Finding why master data changes bypassed the system at Red Bull
Company: Red Bull, Master Data & Process Development team
Duration: February to June 2024 (internship)
Tools & Technologies: SAP MDG, ERP data, Excel Power Pivot (ETL, DAX), Celonis
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Project Overview
Project Overview
Context
An internship in Red Bull's Master Data & Process Development team at the headquarters in Austria. The team runs master data governance for the materials behind Red Bull's products, so the whole company works from one source of truth.
Key Result
I found where and why master data was changed directly in the ERP instead of through the governance tool, and turned that into reporting concepts and a roadmap toward process mining.
Tools
SAP MDG · ERP data · Excel Power Pivot (ETL, DAX) · Celonis · Stakeholder interviews
The Problem
The Problem
Master data changes were meant to go through the MDG tool. In practice, many were made directly in the ERP system, where nobody tracked them.
Untracked changes created inconsistencies and gaps in governance
Lower data quality affected operations and decisions
There was little visibility into bottlenecks and change request cycle times
Adherence was hard to enforce across departments and subsidiaries
Across 177 countries and more than 1,850 active SKUs, that is a real operational risk.
The Approach
The Approach
Narrow the data. Analyzed more than 7 million ERP change records and narrowed them to 40,000 finished-goods changes from 2022 to 2024.
Connect the dots. Built a local data model in Excel Power Pivot (ETL, DAX) linking each change to material ownership, department and timing.
Ask the people. Interviewed stakeholders across departments and subsidiaries to understand why changes bypassed the tool.
Check the tooling. Reviewed the existing Celonis process mining setup and the product request dashboard for integration options.
Outcome
Outcome
Identified seven fields that were frequently changed outside MDG, plus country-specific patterns that needed targeted action
Proposed reporting concepts to track unauthorized changes, with triggers such as RPA alerts to catch them early
Delivered a project roadmap with the requirements and timelines for a future Celonis dashboard
Recommended better training and clearer system restrictions to close the knowledge gaps behind manual changes
Concluded that full process mining integration was not feasible within the project, and documented what has to be in place first
Why this Project Matters
Why this Project Matters
The data showed where the rules were broken; the interviews showed why. Much of the fix was not technical: knowledge gaps and habits mattered as much as the tooling. I look for that balance in every process I work on now.
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