Data Quality Issues

Overview

Data quality issues reduce trust in reporting and slow delivery work.

Common causes include inconsistent statuses, missing Segment assignments, and inconsistent naming.

Key Concepts

• Data integrity: correctness and completeness of work item fields
• Consistency: stable meanings across Projects for statuses and segments
• Governance: standards that keep reporting reliable over time

How It’s Used

Use this page during rollout when importing or standardizing data.

Use it during governance when reporting signals drift.

Benefits

• More reliable dashboards and pivot tables
• Faster governance meetings with fewer debates about accuracy
• Reduced rework caused by ambiguous statuses and fields

Setup Steps

1

Identify the failure pattern

Common patterns include:

• missing Segment values in lists
• inconsistent status meaning across teams
• duplicate items created during imports
• fields left blank that should be required

2

Validate segmentation and scope

1. Confirm Segment Types are defined at the Organization level.
2. Confirm Segments exist for the intended reporting breakdown.
3. Apply Segments consistently across requirements and tasks when used for reporting.

3

Standardize statuses

1. Define entry and exit criteria for each status.
2. Apply meanings consistently across Projects when possible.

Related: Status Types and Meanings

4

Create repair views

Create Save Views that surface records needing cleanup, such as:

• Segment is empty
• Status is Blocked
• Owner is empty

Best Practices

• Keep Segment Types stable to protect analytics continuity.
• Keep shared fields minimal and clearly defined.
• Use Save Views for ongoing data hygiene.

Summary

Data quality improves most from stable governance standards, consistent segmentation, and repeatable cleanup views.