2x Faster Insight Delivery with Streamlined Data Migration

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For a Global Retail Leader

“When processing massive volumes of retail data, delivering faster insights with accuracy and scalability isn’t a one-time goal – it has to be built into the system every day.”

– A Chief Data Officer at a global retail enterprise

When a global retail leader wanted to modernize its legacy data platforms and accelerate access to business insights at scale, MovateAI-driven data migration and modernization solution delivered transformational impact:

Boosted platform performance

Reduced code complexity for leaner operations

Faster business insights

Business problem

Every delayed insight is a missed opportunity to respond to shifting demand, optimize pricing, and elevate customer experience.

In today’s data-driven economy, speed and accuracy of insights are no longer optional but they define competitiveness. For global retailers, delays caused by manual pipelines, poor scalability, and fragmented legacy systems are not just technical inefficiencies; they’re barriers to agility and growth. Incomplete documentation, rising licensing costs, and limited visibility into bottlenecks turn data into a burden instead of a strategic asset, slowing decision-making and putting business performance at risk when timely insights matter most.

Customer challenge

Our client, a global retail leader, relies heavily on data-driven insights to power pricing strategies, demand forecasting, and customer experience initiatives.

However, legacy data platforms posed multiple roadblocks. Manual triggering of critical pipelines delayed insights, while poor scalability and inefficiencies in Spark-based systems created bottlenecks that slowed decision-making. The lack of complete documentation around ML models and business logic introduced significant risks during handoffs, adding complexity to already fragile operations.

Rising annual licensing costs on the legacy platform further strained resources, while limited visibility into processing bottlenecks impacted time-to-insight. As the business grew, the legacy infrastructure could no longer keep pace with the velocity, scale, and accuracy demanded by modern retail operations turning data into a liability instead of a strategic enabler.

Movate’s AI solution

To overcome the limitations of legacy systems, a resilient data modernization strategy was implemented. A three-tier Medallion architecture on Databricks, integrated with Azure Data Lake and PySpark, streamlined workflows and enabled distributed, parallel processing for faster, more reliable insights. Legacy pipelines were simplified into modular, reusable components, while redundant tables were reduced and schemas optimized for performance.

Robust reconciliation, validation, and testing ensured accuracy throughout migration, and scalable ML workflows were built to publish trusted insights from curated data layers. This modernization not only boosted platform performance and reduced complexity but also established a sustainable foundation for analytics reusability and faster decision-making.

Movate AI differentiator

This migration leveraged Databricks on Azure to deliver 2x faster access to business insights, streamline analytics workflows, and cut code redundancy by 10%. By boosting platform performance by 30% through optimized architecture and cloud-native infrastructure, the solution unlocked scalability and reliability at enterprise scale.

With deep expertise in data migration, validation, and ML integration, the engagement empowered this global retail leader to transform data operations, driving analytics reusability, improving process clarity, and accelerating decision-making with timely, trusted insights.