Overview
The Extraction Time calculator displays the date and time when your dataset was last successfully extracted from data sources. This essential metadata calculator helps you verify data freshness and ensures you're making decisions based on current information.
Unlike other calculators that analyze your process data, Extraction Time simply retrieves and displays a timestamp from the dataset's metadata. This timestamp is set automatically when data is imported or refreshed through your ETL pipeline.
Common Uses
- Verify data freshness: Confirm how current your analysis data is before making business decisions
- Dashboard timestamps: Display "as of" dates on dashboards and reports to inform stakeholders
- Data quality monitoring: Identify stale datasets that need to be refreshed
- Audit trails: Track when data was loaded for compliance and troubleshooting purposes
- User awareness: Help analysts understand the recency of data they're working with
- ETL pipeline monitoring: Verify that scheduled data extractions are running successfully
Settings
This calculator has no configuration settings. It automatically retrieves and displays the extraction timestamp from your dataset's metadata.
The only standard fields available are:
Title: Optional custom title for the calculator output (defaults to "Extraction Time")
Description: Optional description to provide context about this metric
Examples
Example 1: Dashboard Data Freshness Indicator
Scenario: Your operations team uses a process mining dashboard to monitor daily invoice processing. They need to know how current the data is to make informed decisions about workload allocation.
Settings:
- Title: "Data Last Updated"
- Description: "Timestamp of most recent data extraction from SAP"
Output:
The calculator displays a single timestamp value, such as:
2025-10-19 06:30 AM EST
This shows when the data was last extracted from your source systems.
Insights: The team can see that data was refreshed this morning at 6:30 AM, meaning they're looking at yesterday's completed work. If the timestamp showed last week's date, they would know to request a data refresh before making operational decisions.
Example 2: Report Audit Trail
Scenario: Your compliance team generates quarterly process mining reports that must include metadata about when the underlying data was extracted, ensuring report accuracy and traceability.
Settings:
- Title: "Source Data Extraction Date"
- Description: "Q4 2024 Accounts Payable Analysis"
Output:
The report header displays:
Process Mining Report - Q4 2024
Report Generated: 2025-01-15 2:30 PM
Data Extracted: 2025-01-14 11:45 PM
Data Age: 14 hours 45 minutes
Insights: The report clearly documents that it's based on data extracted on January 14th, providing full transparency about data currency. Auditors can verify the timeline between data extraction and report generation, ensuring the analysis reflects the stated time period.
Example 3: Stale Data Detection
Scenario: Your process mining platform should alert users when they're viewing outdated data that hasn't been refreshed on schedule. The weekly data refresh from your ERP system failed, but users may not realize they're looking at old information.
Settings:
- Title: "Last Data Refresh"
- Description: "Weekly extraction from Oracle ERP"
Output:
The calculator shows:
2025-10-05 03:00 AM UTC
Current date: October 19, 2025
Insights: The data is 14 days old, indicating the weekly extraction process has failed for two consecutive weeks. The system can automatically display a warning banner: "Data is 14 days old - refresh needed" and alert the data team to investigate the ETL pipeline failure. Users are informed not to rely on this data for current operational decisions.
Example 4: Multi-Region Global Dashboard
Scenario: Your multinational company has process mining users across US, Europe, and Asia who need to see the data extraction time in their local timezone for better understanding of data currency.
Settings:
- Title: "Data Extraction Time"
- Description: "Global Order-to-Cash Process"
Output:
The calculator automatically converts the UTC timestamp to the configured timezone for each region:
- US East Coast Office: 2025-10-19 06:30 AM EST
- London Office: 2025-10-19 11:30 AM GMT
- Tokyo Office: 2025-10-19 08:30 PM JST
All showing the same extraction event in local time.
Insights: Global teams can immediately understand data freshness in their local context. Tokyo users see that data was extracted this evening (their time), while New York users see it was extracted this morning. This prevents confusion about whether "yesterday's data" means yesterday in New York or yesterday in Tokyo.
Example 5: Automated ETL Monitoring
Scenario: Your data engineering team needs to monitor that the nightly data extraction pipeline completes successfully. If today's extraction hasn't run by 8 AM, they need to be alerted.
Settings:
- Title: "Latest Extraction"
- Description: "Nightly ETL Pipeline Status"
Output:
The monitoring system queries this calculator and compares the timestamp:
Expected: 2025-10-19 (today)
Actual: 2025-10-18 03:00 AM
Status: FAILED - extraction is 1 day overdue
Insights: The automated monitoring system detects that the extraction timestamp is still showing yesterday's date when today's extraction should have completed. It automatically sends an alert to the data engineering team to investigate the pipeline failure. This proactive monitoring prevents users from making decisions on outdated data.
Output
The calculator returns a single timestamp value showing when the dataset was last successfully extracted from source systems.
Timezone Display:
- If a timezone is configured in your dataset settings, the timestamp is displayed in that local time
- If no timezone is configured, the timestamp displays in UTC (Coordinated Universal Time)
- The timezone abbreviation or offset is typically shown with the time (EST, GMT, UTC, etc.)
Format: The timestamp typically displays in a human-readable format such as:
- "October 19, 2025 6:30 AM EST"
- "2025-10-19 06:30:00"
The exact format may vary based on your system's display preferences.
Handling Missing Data: If the extraction timestamp is not available (which can occur with manually created test datasets or very old datasets), the calculator may display "Unknown" or an empty value. In production systems with properly configured ETL pipelines, this should always have a valid timestamp.
Dashboard Integration: This calculator is commonly added to dashboard headers to provide constant visibility of data freshness. It can also be included in automated reports, monitoring systems, and audit logs.
This documentation is part of the mindzie Studio process mining platform.