Mainframe Modernisation with MongoDB as Operational Data Layer
Blue Turtle Technologies is hosting a Mainframe Modernization event together with MongoDB.
Mainframes continue to play a critical role in many organisations. As digital demands grow and the need for faster access to data increases, businesses are exploring how they can modernise their environments without disrupting the systems they rely on.
Join us for a morning of practical discussion around mainframe modernisation and the role MongoDB can play as an operational data layer.
Together, we’ll explore a progressive approach to modernisation, allowing organisations to start with the right workload, demonstrate value and evolve over time, rather than taking on an all at once replacement programme.
Event Agenda – 7 October 2026
09:00 – 09:30 | Registration, Breakfast and Networking
09:30 – 09:40 | Welcome and Event Introduction
09:40 – 10:40 | Why MongoDB for Mainframe Modernisation
10:40 – 10:55 | Coffee Break and Networking
10:55 – 11:55 | The Progressive Mainframe Modernisation Journey
11:55 – 12:25 | Technical Q&A and Open Discussion
12:25 – 14:00 | Lunch, Networking and Solution Discussions
Event value proposition
Mainframe modernisation does not need to be an all-at-once replacement programme. organisations can start with a high-value workload and use MongoDB’s operational data layer to reduce avoidable mainframe reads, contention, and consumption; consolidate duplicated read paths; and simplify access to operational data. That same layer can provide queryable, application-ready views for operational dashboards, reporting, and data products, enriched with web, mobile, and third-party context for near-real-time insight. By making current customer, account, product, and transaction context available through modern applications and services, MongoDB can also support AI use cases such as fraud detection, risk scoring, personalization, and next-best action.
The event will show how to identify and measure value by workload or domain, not promise a blanket saving, and how MongoDB complements the wider enterprise warehouse, lake, data, and AI platforms. Attendees should leave with a clear understanding of where MongoDB fits, which workload could be a credible starting point, and how a measured first step can evolve into broader modernisation.
MongoDB value proposition in this context
Cost savings
- Reduce avoidable mainframe reads, contention, and consumption by serving eligible digital, API, reporting, and integration workloads from MongoDB.
- Consolidate duplicated read paths and simplify integration around a scalable operational platform.
- For migrated domains, reduce long-term dependency on mainframe data access and legacy data tiers.
- Frame savings as measurable potential, not a blanket promise: the value depends on workload profile, architecture, platform costs, and successful decommissioning.
BI and analytics
- Provide a queryable, application-ready view of mainframe data for operational dashboards/reporting and data products.
- Combine mainframe data with web, mobile, and third-party context to create richer customer, product, policy, or transaction views.
- Support near-real-time operational insight without placing heavy analytical reads on the core mainframe.
- Position MongoDB as the operational and low-latency layer that complements, rather than automatically replaces, enterprise warehouses and data lakes.
AI
- Make current customer, account, product, and transaction context available to AI-enabled applications and decision services.
- Support low-latency retrieval for use cases such as next-best action, fraud detection, churn intervention, risk scoring, and personalization.
- Provide an operational source for AI features and applications while fitting into the wider enterprise data, model governance, and AI platform.
- For modernised applications, support capabilities such as vector search and retrieval-augmented experiences where they fit the target architecture.