Analyse operational performance, assess market shifts & identify export gaps through structured evaluations, benchmarking insights and decision-support analysis.
According to Sarker, (2021), data analytics and management refer to the processing of the data by conventional technologies, theories, and tools to extract the useful to study data and its environments involving the domains along with other aspects such as social, and organisational aspects. Data facilitates a company by providing objective information relating to the market trends, the behaviors of the customers, and factual insights that are beneficial for its decision-making process. Students who face challenges in applying these techniques can benefit from assignment help writing to ensure clarity and accuracy in their analyses. A wide data record has been made in the context of the German Wine Group (GWG), supported by BUS5015 Data Analytics and Management practices. The four premium co-operatives Cleebronn Alde Gott, DIVINO, and Weinbiet, have combined their portfolio and export services to offer a one-stop shop for German wines (German Wine Group, 2025). The company is considered the leading wine producer and exports German wine worldwide. Moreover, with the help of the data of the company, the preferences of the customers regarding wine can be obtained which will be further beneficial for it to make decisions.
Analytical approaches help organisations make structured and informed decisions. By examining collected information relating to production, market trends, and customer responses, companies are able to strategise more effectively. This leads to improved performance planning and competitive positioning.
a. The way GWG uses data analytics in its function, and it helps in making a vital decision and SWOT analysis
The use of data analytics in GWG, and the beneficial aspects of the decision-making
The GWG uses data analytics to collect data regarding grape yields, soil moisture, and weather patterns. Through the application of BUS5015 Data Analytics and Management, this further leads the company to make the decision about yield optimisation, and harvest planning. This further leads the company to make the decision about yield optimisation, and harvest planning. This technology also helps the company to monitor wine production procedures which further facilitates GWG to optimise the quality of the wine, maintain consistency, and identify the areas of improvement.
b. Research questions
a. Presenting the instances of the weaknesses in GWG
As stated by Dressler, (2023), German wine did not profit well after 2000 for the halved wine export. The export rate of wine in the GWG has also decreased in recent years. Therefore, it can be said that the reduction of wine exportation is one of the most prominent instances of the weaknesses of the GWG.
b. Background information on the problem highlighting a comparison with another company
It can be found on the official web page of the GWG that the company mostly uses its traditional domestic sales channels (German Wine Group, 2018). It further limits the sales of the company in the international wine market. Moreover, the lack of using advanced sales channels can be considered as the background of the reduction of wine exportation. It has been reported by Best, (2024), that the wine exportation rate of the Deutsches Weininstitut was 1.2 million hectolitres, 2% down earlier. Hence, it is evident that like GWG, Deutsches Weininstitut is also facing the issue of declining the exporting rate of the wine.
a. Discussing problem-solving phases
In order to solve a problem, different phases are followed in which the first phase is recognising a problem. A clear structure helps to understand the problem clearly while defining the problem is also essential in the search for the potential solutions of the problem. As per the opinions of Kumar, (2021), 85% of organisations are facing the issue of identifying the right problem in the problem-solving matter. Apart from that, interpreting the results is important to obtain the effectiveness of the solution which further leads to the implementation based on its effectiveness.
b. The solution to the problem and research objectives
c. Justifying the reason for the benefits of the company
As mentioned previously, the export rate of the GWG has been decreased. Hence, to increase the export rate, it is essential to investigate the current operational procedure and identify the areas in which it can improve its performance. It has been discussed that identifying the trends of the market is crucial in making decisions in the wine production industry (Ohana-Levi and Netzer, 2023). Further, investigating into the research of the market trends along with data analytics, and management can enhance the export rate can also be beneficial for the company.
d. Identifying and summarising the problem requires a data analysis solution
It can be found from the previous discussion that GWG uses traditional domestic sales platforms (German Wine Group, 2018). It is one of the key reasons for the reduction of the wine expiration in the company. Therefore, the implementation of data analytics, can facilitate the GWG to obtain the trends of the market along with the behavior of the customers. This further contributes to the mitigation of the decreasing exportation problem in GWG.
Evaluating past outcomes and comparing performance with competitors helps identify areas where improvement is required. When gaps become clear, appropriate strategies can be developed to strengthen operations and increase growth potential. This enables more reliable planning and decision-making.
a. The importance of the data collection and upon which it is dependent
Data collection is one of the most important aspects of a study. As per the opinions of Karunarathna et al., (2024), data collection suggests collecting data that would further answer the question of the research, achieve the objectives of the research, and test the hypothesis. The data collection process helps an individual to get insights into the raw data which is further helpful in making informed decisions. The data collection method is also essential for understanding the trends of the market as well as identifying the problem and preventing any future errors. In such cases, BUS5015 Data Analytics and Management can transform collected data into valuable insights for relevant stakeholders. It has been stated by Taherdoost, (2021), that data collection is regarded as one of the most effective factors in a study which often determines the quality of the research result. Therefore, it can be said that data collection is a most vital factor. However, the appropriateness of the data collection method in a study often depends on the nature of the problem.
b. Identifying and describing the type of data
Secondary data collection is the most suitable method. As discussed in a study, secondary data collection includes all the technical publications such as journal articles, books, web pages, and others (Karunarathna et al., 2024). Moreover, the secondary data sources suggest collecting data from the sources that have already been published. The decision-making can be improved with the help of the incorporating secondary data collection method. The published data from the market research have been collected in this regard. Further, the quantitative data analysis method is used for anlysing the data. The analysis method is helpful to analyse the large database for which it has been selected.
a. Critically analysing the data collection methods
The presented data set related to the pricing, imports, and exports of the German Wine Group was constructed with the help of the qualitative data collection method. Qualitative data is a particular type of data that was collected in both numerical and non-numerical form. In the data set, the survey method of qualitative data collection was particularly used to collect data. The data was gathered through targeted audiences in order to get insight information about the import and export operation of the German Wine Group which includes the product pricing, designation, province, and winery names. To increase the accuracy of the gathered data, the survey participants are specifically chosen who has a collecting with the company operations.
In the constructed data set, the qualitative data collection method of the survey was very suitable for acquiring insight information about the company. The strength of using the survey data collection method lies in its cost-effectiveness and versatility (Pressbooks.pub, 2025). A wider viewpoint of information can be acquired with was ability to give more depth in the data set. However, the reliability of accuracy of the collected data were very low as the verification of the data is going to be difficult.
b. Proposing a method which can better suit
Apart from the survey method, the relevant dataset can also be accrued with the help of gathering data from the publicly available sources of the company. This is one of the best methods of secondary data collection. By collecting the relevant data from German Wine Group, through their published financial report and websites, the accuracy and reliability of the collected data can be ensured. This way, the conducted data analysis can present more value. In addition to that, using secondary data from published and unpublished sources can be more time efficient and able to present valuable insights.
c. The way of using different datasets
The data set related to the German Wine Group’s import and export was consistent with both numerical and information-related data. The main numerical data includes the points and pricing of the products. The main objective of the German Wine Group was to grow and develop its product export activities (german-wine-group.com, 2025). By focusing on that objective, the data was specifically gathered on the company’s export activities on multiple wineries in the country of Germany. The corresponding designation and the points of the company’s import activities are going to be helpful in identifying some active and profitable export areas of the company. In this particular, the company could focus more on those particular areas for growth and improvement.
d. Incorporating theoretical models
The data set related to the import and export of the German Wine Group was connected with the comparative advantage theory. According to the theory, the company to understand its ether export and import operations (Boehm et al., 2022). The company needs to improve these services by selecting particular areas where the operational costs are low, or the profit margins are very high compared to the other areas. This can lead to increasing the trade opportunity of the company and overall economic benefits. By reminding the theory, the data set was focused on presenting the relent information that can give the company a valuable understanding of their economic advantages.
Organisations interpret numerical findings to understand trends, variations, and performance outcomes. The insights gathered guide strategic recommendations that can support expansion, operational refinement, and long-term improvement. These findings also contribute to informed decision-making for future actions.
a. Explaining and providing examples of the data analytics activities
Step of the process
Collection: According to Cote, (2021), data collection suggests collecting data or information about a particular subject. In this step, data have been collected in MS Excel.
Cleaning: It is discussed that data cleaning refers to the procedure of removing, and fixing incorrect, duplicate, corrupted, and incomplete data concerning the data sets (Tableau, 2025). Data cleaning is the process of organizing the data in the most suitable way and eliminating unessential and uncorrelated data such as errors from the data set. The cleaning procedures can be conducted by multiple procedures such as elimination and shorting.
Manipulating: As per the opinions of Chaurasia, (2019), in the data manipulation only the chosen data have been incorporated. Moreover, the manipulation of the data suggests the procedure of changing the data to make it more organised as well as reliable. Data manipulation is the process of editing the pre-occupied data in a data set. The manipulation process was used to present the data in the most appropriate way possible.
Analysis: Data analysis refers to the statical technique of illustrating, and describing the data (HHS Gov, 2024). In this section, the data have been analysed with the help of different statistical tests.
Visualisation
Figure 1: Sum of the column 1 by province
Figure 1 depicts the sum of all province and it interprets the higher value of sum in the province of mosel saar ruwer is the consist in higher value. Rheingau province is also consist in a higher value.
Figure 2: Sum of the price by province
In the above figure sum of the prices of the province consists of a higher portion and the Mosel saar ruwer and mosel is consist of a higher portion.
Figure 3: Price Line Fit Plot
The price line fit plot is presented in visual representations of the data where data drawn is consist in single line chart. It evaluated the over all trending of the price which movements might be showing in the best fit and it directly minimized the distance between itself and the data points.
Duplication
Duplications of the data set mainly refer to the data set and it means the identified copies of the same data entries in the data set.
Incomplete data
The incomplete data suggests incorrect data or that some data are missing in the data.
Missing data
The missing data is suggested when some data becomes missing from the entire data set. It leads to potential errors in the dataset.
a. Analysing the data in relation to the research question and objectives
In this section, certain data analysis methods such as descriptive analytics, regression, correlation, and hypothesis testing will be done to develop the study better with the help of the numerical data to develop the objectives, and research question more appropriately.
a. Interpreting and discussing the results of the analysis
Regression
| Points | Price | ||
|---|---|---|---|
| Mean | 88.62643 | Mean | 38.81974 |
| Standard Error | 0.059146 | Standard Error | 1.172823 |
| Median | 89 | Median | 24 |
| Mode | 90 | Mode | 20 |
| Standard Deviation | 2.928762 | Standard Deviation | 58.07548 |
| Sample Variance | 8.577645 | Sample Variance | 3372.761 |
| Kurtosis | -0.11406 | Kurtosis | 65.27541 |
| Skewness | -0.09473 | Skewness | 6.994182 |
| Range | 17 | Range | 835 |
| Minimum | 80 | Minimum | 1 |
| Maximum | 97 | Maximum | 836 |
| Sum | 217312 | Sum | 95186 |
| Count | 2452 | Count | 2452 |
| Confidence Level(95.0%) | 0.115981 | Confidence Level(95.0%) | 2.299827 |
5 Years | PhD
Table 2: Regression analysis
It can be obtained from the above table that the mean value of the points, and the price are 88.62, and 38.81 respectively. The std value of the points is 2.92 and the std value of the price is 1.17.
The R-value of the regression statistics is 0.38 while the R-square value is 0.14. On the other hand, the adjusted R-value is 0.14.
Correlation analysis
Figure 4: Correlation analysis
The correlation value between the points, and the price is 0.38.
b. Presenting a comprehensive analysis of the results
In the regression analysis, the value of the regression suggests a high level of data dispersion, and widely spread. It further indicates that there is a weak relationship between the independent, and dependent variables. The R, R square and adjusted R square values indicate a positive correlation between independent and dependent variables. It is further obtained from the correlation value that the points and the price are not statistically significant as the correlation value is higher than the ideal value (0.05). In the hypothesis testing, it can be witnessed that the df value is higher which further suggests a large sample size. It usually provides more power for detecting a prominent impact and enables a smaller critical value to reject the null hypothesis.
Recommendations
a. Suggesting well-grounded recommendations based on implications and findings
Recommendation 1: Incorporating clear data collection forms, and mandatory fields
In order to avoid the issues related to missing data, the incorporation of clear data collection forms can be done. Further, the mandatory fields option needs to be implemented to prevent such issues.
Recommendation 2: Implementing data analytics to identify the trends of the market and customer behaviors
It has been previously found that due to the use of traditional domestic sales channels, the exportation of wine has decreased. Therefore, concerning this, data analytics can be implemented which will benefit the GWG to overcome this issue.
Conclusion
It can be concluded from the above figure that GWG is one of the leading wine producers and exporters in Germany. However, the company despite of its efforts is facing an issue regarding decreasing numbers of exportation. Further, a statistical analysis has been made to make the analysis more relevant and practical. In order to mitigate this issue, the company has been recommended to implement data analytics to identify the trends of the market.
References
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