Asset Details

The Asset Details page provides a detailed view of a dataset’s reliability, quality, and freshness. It brings together profiling results, applied policies, and trend analysis to help users understand how data performs over time and which fields may need attention.

Accessing Asset Details

To open the Asset Details page:

Navigate to Data Reliability > Discover Assets.
Search for or select the dataset you want to explore.
Click the dataset name to open its details view.

Overview Tab

The Overview tab gives a high-level summary of the dataset’s current health and performance trends.

Key Metrics

At the top of the page, the following scores are displayed:

Data Reliability Score: The overall dataset health, aggregated from data quality, freshness, and reconciliation results.
Data Freshness: How up to date the dataset is compared to its expected schedule.
Data Quality: Aggregated score based on rule evaluation and policy success rates.
Other Indicators: Include Data Drift, Schema Drift, and Reconciliation (if configured).

Trend Charts

Performance Trend: Visualizes changes in the dataset’s reliability score over time.
Freshness Trend: Displays data freshness intervals, showing how frequently the dataset was updated and when delays occurred.

These trends help identify recurring issues, degradation, or improvements in data quality.

Column-Level Data Quality Reporting

The Column Details section extends asset-level analysis to individual fields, offering granular visibility into data health at the column level.

Behavior

Column-level results appear after a policy execution run completes.
Only columns that are part of an active Data Quality policy display rule counts and reliability scores.
The table reflects the most recently profiled version of the dataset.
Metrics automatically refresh when new profiling or policy runs complete.

Example

In the EMPLOYEES dataset:

The Performance Trend shows changes in reliability over time.
The Freshness Trend tracks when data was last updated.
The Column Details table lists fields such as BIRTHDATE, EMAIL, ID, and NAME, along with data types and quality metrics.

Benefits

Enables fine-grained visibility into data health at the field level.
Helps identify which columns affect overall reliability scores.
Supports faster troubleshooting and more precise policy adjustments.
Combines asset-level and column-level insights in a single view.

In addition to Overview, the Asset Details page includes several tabs that provide specific operational insights:

Tab	Purpose
Policies	Lists all applied data quality, freshness, reconciliation, anomaly, and schema drift policies. Includes details such as policy status, quality score, open alerts, and execution history.
Cadence	Shows the frequency and trend of dataset updates over time. Also includes derived metrics such as change in asset size, row count, and rows added per hour.
Profile	Displays profiling statistics for each column, such as distinct counts, null percentages, patterns, and data type–specific metrics. Helps assess data structure and quality readiness.
Sample Data	Provides a preview of the dataset’s sample records for quick validation. Users can refresh cached data or retry fetching samples if unavailable.
Segments	Allows creation and management of column-based segments by selecting distinct column values. Useful for analyzing specific data subsets.
Lineage	Displays upstream and downstream data relationships. Users can manually add lineage by specifying lineage type, target asset, and process details.
Relationships	Visualizes logical asset hierarchies and parent-child connections, such as source → database → schema → table. Helps in navigating organizational data structures.
Schema Changes	Tracks and compares schema snapshots over time to detect column additions, deletions, or modifications.
Metadata	Displays and manages metadata, including owner, team, description, tags, labels, and user-defined template (UDT) variables. Supports automated description generation.
Settings	Provides profiling configuration options such as engine selection, column scope, pattern detection, notifications, scheduling, and advanced performance settings.
Recommendations	Suggests optimization or configuration actions (for example, adding missing policies or enabling profiling) to improve data reliability and monitoring coverage.
Query Logs	Lists query activity for the dataset, including query text, timestamp, type, and user. Includes access information, row count trends, and most associated tables.
Asset Similarity	Lists datasets with similar schema or content, along with a similarity score. Helps identify redundant or related assets for governance and optimization.

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