Your Payroll Processing Data Template

The Activity Name describes a discrete action taken within the payroll lifecycle, such as 'Time Data Approved', 'Gross-To-Net Calculated', or 'Payment Executed'. Each activity represents a node in the process map and is associated with a specific timestamp.

This attribute is fundamental to process mining as it forms the basis of the process map. Analyzing the sequence, frequency, and duration of these activities reveals the actual process flow, identifies common pathways, highlights deviations from the standard procedure, and pinpoints bottlenecks where the process slows down.

It defines the steps in the process map. Analyzing activities is crucial for understanding what is happening, discovering bottlenecks, and identifying rework loops.

Usually found in system logs, audit trails, or status fields within payroll transaction tables. It may need to be mapped from technical event codes to user-friendly names.

The Event Timestamp records the exact moment an activity was performed. This chronological data is essential for ordering events correctly and calculating the duration between different steps in the process.

In process mining, timestamps are used to construct the sequence of events for each case and to perform temporal analysis. This includes calculating cycle times between activities, identifying delays, measuring waiting times, and monitoring performance against service level agreements (SLAs). Accurate timestamps are the foundation of any performance-related process analysis.

This attribute is critical for sequencing events correctly and for all time-based analyses, including calculating cycle times, durations, and identifying bottlenecks.

Found in system audit logs or as a 'creation date' or 'event time' field in payroll transaction records.

The Payroll Record ID serves as the unique case identifier for the entire payroll process for a single employee in a given pay cycle. Each ID represents one journey from time data submission to payment and reporting.

In process mining, this attribute is essential for stitching together all related activities into a coherent end-to-end process flow. It allows for the analysis of cycle times, variations, and bottlenecks for individual payroll records. Without a unique case ID, it would be impossible to trace the sequence of events and understand the process dynamics accurately.

This is the primary key that connects all process events into a single case, making it possible to analyze the end-to-end payroll cycle for each record.

Typically generated by the payroll system or can be created by concatenating an employee identifier and a pay period identifier from payroll transaction data.

The Last Data Update timestamp provides information on the freshness of the data. It indicates the last time the record was changed in the source system or when it was pulled into the dataset for analysis. This is distinct from the Event Timestamp, which records when the business activity actually happened.

This attribute is primarily used for data governance and validation. It helps in managing incremental data loads and ensuring that the analysis is based on up-to-date information. While not typically used for direct process flow analysis, it is crucial for maintaining the integrity and timeliness of the process mining dataset.

Ensures data freshness and is essential for managing incremental data loads, helping to confirm that the analysis reflects the most current information available.

This timestamp is typically generated during the data extraction, transformation, and loading (ETL) process or is available as a 'last modified date' field in source system tables.

This attribute identifies the source system from which the event data was recorded. In a modern payroll landscape, data may come from various systems, such as a Time and Attendance system, an HRIS, or the core payroll engine itself.

Analyzing the source system helps in understanding the integration points and data handoffs within the overall process. It can be used to filter the process for events originating from a specific application, which is useful for diagnosing integration issues or comparing processes that are managed in different systems.

Identifies where each process step occurs, which is crucial for understanding process fragmentation and identifying system-specific bottlenecks or integration delays.

Often available as a standard field in data extracts, or it can be added during the data extraction process based on the origin of the data tables.

The Cost Center is an organizational attribute that links payroll expenses to a specific part of the business, such as 'Sales', 'IT', or 'Manufacturing'. It is a fundamental element for financial reporting and management accounting.

Using Cost Center as a dimension in process mining allows for slicing and dicing the process data by business unit. This analysis can reveal if certain departments experience more payroll issues, delays, or manual corrections than others. These insights can help target process improvement initiatives to the areas with the greatest need or impact.

Enables organizational analysis to determine if payroll issues, delays, or inefficiencies are concentrated in specific departments or business units.

Available in employee master data, HR organizational structures, or financial assignment tables within the payroll or HR system.

Gross Pay Amount represents the total earnings of an employee for a specific pay period. It is a key financial figure calculated during the payroll run and serves as the basis for subsequent tax and deduction calculations.

While not a direct driver of process flow, this attribute is valuable for contextual analysis. It can be used to segment cases by value, for instance, to check if high-value payroll records are processed differently or are subject to more rigorous checks. It is also essential for financial impact analysis, such as calculating the total value of payments processed with errors or delays.

Provides financial context to the process, allowing for analysis of the monetary impact of delays, rework, or exceptions, and for segmenting cases by value.

A standard output field from the gross-to-net calculation step, found in payroll results or transaction data tables.

This attribute is a true or false flag that identifies events where a user had to manually intervene in what should ideally be an automated process. This can include manually keying in missing data, correcting timesheets, or adjusting calculated results.

Manual corrections are a direct indicator of process inefficiency, risk, and poor data quality. Analyzing where and why these interventions occur is fundamental to process improvement. By filtering for manual corrections, organizations can pinpoint the root causes of rework, identify automation opportunities, and measure the 'First-Pass Yield' KPI. Reducing manual corrections leads to a more efficient, accurate, and compliant payroll process.

Directly measures process friction and inefficiency. High rates of manual correction indicate poor data quality, broken automation, and increased compliance risk.

Can be derived from audit logs that track data changes by users, or from specific transaction codes that signify a manual adjustment.

A Pay Group is a logical collection of employees who are paid on the same schedule, such as 'Bi-Weekly Salaried US' or 'Monthly Executive UK'. This attribute is used to manage and execute payroll runs for specific segments of the workforce.

In process analysis, filtering by Pay Group allows for comparing the performance and efficiency of different payroll cycles. This can uncover issues specific to a certain group, such as higher correction rates for weekly hourly employees versus monthly salaried ones. It also helps in understanding how process variations correlate with different pay schedules or employee populations.

Allows for comparative analysis between different payroll cycles, helping to identify if process issues are specific to certain employee populations or pay frequencies.

Found in employee master data or payroll configuration tables, often labeled as 'Pay Group', 'Payroll Area', or a similar term.

The Pay Period End Date marks the cutoff for the time and attendance data included in a specific payroll run. It is a critical date that defines the timeframe of the earnings being processed.

This attribute is useful for trend analysis over time. By aggregating data based on the pay period end date, analysts can track KPIs like processing time, error rates, and manual corrections across different months or quarters. This helps in identifying seasonality, measuring the impact of process changes over time, and long-term performance monitoring.

Enables trend analysis of payroll performance over time, helping to identify seasonality or measure the impact of process improvements from one pay period to the next.

A standard attribute in payroll calendar configurations and payroll transaction data.

The Payroll Run Type classifies the payroll processing event. A 'Regular' run is part of the standard, scheduled cycle. An 'Off-Cycle' run is an unscheduled payment to correct an error or pay a terminated employee. Other types might include 'Bonus' or 'Commission' runs.

This is a powerful dimension for comparative analysis. Off-cycle payments are often a sign of process failure in the regular cycle and are typically more expensive and time-consuming to process. By filtering by run type, organizations can isolate and analyze the root causes of these exceptions, measure their frequency and cost, and compare the efficiency of different run types.

Distinguishes between standard and exceptional processing. Analyzing off-cycle runs is key to understanding process failures and reducing costly, non-standard payments.

This information is typically available in the payroll run or batch processing tables within the payroll system.

The User Name identifies the individual or automated system responsible for executing a specific process step. This could be a payroll specialist, a manager approving time data, or a system account running an automated calculation.

This attribute provides a human-centric or system-centric view of the process. It is used to analyze workload distribution, individual performance, and team productivity. It can also help identify opportunities for training by highlighting users who frequently deviate from the standard process or are associated with high rates of rework. For automated steps, it helps distinguish between system-driven and manual activities.

Enables analysis of workload, team performance, and automation levels. It helps identify which users or teams are involved in rework or exceptions.

Typically found in 'user ID', 'processed by', or 'action user' fields in system logs or transaction records.

When automated audits are run during the payroll process, they may flag records that violate predefined rules. This attribute captures the category of the exception, such as 'Missing Tax ID', 'Unapproved Overtime', or 'Gross Pay Anomaly'.

Analyzing the type and frequency of audit exceptions provides direct insight into data quality and compliance issues. This information allows organizations to perform root cause analysis, for example, determining if a spike in 'Missing Tax ID' exceptions is due to a faulty onboarding process. Tracking these exceptions is crucial for the 'Audit and Compliance Monitor' dashboard and for reducing risk.

Provides a direct way to measure and categorize data quality and compliance issues, enabling targeted root cause analysis to improve accuracy and reduce risk.

This data is generated by the payroll system's internal audit or validation engine and is typically stored in audit logs or exception reports.

Employee Type is an attribute from the HR master data that categorizes the workforce. Different types of employees often follow different payroll processes, have different data requirements, and are subject to different regulations.

Using this attribute as a dimension for analysis can reveal significant differences in process performance. For example, the process for hourly employees may involve more complex time data integration and be more prone to errors than the process for salaried employees. Comparing KPIs like cycle time or correction rate across different employee types helps tailor process improvements to the specific needs of each group.

Helps segment the process to reveal variations and issues specific to different workforce categories, such as hourly vs. salaried employees.

This is a standard field in the employee master record within the HR or payroll system.

This attribute categorizes the performance of a case against its defined Service Level Agreement. It is typically derived by comparing the actual timestamp of a milestone activity, like 'Bank Transfer File Generated', with the 'SLA Deadline'.

This provides a clear and immediate indicator of performance that can be used in dashboards and reports. Analyzing the Sla Compliance Status helps to quantify the scale of deadline misses and allows for drilling down into the process characteristics of late cases versus on-time cases. This is crucial for identifying the root causes of delays and improving the reliability and predictability of the payroll process.

Provides a simple, direct measure of performance against targets, making it easy to report on compliance and focus analysis on cases that failed to meet deadlines.

This attribute is not found directly in source systems. It is derived by comparing the EventTimestamp of a milestone activity with the SlaDeadline attribute.

The SLA Deadline is a timestamp that represents the commitment for completing a critical part of the payroll process, such as generating the bank transfer file or executing payments. It serves as a benchmark against which actual performance is measured.

This attribute is essential for monitoring and analyzing SLA compliance. By comparing the actual completion timestamp of an activity with its SLA Deadline, organizations can identify breaches, measure the extent of delays, and proactively manage risks. It is a key component for dashboards related to payment performance and process reliability.

Defines the performance target, making it possible to measure SLA compliance, identify late payments, and analyze the root causes of delays.

This may be a fixed field in the system based on pay cycle configuration or can be derived based on business rules (e.g., Pay Period End Date plus three business days).

Tax Jurisdiction specifies the legal or administrative region for which taxes must be calculated and remitted. Payroll complexity can vary significantly between different jurisdictions due to different regulations, tax rates, and filing requirements.

Analyzing the process by Tax Jurisdiction can help identify regional challenges. For instance, it may reveal that payroll processing for a particular state or country is consistently slower or more error-prone. This could point to a need for better system configuration for that jurisdiction's rules, or additional training for specialists handling those regions.

Allows for analysis of process performance by geographic or legal region, helping to identify if complexity or errors are concentrated in specific jurisdictions.

This is part of the employee's tax information setup within the payroll or HR system.