From Dual Legacy Analytics Environments to a Unified Cloud-Core Platform

The Pain

The organization faced a fragmented analytics setup: two separate enterprise BI instances built on the same BI platform, each with its own user accounts, roles/groups, dashboards, schedules and governed data models. This scenario led to several key challenges:

• Redundant administrative overhead: maintaining two environments, two permission matrices, two sets of schedules.
• Inconsistent user experiences: some users existed in both environments under different roles, causing confusion and access disparity.
• Performance and reliability issues: scheduled jobs and queries across the two instances exhibited delays or failures due to legacy design and growth of content.
• Governance complexity: having two platforms increased the risk of inconsistent metrics, lack of a "single source of truth" and fragmented user access patterns.

The Process

RavencoreX approached the migration with a structured, phased methodology, aligning both governance and technical aspects. The main steps included:

• Discovery & inventory: Cataloguing all relevant components in both legacy instances — dashboards, user accounts, roles/groups, scheduled jobs, query models, data-sources and dependencies.
• Target architecture definition: Designing the unified instance (cloud-core) with consolidated user/role/group frameworks, harmonized permissions, and performance-tuned configuration.
• Hybrid-profile modelling: For users who existed in both legacy instances, hybrid profiles were built in the target instance so that each user arrived in the new environment with essentially the same access and continued experience.
• Migration execution: Dashboards, users, roles/groups, schedules were migrated. Queries and job definitions were ported, and schedules re-sequenced.

• Performance tuning & monitoring: During and after migration, performance optimizations were executed — query tuning, schedule optimization, monitoring of dashboards for equivalence of results. Validation with end-users ensured that migrated dashboards displayed the same data as before, while the underlying system delivered improved responsiveness.
• Governance & change management: Throughout the migration, close engagement with business users was maintained to ensure seamless adoption, maintain trust in analytics output, and handle any role/permission edge-cases.

The Optimizations

In the course of migration, several value-add optimizations were embedded:

• User/role/group consolidation: The dual-instance setup's separate user/role models were rationalized into a unified model. This reduced administrative overhead and improved clarity in permission assignment.
• Query & schedule optimization: Legacy instances often carried dated query patterns and schedule definitions. By analyzing execution metrics, queries were tuned, schedules re-scheduled to minimize conflict, and resource usage improved.
• Seamless user experience: Despite the technical re-platforming, the users' workflow remained unchanged — dashboards appeared the same, access rights were maintained, and hybrid profiles ensured no user was "lost" during transition.
• Validation framework: A structured validation pipeline compared pre-migration vs post-migration dashboard outputs, user acceptance testing with business stakeholders, and monitoring performance, enabling early detection and correction of anomalies.
• Fallback strategy: Running the legacy system in parallel for a defined period provided a safety-net. It allowed for rapid rollback if issues emerged, but also pressured the transition to the new system without undue delay.

The Results

The migration produced strong outcomes:

• The organization now operates on a single cloud-based analytics core platform, which simplifies governance, reduces duplication and lowers system complexity.
• Users experienced little to no disruption: their dashboards, access and roles remained consistent, while underlying platform performance improved.
• Dashboard load times and scheduled job stability improved thanks to the optimizations applied during the migration.
• The cost and risk overhead of maintaining two parallel BI instances was eliminated; future analytics operations are more streamlined and maintainable.
• The phased rollout plus fallback design ensured minimal disruption, high user confidence and trust in the migrated analytics environment.

The Advantage of Working with RavencoreX

Partnering with RavencoreX in this project brought strategic benefits:

• Strong expertise in cloud analytics platforms — particularly combining the BI platform with cloud infrastructure — allowed a robust, future-ready design.
• Proven methodology for consolidating legacy analytics instances: role & permission modelling, hybrid profile design, migration planning, performance tuning, user-centric transition.
• Balanced focus on both technical infrastructure and user experience: ensuring that business users saw continuity, while the technical foundation improved significantly.
• Risk-minimized cut-over approach: parallel operation, user validation windows, fallback instance maintained — reducing downtime and migration anxiety.
• Strategic analytics governance mindset: this was not just a one-time migration, but a foundation for scalable, maintainable analytics operations moving forward.