Table of Contents
The counterproductive cycle of traditional master data management is shown below. It includes asking business teams to fill out complex and confusing spreadsheets and forms, chasing them for input, tedious data entry, fixing errors, and being blamed for poor master data.
Traditional Master Data Management solution make it worse
Expensive master data management solutions are no panacea and may even deteriorate the situation.
Claude’s Take
Claude (our in-house stoic AI overlord) gives the following explanation on why master data management is counterproductive, even with a super turbo MDM solution
The Reactive Cleanup Loop
- Teams spend excessive time fixing data quality issues after they occur rather than preventing them at the source
- Each cleanup effort is temporary, with new errors continuously emerging from upstream systems
- Resources are drained on remediation rather than strategic initiatives
Governance Bottlenecks
- Heavy approval processes and manual reviews create delays in data updates and changes
- Data stewards become overwhelmed gatekeepers, slowing business operations
- Bureaucratic workflows make the system too rigid to respond to changing business needs
Perfectionism Paralysis
- Organizations delay value delivery while pursuing 100% data quality
- Projects stall in endless profiling, cleansing, and standardization phases
- “Perfect data” becomes the goal instead of fit-for-purpose data for specific business outcomes
Technology-Driven Rather Than Business-Driven
- MDM becomes an IT project focused on technical architecture rather than solving real business problems
- Disconnect between the MDM team and actual business users who need the data
- Success is measured by technical metrics rather than business value delivered
The Integration Nightmare
- Attempting to create one “golden record” that serves every system and use case
- Complex integration architectures that are fragile and difficult to maintain
- Each new data source or system adds exponential complexity
Inflexibility and Technical Debt
- Rigid data models that can’t adapt to changing business definitions or new requirements
- Legacy MDM platforms that are expensive to modify or extend
- Resistance to incorporating new data types or sources that don’t fit the existing model
How to break the counterproductive cycle of traditional master data management
We are aware of at least one approach that can break the cycle. And that is process orchestration. However, process orchestration alone is not enough. Process orchestration, combined with the following, will break the cycle.
- Mistake proofing at the source (using a variety of mistake-proofing techniques)
- Seamless integration to ERP, CRM, Planning, and Manufacturing systems
- Flexible data modeling that can support data model changes at run time
- Strong governance workflow modeling and execution engine
- Auditability and electronic signatures (especially in regulated environments)
- Out-of-the-box domain models that can be extended
- and more..
Yes, you are asking for a unicorn. You are at the right place if you are in search of this unicorn.