Ataccama ONE — Automated Data Quality Issue Tracking

Operationalizing Data Quality Monitoring with Workflow Automation, ADLS Staging, and Power BI Reporting

Overview

Organizations rely on accurate, consistent, and trusted data to support business operations and decision-making. As enterprise data volumes continue to grow, manual processes for identifying, assigning, and resolving data quality issues become increasingly difficult to manage. An automated approach to Data Quality Monitoring helps organizations detect issues early, improve operational efficiency, and strengthen enterprise Data Quality Management.

This case study highlights how Ataccama ONE was implemented to automate Data Quality Issue Tracking across connected enterprise data sources. The solution leverages Ataccama Orchestration, Workflow Automation, and Azure Data Lake Storage (ADLS) to create, update, and close tasks automatically based on monitoring results. Historical monitoring data is stored in ADLS and visualized through a Power BI Dashboard, giving business and technical teams complete visibility into the data quality lifecycle.

The Challenge

Many organizations perform routine Data Quality Monitoring, but converting monitoring results into actionable tasks often requires manual intervention. This slows down issue resolution, increases operational effort, and makes it difficult to maintain a consistent audit trail across the organization.

The objective was to implement an automated and repeatable Data Quality Automation process that transforms monitoring results into structured workflows within Ataccama ONE. Every identified issue needed to progress through a defined lifecycle—from detection and assignment to resolution and closure—without manual task creation or administration.

The solution also needed to support enterprise Data Governance by maintaining complete visibility into issue history, ownership, and reporting while ensuring that data stewards received timely notifications for newly detected issues.

Key Requirements

• ✓
  Automatically convert Data Quality Monitoring results into actionable tasks within Ataccama ONE.
• ✓
  Detect newly failed records, resolved records, and changes between consecutive monitoring runs.
• ✓
  Maintain complete historical records of Data Quality Management activities for reporting and compliance.
• ✓
  Generate automated notifications for data stewards and stakeholders whenever new issues are detected.
• ✓
  Support native task creation directly from monitoring outcomes.
• ✓
  Enable built-in email notifications for task owners.
• ✓
  Generate custom task identifiers, task descriptions, and detailed task metadata for improved traceability.
• ✓
  Provide historical reporting through Azure Data Lake Storage (ADLS) and Power BI Reporting to improve operational visibility beyond the native task management interface.

The Solution

The solution combines Ataccama ONE, Ataccama Orchestration, an Ataccama Runtime workflow, and Azure Data Lake Storage (ADLS Gen2) to automate the complete Data Quality Management lifecycle. This integrated approach enables organizations to identify, track, assign, and resolve Data Quality Issues without manual intervention while maintaining a centralized history for reporting and governance.

The Data Quality Monitoring project continuously evaluates connected enterprise data sources and publishes monitoring results to Azure Data Lake Storage (ADLS). Once each monitoring cycle is complete, Ataccama Orchestration automatically triggers a workflow that compares current and previous results, determines the status of each issue, and performs the appropriate task actions within Ataccama ONE.

By storing monitoring history in ADLS and connecting it to a Power BI Dashboard, the solution extends reporting capabilities beyond the native Ataccama task interface. Business users and data stewards gain complete visibility into issue trends, task status, ownership, and historical data quality performance, enabling faster decision-making and stronger Data Governance practices.

Solution Architecture

The architecture diagram below illustrates how Data Quality Monitoring results are generated, processed, and converted into automated tasks within Ataccama ONE during every monitoring cycle.

1. Data Quality Monitoring

The Ataccama ONE environment contains a scheduled Data Quality Monitoring project that evaluates data quality rules across configured Catalog Items. After every monitoring run, a post-processing plan exports the results to Azure Data Lake Storage (ADLS Gen2), where they serve as the foundation for automated processing and historical analysis.

2. Workflow Automation

Once monitoring is complete, Ataccama Orchestration automatically triggers the configured Workflow Automation process. This removes the need for manual execution and ensures that every monitoring cycle follows a consistent and repeatable process.

3. Runtime Processing

The orchestration workflow executes on the Ataccama Runtime Server, where monitoring data is validated, processed, and prepared for issue comparison and automated task management.

4. Automated Data Quality Comparison

The workflow compares the latest monitoring results with those from the previous execution using data stored in Azure Data Lake Storage (ADLS). Every monitored record is evaluated individually to identify:

• ✓
  Newly Failed Records: Records failing rules for the first time in the current cycle.
• ✓
  Successfully Resolved Records: Previously failing records that now satisfy all validation rules.
• ✓
  Existing Records with Updated Failures: Active issues where specific rule violation details have changed.

This automated comparison provides accurate Data Quality Issue Tracking while eliminating repetitive manual review.

5. Intelligent Task Lifecycle Management

Based on the comparison results, the workflow automatically performs the appropriate actions within Ataccama ONE.

For newly detected issues, individual tasks are created for each failed record, ensuring every Data Quality Issue is tracked independently.

When all rule violations associated with a record are resolved, the corresponding task is automatically closed.

If an existing issue changes—for example, when one rule is resolved while another fails—the workflow updates the existing task instead of creating duplicates.

Each task includes:

• ✓
  Automatic assignment to the appropriate user or support group.
• ✓
  Detailed descriptions listing failed Data Quality Rules.
• ✓
  Custom task identifiers for easier tracking.
• ✓
  Record-specific business details and endpoint references where applicable.
• ✓
  Individual subtasks for every failed validation rule, providing better visibility into complex issues.

This automated Task Management process significantly reduces manual effort while improving accountability and consistency across enterprise Data Quality Management initiatives.

6. ADLS History and Data Staging

Azure Data Lake Storage (ADLS Gen2) serves as the centralized staging, processing, and historical repository for all Data Quality Monitoring results. Each monitoring execution stores both the current and previous evaluation results, enabling the workflow to accurately compare records across consecutive runs.

In addition to acting as the workflow staging layer, ADLS maintains complete historical records of Data Quality Management activities. This historical data supports trend analysis, compliance reporting, audit readiness, and long-term performance tracking across enterprise data assets.

7. Historical Reporting with Power BI

The workflow maintains both summary-level and detailed historical records of Data Quality Monitoring results, task creation activities, task modifications, and issue resolution history within ADLS.

This historical repository becomes the primary data source for Power BI Reporting, enabling organizations to build interactive Power BI Dashboards that provide deeper visibility into data quality performance than the native Ataccama task management interface.

Business users, data stewards, and management teams can analyze:

• ✓
  Data quality trends across monitoring cycles
• ✓
  Open and resolved task counts
• ✓
  Task ownership and assignment distribution
• ✓
  Historical issue patterns and root causes
• ✓
  Data quality KPIs and SLA compliance
• ✓
  Overall Data Quality Management performance

These dashboards improve operational visibility while supporting informed decision-making across the enterprise.

8. Automated Email Notifications

Upon successful completion of every workflow execution, automated email notifications are sent to designated users, providing confirmation that the monitoring process has completed successfully.

The workflow can also be configured to notify assigned Data Stewards whenever new Data Quality Issues are identified. Each notification includes relevant information such as failed validation rules, issue details, and direct links to the corresponding task within Ataccama ONE, enabling faster investigation and resolution.

9. Open Task Reporting

To improve operational visibility, the workflow can automatically generate and distribute a report containing the current list of open tasks after each monitoring cycle.

This automated reporting helps support teams and business stakeholders monitor outstanding Data Quality Issues, prioritize remediation efforts, and ensure that critical issues receive timely attention without manually reviewing the application.

10. Fully Automated Task Lifecycle

No manual task management is required throughout the Data Quality Issue Tracking process. Every stage—from issue detection and task creation to updates, notifications, reporting, and closure—is driven through Workflow Automation within Ataccama ONE.

This end-to-end automation improves operational efficiency, reduces manual effort, strengthens Data Governance, and ensures that every monitoring cycle follows a consistent and auditable process.

Results & Benefits

By integrating Ataccama ONE Task Management, Ataccama Orchestration, Azure Data Lake Storage (ADLS), and Power BI Reporting, Princeton IT Services implemented a scalable Data Quality Management solution that automates issue tracking while providing complete operational visibility.

Key business benefits include:

• ✓
  Automated Data Quality Task Lifecycle: Task creation, updates, and closure are driven directly by monitoring outcomes.
• ✓
  Record-level Data Quality Issue Tracking: Every task contains detailed information about failed validation rules.
• ✓
  Automatic Task Assignment: Routes tasks to the appropriate user or business group, improving accountability and response times.
• ✓
  Simplified Open Task Monitoring: Operational reporting and automated email notifications keep teams informed.
• ✓
  Complete ADLS Audit History: Maintained within Azure Data Lake Storage (ADLS) for compliance, reporting, and historical analysis.
• ✓
  Interactive Power BI Dashboards: Overcomes native reporting limitations by providing advanced search, analytics, and trend visualization.
• ✓
  Proactive Communication: Workflow notifications keep stakeholders and Data Stewards informed of newly detected issues.
• ✓
  Reduced Manual Effort: Workflow Automation allows data teams to focus on resolving issues rather than managing administrative tasks.

This solution enables organizations to improve enterprise Data Quality Monitoring, strengthen Data Governance, and deliver faster, more reliable data quality operations across business-critical systems.

Conclusion

Ataccama ONE provides a powerful foundation for automating Data Quality Management, enabling organizations to transform routine Data Quality Monitoring into an efficient, end-to-end operational process. By combining Ataccama Orchestration, Workflow Automation, and Azure Data Lake Storage (ADLS), organizations can automatically detect data quality issues, create and manage tasks, notify stakeholders, and maintain a complete audit history without manual intervention.

The integration of ADLS with Power BI Reporting extends visibility beyond the native task management interface, allowing business users and data stewards to analyze Data Quality Metrics, monitor issue trends, track task performance, and gain actionable insights through interactive Power BI Dashboards. This centralized reporting capability supports stronger Data Governance, improves operational transparency, and helps teams make faster, data-driven decisions.

By automating the entire Data Quality Issue Tracking lifecycle—from issue detection and task assignment to resolution and reporting—organizations can significantly reduce manual effort, improve accountability, and accelerate issue resolution. The solution also provides the scalability needed to support growing enterprise data environments while maintaining consistent monitoring and governance practices.

While the native Ataccama ONE task management module may experience performance limitations as task volumes increase, integrating historical data with Azure Data Lake Storage (ADLS) and Power BI Dashboards provides a practical and scalable reporting solution for managing large numbers of Data Quality Issues.

This implementation demonstrates how Ataccama ONE, combined with Workflow Automation, Enterprise Data Quality Monitoring, and modern analytics platforms, enables organizations to build a scalable, automated, and governance-driven Data Quality Management framework that improves data reliability, operational efficiency, and business confidence.