Redundant administrative overhead
Two environments, two permission matrices and two sets of schedules to maintain in parallel.
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Migration
From two legacy environments to one cloud-core platform
A phased migration and consolidation of two enterprise Looker BI instances into a single, high-performance cloud analytics core — with a fallback design that kept users working throughout.
2 → 1
legacy BI environments unified into one cloud-core platform
Little to no
disruption — dashboards, access and roles stayed consistent
~15–20 days
legacy fallback window kept live for safe rollback
The challenge
A fragmented analytics setup across two BI instances
The organization ran two separate enterprise BI instances built on the same platform — each with its own user accounts, roles and groups, dashboards, schedules and governed data models. That split created four recurring problems.
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 showed delays or failures due to legacy design and content growth.
Governance complexity
Two platforms raised the risk of inconsistent metrics, no single source of truth, and fragmented access patterns.
The objective was clear: migrate to a single cloud-based core analytics instance, preserve the user experience (access, dashboards, roles), and drive improvements in performance, maintainability and scalability.
The approach
A structured, phased migration — governance and technical, together
We aligned governance and technical work across four main steps, then ran a careful cut-over with a fallback safety net.
Discovery & inventory
Catalogued every relevant component across both legacy instances — dashboards, user accounts, roles and groups, scheduled jobs, query models, data sources and dependencies.
Target architecture definition
Designed the unified cloud-core instance 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 so each user arrived with essentially the same access and a continued experience.
Migration execution
Dashboards, users, roles and groups, and schedules were migrated; queries and job definitions were ported, and schedules re-sequenced.
The cut-over plan
- Stand up the new instance in parallel and make it available for user validation.
- Provide a verification window (~1 week) where users could access the new system while the old stayed live for fallback.
- Disable user access in the legacy instance (users redirected to the new platform), yet keep the old instance technically active as a fallback for ~15–20 days.
- After monitoring and validation, retire the legacy instances and transition exclusively to the unified platform.
Performance tuning & monitoring
Query tuning, schedule optimization and dashboard monitoring for result equivalence. End-user validation confirmed migrated dashboards showed the same data, while the system delivered improved responsiveness.
Governance & change management
Close engagement with business users throughout ensured seamless adoption, maintained trust in analytics output, and handled role and permission edge-cases.
The optimizations
Value-add improvements embedded along the way
The migration was also an opportunity to clean up and strengthen the analytics foundation.
User/role/group consolidation
The dual-instance setup's separate user/role models were rationalized into a unified model — reducing administrative overhead and clarifying permission assignment.
Query & schedule optimization
By analyzing execution metrics, dated queries were tuned, schedules re-sequenced to minimize conflict, and resource usage improved.
Seamless user experience
Despite the re-platforming, workflows stayed unchanged — dashboards appeared the same, access rights were maintained, and hybrid profiles ensured no user was lost in transition.
Validation framework
A structured pipeline compared pre- vs post-migration dashboard outputs, ran user acceptance testing with stakeholders, and monitored performance — enabling early detection and correction of anomalies.
Fallback strategy
Running the legacy system in parallel for a defined period provided a safety net for rapid rollback — while still pressuring the transition forward without undue delay.
The results
One platform, simpler governance, confident users
A single cloud-based analytics core
The organization now operates on one platform, which simplifies governance, reduces duplication and lowers system complexity.
Little to no disruption
Users' dashboards, access and roles remained consistent, while underlying platform performance improved.
Faster loads, more stable jobs
Dashboard load times and scheduled-job stability improved thanks to the optimizations applied during the migration.
No more duplicate-instance overhead
The cost and risk of maintaining two parallel BI instances was eliminated; future analytics operations are more streamlined and maintainable.
High user confidence and trust
The phased rollout plus fallback design ensured minimal disruption and sustained trust in the migrated analytics environment.
The advantage
Why working with RavenCoreX made the difference
Cloud analytics expertise
Strong command of the BI platform combined with cloud infrastructure allowed a robust, future-ready design.
Proven consolidation methodology
Role and permission modelling, hybrid-profile design, migration planning, performance tuning and a user-centric transition.
Technical depth + user experience
Business users saw continuity, while the technical foundation improved significantly.
Risk-minimized cut-over
Parallel operation, user-validation windows and a maintained fallback instance reduced downtime and migration anxiety.
Governance mindset
Not a one-time migration, but a foundation for scalable, maintainable analytics operations going forward.
Your data, next
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