Outdated workflows and growing datasets strain IT expense analysis
MagicOrange is a cloud-native enterprise IT financial management company dedicated to helping organizations, particularly in financial services, optimize their technology spend. By providing clarity and efficiency in correlating IT, shared services and divisional costs with business activities, MagicOrange empowers clients to make better investment decisions and improve profitability.
MagicOrange leverages the Databricks Data + AI Platform for both customer-facing and internal use cases, transforming how they handle vast and complex datasets. Clients rely on MagicOrange for real-time answers to challenging questions about monthly expenses, cost trends and variances. “Our clients face constant demands for timely financial insights, and MagicOrange ensures they can confidently answer stakeholder questions with accurate data,” Michael Brennan, CTO at MagicOrange, said.
MagicOrange serves enterprises managing terabytes of data per customer, spanning hundreds of billions of rows across all clients. Their solution enables users to query and explore these massive datasets efficiently, particularly for expense analysis and cost optimization. Internally, MagicOrange uses Databricks to support data engineers and analysts in building advanced analytics pipelines and exploring curated datasets. “With our move to Databricks, we can finally scale our data processing and uncover new insights without the bottlenecks of legacy systems,” Bhushan Tambatkar, Azure Architect at MagicOrange, shared.
What were the challenges? Before adopting Databricks SQL and Genie, MagicOrange faced the challenge of scaling processing and storage capabilities to handle massive customer data volumes while controlling costs. They also sought a platform that could seamlessly integrate a natural conversational experience, enabling self-service analytics while enhancing their existing embedded analytics offerings.
Relying on technical teams for data queries and analytics often made it challenging for users to access the insights they need quickly. Additionally, managing the sheer scale of data — terabytes per customer — while maintaining security and efficiency was a growing challenge. Bhushan noted, “Building a scalable, secure and cost-effective solution for advanced analytics required us to rethink our entire approach.”
