Optimizing Marketing Analytics: A Data Engineering Data Engineering Solution for a Non-Alcoholic Beverage Company

Client Background

A leading non-alcoholic beverage company operates across multiple regions and runs various marketing campaigns through digital, TV, print, and social media. They aim to measure the effectiveness of their marketing efforts, optimize advertisement spending, and derive insights to improve customer engagement.

Business Challenges

  • Data Silos: Marketing and advertisement data exist in disparate systems (social media platforms, CRM, ad networks, etc.).
  • Scalability Issues: Existing on-premise data processing systems struggle to handle high data volume and variety.
  • Data Quality: Inconsistent and duplicate data from multiple sources create challenges in accurate analysis.
  • Lack of Real-time Insights: Marketing teams lack real-time insights to optimize campaigns dynamically.
  • Compliance & Governance: The company must ensure compliance with data regulations and maintain data security.

Proposed Solution

Ams
Asset Management

Technology Stack

  • Azure Data Factory (ADF): Data ingestion and ETL pipeline orchestration.
  • Azure Databricks (ADB) with Unity Catalog: Data transformation, processing, and governance.
  • Python: Data processing, analytics, and automation.
  • Power BI: Data visualization and reporting.
  • Medallion Architecture: Ensuring a structured approach to data processing (Bronze, Silver, Gold layers).

Solution Architecture

1. Data Ingestion (Bronze Layer - Raw Data Storage)
  • Azure Data Factory (ADF): Extracts data from multiple sources (Google Ads, Facebook Ads, CRM, TV analytics, customer surveys).
  • Azure Data Lake Storage (ADLS): Stores raw data in its native format in a Bronze Layer.
2. Data Processing & Transformation (Silver Layer - Cleaned & Processed Data)
  • Azure Databricks (ADB): Cleans, deduplicates, and normalizes data using Unity Catalog.
  • Medallion Architecture: Implements progressive refinement from raw to clean data.
  • Python Scripts: Handles transformations and data quality checks in Databricks notebooks.
  • Unity Catalog (Silver Layer): Stores cleaned data in the Silver Layer.
3. Data Aggregation & Enrichment (Gold Layer - Analytical Data)
  • Advanced Analytics: Databricks enables customer segmentation, sentiment analysis, and ROI calculations.
  • Unity Catalog (Gold Layer): Stores enriched, business-ready data in the Gold Layer.
4. Visualization & Reporting
  • Power BI Dashboards: Provides interactive insights into marketing performance.
  • Dynamic Filtering: Enables drill-down analysis for campaigns, customer engagement, and ad spend ROI.
5. Data Governance & Security
  • Unity Catalog - RBAC: Ensures role-based access control for secure data management.
  • Data Lineage Tracking: Provides audit trails and compliance monitoring.

Conclusion

By implementing an end-to-end Azure-based Data Engineering Solution, the non-alcoholic beverage company successfully optimized its marketing and advertisement analysis. The Medallion Architecture ensured a structured data processing approach, while Power BI dashboards provided actionable insights, leading to better campaign performance and higher customer engagement.