Why poor master data is becoming a major cost factor in 2026
The growing demands of digital landscapes
As business processes become more digital, the quality of master data is no longer optional. Automated workflows, SAP S/4HANA migrations, and AI-powered analytics can only function reliably if the underlying data is complete, consistent, and unique.
In reality, many organizations struggle with:
- Data Redundancy: Persistent duplicates and fragmented legacy structures.
- Inconsistency: Missing attributes and misaligned data sets.
- Operational Friction: Erroneous reporting and excessive manual intervention.
From "Manageable" to a Financial Liability
What used to be viewed as a minor operational flaw is now a significant cost driver. Flawed decision-making, process inefficiencies, and the rising cost of data maintenance are direct consequences of poor data management.
Despite this, most companies are unable to answer a critical question: "How reliable is our master data really -and where are our hidden risks?"
Building the Foundation for Success
Without transparency, data issues often remain hidden until they cause costly disruptions. A structured assessment of master data quality is the essential first step to mitigating risk and ensuring that future initiatives - from automation to S/4HANA - are built on a solid foundation.
We will soon share more insights on how to measure your data quality and effectively minimize risks.