Data Analytics Study Case : SuperMarket Sales

Hi sowmya12345 

This is an interesting and challenging question, especially for someone who is just starting in data analysis. To answer the user's query, I would suggest the following steps:

1. Data Review:

• Start by reviewing the data structure in the CSV file. Are all the columns mentioned in the description present in your file? Are there additional columns that were not mentioned?
• Check the completeness of the data – are there any missing values or other quality issues?

2. Examining Relationships Between Data:

• Ensure that you are using the correct relationships between the different tables or fields. For example, is every field directly or indirectly related to the "Total" or "Gross Income" fields? Are there other logical relationships that could provide interesting insights?
• Consider analyzing dependencies between different fields, such as the impact of "Customer type" on gross income or the impact of "Product line" on customer rating.

3. Use of Visualizations:

• Ensure you cover all aspects of the data using appropriate visualizations. For instance, have you used charts that show relationships between different data points? Are there insights that are not clearly presented?
• Try creating separate reports based on different segments (e.g., by customer type or product type) to see if there are additional insights that weren't revealed in the general report.

4. Using Models and Functions:

• Consider using advanced Power BI functions to explore more complex relationships in the data. For example, is there a correlation between the time of day and the type of product sold or the payment method used? Is there a field from which you can calculate an additional metric that might interest you?

5. Comparative Analysis:

• Make sure you are comparing the data to industry averages or standards if possible. Check for any anomalies that may indicate the need for further investigation.

6. Requesting Feedback:

• Post the report (not a snapshot) on the forum and ask for feedback from the community. You can request suggestions for improvements or additional checks that you might have missed.

This is a process that requires patience, curiosity, and the use of various data analysis tools. As you gain more experience, you'll be able to discover and understand more relationships between the data and generate new insights