Configure observers - Precisely Data Integrity Suite

Data Integrity Suite

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Spatial_Analytics

Data_Integration

Data_Enrichment

Data_Governance

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Data_Observability

Data_Quality

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Services

Spatial Analytics

Data Integration

Data Enrichment

Data Governance

Geo Addressing

Data Observability

Data Quality

Core Foundation

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Data Integrity Suite

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en-US

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pt_product_guide

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2000

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2026

An observer is a set of rules you define and apply to a data asset. The observer monitors your selected data assets and generates alerts based on the rules you choose. Observers monitor your data assets for anomalies and generate alerts based on rules you define. Use observers to proactively identify and resolve data quality issues.

For example, an observer might check daily whether columns have been added to or deleted from the Customer_Cleansed_Table. If a change is detected, it creates a critical alert and sends it to specified recipients.

Each observer includes a schedule, the dataset to observe, the rules to apply, alert thresholds, alert severity levels, and the recipients who receive notifications.

Go to Observability > Observers where you can create, view, edit, or delete observers.

The Observers page includes these key fields and areas:

Search: Type the complete or partial name of an observer to find matching results.
Name: Displays the observer name. Click the ellipsis icon to edit or delete the observer. Click the observer name to view associated alerts, assets, and profile information.
Scheduler: Displays the observer schedule status.
- Enabled: The observer is scheduled. Click to view observer run history.
- Suspended: The scheduler stopped after ten consecutive failures. Click the Suspended status to investigate or re-enable the schedule.
- Unavailable: The scheduler service is unavailable and cannot provide observer run history.
Alerts: Displays alerts and warnings for the observer.
- Red: Critical alert. The number indicates how many critical alerts exist.
- Yellow: Warning alert. The number indicates how many warning alerts exist.
- Green: No alert. Zero indicates no alerts exist.
Created By: Displays the name of the user who created the observer.
Bell icon without strike-through: You're subscribed to email notifications.
Bell icon with strike-through: You're unsubscribed from email notifications.
Dimmed bell icon (with or without strike-through): Email notifications are disabled for this observer, or the maximum subscription limit of 25 has been reached.

Determine observer creation prerequisites

Understand the patterns of your data and the types of anomalies that indicate problems. For example, determine whether a 5% change in data volume requires attention, or if you need a change greater than 30%. Once you understand your data, you can choose which alert types are relevant.

Verify prerequisites:

The dataset is connected and cataloged.
The dataset is not already assigned to another observer.
You have access permissions for the dataset, including its tables and columns.
Table and column names do not contain single (') or double (") quotes.
Required observer rules are identified.
An appropriate run frequency (daily or weekly) is selected.
Email notification preferences are configured.
Observation activities comply with applicable data privacy requirements.

Understand observer rules

Observer rules define when alerts should be generated by identifying significant changes or anomalies in your data assets. The Data Integrity Suite supports four rule types, each designed to detect different types of data issues.

Each observer includes one or more rules that monitor your data for specific conditions. When a rule detects an anomaly, it generates an alert and notifies observers.

Available observer rules

Freshness: Alerts are generated when the data fails to update at the expected frequency.
Volume: Alerts are generated whenever there is a change in the number of rows in the data.
Data drift: Alerts are generated when there is a change in data beyond a specified range.
Schema drift: Alerts are generated whenever there is a change in a table or column.

Note: Freshness rule cannot be configured for views and is only supported for tables.

Confidence based versus threshold based rules

For freshness, volume, and data drift rules you indicate if you want to create a confidence based rule or a threshold based rule. This distinction does not apply to schema drift rules.

Confidence based alerts: Generate alerts based on the certainty that the change meets your criteria for an alert. For example, a confidence based freshness alert says, only generate that alert if you are 80% confident (as an example) that the table failed to update as expected.
Threshold based alerts: You manually assign limits to trigger alerts. For example, you can specifically say that you want to generate an alert if a table fails to update every three days.

Create observers

An Observer lets you select data assets to monitor, define alert rules, and set a run schedule. You can monitor multiple assets from different data sources, including assets cataloged in Data Integrity Suite and other Precisely applications. Each asset can be used in only one Observer.

Tip: Each asset can be used in only one Observer. You can create multiple Observers for a single data connection.

Create Observers to monitor your data assets and generate alerts when unexpected changes occur.

Go to Observability > Observers and select + Create Observer.
On the Source Data page, select the datasets and fields you want to observe.
Click Next.
On the Observer Rules page, configure the rules you need. Available rules include Freshness, Volume, Data drift, and Schema drift.
Click the gear icon in the Configure column for each rule you want to configure.
Click Next.
On the Finalize page, enter the Observer name and description.
In the Scheduler section, select the frequency and timing for the run schedule.
In the Start Date field, select when the Observer should begin considering profiling statistics for alerts. This is optional and helps you skip past initial profile runs.
In the Notifications section, verify that Email Notifications is turned on (enabled by default).
In the Recipients field, add email addresses for each person who should receive alerts from this Observer.
Click Save.
The Observer is saved and you're redirected to the Observers page. The Observer runs at the scheduled time and sends notifications about unexpected changes in your data assets. If email notifications are enabled, you'll receive emails for generated alerts and failed Observers. You can edit the Observer anytime to change rule configurations.

What happens after you create an observer:

First runs: After the first scheduled run, view profiling information from the Assets and Profiling tab.
Monitoring: After a few runs, the Observer identifies trends and patterns. When the system detects an anomaly, it creates an alert and sends a notification. You can adjust alert criteria and notification settings to fine-tune monitoring.
Visualization: Alerts show the reason for the change and a visual representation of value changes, helping you spot trends and patterns quickly.
Updates: Edit Observers after initial configuration to change rules, data assets, notification settings, and scheduling.
Historical data monitoring: The Observer continues monitoring data changes over time, providing historical context for additional insights.

Search for observers: In the search field, enter the complete or partial name of an Observer. Matching results display.

Edit an observer: Click the ellipsis for the Observer you want to edit and select Edit.

Delete an observer: Click the ellipsis for the Observer you want to delete and select Delete.

Warning: Deletion cannot be undone. Asset history is retained, but existing alerts associated with the Observer are deleted.

View profiling details for an observer

Access profiling details associated with an Observer to review analysis results.

Go to Observability > Observers.
Select the Observer whose profiling details you want to view.
Click Assets and Profiling.
Review the profiling details associated with the Observer.

Tip: For Snowflake and Databricks datasources, profiling statistics are generated automatically when you run an Observer. For other datasources, enable the Profile datasets option on the Insights page when you set up a new datasource to generate profiling statistics after a successful Observer run.

Manage alert notifications

Configure email notifications so you're alerted when anomalies are detected or when an Observer fails to run.

Only the creator of an Observer can turn notifications ON or OFF and add recipients to the notification list. Notifications are sent by email when:

An alert is generated for a monitored data asset.
An Observer fails to run ten times consecutively.

Note: Alert notifications are sent from a no-reply email address. If you don't see expected emails in your inbox, check your spam or quarantined folder. If you haven't received any emails, contact your organization's IT support to allow receipt of emails from the Data Integrity Suite domain.

When creating or editing an Observer, go to the final configuration page.
In the Notifications section, review the notification status. Notifications are set to on by default.
To disable notifications, toggle Email Notifications off.
To add notification recipients, type their email address in the Recipients field.
You can add multiple recipients by entering each email address.
Click Save to apply your notification settings.

Your notification preferences are saved, and recipients will receive email alerts when anomalies are detected.

Observer constraints and limitations

Consider these factors when observing tables and columns:

Data Access: Not all users have access to observe all tables and fields. Make sure that users have the right permissions to observe the data they need.
Data Volume: Observing large datasets can be resource-intensive and may require additional processing power or storage. It will also take more time to complete a successful run on large datasets.
Data Privacy: Observing certain types of data, such as personal data, may require additional security measures to ensure compliance with data privacy regulations.
Data Quality: Observing tables and fields with poor data quality can lead to inaccurate or unreliable results.
Data Governance: Observing tables and fields may be restricted by the data governance policies of your organization.