Neoware Technology Solutions

The client needed to migrate its Snowflake data warehouse from Azure to AWS within a strict contractual timeline driven by an M&A initiative. At the same time, the organization sought to modernize its data ecosystem, improve enrollment conversion, and establish a scalable foundation for analytics, machine learning, and Generative AI initiatives.

Neoware executed a 16-week migration of the Snowflake data warehouse from Azure to AWS, rebuilding Azure Databricks pipelines using PySpark, Amazon EMR, and dbt. The implementation migrated machine learning models, including Propensity-to-Enroll and Enrollment Revenue models built on XGBoost, to Amazon SageMaker, converted Excel-based reports into Tableau dashboards, and modernized the enterprise data platform on Snowflake while establishing a unified customer data platform and self-service BI capabilities.

The client lacked a centralized enterprise data warehouse and had inconsistent KPI definitions across marketing, product, and editorial teams. Fragmented analytics limited enterprise-wide reporting, slowed decision-making, and reduced visibility into audience engagement, subscription performance, and revenue insights.

Neoware implemented a governed enterprise data platform on Google Cloud that integrated multiple source systems while allowing business teams to continue using their existing analytics and business intelligence tools. The platform established standardized KPI definitions, a single source of truth, and real-time executive dashboards to support cross-functional analytics.

The client operated in a fragmented data environment with business-critical information distributed across finance, operations, and human capital management (HCM) systems. The fragmented landscape prevented a unified view of business performance, required manual reporting processes, and limited real-time access to trusted enterprise data.

Neoware designed and implemented a unified data platform on Databricks running on AWS to centralize and standardize data across finance, operations, and HCM systems. The implementation introduced scalable data engineering pipelines capable of processing structured and semi-structured data while delivering enterprise-wide reporting through Power BI and establishing a single source of truth for the organization.