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Case Study – Data Integration & Transformation – Azure Platform

Industry

Digital Marketing Technology Firm

Function

Infrastructure: Data Transformation

Business Challenge

The client, a prominent performance marketing organization based in Dubai with global offices, serves the fashion and lifestyle industry. They faced significant challenges in information management, including data fragmentation, manual data loading processes, disparate data storage, and inefficient reporting. These issues hindered effective data management and decision-making.

Objective

  • Develop a unified view of the customer and their coupon management system through a data
    warehouse.
  • Create a DataMart and semantic layer to meet faster and more integrated reporting needs.
  • Automate the data management process, including the onboarding of new data sources.

Approach & Solution

A systematic approach was taken to understanding the client’s data challenges, including data sources, formats, systems, business functions, and processes. This thorough analysis allowed us to define clear project goals, such as achieving a unified customer view across business operations and delivering fast, accurate, scalable, and on-demand ROI and business performance reports.

The project was implemented on the Azure platform, utilizing Azure Data Factory to build data pipelines from various sources, including ad platforms, Google Sheets, social media platforms, MySQL, and AWS Redshift. The DataMart and semantic layer were constructed using Azure technologies.

By leveraging advanced data modeling tools, we developed automated tabular data models that
accounted for dynamic factors, such as changing contractual terms across different client locations,
campaigns, products, and pricing structures. This enabled the client to easily access all the key
performance indicators (KPIs) necessary for comprehensive customer, ROI, and performance reporting.

Project Outcome

The solution significantly reduced the reliance on manually generated reports, which previously required
extensive data manipulation for each client and every change in contract terms, products, or pricing. This transformation streamlined reporting processes, eliminating inefficiencies and resulting in faster,
more efficient reporting.

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