The Challenge

As a global foodservice leader serving **millions of meals daily across dozens of countries**, Sodexo faced mounting pressure to operate with unified visibility and control. However, their decentralized data infrastructure prevented them from functioning as one cohesive organization, creating costly operational blind spots. ### Critical Data Fragmentation Issues Three fundamental challenges undermined Sodexo's global operations: **Global spend visibility was impossible.** When operations teams asked "How much are we spending on produce globally?" each region tracked expenses differently. Comparing costs across markets required **weeks of manual reconciliation**, preventing strategic procurement decisions and supplier negotiations. **Menu standardization hit technical roadblocks.** Questions like "Can we standardize this menu across our European sites?" couldn't be answered efficiently. Each location operated different food management systems with incompatible data formats, making it impossible to compare production costs or assess ingredient availability across regions. **Real-time inventory accuracy was compromised.** Critical discrepancies emerged between retail and ordering systems because data moved in overnight batch updates. By the time headquarters accessed inventory numbers, the information was already stale and unreliable. This data fragmentation cost Sodexo significantly: procurement couldn't secure global contracts without comparable spend data, successful operational practices couldn't be replicated across regions due to inconsistent data definitions, and finance teams spent days manually reconciling systems during book closures.

Results Delivered

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The Challenge

Nestlé's Nutrition division, a multi-billion dollar consumer packaged goods operation, faced critical data accessibility barriers that were undermining strategic decision-making in an increasingly competitive market. Consumer preferences shift weekly in CPG categories, making real-time intelligence essential for maintaining market position and optimizing shelf space allocation. ### What Were the Core Data Access Challenges? **Reporting bottlenecks were creating strategic blind spots** for brand managers responsible for million-dollar market decisions. Campaign performance analysis across regions required submitting formal requests to central analytics teams, while quarter-over-quarter sales mix comparisons meant waiting for IT departments to build custom dashboards. Competitive intelligence investigations depended entirely on data team availability and bandwidth. **Legacy systems were costing weeks in delayed market response time.** Each analytics request involved multiple touchpoints: explaining requirements to intermediaries, waiting days or weeks for delivery, then discovering reports failed to address the original business question. Data silos meant strategic decisions took weeks instead of hours, as analysts lacking market context delivered incomplete intelligence. **Without real-time data access capabilities**, brand managers were making strategic decisions on stale information or instinct alone. This accessibility bottleneck represented a strategic liability in fast-moving consumer categories where competitive moments pass quickly and market intelligence determines success or failure.

Results Delivered

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The Challenge

## The Challenge: When Financial Intelligence Systems Can't Keep Pace Financial services companies face unprecedented pressure to deliver real-time insights to clients while maintaining operational efficiency. For MSCI, a global leader providing investment intelligence to the world's largest institutions, their own financial intelligence platform had become a critical constraint on business growth. **Trapped by Technical Debt and Extended Close Cycles** MSCI's finance teams were locked into a 10-day month-end close cycle, not due to business complexity but because their SAP ECC and BPC infrastructure couldn't match operational demands. Month after month, allocation runs consumed hours to complete while manual reconciliations absorbed days of senior analyst time. Finance leadership found themselves making strategic decisions based on data that was already outdated by the time it reached executive dashboards. **Legacy Architecture Driving Real Business Costs** The technical debt was substantial: outdated SAP BPC and BW versions, over 72 custom allocation logics built across multiple years that teams hesitated to modify, inadequate security controls that failed to properly segregate duties, and an inflexible architecture where simple reporting changes required multi-week implementation cycles. More critically, finance operations couldn't operate at the velocity the business demanded, creating a competitive disadvantage for a company advising clients on investment intelligence while struggling with their own financial data processing capabilities.

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The Challenge

## What Challenges Did RaceTrac Face with Legacy Data Systems? Retail fuel and convenience store companies face intense pressure to optimize operations across hundreds of locations while maintaining razor-thin profit margins. RaceTrac, operating over 800 locations across the Southeast, needed real-time visibility into performance metrics that legacy systems simply could not provide. **Fragmented Data Created Decision-Making Delays** RaceTrac's existing data infrastructure consisted of disconnected point-of-sale systems, inventory management platforms, and operational databases. Data silos meant critical business decisions took 3 to 5 days instead of hours, preventing rapid response to fuel price fluctuations and inventory shortages. Without integrated analytics, store managers lacked visibility into performance patterns that could drive revenue optimization. **Manual Reporting Processes Cost $200K Annually** Legacy systems required 15 hours per week of manual data compilation across regional teams. This manual effort was costing the company approximately $200,000 annually in labor while creating opportunities for human error in critical financial reporting. **Competitive Disadvantage in Dynamic Fuel Markets** Without real-time data analytics capabilities, RaceTrac struggled to respond quickly to competitor pricing changes and optimize fuel margins. Based on our experience with 80+ retail deployments, companies typically lose 2 to 4% in potential fuel margin revenue when operating with delayed pricing intelligence.

Results Delivered

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