Financial institutions are experiencing one of the fastest technology shifts in history. With digital banking, embedded finance, mobile-first experiences, and real-time payment networks growing rapidly, the pressure on analytics systems has never been higher. In this environment, BI Migration for F2inancial Services Companies is becoming a strategic priority rather than a technical upgrade.
Today’s financial organizations generate massive amounts of data transactions, risk assessments, credit scores, AML/KYC checks, and customer interactions. Yet, 67% of banking and financial services leaders say legacy systems are their biggest barrier to digital transformation, according to Accenture.
Legacy BI platforms such as SSRS, SAP BO, Cognos, Tableau, and Qlik often struggle to meet modern requirements for agility, governance, and real-time insights. Hence, they lack the flexibility required for AI-driven analytics, predictive reporting, and self-service capabilities.
Furthermore, rising regulatory pressure from Basel III and IFRS9 to RBI/FDIC guidelines demands stricter data governance , lineage tracking, and audit-ready reporting, which outdated BI systems cannot support efficiently.
Key Learnings BI migration is now essential for financial institutions as legacy reporting tools cannot support real-time analytics, AI-driven insights, or modern regulatory requirements. There is a contemporary BI architecture that will enhance governance, performance, and accuracy of data and allow a steady reporting of risk, finance, compliance, treasury, and customer analytics. Financial services benefit from cloud-native BI platforms such as Microsoft Fabric , Power BI, Databricks, Snowflake, and Looker, which deliver scalability, strong security, and advanced analytics. Successful migration requires a structured roadmap starting with assessment and governance, building a strong data foundation , modernizing dashboards, validating results, and training business users. Why Financial Services Need BI Migration? Financial services organizations are experiencing a rapid explosion of data ranging from daily transactions and risk scores to KYC/AML checks, credit bureau reports, system logs, and continuous customer interactions. As well as, traditional tools are often slow, expensive to maintain, and not designed for real-time insights or cloud-scale processing . As a result, they limit the organization’s ability to respond quickly to market conditions and regulatory requirements.
Additionally, regulatory pressure continues to rise. Frameworks such as Basel III, IFRS 9, MiFID II, and RBI/FDIC guidelines require accurate, timely, and audit-ready reporting. In addition, legacy systems tend to perform poorly in tracking lineage, fine-grained access control, and documentation, which makes them dangerous in the current controlled world.
Moreover, financial institutions now require real-time dashboards for fraud detection , liquidity monitoring, credit exposure analysis, and trading insights. Older BI systems, with their batch of refresh cycles, cannot deliver the speed or reliability needed for these high stakes use cases.
Concurrently, there is an increasing need towards cloud-native analytics applications, which incorporate AI and machine learning. At the same time, there is growing demand for cloud-native analytics that integrate AI and machine learning . Thus, these modern capabilities help banks automate risk scoring, forecast market movements, and detect anomalies more efficiently something legacy BI systems cannot support.
Finally, rising operational costs and competitive pressure push financial institutions to improve efficiency . Correspondingly, BI migration helps reduce infrastructure costs, standardize reporting , improve performance, and enable self-service analytics across the organization.
Modern BI Architecture for Financial Services A contemporary BI system in the financial industry should be able to handle large volumes of data, high level of regulatory compliance, and real time analytics. To do this, financial institutions depend on a modular and layered structure, which can be scaled, governed and perform well. Hence, the architecture of this is a clear breakdown as indicated below.
1. Data Ingestion Layer The ingestion layer is responsible for bringing data from multiple financial systems into the analytics environment.
It supports batch ingestion using ETL/ELT tools to move large datasets such as historical transactions, credit files, or policy data. At the same time, streaming ingestion through platforms like Kafka, Azure Event Hubs, AWS Kinesis, or MQ enables real-time processing of payments, fraud signals, trading events, and logs. As well as, this layer also integrates with core banking systems (CBS), insurance platforms, treasury systems, payment networks like UPI and SWIFT, and external sources such as credit bureaus or market data feeds.
2. Storage Layer Once the data is ingested, it is stored in scalable and secure cloud environments.
Financial services organizations typically rely on cloud data lakes such as ADLS, Amazon S3, or Google Cloud Storage to store raw, semi-structured, and historical data. Lakehouse with Microsoft Fabric, Databricks, or Snowflake is the best choice to use in analytical workloads because of its low-cost storage and high-performance computers.
Moreover, this layer should also be able to maintain regulatory compliant storage, encryption, retention, access auditing and disaster recovery.
3. Processing & ETL Layer This layer transforms raw financial data into clean, structured, and analysis-ready formats.
Good data quality guidelines guarantee the correctness of sensitive figures of exposure, capital sufficiency, risk rates, and NPA typology.
4. Semantic & Governance Layer The semantic layer ensures consistency and trust across the organization.
It defines governed datasets, certified tables, and shared business logic. A business glossary standardizes terms such as NPA%, RoA, RoE, risk exposure, liquidity ratios, or underwriting metrics. Lineage tracking and access control support regulatory audits and internal governance.
5. BI Consumption Layer This layer provides insights to business users, analysts, and regulatory teams.
Tools like Power BI, Tableau, and Looker deliver dashboards, scorecards, and visual analytics . Embedded analytics allows insights to appear directly inside banking portals, CRM systems, or internal risk tools. Tools and Platforms for BI Migration The migration of BI systems within the financial services sector will need a mixture of up to date BI tools, cloud information platforms, governance tools, and speedy integrators. In addition, both categories are important in terms of performance, compliance, and scalability in the long run. Thus, a breakdown of the key tools and platforms to be employed during BI migration projects is given below.
1. Modern BI Tools The recent BI systems provide interactive dashboard, real-time analysis, and robust connectivity with cloud solutions.
Power BI – Ideal for financial services due to deep integration with Microsoft environments, strong governance, and enterprise-grade semantic models . Looker – Excellent for organizations needing governed metrics and a strong semantic modelling layer using LookML.
These tools enable self-service reporting while maintaining controlled access for compliance.
2. Cloud Data Platforms Cloud platforms serve as the analytical backbone for modern BI migration.
Microsoft Fabric – Unified Lakehouse, governance, pipelines, and Power BI all in one platform. Databricks – Ideal for large-scale ETL, ML, real-time processing, and interactive analytics. Snowflake – Highly scalable for financial data warehousing, multi-cloud support, and secure data sharing.
These platforms offer security, elasticity, and compliance necessary for financial workloads.
3. Governance Tools Strong governance is essential for meeting regulatory requirements.
Azure Purview – Provides lineage, classification, and metadata management across Microsoft ecosystems. Alation – Enterprise data catalog with user-friendly governance workflows. Collibra – Comprehensive governance for regulated industries requiring strict oversight.
These tools ensure data integrity , auditability, and controlled access.
4. Migration Accelerators Accelerators help financial institutions modernize legacy BI with minimal downtime.
Informatica-to-Fabric/Databricks connectors for automated ETL migration .
They reduce manual effort, improve accuracy, and shorten migration timelines.
5. Integration Tools Reliable integration tools ensure consistent data flow across the ecosystem.
Fivetran, Informatica IICS, Talend – Automate ingestion from core banking, CRM, underwriting, and trading systems.
Step-by-Step BI Migration Roadmap Migrating BI systems in financial services requires a structured and disciplined roadmap. Consequently, because banking, insurance, and capital markets operate in highly regulated environments, each step must ensure accuracy, governance, and minimal disruption. Thus, the following roadmap provides a clear, enterprise-ready approach to executing a successful BI migration.
Step 1: Assess Current State The migration begins with understanding what exists today.
Inventory all reports, dashboards, data sources, semantic models, and data pipelines . Identify dependencies between systems such as core banking, underwriting engines, risk systems, CRM, and treasury platforms. Categorize reports into “critical,” “medium,” and “low usage” to avoid migrating redundant assets. Analyze performance, refresh frequency, security models, and data lineage .
This assessment helps define the scope and reduces migration risk.
Step 2: Define Future BI Strategy Next, financial institutions must define what their modern BI landscape should look like.
Decide whether the organization will adopt a cloud-first, Lakehouse, or multi-cloud strategy. Align BI goals with business KPIs such as fraud detection, risk exposure, regulatory reporting, and customer analytics. Specify the governance or compliance and data privacy requirements at an early stage.
This ensures that migration supports long-term transformation goals.
Step 3: Select Tools & Architecture Choosing the right tools is essential for long-term scalability.
Select the future BI platform such as Power BI, Tableau, or Looker.
A clear architectural blueprint ensures consistency across teams.
Step 4: Build the Data Foundation A strong data foundation is the backbone of successful BI migration .
Build curated layers in the data lake or warehouse for consumption. Use data quality checking in form of rules, validation logic and standardized schemas.
This ensures reliable, audit-ready reporting.
Step 5: Rebuild or Migrate Reports Once the data foundation is ready, BI assets can be migrated.
Rebuild critical dashboards with better UX and refined KPIs. Eliminate duplicate reports and unify business definitions.
This stage delivers cleaner, more efficient reporting.
Step 6: Validate & UAT Financial analytics require high accuracy.
Confirm the arithmetic of the important ratios like the NPA, exposure, NAV, liquidity and risk weight.
Perform user acceptance testing (UAT) on business teams.
Trusting and regulatory compliance are provided through validation.
Validation ensures trust and regulatory compliance .
Step 7: Deploy to Production After approval, the BI system is deployed.
Use CI/CD pipelines for secure and repeatable deployment. Publish certified datasets, semantic models, and dashboards. Apply proper access control and row-level security.
Production deployment ensures stability and governance.
Step 8: Train Users & Drive Adoption User adoption determines whether migration succeeds.
Risk teams, traders, executives, and train analysts. Provide user guides , onboarding modules, and demo sessions. Apply change management to aid the transition.
Adoption ensures value from migration.
Step 9: Ongoing Monitoring & Optimization Migration does not end in deployment.
Track dashboard usage, performance, and refresh reliability. Continuously improve dashboards, semantic models, and pipelines.
This ensures long-term performance and governance.
Best Practices for BI Migration in Financial Services Migrating BI systems in financial services requires a disciplined, compliance-driven, and scalable approach.
1. Start with Regulatory & Compliance Needs In financial services, compliance must guide every architectural and migration decision.
Establish data lineage tracking so every metric, report, and dataset can be traced back to its source. Maintain comprehensive audit logs for report usage, changes, refreshes, and data access . Implement role-based access controls (RBAC) and least-privilege permissions for sensitive data. Use data masking , tokenization, and PII anonymization for customer-level data. Align with cross-region regulations such as GDPR, PCI-DSS, SOX, Basel III, and RBI/FDIC rules.
Starting with compliance reduces risk and ensures regulatory readiness from day one.
2. Create a Unified Semantic Layer A unified semantic layer prevents reporting inconsistencies and improves trust in BI outputs.
Standardize all business metrics such as NPA %, RoA, RoE, VAR, ECL, capital adequacy, and exposure limits. Build a strong data dictionary that documents definitions, formulas, and usage rules. Ensure semantic models are centrally governed rather than built independently by teams. This promotes consistency across risk, finance, treasury, operations, and regulatory reporting.
3. Build a Cloud-Native Lakehouse Foundation A modern BI environment must operate on scalable and secure cloud infrastructure .
Use Delta or Parquet formats to support ACID transactions and efficient analytics for regulatory reporting. Select warehouse or Lakehouse architecture based on reporting latency, workload size, and cost. Store raw, curated, and governed layers separately for transparency and auditability.
Lakehouse maximizes performance while ensuring compliance and flexibility.
4. Adopt Metadata-Driven Pipelines Metadata-driven systems reduce manual work and improve governance.
Use tools that auto-generate documentation for data pipelines and transformations. Apply policy-driven data quality rules for thresholds, null-checks, business validations, and deduplication. Track pipeline lineage for every field used in financial calculations.
This ensures transparency and consistent data quality across all BI assets.
5. Prioritize High-Impact Dashboards Not all dashboards provide equal business value .
Migrate high-value dashboards early: risk dashboards, fraud detection dashboards, liquidity and treasury dashboards, and credit exposure dashboards. Rebuild executive dashboards focusing on KPIs such as profitability, portfolio performance, and operational efficiency.
This drives immediate business impact and accelerates adoption .
6. Implement Strong Governance Without governance, BI migrations become chaotic and unscalable.
Mask PII and sensitive financial fields by default. Centralize governance with Azure Purview, Collibra, or Alation for cataloguing, lineage, and classification. Governance ensures trust, compliance, and long-term maintainability.
7. Ensure Performance Optimization Performance issues can undermine BI adoption.
Use partitioning, caching, clustering, and Z-ordering where applicable. Tune ETL pipelines to avoid excessive shuffles and unnecessary transformations.
8. Enable Self-Service BI Modern BI migration should empower users, not limit them.
Provide predefined datasets, certified models, and reusable semantic layers. Allow controlled access to build self-service reports without compromising governance. Publish standard KPIs so all teams use consistent definitions.
Self-service BI accelerates insights across finance, risk, operations, IT, and compliance teams.
Top Banking and Finance Firms Benefitting with BI Modernization
Case Study 1: ABN AMRO Migrates Legacy Analytics to Microsoft Power BI ABN AMRO , one of Europe’s largest banks, modernized its analytics landscape by shifting from a mix of legacy BI tools to Microsoft Power BI and Azure.
Challenges:
Multiple legacy BI systems created fragmented reporting. Manual processes slowed down risk and finance reporting. Solution:
Migration to Power BI, Azure Synapse , and Azure ML for unified analytics. Creation of semantic models for governed KPIs. Outcomes:
Case Study 2: Standard Chartered Bank Moves Analytical Workloads to Looker Standard Chartered modernized its analytics stack by migrating to Google Cloud + Looker, replacing traditional BI workflows and decentralized reporting.
Challenges:
Difficulty in maintaining consistent metrics across markets. Governance issues with legacy BI dashboards. Solution:
Centralized semantic layer using LookML. Migration of dashboards into governed Looker models. Outcomes:
Global consistency in financial KPIs and regulatory metrics. Faster development and improved compliance reporting.
Case Study 3: Morgan Stanley Builds Unified Lakehouse Analytics on Databricks Morgan Stanley leverages the Databricks Lakehouse to unify quantitative research, risk modelling, and portfolio analytics.
Challenges:
Siloed data sources for risk, markets, and trading. Solution:
Migration of large-scale financial datasets to Databricks. Outcomes:
Ability to deliver AI-driven insights across multiple investment teams.
Future Trends in BI Migration for Finance As financial institutions continue modernizing their analytics landscape, several emerging trends are shaping the future of BI migration. Also, these trends focus on automation , intelligence, real-time insights, and seamless interoperability across platforms. Below are the key developments to watch.
AI-Powered Semantic Modelling – Modern BI platforms are beginning to use AI to automatically generate data models , metric definitions, and relationships. Moreover, this speeds up migration and ensures consistency across risk, finance, compliance, and treasury teams. LLM-Driven BI Assistants (e.g., Copilot for Power BI) – Large Language Models will enable users to create reports, write DAX, build dashboards, and explore insights using natural language. As well as, this reduces the skill barrier and accelerates self-service analytics. Real-Time Reporting at Scale – Financial institutions will increasingly adopt streaming architectures for fraud detection, liquidity management, credit exposure, and trading analytics. Hence, BI migration will move from batch to real-time insights. Automated Lineage and Governance – Tools like Microsoft Purview , Collibra, and Alation will automate metadata capture, lineage diagrams, and data classification reducing compliance effort and improving audit-readiness. Predictive Dashboards Instead of Descriptive BI – AI and ML outputs will be embedded directly into dashboards, enabling proactive decision-making, early risk detection, and scenario forecasting. Cross-Cloud BI Interoperability – BI systems will integrate seamlessly across platforms like Microsoft Fabric, Snowflake, and Databricks, allowing financial institutions to query data wherever it resides. BI Modernization: How to Migrate from Legacy BI Tools in 2025 Explore BI modernization strategies to improve analytics and decision-making.
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Kanerika: Accelerating BI Migration for Financial Services Companies Kanerika assists organizations that provide financial services to update their analytics and data infrastructure with fast, secure, and intelligent migration strategies. Further, legacy BI systems usually have difficulties with large transactional volumes, regulatory reporting, and real-time risk information. To that, our strategy will secure an unobtrusive shift to the new platforms without any interference with the key financial processes.
We provide end-to-end migration services across multiple areas:
BI Migration: Move from legacy tools like Tableau, Cognos, SSRS, and Crystal Reports to Power BI for interactive dashboards, regulatory reporting, and real-time portfolio insights. Data Warehouse to Data Lake Migration: Shift from rigid warehouse setups to flexible data lakes or Lakehouse platforms capable of handling structured, semi-structured, and real-time financial data. Cloud Migration: ETL and Pipeline Migration: Modernize data pipelines for faster ingestion, risk calculations, fraud analytics, and automated data transformations . RPA Platform Migration:
Our proprietary platform, FLIP, powers these migrations with Smart Migration Accelerators. Additionally, FLIP automates up to 80% of the migration process, reducing risk, preserving business logic, and enabling financial institutions to adopt cloud-native, AI-ready architectures in weeks instead of months. Hence, it supports complex transitions like Tableau to Power BI, SSIS to Microsoft Fabric , and Informatica to Talend, while ensuring zero data loss and operational continuity.
Kanerika ensures compliance with global standards, including ISO 27001, ISO 27701, SOC 2, and GDPR, throughout the migration process. Additionally, with deep expertise in automation, AI, and cloud engineering, we help financial services companies unlock predictive insights, enhance operational efficiency , strengthen compliance, and build a future-ready data ecosystem.
Migrate to Power BI for Smarter Analytics and Real-Time Insights! Partner with Kanerika for Seamless Migration Services.
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FAQs 1. Why is BI migration important for financial services companies? Financial services firms rely on timely, accurate data for risk management, compliance, and decision-making. Legacy BI tools often struggle with performance, scalability, and regulatory needs. BI migration enables faster reporting, better data governance, and improved security. This helps financial institutions stay competitive and compliant.
2. What BI systems are commonly migrated in financial services? Banks and financial institutions often migrate legacy BI platforms such as Cognos, BusinessObjects, and on-prem reporting tools. These systems typically support risk reporting, regulatory submissions, and financial dashboards. Migration modernizes analytics while maintaining historical data and audit trails. A structured approach ensures continuity during transition.
3. How can financial services companies ensure regulatory compliance during BI migration? Compliance must be built into the migration process from the start. This includes enforcing access controls, encryption, and audit logging throughout the migration. Regulations such as GDPR, SOX, and PCI-DSS require traceability and data lineage. Strong data governance ensures compliance is maintained at every stage.
4. What are the key challenges in BI migration for financial services? Common challenges include sensitive data handling, complex reporting logic, and minimal tolerance for downtime. Financial systems often have tightly coupled dependencies that increase migration risk. Without careful planning, migration can disrupt critical reporting. Phased execution helps reduce these risks.
5. How can organizations minimize risk and downtime during BI migration? Financial services companies can reduce risk by using phased migrations and parallel report validation. Testing migrated reports alongside legacy systems builds confidence. Automation and migration accelerators also reduce manual errors. This approach ensures uninterrupted access to critical financial insights.
6. What role does data governance play in BI migration success? Data governance ensures data accuracy, security, and consistency throughout the migration process. It defines ownership, standards, and validation rules. For financial services, governance is essential to meet audit and regulatory expectations. Governed BI migration builds trust in post-migration reporting.
7. How do companies measure success after BI migration? Success is measured through faster reporting cycles, improved data accuracy, and reduced BI costs. User adoption and self-service capabilities are also key indicators. Additionally, improved compliance readiness and audit confidence signal long-term success. A successful BI migration supports business growth and resilience.
