Complex data modeling and challenges

 

Project: Sales Performance Analytics for a Retail Chain (End-to-End Data Modeling)

Objective:
The goal was to build a scalable and insightful data model to track sales performance, customer behavior, and product trends across multiple regions and stores.

1. Understanding the Requirements:

I collaborated with business stakeholders to define key KPIs like total revenue, profit margin, average basket size, and customer retention rate. They needed both high-level dashboards and the ability to drill down by region, store, category, and time.

2. Data Sources and Challenges:

• Data Sources:

  • POS transactions from multiple stores (CSV files and SQL DB)

  • Product master data

  • Customer loyalty data (from CRM)

  • Regional targets from Excel reports

• Challenges:

  • Data inconsistency between systems (e.g., mismatched product codes).

  • Data volume—millions of transactions requiring efficient processing.

  • Frequent schema changes in source files.

  • Need to enable self-service BI without users writing SQL.

3. Data Modeling Approach:

• Chose a star schema for performance and simplicity.

• Fact table: Sales transactions (granular to each item per invoice)

• Dimension tables: Product, Store, Date, Customer, Region

• Built this model in Power BI using Power Query for transformation and DAX for measures.

• Used Power BI Dataflows to centralize and reuse ETL logic.

• Created a semantic layer using DAX measures for consistent KPIs.

4. Overcoming Challenges:

• Implemented data validation scripts in Power Query to flag mismatches early.

• Used incremental refresh in Power BI to handle large datasets.

• Created data dictionaries and a KPI glossary to align definitions with stakeholders.

• Built a role-based security model so regional managers saw only their data.

5. Results and Impact:

• Reduced reporting turnaround time from 2 days to under 1 hour.

• Empowered 30+ business users with self-service dashboards.

• Improved data accuracy and trust, leading to data-driven decision-making.