data without any errors is regarded as paramount in decision-making.

SUMMARY TABLES

Table 1

Table 1 reports the statistics of the UK, Japan, and the USA from 2018-2020. In addition, it showed the count concerning the subtypes integrating bracelet, ring, necklace, bracelet, hair band, and accessory. The table shows that the highest sales were of bracelets in three consecutive years in all nations (bracelets=109). Moreover, the sales of items were most seen in the USA (USA=174) followed by Japan (Japan=164) and then the UK (UK=160). Hence, the total number of jewellery items sold in three nations from 2018-2020 was 498. 

                                                           Figure 2: Product Analysis

Figure 1 demonstrated the visual representation of table 1 and showed that the highest sale was of the bracelet in the USA in 2018. At the same time, the least sale was observed to be accessories in Japan in 2018. However, the sales of necklaces and hairbands were equal in all three nations in three consecutive years. 

Introduction to the report

According to Kaarakainen, Kivinen, and Vainio (2018), Information and Communication Technology skills are regarding comprehending and implementing various computer programs, software and different applications. These incorporate words processing, spreadsheets, PowerPoint. Search engines and databases. Besides technical skills, the associated skills involve creativeness and analytical skills to employ the appropriate ICT skills to an activity. Moreover, basic Information and Communication Technology skills are vital and required in any role (Picatoste, Pérez-Ortiz, and Ruesga-Benito, 2018). To comprehend and be more skilled in ICT tools, it is imperative to comprehend the basic statistics as using different computer programs and software required statistics. The main reason to comprehend basic statistics is that statistical knowledge assists individuals in utilizing the appropriate methods to gather the data, implement the appropriate analysis, and efficiently demonstrate the outcomes. Statistics is a critical procedure behind how an individual makes scientific discoveries, makes decisions based on data and makes interpretations (Bailey, 2021). Regarding Lehner (2018), possessing ICT skills would assist in organizing the workload, streamline procedures and access to digital info. Enhancing ICT skills is a levelheaded way to stretch a competitive advantage over other applicants when applying for job vacancies.

This report presents the comprehending of the usage of spreadsheets to enter, analyze and report data using appropriate functions, formulas, descriptive statistics and graphical representations. Specifically, the application of MS Excel spreadsheet for the purposes is demonstrated. The data utilized for demonstration purposes concerns the GDP and COVID-19. To guarantee inclusive and right data analysis, it is vital to perform a series of processes. The report is subdivided into three tasks. The particular procedures discussed in this report integrate:

1. Data entry (spreadsheet formatting and data entry)
2. Data summary (performing descriptive statistics including mean, total, minimum, and maximum)
3. Data visualization (i.e. using a pie chart, line chart, and bar chart)
4. Data reporting (demonstration of analyses outcomes)

Task 1 – Impact of COVID-19 on daily life in the UK

Introduction

This task involves the essay on the impact of the COVID-19 on daily life within the UK in the context of healthcare, economic and social settings. For this task, some statistics have been presented using different charts such as line charts, bar charts, and pie charts. The data utilized for this task is COVID-19 death, total cases of COVID-19 in the UK, and monthly unemployment within the UK due to COVID-19 measures.

Main body

As per Milas (2021), in January 2020, economists surveyed through the Financial Times and showed that the economy of the UK progressed by about 1.4% in 2020. This approximation was hardly off the mark as the UK economy shrunk by 9.8% in 2020 due to the negative impacts of COVID-19. With the fast widespread of the pandemic and the death toll rate within the UK mounting to an unimaginable extent (over 128,000 to date), social unfriendliness and restrictions of lockdown measures turn out to be a one-way street. Milas (2021) showed average yearly GDP growth of -9.1% ever since the epidemic has started (amid March 2020 to January 2021) equated to 0.6% in the counterfactual situation where no constraints were enacted. Thus, on average, constraints/boundaries diminished yearly UK progress by 9.7% compared to the state of no action by the government.

Concerning the impact of COVID-19 within the UK, Bloom et al. (2020) present the larger impact of a pandemic on UK businesses. The mean probability of adverse influence of COVID-19 on sales in the UK was projected at 71%, whereas the probability of positive influence was only 7%, as shown in figure 1. Nevertheless, these sales influences were temporary.

Following is the demonstration of data of COVID-19 deaths within the UK. Figure 2 demonstrates the average number of deaths from Jan '21 to Mar '21. Figure 2 shows the great decline in the death rate from COVID-19. As per Ross, Morales, and Ashton (2 report021), the decline rate was due to the strict lockdown measures imposed by the government after the massive death rate of people.

Figure 2: COVID-19 deaths from Jan'21 - Mar'21

Moreover, figure 3 demonstrates the average number of deaths from Mar '20 to Dec '20. Figure 3 shows that the death rate was at a peak in April. According to Kumar et al. (2020), the rise in the death rate meant there were no strict lockdown measures. As a result, social distancing was becoming elevated results in more cases of coronavirus. However, at the end of April, the UK government impose strict lockdown measures. This slowed down the surge of an epidemic of COVID-19 by the end of April, which consequently resulted in a very low number of deaths. Additionally, due to a low number of cases and deaths, people turned back to normal routines and avoided lockdown measures. However, this worsens the situation even more and an upsurge in death rates. Figure 3 showed that there was a sudden rise in deaths in October '20. According to Looi (2020), the government of the UK declared the second spell of COVID-19 in October '20. Hence, the rise in several deaths was due to the second wave of covid-19.

Figure 3:COVID-19 deaths from Mar'20-Dec'20

HEALTHCARE

The covid-19 epidemic has changed healthcare delivery and access across the UK. For instance, emergency department visits were down by an approximation of 40% in various communities all over the UK (Lundberg et al., 2021). As per Lundberg et al. (2021), various in-person office visits were also postponed due to the adverse impact of covid-19 and were altered to telehealth visits. Moreover, different forms of healthcare delivery have been altered as a result of measures of social distancing. The major impact of covid-19 on the healthcare sector was the lower number of beds and staff per capita to treat the patients with a virus (Thorlby, Tinson, and Kraindler, 2020).

Moreover, working 24/7 was stressful for healthcare workers due to the low number of staff and higher number of infected people, which adversely impacted healthcare delivery (Thorlby, Tinson, and Kraindler, 2020). Figure 4 shows the cases of COVID-19 on 25th March '21, which demonstrate that a greater number of cases were reported on a single day. However, the majority of them was recovered, and a very lower number of deaths occurred. Moreover, fewer people were admitted to the hospital on 25th March '21, as shown in figure 4.