From Data Cleaner to AI Orchestrator: The Evolving Role of the Data Steward in MDM
Introduction: The Data Steward's Transformation
For years, the Data Steward's job has been defined by the arduous work of manual data cleansing: finding duplicates, correcting entries, and enforcing data quality standards one record at a time. This necessary, but labor-intensive, process has been a significant bottleneck in enterprise MDM initiatives.
The integration of AI and Machine Learning (ML) into modern MDM platforms is fundamentally changing this role. The Data Steward is moving from being a manual data cleaner to becoming an AI Orchestrator a high-value specialist who manages automation, handles complex exceptions, and strategically aligns data policy with business outcomes like SPM and RevOps goals.
The AI-Driven Shift in Data Governance
The power of AI in MDM is its ability to handle scale and complexity far beyond human capacity, allowing for Augmented Data Management.
1. Automation of the Mundane
AI algorithms now automatically manage the overwhelming majority of data quality tasks:
Intelligent Matching & Merging: ML continuously refines matching algorithms to find and resolve duplicate records across massive datasets with minimal human intervention.
Automated Enrichment: AI services standardize addresses, append industry codes, and validate contact information in real-time.
Policy Enforcement: Rules (e.g., required product classifications for commission) are automatically enforced, flagging only the most ambiguous cases for human review.
2. The New Focus: Managing the Exceptions
With automation managing the 80% of simple cases, the Data Steward’s focus shifts to the critical 20%:
Managing AI Bias: Monitoring the ML models to ensure their matching and merging rules do not introduce systemic biases, particularly in critical customer and territory data used for SPM.
Handling Complex Exceptions: Using advanced MDM workflows to resolve multi-domain conflicts (e.g., a change in a customer's legal entity requiring a cascade change across their product contracts and sales credits).
Strategic Policy Design: Working closely with RevOps and Finance to design new data policies (e.g., "how we define a renewal sale") that the AI system can then encode and enforce.
The Impact on Revenue Operations (RevOps)
For RevOps, this shift means a massive leap in data reliability. When Data Stewards focus on governance strategy and AI management, the integrity of the data feeding the SPM system becomes rock solid. This results in:
Real-time Payout Assurance: Commissions are calculated on AI-cleansed, governed data, providing instant trust.
Faster Strategic Pivots: New data domains or business rules required for an agile compensation plan can be orchestrated and deployed by the Steward faster than ever before.
The strategic CDO understands that the true ROI of MDM is realized when the human talent is augmented by automation, transforming governance into a continuous, high-speed business service.
Ready to augment your data governance team? Contact Ackle Consulting Group for an AI-Readiness Assessment