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Case Study: Migrating a Global Bank’s BI Ecosystem to Power BI

  • Microsoft Fabric Guy
  • Dec 25, 2024
  • 3 min read

Migrating a complex Business Intelligence (BI) ecosystem from legacy tools to Power BI can be a daunting task. However, with the right strategies, automation, and collaboration, it’s possible to execute a seamless transition that transforms how an organization works with data.

In this blog, I’ll share a detailed case study of a global bank’s BI migration journey to Power BI, highlighting challenges, solutions, and outcomes. We’ll focus on how automation and adoption strategies played a crucial role in achieving success.

The Challenge: A Fragmented BI Landscape

The global bank had a fragmented BI environment with multiple tools such as SAP BusinessObjects (BO), Qlik Sense, and Tableau, leading to:

  • High Costs: Maintaining multiple tools required significant licensing, infrastructure, and training expenses.

  • Inefficiency: Data silos and redundant reports made it difficult to access reliable insights.

  • Poor User Experience: Non-standardized dashboards and complex workflows led to inconsistent reporting.

Key Objectives of the Migration:

  1. Consolidate BI tools to Power BI as the single platform.

  2. Streamline reporting by rationalizing and standardizing reports.

  3. Enable self-service analytics for business users.

  4. Improve scalability and reduce operational costs


Phase 1: Discovery and Planning

Activities Undertaken:

1. Inventory of Reports and Dashboards:

  • Conducted a metadata analysis of over 5,000 reports across legacy tools.

  • Categorized reports into high priority, to consolidate, and to retire.

2. Stakeholder Engagement:

  • Interviewed business users to identify critical reporting needs.

  • Validated which reports were actively used and necessary for migration.

3. Technical Assessment:

  • Documented data sources, pipelines, and integrations in the existing BI environment.

  • Assessed compatibility of legacy transformations with Power BI.

Phase 2: Rationalization and Automation

Key Actions:

1. Report Rationalization:

  • Retired 60% of outdated or duplicate reports using our custom built Rationalization tool.

  • Consolidated 1,200 overlapping reports into 300 streamlined dashboards.

2. Automation Tools:

  • Used Databricks for data preparation and transformation to feed into Power BI.

  • Automated the creation of semantic models using Tabular Editor to convert calculations from legacy tools into DAX.

3. Streamlined ETL Workflows:

  • Leveraged Data Flows and Power Query Editor to simplify data transformation workflows.

  • Implemented Azure Data Factory to automate data ingestion pipelines.

Phase 3: Migration Execution

Approach:

Pilot Migration:

  • Selected a high-priority business unit to pilot the migration process.

  • Delivered the first batch of Power BI dashboards in six weeks to showcase value.

Iterative Development:

Used an Agile methodology to migrate reports in sprints, ensuring continuous feedback and refinement.

Data Governance:

Established a governance framework for data quality, security, and compliance using Power BI’s built-in tools.

Testing and Validation:

Conducted rigorous testing to ensure migrated dashboards matched legacy reports in accuracy and functionality.

Phase 4: User Adoption and Enablement

Adoption Strategies:

1. User Training:

  • Conducted hands-on training sessions to empower users with Power BI’s self-service analytics capabilities.

  • Created quick-start guides and video tutorials for reference.

2. Change Management:

  • Set up a feedback loop with users to address issues and improve dashboards.

  • Appointed Power BI champions in each business unit to drive adoption.

3. Monitoring Usage:

  • Used Power BI’s Usage Analytics to track adoption rates and identify areas needing additional support.

Challenges and Solutions

1. Complex Transformations:

Legacy tools like SAP BO had deeply nested calculations and formulas. We had to automated conversion of complex logic into DAX using Tabular Editor and custom scripts.

2. Resistance to Change:

Users accustomed to legacy tools were hesitant to adopt Power BI. We demonstrated early wins with high-impact dashboards and provided continuous training and support.

3. Performance Optimization:

Large datasets caused slow performance during initial testing. We implemented incremental refresh and partitioning to optimize performance.

Migrating a Global Bank’s BI Ecosystem to Power BI Led to a Modernized BI Ecosystem

The migration delivered significant benefits:

  • Cost Savings: Reduced BI costs by 40% through tool consolidation and optimized infrastructure.

  • Improved Efficiency: Streamlined workflows reduced the average report generation time by 50%.

  • Enhanced User Experience: Business users embraced self-service analytics, leading to a 30% increase in data-driven decision-making.

  • Scalability: The Power BI ecosystem easily scaled to accommodate additional data sources and user groups.

Key Takeaways


Migrating a Global Bank’s BI Ecosystem to Power BI Led to a Modernized BI Ecosystem
Steps that Led to a Successful Migration to Power BI

  1. Discovery and Rationalization are Critical: Identifying and retiring redundant reports can save time and costs.

  2. Automation is a Game-Changer: Leveraging tools like Databricks and Tabular Editor accelerates migration and ensures accuracy.

  3. User Adoption Drives Success: Continuous engagement and training are essential for long-term success.

  4. Governance Cannot Be Overlooked: A strong governance framework ensures data quality, security, and trust.

Conclusion

Migrating a global bank’s BI ecosystem to Power BI required careful planning, automation, and collaboration. By addressing challenges head-on and focusing on user adoption, the bank transformed its BI capabilities and empowered teams with modern analytics tools.


Are you planning a similar migration? Share your thoughts or questions in the comments—we’re here to help you succeed!

 
 
 

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