Accelerate your shift from Tableau to Power BI with our Metadata Extraction Accelerator and proven migration framework.
As enterprises shift toward integrated, Microsoft-first analytics ecosystems, migrating from Tableau to Power BI becomes a strategic move—reducing licensing costs by 40–75% and experiencing scalable, enterprise-grade reporting.
LevelShift enables this transition with a structured migration framework and our Tableau Metadata Accelerator, which cuts assessment time by 40–50% and provides clear insight into what should be migrated, redesigned, or retired.
LevelShift’s Tableau to Power BI Migration Approach
• Tableau estates often contain hundreds of dashboards, custom SQL, and unused assets; the real challenge is deciding what to migrate.
• Our Tableau to Power BI Metadata Accelerator provides a full 360° view of your environment through secure server scanning.
It generates two key reports:
• Metadata Analysis – Inventory, complexity tiers, dependencies, and usage patterns.
• Migration Analysis – Effort estimates, priority sequencing, dependency mapping, and ROI comparison.
• This intelligence removes guesswork, reduces migration workload, and cuts cost and time significantly.
•Prioritize high-value, frequently used dashboards.
• Remove duplicates and unused assets to shrink migration scope.
• Analyze data sources, security, and visualization logic.
• Define the target architecture, semantic model design, and workspace plan.
• Create a phased migration roadmap aligned to complexity and business priority.
• Align Tableau data sources with Power BI and Microsoft data services.
• Modernize or rebuild semantic models for performance.
• Apply row-level security and map authorization rules to Power BI.
• Convert dashboards using accelerator insights for faster replication.
• Recreate calculations, logic, and interactions with Power BI-native capabilities.
• Apply standardized themes, styles, and workspace structures.
• Validate data accuracy and visual parity between Tableau and Power BI.
• Use Azure Monitor and Power BI tools to confirm performance and refresh reliability.
• Optimize models, visuals, and queries for long-term stability.
• Deploy reports through structured release rules.
• Train end users, analysts, and admins on Power BI features and best practices.
• Support change management to drive smooth adoption.
*The typical implementation duration is 4–6 weeks. Actual timeline and cost may vary based on the existing data architecture, data volume, and scope of work (SOW).