Analyse seasonal trends in the data using dax calculations

Assignment Help Computer Engineering

Reference no: EM133217798

Part (a)
The owner for FL Clarity Pty Limited (FLC), Adam, is impressed with your findings as to why the business's profits have stagnated and your insights into the business's wider issues. During a meeting you have with Adam, he confesses that operating stores in three locations has been stressful and has taken up significantly more time than he anticipated. He has had limited time to think strategically about the future direction of the business; however, he is excited about your recommendations and impressed that they were informed by data. He is confident there is further opportunity to use the financial and non-financial information that the business collects to better inform its decision making. He is particularly excited about the prospect of using evidence to inform FLC's strategic direction. He is very conscious of the competitive nature of the jewellery market and wants to ensure that the business remains competitive.

Your investigations identify that FLC has been doing basic forecasting, but its forecasting is often rushed and lacks accuracy. Adam explains that not all the decisions made by FLC have been fully informed, which has cost the business money. He approaches you with the challenge of assisting him with FLC's forecasting. He wants to ensure that the decisions that FLC makes in the future are based on evidence, and not on intuition.

You confirm that FLC prepares its forecasts in Microsoft Excel. Preparing these forecasts is very time consuming, and it accounts for only a limited numbers of potential performance drivers.

Adam requests that your first area of focus be on understanding the current performance of FLC's stores. Adam provides you with a dataset, created by FLC's database administrator. The dataset includes sales data for each store across the product categories. The dataset also includes the following additional tables that could help you to prepare the forecast:
• DataSales - This table includes sales information for each store based on the different product categories and calendar weeks.
• DimStore - This tables includes further information on the different stores, including store names, density of the suburbs the stores operate in (measured in people per square kilometre), and the total populations of the suburbs the stores operate in.
• DimCalendarWeek - This tables includes information on calendar dates. It also includes information on the seasonality (for example, based on past data, FLC considers wedding season to be in December and January, Valentines' Day season is in February, and Mother's Day season is in April and May).
• DimCategory - This table includes information on FLC's product categories (rings, earrings, necklaces) and a description for each product category.
• DimMaterial - This table includes a description for each material used by FLC for the different product categories.
The database administrator has already cleansed the data and provided you with the following data model that you
should use for your analysis of?the dataset:

Task 1:
Analyse seasonal trends in the data using DAX calculations.
Requirement

Use Dataset Part (a) for the activities in Task 1. Save your answers for Task 1 in a Microsoft Word document. This will form the first part of your 2000-word written assignment.
Use DAX calculations in Power BI to create the following measures. Copy the formula for each measure into your report document and provide a brief explanation of the logic of each formula used to create the measures:
i. A measure that calculates the total amount of sales generated by all stores for the 52 calendar weeks. Name this measure ‘TotalSal'.
ii. A measure that calculates the weekly average sales that all stores generate. Name this measure ‘WeeklySalStore'.
iii. A measure that calculates the total amount of sales during the Wedding Season. Name this measure ‘TotalWeddingSeason'
iv. A measure that calculates the total sales generates by all products that use gold as a material. Call this measure ‘TotalSalMaterial'
In Power BI, use the measures from Task 1.a to create the following data matrices. Take screenshots of the matrices and paste them into your report:
i. A matrix that displays values for ‘TotalSal', ‘WeeklySalStore', ‘TotalWeddingSeason', ‘TotalSalMaterial'.

ii. A matrix that displays the total sales generated by each product category.

iii. A matrix showing ‘TotalSal', ‘WeeklySalStore', ‘TotalWeddingSeason', ‘TotalSalMaterial' for all stores, except the online store.
iv. A matrix that shows the sales generated by the online store during the Valentine's Day season only.

Task 2:
Use Power BI to generate insights into the drivers of sales peaks
Requirement

Use separate pages in Power BI for each visual required in Task 2. Take screenshots of each page and paste them into the Microsoft Word document you used in Task 1. This will form the second part of your 2000-word written assignment.
Identify the store with the highest total sales generated during the wedding season. For this store, create a visualisation to identify the highest sales peak over the course of 52 weeks. Use Power BI to identify significant contributing factors that have accounted for the increase in sales for the identified peak. Include screenshots of the visualisation and your Power BI output for the identified contribution factors in your 2000-word assignment.
Interpret the results generated by Power BI in task 2a.

Create a 20-week sales forecast (based on calendar week) for each store. The forecast should be based on a seasonality of 25 and a 95% confidence interval.

Task 3:
Evaluation and Recommendations
Requirement

Save your answers for Task 3 in the same Microsoft Word document you used in tasks 1 and 2. This will form the third part of your 2000-word written assignment.
Evaluate the results of the sales forecast in Task 2c and determine the store that has the most promising sales forecast.

Recommend two datasets (in addition to the data already provided) that could help generate insights on FLC's performance. Explain how each type of data analytics (descriptive, diagnostic, predictive and prescriptive) could be applied to the datasets to generate more insights on FLC's performance.

Attachment:- Assessment - Case study.rar

Reference no: EM133217798

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