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Data Observability
Proactively monitor and manage the health of your data. Receive actionable insights through alerts that drive business decisions.
Observers
You can create observers to keep a watch on your data assets and take timely measures in case of data anomalies. The observer monitors your selected data assets and provides alerts based on the selected observer rules.
Manage Observers
You can manage your observers on the Observers page.
Create an Observer
You can create multiple observers for a single data connection, but you cannot use the same assets across different observers. You must set up a successful data connection before creating an observer. To create a data connection, refer to DISuite/Observability/data-connections/Creating_Snowflake_Data_Connection.html.
Observer Rules
Observations profile your data on a regular cadence. It observes and monitors your data to identify anomalies. Based on your selected observations, you will receive alerts for identified anomalies. You can use observer rules to generate trends that can help you to identify business impacts and take prompt actions.
Volume Rule Configuration
The data volume is defined by the number of rows in the data. The unexpected changes—addition or deletion of rows in the data, can impact the overall decision-making. Volume rule configuration helps to generate alerts for these unexpected changes.
Threshold-based alerts
The threshold is the limit set manually to trigger the alerts. This threshold range is in percentage and defines the change in the data volume.

Welcome
The Precisely Data Integrity Suite is designed to deliver trusted data with accuracy, consistency, and context – built on the core capabilities of data integration, data quality, data governance, location intelligence, and data enrichment.
Data Integration
Replicate data from mainframe, IBM i, and relational databases to cloud analytics platforms.
Data Observability
Proactively monitor and manage the health of your data. Receive actionable insights through alerts that drive business decisions.
- Observe Data Assets
  Observing your data assets allows you to take actions based on the alerts received. Alerts are provided after successful scheduled runs. The trends generated provide alerts about data anomalies. You can observe data assets only once and cannot use them again for another observer. Before you create an observer, identify your data source and set up a data connection. Scheduling is mandatory for creating an observer.
- Profile Data Assets
  Learn about how to perform end-to-end Data Profiling. It provides a complete workflow of Data Profiling.
- Data Connections
  Your organization may have diverse data sources that you want to process. Use Data Connection to configure these data sources and connect them to the Data Integrity Suite.
- Data Profiles
  Data profiling scans your data records from all the data sources - irrespective of their volume and complexity. It identifies problems related to correctness, completeness, and validity in the data, and suggests actions to fix the issues. Thus, it improves the quality and utility of your data with very little manual effort.
- Observers
  You can create observers to keep a watch on your data assets and take timely measures in case of data anomalies. The observer monitors your selected data assets and provides alerts based on the selected observer rules.
  - Manage Observers
    You can manage your observers on the Observers page.
    - Create an Observer
      You can create multiple observers for a single data connection, but you cannot use the same assets across different observers. You must set up a successful data connection before creating an observer. To create a data connection, refer to DISuite/Observability/data-connections/Creating_Snowflake_Data_Connection.html.
      - Observer Rules
        Observations profile your data on a regular cadence. It observes and monitors your data to identify anomalies. Based on your selected observations, you will receive alerts for identified anomalies. You can use observer rules to generate trends that can help you to identify business impacts and take prompt actions.
        Volume Rule Configuration
        The data volume is defined by the number of rows in the data. The unexpected changes—addition or deletion of rows in the data, can impact the overall decision-making. Volume rule configuration helps to generate alerts for these unexpected changes.
        Confidence-based alerts
        Threshold-based alerts
        The threshold is the limit set manually to trigger the alerts. This threshold range is in percentage and defines the change in the data volume.
        Data Drift Rule Configuration
        You can track a set of statistics for Numeric and Textual data such as Cardinality (number of distinct values), min/max values, min/max length, number of empty/blank values, new values found, and so on. The unexpected changes in the historical trends of any of these statistics can impact overall decision-making. Data drift configuration generates alerts for unexpected changes in the selected statistics. The alerts will be generated if the value of the selected statistics increases or decreases than its expected value.
    - Edit an Observer
    - Delete an Observer
  - Search Observers
    You can search for a specific observer.
  - Reference
    This section provides reference information for Observers.
- Alerts
  Alerts are generated as per the observer rules and the assets that are selected during observer creation. The alerts are either critical level or warning level. This level is set during the observer rule configuration.
Data Governance
Manage data policy and processes with greater insight into your data's meaning, lineage, and impact.
Data Quality
Assure reliability, consistency, and accuracy data from different sources to deliver value to employees and customers.
Administration
Manage your subscription, your users and their roles, and other system tasks.

Threshold-based alerts

The threshold is the limit set manually to trigger the alerts. This threshold range is in percentage and defines the change in the data volume.

For example, a threshold range set to 3% to 8% of data volume means that the alerts will be generated for an increase or decrease in the volume by 3% to 8%.

Warning alerts will be generated for a change in data between 3% and 8%.
Critical alerts will be generated for a change in data above 8%
No alerts will be generated for a change in data below 3%.

To set threshold-based alerts:

Select any one of the options to calculate the alerts against the following.

Field	Description
Moving Average	Uses the specified data points which are the number of profile runs to detect any changes in the data volume. Note: The default number of profiles for moving average is 7. For example, if the data points are set to 7, the moving average will be calculated after 7 profile runs, to detect the change in data volume.
Last Profile Run	Uses the last profile run to detect any changes in the data volume.

Enter the lower and higher value in the text box to set the range.

Note: The default range is set as 3% to 8%.
Click Save to save the changes.
Tip: The moving average filters noise in the alerts. It identifies the change in data volume based on the average of collected data points. The data points can be set depending upon the frequency of the schedule.
Example:
If the scheduler is set to daily, you may set the data points to a maximum number. This is because the frequency of runs will be more, and more data points can be collected to calculate the moving average.

If the scheduler is not very frequent, you may set the data points to a minimum number. This is because the frequency of runs will be less, and it will take more time to collect more data points.
The last profile run uses the latest run to identify the data volume change. This is used to reduce noise in the alerts.
Example:

If the scheduler is set to daily, it will analyze the data after every last run and generate an alert when there is a significant change in the data volume.

If the scheduler is not very frequent, this is an ideal setting to receive alerts for change in data volume.