Apply Policies and Monitor Reliability

After profiling a dataset, the next step is to define what “good data” looks like and monitor it continuously. In ADOC, a policy is a set of Rules or Rule Sets that defines your expectations for a dataset. Each policy can include one or more rules, and a policy is considered passing only if all its rules are met.

Policies help you enforce data quality, freshness, reconciliation, anomaly detection, data drift, and schema drift rules, ensuring datasets remain reliable over time.

All policy actions are performed per dataset in the Asset Details page, which serves as the hub for monitoring and management.

Example:

• A Data Quality policy might include rules such as “customer email cannot be null” and “order total must be greater than zero.”
• A Freshness policy might include rules like “sales data must refresh daily before 6 AM.”

Workflow Overview

1. Open the Asset Details Page

1. In Discover Assets, locate the dataset you want to monitor.
2. Click the dataset name to open the Asset Details page.

2. Access Policies

1. Navigate to the Policies tab in the Asset Details page.
2. Here you can:
   • View existing policies
   • Create new policies
   • Modify rules or thresholds

Apply Policies

Policies are applied per dataset, and you can configure multiple policies for the same dataset depending on business needs.

Each policy type has its own criteria and configuration:

• Data Quality Policy
• Reconciliation Policy
• Data Freshness Policy
• Data Anomaly Policy
• Data Drift Policy
• Schema Drift Policy

Examples:

• Data Quality: Customer email columns cannot have null values.
• Freshness: Daily sales data must refresh before 6 AM.
• Reconciliation: Daily transactions in CRM must match the billing system.
• Anomaly Detection: Revenue spikes beyond normal thresholds trigger alerts.

Monitor Compliance

Once policies are applied, ADOC continuously checks your data.

• Alerts are raised if any policy fails, allowing early intervention before downstream impacts.
• Execution history shows which rules passed or failed.
• Quality scores help track overall data health.

Example alerts:

• Missing or null values in a critical column.
• Daily data not arriving on schedule.
• Totals mismatching across systems.
• Revenue spikes outside normal trends.

Take Action

• Investigate alerts directly in the Alerts page.
• Use system recommendations to fix data issues.
• Adjust policy thresholds or rules as needed.

Managing Policies

• Export & Import: Share policies across teams or environments.
• Policy Groups: Organize related policies for easier management.
• Combine with Profiling: Policies rely on profiling statistics, so ensure profiling is up-to-date before applying policies.

Next Steps

• Choose a dataset that has been profiled.
• Explore policy types.
• Apply a policy and monitor alerts to ensure data reliability.

Note Keep this page as a central guide to policies, linking to separate detailed docs for each policy type. Every dataset action (profiling, applying policies, investigating alerts) happens in the Asset Details page.