The Alshaya Group is a leading name in international brand franchising. They have a comprehensive portfolio of businesses and investments in various divisions including food, fashion, pharmacy, health and beauty, leisure, home furnishings, and entertainment. Today, the retail giant owns over 4000 stores and carries over 90 renowned brands including Starbucks, H&M, American Eagle, and The Body Shop.
One of Alshaya Group’s core values is delivering stellar customer experiences. With rapid shifts in consumer behavior across its diverse range of brand categories, the company needed to stay updated with evolving customer requirements. Alshaya decided it was time to embrace digital transformation and overhaul its existing legacy data architecture.
The global brand franchiser planned to implement modern retail and merchandising solutions for better order, inventory, and warehouse management across all of their locations. As a byproduct of these implementations, the company was looking to streamline processes for onboarding new brands, deploying new projects, and developing 360 degree views of consumer and brand data.
Their primary focus was to deploy a standardized data migration framework that could intelligently handle their high volume of historical data. Management opted for a hybrid cloud approach, meaning that the company needed to move their historical data from their legacy ERP systems into an enterprise data warehouse. This made trusted data available for real-time analytics through cloud applications. Unfortunately, Alshaya didn’t have the technical resources, dedicated processes, or the right technology stack to build and sustain a standardized framework for moving historical data.
Migrating the historical data required robust mapping, profiling, and validation workflows to ensure data quality. Alshaya Group’s historical data of thousands of stores, digital channels, and internal systems was scattered across different systems. Sales, products, customer, and inventory information was stored in the production database system, while the rest of the data was stored in siloed CSV and Excel files.
Enter Astera Centerprise
After evaluating several data management solutions, Alshaya Group partnered with Astera Software and DvSum to combine their technologies.
Centerprise is a high-performance, scalable data management platform that allows businesses to meet today’s advanced business needs. Powered by an enterprise-grade parallel-processing engine, Centerprise offered Alshaya data extraction, transformation, integration, and data warehousing functionalities all through a single platform.
Astera Centerprise served as the primary data integration tool to populate the enterprise data warehouse with clean, trusted data. The DvSum Enterprise Data Quality Tool provided data discovery capabilities to build a comprehensive catalog for profiling and enriching the source data. Once the data catalog was developed, the DvSum tool cleaned, transformed and validated the data for errors. Centerprise pulled the verified data from the staging database and stored it in XML and flat file formats which were then delivered to the target data warehouse.
The Alshaya Group is an international franchising giant with over 4000 stores worldwide.
Astera Centerprise and DvSum Enterprise Data Quality
Alshaya used Centerprise to migrate legacy data and build an enterprise data warehouse.
With Astera and DvSum, Alshaya migrated more than two billion records and increased the quality of their migration outputs by 20 %.
With the new framework in place, Alshaya Group has executed 16 migration runs and processed over two billion records in over 5000 database tables. With Astera Centerprise, the global franchiser has seen a 20 percent increase in the quality of migration outputs, and has increased overall company efficiency.
DvSum and Astera Software have successfully implemented an end-to-end data migration framework for one of the brands. With category specific iterations, the data solutions providers are working towards creating similar processes for all 90 brands.