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    Built an Anaplan-led data, planning, and GenAI backbone for a global alcoholic beverage GCC

    Client: A Global Alcoholic Beverage
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    case study
    • Alcoholic Beverages
    • GCC
    • Finops
    Problem Statement Problem Statement

    The client is the GCC of a global alcoholic beverage enterprise supporting Supply, Commercial, Finance, and Consumer functions. Polestar Analytics evolved the engagement from a single Anaplan partnership into a multi-function data, planning, and GenAI backbone, supported by 80+ experts across 30+ geographies.

    Key Challenges Key Challenges
    • Disconnected planning and forecasting across Finance, Commercial Finance, and Supply teams.
    • Fragmented data architecture causing metadata inconsistencies and data translation gaps.
    • Stronger platform security was needed without slowing global delivery.
    • Manual tracking and dashboard dependency limited decision speed.
    • External market data needed standardization across products, outlets, categories, and regions.
    • DataOps, governance, and AI-readiness had to scale across domains and geographies.
    Architecting with the Best Tech Stack
    • Anaplan Logo
    Solution ImplementedSolution Implemented
    • Secure Data Platform: Built a zero-trust Azure architecture with private connectivity across Databricks and core data services.
    • Serverless Migration: Moved Supply workloads to Databricks serverless for faster execution and better utilization.
    • Metadata Governance: Created a single source of truth with traceability and audit compliance.
    • Governed Lakehouse: Implemented Unity Catalog, automated quality checks, and observability to reduce data drift.
    • Anaplan Planning: Enabled forecasting, A&P planning, cash visibility, and pricing automation across 30+ countries.
    • DataOps & MIA: Standardized DES operations and enabled conversational AI insights through MIA.
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    Business Impact
    • 0 critical/high security vulnerabilities open
    • 40–60% reduction in Databricks job execution time
    • 70–90% improvement in CPU utilization on migrated workloads
    • 98% reconciliation drop with 40+ man-months saved
    • $200K direct cost savings through the harmonized data hub
    • >30+ countries enabled on a single forecasting model

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