Project overview
Our client, a global food and beverage conglomerate, required a large-scale data and reporting migration from SAP BusinessObjects (BOBJ) to Microsoft Power BI. The organization faced challenges with static reports and slow performance in BOBJ, prompting a strategic shift to Power BI—a more agile and integrated analytics platform that had already become the standard for most of their internal reporting initiatives.
For on-site product managers, a key daily activity involves reviewing the previous day’s sales performance to inform task assignment and operational planning. This requires fast, reliable access to detailed KPIs—including total sales, volume in kilograms, and volume in cases—presented in reports optimized for rapid decision-making.
Project scope
· Reports migrated: 120+
·
Data volume: 100 million+ rows
·
Data sources: Structured data from SAP and EG News page
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Impacted business units: Sales and Go-to-Market (GTM) operations
Key challenges
1.
Large-scale migration complexity:
·
Migrating over 120 reports with more than 100 million rows of data posed a significant technical challenge. Each report had to be replicated in Power BI while maintaining the functionality, logic, and business rules of SAP BOBJ.
2.
Multiple data sources:
·
The project involved consolidating structured data from SAP and EG News page (a mobile-based field sales platform). These systems had different schemas and refresh cycles, demanding a robust data pipeline architecture to unify them under a consistent model.
3.
Data export limitations in Power BI :
·
SAP BOBJ supported detailed, cell-level exports of large tables—a feature not natively available in Power BI. To replicate this functionality, we implemented Power BI Paginated Reports, enabling high-volume data exports while maintaining the visual and functional expectations of SAP BOBJ.
4.
Performance optimization at scale:
·
Some reports initially suffered from slow performance due to complex models and inefficient queries. We tackled this by optimizing queries and data models, separating business logic into flat tables where appropriate, and leveraging Azure Synapse Analytics to improve efficiency.
Solution
The data pipeline consisted of five main stages:
· Ingestion: Extracted data from SAP ECC, SAP BW, and EG News page using Azure Data Factory (ADF)
·
Storage and Processing: Stored raw and cleaned data in Azure Data Lake Storage Gen2 and processes it using Apache Spark via Azure Databricks.
·
Serving: Refined datasets are stored in Synapse SQL (Azure Synapse Analytics) for fast querying.
·
Visualization: Dashboards and reports are integrated with Power BI service for interactive reporting, and Power BI Report Builder for paginated reports.
·
Supporting Services: Ensured security, access control, and execution with Azure Entra ID, Key Vault, Integration Runtimes, and Security Center.
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Figure 1: Solution architecture
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Outcomes
· Faster report load times: Product managers can now filter and view daily reports within seconds, improving agility and responsiveness in sales planning and task allocation.
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Enhanced visual insights: The broader visualization capabilities of Power BI enabled users to analyze trends and patterns specific to their needs, facilitating better decision-making.
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Mobile accessibility: Reports are now accessible through the Power BI mobile app, allowing secure access to insights from anywhere—without reliance on desktop environments.
·
Improved data refresh: With Azure Synapse pipelines supporting data refresh cycles, performance and reliability improved significantly compared to the old BEX query model under SAP BOBJ.
Looking ahead
After a successful migration of all key reports, the client was positioned to embrace a self-service analytics culture. This empowered product managers to make faster, more informed decisions based on timely, customized reports.
Interested in learning more?
Beyond SAP BusinessObjects, MAQ Software offers end-to-end migration services from platforms including Tableau, MicroStrategy, Cognos, and others.
Organizations today face a rapidly changing business landscape, where data is the key to staying competitive. As the demand for scalable, cost-effective solutions grows, many companies are making the shift from Tableau to Power BI. With AI-powered capabilities embedded in the Power BI workload within Microsoft Fabric, businesses can harness automation, self-service analytics, and advanced decision-making tools at a lower total cost of ownership.
The migration process from Tableau to Power BI can be overwhelming due to the complexities of planning, minimizing business disruptions, rebuilding reports and dashboards, adapting to a new interface, and managing costs—all while maintaining operational efficiency. MigrateFAST simplifies data and report migration through automation, reducing time-to-market and enabling a smooth transition with minimal disruptions.
6-step migration process with MigrateFAST
Transitioning from Tableau to Power BI can be smooth and efficient with the right approach. MigrateFAST’s highly automated process enables a seamless migration across six key stages, reducing costs and time-to-market.
1. Inventory Analysis: Laying the Foundation
Before the migration can take place, a complete inventory of Tableau workbooks, extracts, data models, and reports is essential to evaluate what needs to be migrated, what can be retired, and what may require re-engineering. Accurate and thorough inventory analysis ensures that you know exactly what you’re working with before diving into the migration process.
This step alone can take weeks without automation. MigrateFAST automatically analyzes Tableau data models and connections and accelerates the creation of metadata documents with a few clicks.
2. Estimation & Planning: Clarity from Day One
Successful migrations require a clear estimate of costs, risks, and unknowns. A well-laid-out plan can help prevent unexpected issues during migration and ensure that the project remains within scope and budget.
MigrateFAST automates the estimation of licensing costs, infrastructure requirements, and the creation of a detailed migration timeline. Using historical data and pre-built migration pathways, MigrateFAST reduces uncertainty, providing a clear and actionable plan for each phase of the migration.
3. Semantic Model & Report Creation: Delivering Consistent Insights
Recreating semantic models and reports is the most visible part of the process for end users. It is essential that reports in Power BI look and behave the same way as in Tableau, if not better. Reports should be optimized for Power BI’s interface and features. Moreover, identifying and removing redundant reports helps improve the efficiency of the migration and performance of Power BI reports after deployment.
MigrateFAST reduces manual efforts by:
· Migrating LOD expressions to DAX measures
· Automatically creating Power BI semantic models
· Recreating Tableau visualizations in Power BI
4. Review: Ensuring Data Accuracy and Integrity
Migrating large amounts of data and reports from one platform to another can lead to performance issues, incorrect data mappings, or errors in calculations. Without thorough review, there is a risk that your reports could deliver incorrect or suboptimal results.
MigrateFAST automates the validation of the migrated reports by running performance tests, optimizing queries, and ensuring data accuracy through validation and certification.
5. Governance: Maintaining Control and Integrity
Data governance is essential in migration to support data validation and certification processes while ensuring dataset integrity. A well-defined governance framework provides proactive alerts and real-time monitoring of capacity, usage, report availability, and platform issues.
Our approach automates performance optimization to ensure adherence to best practices and pre-defined SLAs.
6. Center of Excellence (CoE): Supporting Long-Term Success
Migration doesn’t end once the reports are deployed. Post-migration support is essential for monitoring system performance, optimizing costs, and ensuring that the organization can effectively adopt and use the new platform.
We provide ongoing support to optimize costs, monitor system performance, and improve change management. This approach drives higher adoption and long-term success—helping your organization move from reactive fixes to proactive innovation.
Customer success story
A leading retail client with 25,000+ users needed a scalable, secure, and resilient centralized data platform. Their existing infrastructure included 300+ workbooks, 350+ extracts, and a 200 TB Teradata database, supporting 200+ concurrent users. After migrating from Tableau to Power BI, the client achieved:
• 60% reduction in overall maintenance and new development effort
• 35% cost savings on platform and maintenance
• NSAT score improvement from 2 to 3.5
• 300% increase in adoption within a year through CoE setup
With MigrateFAST, the client’s migration effort was automated by approximately 60%, dramatically reducing manual intervention and shortening the migration timeline.
| Migration Steps |
Automated using toolkit |
Efforts without MigrateFAST (in hrs.) |
Efforts with MigrateFAST (in hrs.) |
| Analyze Tableau data model and connections |
✓ |
10 |
0 |
| Create metadata document |
✓ |
5 |
0 |
| Create Power BI Semantic model |
✓ |
16 |
0 |
| Understand calculations and recreate them in DAX |
✓ |
24 |
0 |
| Validate and fix calculated columns and measure definitions |
✗ |
16 |
20 |
| Understand and create field parameters |
✓ |
8 |
4 |
| Create Power BI reports with base measures |
✓ |
16 |
4 |
| Implement field parameters in the report |
✓ |
4 |
2 |
| Identify alternate visuals for gaps |
✗ |
4 |
4 |
| Optimize performance (CertyFAST) |
✓ |
8 |
4 |
| Data validation |
✗ |
8 |
8 |
| Total |
|
119 (~25 days) |
46 (~6 days) |
Figure 1: Migration effort with MigrateFAST per workbook
MigrateFAST ensures a seamless transition to Power BI, preserving Tableau’s look and feel while enhancing usability with AI-powered capabilities.
Figure 2: User interface of Tableau vs. Power BI
Expanding MigrateFAST Capabilities
Beyond Tableau, MigrateFAST streamlines migration from multiple platforms, including MicroStrategy, Cognos, SAP BOBJ, and more. Our customized approach aligns with your data architecture requirements, ensuring a seamless transition to help you harness Power BI’s full capabilities.
In a rapidly evolving market, Independent Software Vendors (ISVs) must
integrate innovative solutions to remain competitive. AI-driven features and
self-service solutions have become critical business requirements. To address
these challenges, ISVs can implement a Custom Copilot with Power BI Embedded
to significantly enhance user engagement and operational efficiency, driving
future business growth.
The issue
A leading information services provider sought to enhance data analytics
capabilities within its products. The goal was to decrease the number of
support tickets, minimize reliance on custom dashboards, and strengthen the
products’ market position.
The solution
A Custom Copilot agent was designed to enable self-service capabilities
by streamlining the extraction of actionable insights from Power BI
reports. The solution contained the following components:
· AI: The solution used Azure OpenAI to create the
chatbot and to respond to users.
·
Databases (DB): The solution used Azure SQL DB to
store and manage feedback on the bot usage.
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Applications (Apps): Azure WebApp served as the
primary application to host the chatbot.
Our accelerators helped the client achieve faster time-to-market by
building the solution quickly and efficiently. We implemented the solution
in alignment with the Azure Well-Architected Review Framework, ensuring
optimal performance, security, reliability, cost-efficiency, and
operational excellence.
Solution flow
User authentication
· Users log in securely with their credentials.
· Access permissions are validated to ensure the right level of data access.
Interactive chat interface
· Users enter queries in natural language.
· The bot processes the query using AI and NLP to understand intent.
Power BI data retrieval
· The bot retrieves relevant data from Power BI reports.
· Text-based insights are generated alongside visual elements (charts, graphs, tables).
Visual insights generation
· The bot extracts and displays dynamic visual insights such as charts, graphs, and tables from Power BI for better clarity.
User interaction and feedback loop
· Users engage with insights and provide feedback via like/dislike buttons or additional comments.
· The bot learns from feedback to improve the relevance and accuracy of future responses.
Security and compliance
· The bot adheres to CoreLogic and Microsoft security standards, ensuring data protection.
· Role-based access control and data encryption are enforced for compliance and privacy.
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Figure 1: Solution architecture
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Business impact
The implementation yielded significant improvements in key operational areas:
· Self-service access: Enabled users to access and interpret data without technical expertise.
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Quick decision-making: Real-time insights allowed users to proactively respond to market shifts and business challenges.
·
Enhanced user engagement: The interactive chat and visuals improved user experience and adoption.
·
Operational efficiency: Optimized workflows by automating data exploration and reducing manual effort.
Summary
By implementing the Custom Copilot agent with Power BI Embedded, the ISV met critical business requirements, from self-service analytics to enhanced decision-making. This strengthened the company’s competitive position by improving user engagement, optimizing productivity, and achieving faster time-to-market.
Interested in learning more?
When it comes to adopting AI and analytics solutions, you need a trusted partner to help deliver impactful results. Contact
CustomerSuccess@MAQSoftware.com to explore how MAQ Software can help you achieve your business goals.
Project overview
A leading office supply retailer struggled to manage its massive inventory dataset, which exceeded 10 billion records over a rolling two-year period. Reporting in Import or DirectQuery mode led to performance issues, refresh failures, and inefficiencies. Users also faced challenges with Snowflake mirroring in Microsoft Fabric, including inconsistent incremental updates and the need to restart refresh processes, causing delays and redundant reprocessing. To address these issues, we integrated Apache Iceberg tables into Fabric, providing a scalable and efficient solution for handling large volumes of data.
Solution implementation
The project accessed Apache Iceberg tables in Microsoft Fabric to process large-scale inventory and sales data. The technology stack included Snowflake and ADLS as data sources, with Fabric and Power BI as the visualization tools. The data pipeline involved:
· Fetching data from ADLS and creating Iceberg tables in Snowflake.
·
Establishing cloud storage structures and linking them to Fabric Lakehouse.
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Connecting Iceberg tables from ADLS to Microsoft Fabric via shortcuts.
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Replicating inventory and sales ad hoc reports in Power BI.
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Conducting performance, functionality, and data integrity checks before deployment.
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Figure 1: Solution architecture
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Technical challenges and resolutions
We encountered authentication issues when creating shortcuts with Service Principal and Org Accounts. To resolve this, we tested alternative authentication mechanisms and scheduled data synchronization to ensure seamless updates.
Key benefits
· Improved performance: Iceberg-based reports load in 10–12 seconds, compared to 14–18 seconds for standard reports, even with 20 concurrent users.
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Efficient data processing: Resolved dataset refresh and load time issues under complex filtering and high concurrent usage.
·
Robust data integrity: Ensured strong guarantees for atomicity, consistency, isolation, and durability (ACID), optimizing large-scale data management in Power BI.
Interested in learning more?
As a Microsoft Fabric Featured Partner, MAQ Software brings deep expertise in helping organizations unlock the full potential of Microsoft Fabric. Whether you're looking for guidance on implementing data solutions or optimizing your existing platform, we’re here to support you every step of the way.
Reach out to CustomerSuccess@MAQSoftware.com to discover how Power BI and Snowflake can enhance your business operations, improve customer satisfaction, and drive cost savings.
In today’s fast-paced business environment, effective supply chain analytics is crucial for success across industries. By integrating Power BI for reporting with Snowflake as a backend data platform, organizations can transform their approach to supply chain data. This integration enables real-time insights, facilitating smarter decision-making and responsiveness to market demands, optimizing operations and enhancing efficiency.
The issue
A leading U.S. retailer with over 300 stores faced challenges in managing its supply chain data and reporting systems. Despite a strong legacy of quality, customer satisfaction, and sustainability, their reliance on MySQL for data management and Qlik Sense for reporting was becoming increasingly inefficient. MySQL’s limited scalability led to performance bottlenecks as data volumes grew, while complex queries slowed processing times, making it difficult to manage large datasets effectively. To address these issues, the company decided to migrate to Snowflake, a scalable, cloud-based solution that supports real-time analytics and seamless integration with various data sources.
On the reporting side, the client faced difficulties with Qlik Sense, such as high costs, maintenance, and performance problems with large datasets. Additional expenses for certain add-ons further complicated their reporting infrastructure. By transitioning to Microsoft Power BI, they will gain a more cost-effective and user-friendly solution. This migration, combined with Snowflake’s integration, will improve data connectivity and streamline their reporting processes.
Our solution
Migrating from Qlik Sense to Microsoft Power BI provided the client with cost savings and a more user-friendly interface, simplifying report creation and sharing across the organization. Power BI’s seamless integration with Snowflake enables real-time analytics and advanced data visualizations, enhancing decision-making in supply chain operations. To further improve scalability and performance, the client transitioned from MySQL to Snowflake, addressing MySQL’s limitations with faster data processing, efficient handling of large datasets, and high concurrency support through its multi-cluster architecture and caching capabilities.
The migration followed a structured, multi-step process to ensure a seamless transition:
· Data transformation: Transformed raw data in Snowflake using a dedicated layer to process and generate Power BI-ready views.
·
Data loading: Configured a unified semantic model, defining relationships in Power BI.
·
Report building: Developed intuitive Power BI reports covering all necessary data points.
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Power Automate: Automated data updates by detecting changes in raw tables.
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GitHub integration: Enabled version control and collaborative development across Snowflake and Power BI.
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Figure 1: Power BI reporting powered by Snowflake
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Furthermore, we implemented best practices in Power BI with Snowflake as the data source to optimize performance.
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Efficient reports: Limited visuals in Power BI to enhance performance with large datasets. Snowflake’s query optimization retrieves only necessary data, reducing query complexity and compute costs.
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Optimal connection: The data gateway keeps data in Snowflake via standard mode, minimizing duplication and processing.
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Seamless data querying: Power BI’s DirectQuery mode enables real-time interaction with Snowflake, leveraging its capacity for large, concurrent queries.
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Elastic scaling: Snowflake’s multi-cluster warehouses and auto-scaling compute model adjust resources based on workload demands, ensuring smooth Power BI queries even at peak times.
·
Data model design: Star schemas simplify the model and accelerate queries, while denormalizing frequently used metrics improves efficiency.
·
Row-Level Security (RLS): Implemented RLS using Snowflake’s native features to ensure users see only relevant data, with complex calculations managed at the source for greater efficiency.
Business outcomes
Within four months, we migrated eight critical reports to Power BI, delivering a near-real-time analytics solution. This transition reduced operational costs, improved ROI, and provided a unified, automated system. The client can now seamlessly access insights, enabling confident, data-driven decisions.
Key improvements include:
· Faster report performance: Reports now load in near-real-time, eliminating data latency issues.
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Cost savings: Optimized data retrieval in Snowflake and Power BI’s affordable licensing reduced expenses.
·
High user adoption: Over 80% of end users transitioned to Power BI, driving a data-centric culture.
·
Enhanced accuracy: The Power BI semantic model simplified maintenance and improved reporting precision.
Conclusion
By integrating Power BI with Snowflake, we transformed the client’s reporting capabilities, boosting efficiency, reducing costs, and strengthening decision-making. These advancements position them for continued success in a data-driven landscape.
Contact CustomerSuccess@MAQSoftware.com to discover how Microsoft Fabric and Snowflake can optimize your business, enhance customer satisfaction, and unlock significant cost savings.
A leading office supplies retailer sought to transform its demand forecasting and inventory management to enhance customer engagement and operational efficiency. Facing inaccurate demand predictions, complex customer behaviors, and a lack of AI-driven insights, the company needed a scalable, data-driven solution. We developed an AI-powered forecasting model using Microsoft Azure to refine demand predictions, optimize inventory, and drive strategic decision-making.
Key challenges
Inventory management in a dynamic retail environment is complex. The company struggled with:
· Inaccurate demand forecasting: Traditional methods failed to predict spending behavior, leading to overstocking and stockouts.
·
Customer behavior complexity: Diverse purchasing patterns required a more granular understanding of demand drivers.
·
Limited AI utilization: The absence of AI-driven insights hindered the ability to forecast demand accurately across multiple categories.
·
Customer retention: Identifying high-value customers for targeted marketing was a challenge.
·
Holistic data integration: Internal data (historical transactions, website activity) needed to be combined with external market insights (economic trends, industry news) for more accurate forecasting.
The solution
To address these challenges, we developed an AI-powered demand forecasting model on Microsoft Azure. The solution featured:
· Retail-specific AI foundation model: A machine learning-driven forecasting model, fine-tuned with Azure AI services.
·
Classification and regression models: Predictive analytics to identify high-potential customers and estimate spending trends.
·
Feature engineering: Key customer attributes such as purchase recency, firmographics, and product preferences were incorporated.
·
MLOps for model deployment: Azure MLFlow and Databricks enabled seamless monitoring and optimization.
·
External data integration: Economic indicators, industry news, and seasonal demand factors were incorporated to enhance forecasting accuracy.
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Figure 1: Solution architecture
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Implementation process
1.
Data preparation and engineering:
·
Aggregated and cleaned historical transaction data, marketing insights, and external sources.
·
Identified key predictive features, filtering out non-relevant customer segments.
2.
Model development and training:
·
Developed and fine-tuned AI models using Azure AI services.
·
Tested multiple algorithms (LightGBM, XGBoost, Huber, KNN) for optimal performance.
3.
Optimization and deployment:
·
Hyperparameter tuning for improved accuracy.
·
Integrated with Azure MLOps for automated tracking and real-time feedback loops.
·
Enabled continuous learning to adapt to market fluctuations.
Business impact
The AI-powered forecasting solution delivered measurable improvements:
· Increased forecast accuracy: Achieved a 50% improvement in precision and 69% in recall for demand predictions.
·
Optimized inventory costs: Reduced overstocking and stockouts, leading to a 15% decrease in inventory costs.
·
Higher customer retention and sales: Targeted marketing campaigns resulted in increased revenue and customer engagement.
·
Enhanced operational efficiency: Streamlined inventory management, improving supply chain responsiveness by 20%.
Conclusion
By leveraging AI and Microsoft Azure, we enabled the retailer to revolutionize its demand forecasting and inventory planning. The solution not only optimized inventory but also enhanced customer engagement and sales growth. With integrated AI-driven insights, the company can now navigate market fluctuations with confidence, ensuring a competitive edge in the retail industry.
To learn how MAQ Software can help optimize your demand forecasting and inventory management, contact our team at CustomerSuccess@MAQSoftware.com.
