Pharmaceutical Data Analysis - Psyliq Internship

Key Problem Statements and Solutions:

1. Schema Design:
   • Developed a robust Power BI data model, incorporating appropriate tables and relationships for essential entities such as Distributor, Customer Name, City, and other relevant columns.
2. Relationships:
   • Established crucial relationships between tables, creating a cohesive data model. For instance, connected the "Sales" table to the "Customers" table for comprehensive analysis.
3. Role-Playing Dimensions:
   • Demonstrated adept handling of role-playing dimensions for "Sales Rep" and "Manager" within the data model, ensuring flexibility in analysis.
4. Schemas:
   • Constructed a star schema to optimize report performance, explaining how the schema design enhances data retrieval and analysis efficiency.
5. Row-Level Security:
   • Implemented row-level security to restrict access for a specific sales team, showcasing the impact on measures affected by the security setup.
6. Calculated Columns vs. Measures:
   • Calculated total sales for each product both as a calculated column and a measure, highlighting differences in results and explaining the rationale behind each approach.
7. Time Intelligence:
   • Utilized DAX to create a measure calculating year-to-date (YTD) sales for each month, facilitating temporal trend analysis.
8. Filter Context vs. Row Context:
   • Crafted a DAX calculation showcasing the total quantity sold by each sales rep, providing insights into the application of filter and row contexts.
9. Ranking:
   • Developed a DAX measure ranking products by sales and visually represented the top 5 products by rank.
10. Parent-Child Hierarchies:
    • Created a DAX measure summarizing sales at the subcategory level, addressing hierarchical data structures.
11. Drill-Through:
    • Built a report allowing users to drill through from a summary to detailed data, enhancing interactivity and user exploration.
12. Custom Visuals:
    • Integrated a custom visual into the report, explaining the choice and demonstrating a unique way of visualizing sales data.
13. Bookmarks and Buttons:
    • Created a report with bookmarks and buttons, enabling users to navigate seamlessly between different report pages or states.
14. Conditional Formatting:
    • Applied conditional formatting to a measure, changing color when sales exceeded a predefined target value for quick visual identification.
15. Calculated Columns:
    • Added a calculated column to calculate the total cost of each product (Quantity x Price), enriching the data model with additional insights.
16. New Column from Example:
    • Utilized the "New Column from Example" feature to categorize cities into regions based on a predefined mapping, enhancing geographical analysis.
17. Time-Based Calculations:
    • Developed a measure calculating year-over-year (YoY) growth in sales for each month, providing insights into performance trends.
18. Cumulative Total:
    • Implemented a measure showcasing the cumulative total of sales over time, visualized in a line chart to understand the overall sales trajectory.

Conclusion: The Pharma Data Analysis project showcased my expertise in Power BI, data modeling, and advanced analytics. By addressing key problem statements, the project not only provided actionable insights for the pharmaceutical industry but also demonstrated my ability to optimize reporting for informed decision-making.