Remko Stas - RBMasterData

Streamlining Master Data Adherence and Process Mining Analysis for Change Process Optimization at Red Bull
Company: Red Bull (Global Beverage Company)
Duration: 4 months (2024)
Tools & Technologies: SAP MDG, ERP Systems, Celonis Process Mining, Excel Power Pivot, Data Analysis

Project Overview

Context
Junior consulting internship within Red Bull’s Global Data Department, specifically the Master Data & Process Development team at their headquarters in Elsbethen, Austria. The team manages master data governance (MDG) for all materials related to beverage products, supporting Red Bull’s global operations with a single source of truth.

Key Result
Identified critical areas of system non-adherence and process inefficiencies in master data changes for finished goods, delivering actionable reporting and a clear project management roadmap. This enabled Red Bull to pinpoint root causes of manual data changes and provided a foundation for future automated monitoring and enhanced data governance, directly supporting operational accuracy and efficiency.

The Problem

Red Bull’s master data management process saw frequent manual changes made directly within the ERP system, bypassing the established MDG tool. This resulted in:

Data inconsistencies and governance lapses due to untracked manual changes

Reduced data quality impacting operations and decision-making

Lack of visibility into process bottlenecks and change request cycle times

Challenges in enforcing system adherence across multiple departments and subsidiaries, notably in countries like Japan, Denmark, and Romania

These issues compromised the integrity of Red Bull’s master data, creating inefficiencies and potential operational risks across a network spanning 177 countries with over 1850 active SKUs.

The Approach

Conducted extensive quantitative analysis of over 7 million ERP change records, narrowing the focus to 40,000 relevant data points related to finished goods changes from 2022 to 2024.

Built an integrated local data repository using Excel Power Pivot, applying ETL methods and DAX formulas to link material ownership, departmental involvement, and timing of changes.

Performed qualitative stakeholder interviews across multiple departments and subsidiaries to understand root causes and knowledge gaps behind manual changes.

Reviewed existing process mining infrastructure via Celonis and the Product Request Database (PRDB) dashboard to assess potential integration and dashboarding opportunities.

Developed a detailed project management report outlining requirements and timelines for future Celonis dashboard implementation and barriers encountered.

Formulated strategic recommendations for better knowledge transfer, training enhancement, and operational system restrictions to enforce MDG usage.

Outcome

Mapped and quantified manual ERP changes, identifying seven key fields frequently modified bypassing MDG, and highlighted country-specific challenges requiring targeted intervention.

Produced actionable reporting concepts to track unauthorized manual data changes and suggested triggers (e.g., robotic process automation alerts) to proactively prevent data governance breaches.

Delivered a comprehensive analysis and project roadmap informing Red Bull’s master data governance strategy and future process mining implementation efforts.

Recommended redesign of master data training programs to close knowledge gaps and clearer operational system restrictions to reduce unauthorized manual changes.

Determined that full process mining integration within the project’s timeframe was unfeasible but identified critical preconditions for its successful future adoption.

Strategic Insight

This project exemplifies translating complex enterprise data governance challenges into structured, data-driven insights and practical business recommendations. By blending rigorous data analysis with cross-functional stakeholder engagement, the project highlighted systemic behavioral and operational gaps affecting master data quality at a global scale. Through focused reporting and process visibility, the work laid the groundwork for sustainable governance improvements and demonstrated the critical balance between technology solutions and human factors in business system optimization.