Modern Data Stewardship, Powered by AI
We built the AI Data Steward Copilot ® to solve the biggest gap in modern MDM.
"Stewards don’t trust match scores - and leaders don’t know what’s driving match risk."
Our Services
Understanding Conflicting Match Decisions
The Problem:
In complex MDM environments, stewards frequently encounter match outcomes that appear inconsistent or difficult to explain. Two records may match unexpectedly, while other obvious duplicates remain separate. These situations require manual investigation to understand which rules fired and which attributes influenced the outcome.
The Challenge
Stewards must often reconstruct the reasoning behind match decisions by reviewing rules, scores, and attribute comparisons. This process slows down stewardship workflows and introduces uncertainty in merge approvals.
The Solution
AI Data Steward Copilot provides explainable insights into match outcomes by analyzing the signals that influenced each decision. Stewards can quickly understand why records matched or did not match and determine whether a merge is appropriate.
Outcome
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Faster stewardship decisions
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Reduced investigation time
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Improved trust in automated matching
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Higher quality entity resolution outcomes
The Problem
Stewardship teams often spend more time investigating match outcomes than resolving actual data issues. Understanding which rules fired, which attributes influenced the score, and whether a merge is safe can require multiple system checks.
The Challenge
As data volumes grow, investigation time becomes a major operational bottleneck for stewardship teams.
How AI Data Steward Copilot Helps
The Copilot surfaces the key drivers behind match decisions, allowing stewards to quickly interpret results without manually tracing rule execution.
Outcome
• Accelerated record review workflows
• Reduced manual investigation effort
• Improved operational efficiency
The Problem
When stewards override match decisions, valuable insights about entity resolution quality are often lost. Most systems capture the override event but not the reasoning behind it.
The Challenge
Without understanding why stewards disagreed with a match decision, improving matching logic becomes slow and reactive.
How AI Data Steward Copilot Helps
The Copilot captures steward feedback/decisions and analyzes patterns across overrides and reviews, providing insights into where match rules may need refinement.
Outcome
• Continuous improvement of entity resolution
• Better alignment between automation and human expertise
• Stronger governance over time
The Problem
Even when match algorithms perform well, stewards may hesitate to approve merges if they do not fully understand the decision.
The Challenge
Low confidence in automated decisions can slow stewardship workflows and lead to unnecessary overrides or escalations.
How AI Data Steward Copilot Helps
By providing explainable insights into match outcomes, the Copilot helps stewards understand the reasoning behind automated decisions and approve merges with greater confidence.
Outcome
• Increased trust in automated matching
• More consistent stewardship decisions
• Stronger adoption of MDM automation