Remediation and Issue Management

Reliably manage manual data quality remediation!

Data quality issues cannot always be automatically resolved.

Once off errors may not warrant a repeatable resolution process, or human intervention may be required to add missing information or verify a disputed value.

A structured approach to handling issues and requests ensures that data quality issues are managed in a consistent and efficient way, whether they are discovered by the data management team (through data profiling or monitoring), or reported by business users who are suffering an operational impact. Our approach allows issues to be assigned to the responsible person or team, and tracks the progress of the remediation effort to ensure compliance to data governance policies.

Of course, the goal of data management must be to reduce the need for manual intervention, both to reduce the workload on already stretched operations staff and to ensure consistency of application. Where it makes sense, automated data quality processes can be enhanced to manage newly identified data issues.

A formalised data quality remediation workflow replaces the unmanageable mishmash of Excel spreadsheets and emails that have historically driven data quality, ensuring accountability and increasing trust. Remediation workflows also enhance collaboration between team members, enabling input and decision making to define business policies, data standards and similar data governance artifacts in a formal way.

Watch the video for more information and a demonstration of TS Case Management in action.

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    Data Quality Matters Jan 15, 2019 | 08:05 am

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