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Fortune 500 Retailer Standardizes Finance Reporting with Power BI

How one of the world's largest retailers transformed their finance organization by processing billions of transactions with real-time, self-service analytics.

Billions Transactions Analyzed
10% Inventory Improvement
Real-time Data Access
Self-Service Analytics

The Challenge

As one of the largest companies in the world, this Fortune 500 retailer processes trillions of data transactions annually. The finance organization faced a monumental challenge: democratizing data access while maintaining governance, accuracy, and performance at unprecedented scale.

The legacy environment presented significant obstacles:

  • Excel-based reporting systems couldn't handle the data volume
  • Analysts spent more time preparing data than analyzing it
  • Duplicate reports created inconsistent numbers across teams
  • No self-service capabilities meant IT bottlenecks for every request
  • Leadership lacked real-time visibility into financial performance

The Solution

The finance data and analytics team, led by directors with over 14 years of combined Power BI experience, championed a comprehensive transformation. They knew Power BI could become a key component of the company's data analysis, reporting, and standardization success.

Power BI Semantic Models DirectQuery In-Memory Analytics Row-Level Security

The solution architecture leverages Power BI's hybrid query capabilities to handle both aggregated views and granular drill-downs:

  • Semantic Model: A centralized Power BI semantic model provides analysts with easy access to daily journal entry transactions across the US operations
  • Hybrid Query Mode: In-memory data delivers instant performance for executive summaries, while DirectQuery seamlessly handles drill-downs into billions of detail records
  • Self-Service Layer: Business users can create their own reports and dashboards without IT involvement
  • Governed Data: Row-level security ensures users only see data appropriate to their role

"The flexibility in which a user can interact with their data in Power BI has proven to be a critical enabler of our finance transformation process by helping the team transition from legacy Excel-based systems to Power BI."

— Director of Finance Data & Analytics

Technical Innovation

To effectively summarize billions of records, the solution presents aggregated results using Power BI's in-memory layer. In-memory data is extremely performant and enables leadership to quickly consume and interact with high-level views of their business.

As analysts dig deeper into more granular levels of data, the model seamlessly switches to DirectQuery mode and efficiently queries billions of rows of data. This hybrid approach delivers the best of both worlds: speed for common queries and unlimited depth for detailed analysis.

The Results

Real-Time Financial Visibility

Leadership now has instant access to financial performance metrics that previously required days of manual report preparation.

Eliminated Duplicate Efforts

Centralized semantic models ensure everyone works from the same data, eliminating conflicting numbers and wasted reconciliation time.

Self-Service Analytics

Finance analysts can now answer their own questions without waiting for IT, dramatically accelerating decision-making.

Supply Chain Optimization

AI-powered forecasting improved demand prediction, contributing to a 10% improvement in inventory turnover rates.

"One of the key criteria in selecting our solution was knowing that Power BI works so well with a vast number of partners—not necessarily just Microsoft products. At our scale, we use a myriad of different vendors in our technology stack, so having a tool like Power BI that can bring everything together was critical."

— Director of Finance Data & Analytics

Key Benefits Realized

  • Reusability: Semantic models are built once and used across hundreds of reports
  • Speed to Insight: Questions that took days now take minutes
  • No Wasted Time: Analysts focus on analysis, not data preparation
  • Centralization: Single source of truth for financial data
  • Automatic Refresh: Data stays current without manual intervention

Key Takeaways

  • Champion from Within: Having experienced Power BI advocates in leadership accelerated adoption
  • Design for Scale: Hybrid query modes enable both speed and depth at enterprise scale
  • Centralize First: Building semantic models before reports ensures consistency
  • Enable Self-Service: Reducing IT dependency accelerates time-to-insight
  • Embrace Integration: Power BI's connector ecosystem enables consolidation of diverse data sources

Technology Stack

  • Power BI Service: Cloud-based reporting and dashboard hosting
  • Power BI Semantic Models: Centralized data models with business logic
  • DirectQuery: Real-time queries against source systems for detail data
  • Import Mode: In-memory caching for aggregated performance
  • Row-Level Security: Role-based data access controls

References

This case study is based on the publicly shared success story from Walmart:

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