Project Name
AI-Ready Big Data Platform Designed for 5 TB Oil and Gas Analytics, GCC
A national oil marketing company in Oman operates across four downstream business segments: fuel retail, lubricants, aviation, and logistics. With 5 TB+ of structured data and 1 to 2 TB of unstructured content locked inside separate source systems and no centralized data platform, leadership had no cross-functional visibility and no foundation to begin AI/ML-driven decision-making.
As digital transformation accelerated across the GCC energy sector, the company recognised that a modern Big Data platform was essential to remain competitive and operationally efficient. Ksolves was engaged to design an AI-ready Big Data platform that would unify all data sources and enable smarter operations across every segment.
- Fragmented Multi-Source Data Environment: Data across fuel retail, lubricants, aviation, and logistics was held in separate systems with no common schema or integration layer, preventing cross-functional analytics.
- No AI/ML Foundation: The company wanted to use AI for pricing optimisation, demand forecasting, and operational efficiency, but had no governed data platform to build AI models on.
- Unstructured Data Excluded from Analytics: 1 to 2 TB of unstructured content (documents, reports, sensor logs) was entirely inaccessible to analytics teams, representing significant untapped intelligence.
- Real-Time Streaming Gaps: Operational data from fuel retail and logistics was produced continuously but only available in batch, preventing real-time operational monitoring and alerting.
- No Centralised Reporting Layer: Business leaders relied on siloed departmental reports with no enterprise-wide dashboard. Cross-segment performance analysis required days of manual data collation.
Ksolves proposed a modern Big Data platform architecture combining structured and unstructured data ingestion, a governed data lake, real-time streaming, and an AI/ML-ready analytics layer. The design was built for Oman's regulatory context and the company's multi-segment operational model.
- Unified Data Lake Architecture: A centralised data lake was designed to ingest both structured operational data (5 TB) and unstructured content (1 to 2 TB) from all four business segments into a single governed repository.
- Real-Time Streaming Ingestion: A real-time streaming layer was designed to ingest continuous operational data from fuel retail and logistics, enabling live monitoring dashboards and near-real-time alerting.
- AI/ML Analytics Framework: Ksolves defined an AI/ML analytics framework aligned to the company's priority use cases, including pricing optimisation, demand forecasting, and HSE compliance monitoring.
- Structured and Unstructured Data Processing: A unified processing architecture was designed to handle both structured relational data and unstructured document and log content, enabling analytics across the full data estate.
- Multi-Segment BI Layer: A centralised BI layer was architected to deliver cross-segment performance dashboards for leadership, replacing manual report collation with governed, real-time analytics.
Technology Stack
| Category | Technology |
|---|---|
| Architecture | Big Data Platform |
| Streaming | Real-Time Streaming |
| Storage | Data Lake |
| AI/ML | AI/ML Analytics |
| Frontend | BI Dashboards |
- Cross-Segment Data Unification Designed: 5 TB+ of data was siloed across 4 business segments with no unified analytics layer. The unified Big Data platform architecture now consolidates all structured and unstructured data, enabling single-platform analytics across all segments (target: operational within 6 months).
- AI/ML Foundation Established with 3 Prioritised Use Cases: There was no governed data platform, so AI/ML use cases could not be safely prototyped or deployed. An AI/ML analytics framework covering pricing optimisation, demand forecasting, and HSE compliance has now been defined with a full data readiness assessment, ready for immediate model development post-deployment.
- Real-Time Operational Visibility Enabled: Operational data from fuel retail and logistics was only available in batch with no real-time monitoring or alerting capability. A real-time streaming architecture now supports live dashboards and sub-hour alerting on critical KPIs (target).
- Unstructured Data Brought into Analytics Scope: 1 to 2 TB of unstructured documents and logs was entirely excluded from analytics, leaving significant operational intelligence untapped. A unified ingestion architecture now makes this content queryable and analytics-ready.
“We had data everywhere but insight nowhere. Ksolves gave us a clear architecture to bring it all together, and for the first time, we can see a path to AI-driven decisions across every part of our business.”
– Chief Digital Officer
The company came in with 5 TB+ of fragmented data across four business segments, no unified platform, no AI capability, and no way to produce cross-functional analytics without days of manual effort. Ksolves designed a modern Big Data platform that brings all structured and unstructured data into a single governed layer, with real-time streaming and a clear AI/ML roadmap built on top of it.
The platform positions the company as a data-driven operator within the GCC energy sector, with a foundation to execute AI models on live data covering pricing, demand, HSE, and logistics, driving measurable operational efficiency and margin improvement. For energy and industrial organisations ready to modernise their data estate, Ksolves’ Big Data consulting services and data engineering services deliver governed, AI-ready platforms built for operational scale.
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