Data Stewardship: manage the quality of your customer data

Mai 27, 2026

Data Stewardship: take control of your customer data quality in DQE One Standalone

In a data quality project, detecting anomalies is only a first step. The real challenge lies in deciding how to handle them: should AI be used? How should a complex merge between duplicate records be resolved? This is precisely where Data Stewardship comes into play.

An often underestimated operational challenge

Data quality teams face volumes of anomalies that neither purely manual processing nor full reliance on AI can handle effectively. What they need is a tool that gives them control over when to activate AI, how to arbitrate complex cases, and how to trace every decision.

Key challenges encountered:

Complex duplicates that are difficult to arbitrate. When two records are only partially correct, which one should be kept? Which fields should be merged? Without a suitable interface, it becomes difficult to precisely select the data to retain field by field.
An AI that is either imposed or absent. Some solutions enforce automation with no possibility of manual override, while others offer no intelligent assistance at all.
Lack of traceability. It is impossible to reconstruct who approved a merge, based on which logic, or on what recommendations.

What the Data Stewardship module delivers

1. Full control over the processing mode

For each anomaly, the steward can freely choose between:

High-confidence outcomes can be applied automatically according to defined thresholds, whilst ambiguous cases are submitted for human validation.

2. A duplicate record merge interface

Candidate records are displayed side by side, field by field. The data steward can then:

3. Activation of the deduplication agent

For duplicate cases, the data steward can choose whether or not to activate the deduplication agent. Each recommendation is accompanied by:

4. Complete action traceability

To meet governance, internal audit and regulatory compliance requirements, all operations are recorded in a comprehensive audit log, including:

Business Value Summary

The Data Stewardship module redefines the role of the Data Steward: AI handles repetitive tasks, while humans focus on cases that require business judgement. The result is a more efficient organisation, more reliable data, and complete traceability of every decision.

To learn more about the Data Stewardship module or speak with one of our experts:

Über DQE

Datenqualität ist essenziell für ein umfassendes Kundenbild und den Aufbau einer langfristigen
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