BI migration from legacy reporting to a cloud data platform
How an international organization can move from legacy reporting tools and fragmented data extracts toward a cloud-ready BI foundation with clearer KPI governance.
This case study is anonymized and generalized to protect confidential client information while preserving the business and technical learning points.
Context
A business environment where reporting could no longer scale.
The organization relied on legacy reports, manual extracts and multiple local reporting practices. Business teams needed more reliable dashboards, stronger KPI alignment and a scalable data foundation that could support multiple countries and business functions.
Business impact
Decision cycles slowed down because teams had to validate numbers manually.
Different business units interpreted similar KPIs differently.
Reporting changes were hard to prioritize and difficult to trace.
The existing model created dependency on legacy tools and local workarounds.
Problem
The problem was not only technical; it was functional and organizational.
The reporting environment contained unclear KPI definitions, fragmented data ownership, inconsistent transformation logic and a gap between business requests and technical delivery.
Datilog approach
Datilog’s approach: align business meaning before rebuilding dashboards.
The project should start by mapping reports, source systems, KPIs, business owners and data dependencies. The objective is to create a migration roadmap that connects business definitions with the target cloud data model.
Delivery roadmap
How the project could be structured.
The delivery logic combines business analysis, technical architecture, data/workflow design and progressive implementation.
Phase 01
Report and KPI inventory
List existing dashboards, reports, KPIs, source systems and known inconsistencies.
Phase 02
Business definition alignment
Clarify metric definitions, filters, dimensions, ownership and validation rules.
Phase 03
Cloud data model design
Define fact tables, dimensions, transformation logic and BI-ready datasets.
Phase 04
Progressive dashboard migration
Move reporting use cases progressively, validate with users and document governance rules.
Architecture logic
A cloud-ready reporting architecture with governed business logic.
The target architecture can combine source extraction, cloud storage, transformation layers, semantic models and Power BI dashboards. The key is not only moving data to the cloud, but making business meaning explicit and reusable.
Results to target
More consistent KPI definitions across teams.
Reduced manual reconciliation during reporting cycles.
A clearer roadmap for decommissioning legacy reports.
A stronger foundation for future BI, AI and operational intelligence use cases.
What similar companies can learn
Lessons from this type of project.
A BI migration should start with business definitions, not only with technical migration tasks.
Cloud data platforms create value when data ownership and reporting rules are clear.
Power BI adoption improves when users can understand and trust the semantic layer.
Migration projects need both functional analysis and technical execution.
Related Datilog pages
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Project discussion
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