HI6037 Business Analytics Fundamentals Assignment Sample
Assignment Details
• All answers must be entered in the answer boxes provided after each question.
• Your assessment must be in MS Word format only.
• Reference sources must be cited in the text of the report and listed appropriately at the end in a reference list using Holmes Institute Adapted Harvard
Referencing. Penalties are associated with incorrect citation and referencing.
Question 1
1A: Describe the steps you would take to merge the cleaned_Location.xlsx and cleaned_Budget.csv datasets in Power BI. What key factors must you consider to ensure data integrity?
1B: Using the merged dataset, create a visualization in Power BI that displays the correlation between budget allocations and property price changes over time. Explain how your visualization can be interpreted.
Datasets Used:
1 cleaned_Location.xlsx
2 cleaned_Budget.csv
Question 2
2A: Explain how you would assess the accuracy of sales forecasts using the cleaned_Forecast.csv and cleaned_dim_tables_final.xlsx datasets. What specific Power BI functionalities would you use?
2B: Develop and describe a geographic visualization in Power BI that highlights sales distribution and high-performing regions. Include details on any filters or slicers you would add to enhance interactivity
Datasets Used:
cleaned_Forecast.csv
cleaned_dim_tables_final.xlsx
Question 3
3A: What variables have been included in this synthetic dataset for simulating tourism data? Can you briefly explain each one?
3B: Create a dynamic map in Power BI using your synthetic dataset that shows tourism density and its economic impact on local economies. Describe how you would set up this visualization to provide insights into peak times and spending patterns.
Datasets Used:
cleaned_Synthetic_Tourism_Data.xlsx
Question 4
4A: Discuss how you would analyze customer demographics, purchasing patterns, and profitability using the synthetic transaction data. What analytical techniques would you apply?
4B: Design a dashboard in Power BI to visualize distinct customer segments based on profitability and purchasing behavior. Explain how each element of the dashboard contributes to understanding customer segments.
Datasets Used:
cleaned_Synthetic_transaction_data.csv
Question 5
5A: Describe the process you would use in Power BI to cleanse and prepare a complex dataset for analysis. What challenges might you encounter and how would you address them?
5B: Prepare a presentation in Power BI to showcase findings from one of the datasets provided. Explain how you would use narrative elements and annotations to guide viewers through your analysis.
Datasets Used:
Any provided dataset (e.g., cleaned_Budget.csv)
Solution
Question 1A
To merge cleaned_Locationxlsx and cleaned_Budgetcsv in Power BI, first, it is required to import both tables: Pinpoint the position of the area/areas of concern in these fields and establish the main indexing instrument such as ‘LocationID.’ As subsequent merge is done based on this key, it is adequate to complete this process in the PQ Editor (Sharma et al. 2021, pp. 1-11). In making decisions here one is able to manage conflicts, freeze or expel members and make decisions regarding short comings concerning the quality of contents within the said set. It is also very crucial to highlight the fact that all the columns that you are to include in one should have the same data type. As for the other aspects concerning this process, they include; quality check of the data to be merged is conducted while missing values as well as synchronization of key fields is also done in order to have an efficient merge of data for university assignment help.
Question 1B
.png)
Figure 1: Correlation of Budget Allocation and Budget Property Price Change
(Source: Power BI)
(Source: Power BI)
Looking at the budget on sum of comparison between 2020-2024, again the values showed negative up move indicating that the changes in property prices. So the actual reduction was 0.67% or 3425.40 units below 512,212.18 to 508786.78. This period was witnessed by the budgeting period which was lower than any other period indicating a lot of ignorance of the budget. The monthly percentage change in property prices was also lower, at 69% during the same period. Within the last few years, all the aspects other than the budget have set quite stable trends; the significant drop in the budget commenced in the year 2020. It has been seen that it has been on a decline every year for four consecutive years; it thus indicate changes that are major on budget priorities, which may not be independent but are most likely to be allied to other changes of an economic or an organisational nature.
Question 2A
To finally determine the degree of cleanliness of cleaned_Forecast, which denotes the extent of accuracy of the sales forecasts, the corresponding measure will be computed. csv and dim_tables. On Power BI, go through the Import section and joinery processes to enrich the forecast data for the analysis with the reference data including Cost Element and Business Area. To formulate definitions of the accuracy parameters, generate DAX measures for MAE, MAPE, and RMSE (Houssein et al. 22, pp. 1-36). The use of line graphs to illustrate the actual and forecasted sales of a particular product; use of scatter plots to assess the accuracy of the forecast; and use of tables to indicate the number of errors incurred. For slicing into other finer subcategories use time and dimensional slicers. Developing plausible analytical front-end interfaces to enable time-synchronized viewing and control over the forecast accuracies to facilitate strategic business decisions.
Question 2B
.png)
Figure 2: Geographic Visualisation
(Source: Power BI)
While comparing the data for each country in each of the regions and the consolidated global data it can be deduced that the actual number of countries which made sales were less with the maximum being 62 units, 984.90 and 245.05. Thus, the total count of the country shares with respect to response rate, which is 45.59% is thus captured by 91.35 of the total count of countries. The actual counts of all the twelve data include all the continental and most of the developed countries across the world ranging from 2 upto 62. When using Power BI there is an option of geographic visualisation which can be used to show the above distribution and even possibly identify regions that score high. This would provide geographical perspective on how countries are offers and extent of dispersion, taking color intensity or size of bubble as count and sales contribution. For instance, if describing the effective date differently or sales metrics as a filter or slicer would enhance the interaction level, where the user evaluates the dispersion of sales in a particular period or for specific coefficients.
Question 3A
The synthetic dataset cleaned_Synthetic_Tourism_Data. xlsx includes the following variables: Date is to record the specific day of the following; Attraction is to list down various tourism attraction sites; Visitor_Count is to record the number of visitors on the particular day in each site; Average_Spend to record the spending of the visitors in each site; Local_Business_Revenue is to record the total revenue from tourism business in each site. Combined, these variables offer more details regarding tourism density, tourists spending and the effects on the economy for proper analysis of visitors’ profile, business income and tourism strategy and development.
Question 3B
.png)
Figure 3: Dynamic map in tourism density and its economic impact on local economies
(Source: Power BI)
Analyzing the dynamic map formulated in Power BI based on the synthetic dataset, it can be identified that the Kakadu National Park was visited by a total of 73,430 tourists and Blue Mountains having 32 tourism points had the lowest tourists’ turnout of 72,486 visitors for entire attraction type which is even 1.30% more than Blue Mountains. The visitor count for Kakadu National Park as compared to the total number of visitors for the ten attractions was a thousand and six percent. The average of the visitor counts was fluctuating in the specified range with the lowest number being 72,486 and maximum of 73,430. As a result of using the time slicer, the most popular hours for visiting are specified and the number of visits is determined, which contributes to evaluating the significant trends and effects on enterprises and helps to improve the perception of the time and money necessary for planning and investments.
Question 4A
The analysis of the magnitude of the customer base, the volume and frequency of their purchases, and the profitability of the various classifications involves different analytical tools when done with the synthetic transaction data. Firstly, according to the method called segmentation analysis it is possible to classify customers based on the factors like age, gender, and geographical location. Secondly, the RFM (Recency, Frequency, Monetary) analysis helps in defining the clients with the higher value due to their purchasing activities more easily. Last but not least, in profitability analysis, one can determine the relation between the revenues and costs which help in identification of profitable customer segments (Christy et al. 2021, pp. 1251-1257). These and other methods such as cohort analysis, Customer Lifetime Value calculation, churn prediction will help to improve knowledge and make effective decisions regarding marketing, product creation or development and CRM approaches.
Question 4B
.png)
Figure 4: Visualise Distinct Customer Segments
(Source: Power BI)
The information that was used in the above analysis is all about the financial performance of the customers and the patterns of purchases. Using the total quantity sold by the customers’ total quantity as a criterion, this research reveals that Customer 17 is the most active in this respect. But achieving the large proportionality of this customer’s contribution in terms of total quantity, 10.64% in fact, may yield great profit. Further Total Quantity reciprocally clarifies the relativity between both these variables on the generation of sales figures Total Sales. In this way, we are able to compare Total Sales and Total Quantity data and can note, for instance, that ProductID 17 can be considered an opportunity for improvement. The effort of constructing Power BI dashboard with characteristics of this knowledge will involve stacking bar oriented graphs and cards while using the measures of profitability and purchasing behavior for segmentation.
Question 5A
When you have a large and unfair data to analyze in Power BI, load the data and use the query editor. Removing duplications if data missing it should be either imputed or the data conversion should be done. Format too needs to be coherent from one format to another, such as date format and currency format. If the final database layout as a vision requires splitting or merging of rows and/or columns then it is done in an as needed basis. For example, incoherent data, data that is missing some values and big data; there should be systematic profile and validation of data. Use profiling where one can be in a position to identify the extent of the inconsistency and gain insights about the data through Power BI. Provide the right way of updating and cross-checking the data to make it healthier and ready for analysis.
Question 5B
.png)
Figure 5: Showcase Findings on a Dataset
Based on the general observation of the Power BI presentation insights described above and the other similar ones, it is apparent that the Administration department was provisioned with a total of 222 units of budget. Also, the financial breakdown of the federation budget on 1st January,2024 show that United State of America has contributed the biggest chunk with 60.81% of total budget. Thus, the following conclusions can be made based on the data considered above: the Administration department has significant impact the budget sensitisation; meanwhile, a great many of budget is used in the operations in USA. An additional resource may be the running text with the details of order presented and comments to them useful in drawing the attention of the viewer to these observations and what they imply.
References
Sharma, K., Shetty, A., Jain, A. and Dhanare, R.K., 2021, January. A Comparative Analysis on Various Business Intelligence (BI), Data Science and Data Analytics Tools. In 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-11). IEEE.
Houssein, E.H., Dirar, M., Abualigah, L. and Mohamed, W.M., 2022. An efficient equilibrium optimizer with support vector regression for stock market prediction. Neural computing and applications, pp.1-36.
Christy, A.J., Umamakeswari, A., Priyatharsini, L. and Neyaa, A., 2021. RFM ranking–An effective approach to customer segmentation. Journal of King Saud University-Computer and Information Sciences, 33(10), pp.1251-1257.
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