How a Global Confectionery Company Unified Business Insights with a Centralized Data Lake and Standardized Dashboards

Key Highlights: What This Case Study Covers

• Centralized data lake combining sell-in, sell-out, and SISO data in global markets
• Data standardization through SISO mapping, data squaring, and data harmonization
• Global semantic data model structured using a star schema aligned to reporting needs
• Power BI dashboards built with unified filters, layouts, and KPI definitions for RGM and BPM
• Validation workflows, including testing, UAT, and stakeholder-driven iterations

Client Overview

The client is a global confectionery company with operations spanning multiple countries. It manages a wide portfolio of products and supports local/global decision-making through revenue and business performance analytics.

The Ask

The client needed to build a centralized data lake and design global dashboards for Revenue Growth Management (RGM) and Business Performance Management (BPM). The objective was to harmonize data across countries, standardize reporting through Power BI, and establish a scalable framework for future expansion.

Challenges

• Data quality gaps: Masked private label data caused mismatches between UPC and brand-level revenue.
• Manual harmonization: Business teams relied on manual processing to map SISO data and calculate value pools.
• Dashboard inconsistency: Country-specific dashboard customizations made it difficult to maintain global report standards.
• Lack of alignment: Global and local teams followed different KPI definitions and reporting formats.
• Scattered sources: Sell-in, sell-out, and SISO data existed in disconnected systems with inconsistent structures.

Our Solution: Centralized Reporting Through Data Lake and Power BI Dashboards

• Requirement Gathering: We conducted detailed sessions with global and local stakeholders to capture reporting needs, KPIs, and metrics. Existing Excel reports and data sources were analyzed to define the scope for RGM and BPM dashboards, including filter logic and dashboard views.
• Data Modelling: Source data was cleaned for errors, duplicates, and missing values. SISO mapping and data squaring logic harmonized sell-in, sell-out, and intermediary data. A star schema–based semantic model was built for scalability, with business rules embedded in the transformation layer to ensure consistent and traceable metrics.
• Dashboard Development: With the model in place, Power BI dashboards were designed using standardized layouts, filters, and visual formats. Country-specific views were integrated within a common framework to support global consistency and local relevance. Workshops were conducted with business users to validate dashboard flows and adjust design elements based on market-specific needs. Access levels were also defined for multiple teams to align with user roles.
• Testing and Deployment: Unit-level tests were carried out for data integrity, UI elements, and metric accuracy. Business use cases were translated into test cases to support structured UAT sessions with client teams. Post-validation, dashboards were deployed to the production environment along with cloud-based data pipelines. Clear documentation was created to cover business rules, design logic, and user guidance, ensuring smooth handover to support and adoption teams.

Impact Delivered

• Thought leadership in bringing standardization across report KPIs and wireframes
• End-to-end data model completed for multiple regions – mapping, transformations, and documentation
• Standardized test cases for user testing that helps in quick onboarding of the users
• Structured rollout approach and adoption planning to enable usage of these dashboards within client teams
• Conducted workshops for Global teams to enable alignment across board for a unified solution
• Standardized ways of working and reporting processes to improve time to insights
• Customized case questions created for UAT sessions that enable users to understand the full spectrum of the dashboards