About the Client
A leading multinational oil and gas company known for its focus on sustainable practices and cutting-edge technologies with global operations across exploration, production, and refinement. As a key player in meeting global energy demands, the company sought enterprise-wide data visibility and robust data management to support its next phase of growth and operational excellence.
Challenges
The client initially sought to unify data across its globally distributed systems to improve enterprise visibility. However, as their operations scaled and digital maturity progressed, new business needs emerged—requiring a more advanced, intelligent, and secure platform to support real-time analytics, self-service capabilities, and scalable governance.
Key challenges included:
- Disparate systems (ERP, CRM, terminal management) across 140+ countries with no unified view
- Time-intensive manual reporting for processes like monthly balance sheets
- Delayed data refresh cycles, affecting decision-making
- Inconsistent data governance and compliance risks due to fragmented access control
- Limited self-service data access for business users
- Inability of legacy infrastructure to support scale, performance, or advanced analytics
The organization needed a secure, scalable, and AI-enabled architecture to unify data, automate workflows, and empower business teams without compromising on control or compliance.
Solution
To address both foundational and advanced needs, Bridgenext deployed a multi-phase data modernization program anchored on Databricks and an enterprise-grade data lake strategy.
Phase 1: Data Lake Architecture and Data Strategy
- Enterprise Data Lake Architecture: Implemented a centralized data lake to ingest data from 18+ enterprise systems and standardize it into a unified analytics layer
- Data Aggregation at Scale: Consolidated ~7TB of structured and unstructured data across 10 regions and 140+ countries
- Data Catalog and Lineage: Initiated structured metadata and lineage tracking for traceability and governance
- Common Analytics Model: Standardized reporting across business units with a unified data schema
Phase 2: Intelligent Enablement and Automation
- Centralized Platform on Databricks: Re-architected to support AI-driven use cases with a scalable, secure platform
- Unity Catalog Integration: Enabled secure, role-based access, data masking, lineage tracking, and encryption
- Self-Service BI Portal: Unified access experience across Power BI, Tableau, and Databricks dashboards
- GenAI Assistant (Databricks Genie): Integrated a natural language interface to simplify data querying for business users
- Financial Report Automation: Automated monthly balance sheet processes to reduce turnaround time from days to minutes
- TCO and Performance Benchmarking: Measured efficiency improvements and cost savings
Results
What began as a data visibility challenge transformed into a robust, AI-enabled data ecosystem. By responding to the client’s evolving business needs with upgraded solutions, Bridgenext not only modernized their data architecture but also elevated the end-user experience—empowering teams to make faster, smarter, and more secure decisions that resulted in:
- ~90% Faster Financial Reports – From days to minutes
- Stronger Data Security – Unified governance across countries
- Higher BI Tool Adoption – More users accessing insights independently
- AI-Driven Interactions – Business users querying data via natural language
- Operational Excellence – Standardized acquisition and data processing methodologies
- Empowered business users with secure self-service analytics
