Process Mining Event Logs: Validate Case IDs and Timestamps Before Mapping Bottlenecks

Before you map a single bottleneck, validate your process mining event logs first. Ambiguous case IDs, timestamp errors, and missing events create false process pictures that lead analysts toward wrong conclusions. The fix is not complex, but it requires a documented data collection plan and disciplined audit before discovery begins.

This article walks improvement analysts through a practical event-log audit covering case ID quality, timestamp validation, event duplication, and reconciliation to source counts. You will also find a small illustrative audit table, lifecycle field guidance, and timezone handling notes to help you build a clean foundation for accurate process discovery.

Key Takeaways

• Confirm every event has a valid case ID, activity name, and timestamp.
• Reconcile case and event counts with the defined source-system scope.
• Normalize time zones while preserving each timestamp's original offset.
• Investigate suspected duplicates before removing any repeated event.
• Use lifecycle values consistently when calculating activity durations.

What Process Mining Event Logs Must Contain Before Analysis

Process mining starts with event data. In a traditional event log, each event refers to a case, an activity, and a point in time, as outlined in the process mining event-data guidance. Missing or incorrect values in these fields can distort the discovered sequence and performance measures.

For traditional case-centric analysis, the required fields are a Case ID, Activity name, and Timestamp; Microsoft's process-mining data requirements make the same distinction between event logs with one timestamp and activity logs with start and end timestamps. Resource, lifecycle, and process-specific attributes are optional enrichments.

An event log can look complete at a glance: row counts appear reasonable, timestamps are populated, and case IDs seem present. The audit must test the values and relationships inside the data, not just the visible schema.

How to Conduct a Case ID Quality Audit on Process Mining Event Logs

Case ID quality is the first thing to check. A case ID groups all events belonging to one process instance, such as one purchase order, one patient visit, or one loan application. When case IDs are ambiguous or incorrectly assigned, events from different process instances merge into one artificial case.

That merge creates ghost traces in your process map, paths that no real instance ever followed. It also inflates cycle times and produces bottleneck signals at activity transitions that are actually boundaries between two separate cases.

1. Check for Null or Blank Case IDs

Filter your event log for rows where the Case ID field is empty or null. Even a small percentage of null IDs can fragment traces and produce orphaned events that distort frequency counts during process discovery.

2. Identify Non-Unique ID Formats Across Source Systems

When logs are extracted from multiple systems, the same physical case may carry different ID formats in each source. A purchase order numbered PO-10042 in ERP may appear as 10042 or ORD10042 in a fulfillment system. Without normalization, process mining treats these as separate cases.

3. Flag Cases With Implausible Event Counts

Sort cases by event count and examine outliers at both ends. A one-event case or a case far above the typical event count is a review signal, not proof of an error; confirm it against process rules and source records.

4. Reconcile Case Counts to Source Records

Pull the total case count from the originating system, such as the ERP transaction count or the ticketing system record count, and compare it to the distinct Case ID count in your event log. A gap signals missing cases or duplicate IDs that collapsed multiple cases into one.

5. Verify ID Stability Across the Full Case Lifecycle

Some systems reassign or reformat IDs mid-process, especially during status transitions. Check that the Case ID value remains identical from the first event to the last event for each process instance in the log.

Once case ID quality is confirmed, the next layer of the audit shifts to time data, where errors are equally common and arguably harder to spot without a structured check.

Timestamp Validation Steps for Accurate Process Discovery

Timestamps determine event order and support duration and waiting-time calculations. An incorrect or unlabeled offset can reorder events or shift apparent waits, especially when data comes from several systems or locations.

Timestamp validation is not just about checking for missing values. It includes sequence logic, timezone consistency, precision level, and alignment with system-recorded event times in the source.

Step 1: Check for Missing Timestamps

An event without a timestamp cannot be ordered reliably. Filter for nulls and investigate the source; exclude or impute a value only under a documented rule that fits the analysis purpose, because an invented timestamp can create false precision.

Step 2: Detect Sequence Violations Within Cases

Within each case, sort events by timestamp and verify that the sequence matches defined process logic and rules. An approval event timestamped before the submission event it depends on is a clear data error, not a fast process.

Step 3: Standardize Timezone Across All Events

Multi-site or multi-system logs may mix UTC, local time, and system-default times. Preserve the original offset, convert analysis timestamps to one documented reference zone, and handle daylight-saving transitions explicitly; even a one-hour mismatch can distort sequence and waiting-time calculations.

Step 4: Confirm Timestamp Precision Is Sufficient

If your process operates in minutes but timestamps are recorded only to the nearest hour, sequence within that hour becomes arbitrary. Verify that the granularity of your timestamps matches the granularity of the process you are analyzing.

Step 5: Cross-Check Timestamps Against Source System Logs

Pull a sample of events and compare their timestamps in the event log against the originating system's audit trail or transaction log. Discrepancies indicate extraction errors, transformation bugs, or ETL timezone mishandling that must be corrected at the source.

With timestamps validated, the audit moves to a check that analysts often overlook: duplicate events and lifecycle field consistency.

Event Duplication and Lifecycle Field Checks in Process Mining Event Logs

Duplicate events are rows that represent the same real-world action more than once in the log. They inflate activity frequencies, extend apparent case durations, and create loops in the process map that do not exist in reality. Identifying them requires more than a simple row-level deduplication.

Rows with the same Case ID and Activity but different timestamps may be legitimate rework, separate lifecycle transitions, or extraction duplicates. Use an event identifier and source-system evidence where available before deleting them.

Checks to run for event duplication:

• Identify rows with identical Case ID, Activity, and Timestamp combinations and remove confirmed duplicates.
• Review near-simultaneous rows with the same Case ID and Activity, then confirm whether they represent rework, lifecycle transitions, or system double-writes.
• Compare total event counts in the log against source system transaction counts to detect over-extraction.
• Check whether duplication rates differ by activity type, as some system integrations are more prone to double-logging specific event types.

A lifecycle transition records the state represented by an event; the IEEE lifecycle extension guidance defines this field for events. If some activities log only completion while others log both start and completion, activity-duration calculations are not comparable without an explicit handling rule.

Lifecycle consistency checks include:

• Confirm that each activity type uses the same set of lifecycle values throughout the log.
• Verify that start events always precede complete events within the same case and activity.
• Identify completion events without a corresponding start; they prevent direct start-to-complete duration calculation and should not be assigned an automatic zero or negative duration.

Note: The audit table below uses illustrative data created for demonstration purposes only. It does not represent a specific client dataset.

Four Air Academy Associates Resources That Support Process Mining Event Log Work

Validating event logs draws on data measurement skills, process knowledge, and the ability to ask the right questions about data quality. The following Air Academy Associates resources build the analytical foundation that supports this kind of structured audit work.

Graphical and Measurement Certification Classes

Accurate data visualization is a core skill when reviewing event log distributions, outlier case counts, or timestamp spread across a dataset. Air Academy Associates offers Graphical and Measurement Certification Classes that support data visualization and measurement skills relevant to structured data review work.

• Builds ability to read and interpret data distributions relevant to log audits.
• Supports recognition of outlier patterns that may indicate case ID or timestamp errors.
• Verify the current syllabus directly with Air Academy Associates to confirm specific coverage areas before enrolling.

Measurement System Analysis

Before trusting any event log, analysts need to assess whether the recorded observations are trustworthy in the first place. The Measurement System Analysis course from Air Academy Associates addresses the principles of evaluating whether a data collection method produces reliable, consistent readings, a question directly relevant to event log extraction and system logging behavior.

• Introduces concepts of repeatability and reproducibility applicable to system-recorded data.
• Helps analysts frame questions about whether the source system logs events consistently.
• Supports the reconciliation step by building awareness of measurement error sources.

IPO Courses and Six Sigma Value Stream Mapping

Once event log data is validated, analysts need to connect those records to actual process flow. The IPO Courses and Value Stream Mapping program from Air Academy Associates teaches how to map inputs, process steps, and outputs in a structured way that complements validated event data.

• Connects cleaned event records to a visual representation of the actual process sequence.
• Supports identification of where validated bottleneck signals align with known process constraints.
• Provides a structured framework for communicating findings to process owners and stakeholders.

Coaching and One-to-One Training

Some event-log problems are not technical. They stem from unclear business questions, ambiguous scope definitions, or uncertainty about which case ID to use when multiple options exist. Air Academy Associates offers one-to-one coaching that allows analysts to review their specific business question and event-data limitations with an experienced practitioner.

• Provides a structured conversation about data scope and case definition before analysis begins.
• Helps analysts identify which audit checks matter most for their specific process context.
• Offers practical guidance without requiring a full training enrollment for targeted questions.

Putting the Audit Into Practice Before Process Discovery

A validated event log does not guarantee a perfect process map, but it removes the most common sources of false bottleneck signals. Analysts who skip the audit phase often spend more time explaining unexpected findings than they would have spent fixing the data upfront.

The standard event-log data model makes the case notion central to traditional process mining: each event must be correlated to the intended process instance. That requirement applies across healthcare, manufacturing, government, and financial-services processes.

Run the seven audit checks in the table on a representative sample before full-scale discovery. Investigate every unexplained reconciliation gap against the documented extraction scope rather than relying on a universal percentage threshold, and resolve timezone inconsistencies before calculating waiting times.

Air Academy Associates supports improvement analysts in building the measurement and process-mapping skills used in structured analysis. Explore the training resources above or request a coaching conversation to discuss how these skills apply to your process-improvement context.

Final Thoughts on Validating Process Mining Event Logs

Validate case IDs and timestamps before mapping any bottleneck in your process mining event logs. Clean data produces accurate process discovery, while unvalidated logs produce findings that mislead improvement decisions. A structured pre-analysis audit is not optional work; it is the foundation on which reliable process insight is built.

Air Academy Associates helps teams strengthen practical process-improvement, measurement, and analysis skills. Explore our training and services to find the support that fits your improvement work.

FAQs

What Fields Does a Usable Process-Mining Event Log Need?

At minimum, you need a unique case ID (to group events into one instance), an activity/event name (what happened), and a reliable timestamp (when it happened). Helpful additions include resource/owner, status, location/system source, and key attributes (e.g., customer type, priority) to enable filtering and root-cause analysis—an approach we emphasize in our Lean Six Sigma and DOE work to ensure conclusions are data-supported.

How Can Case IDs and Timestamps Create False Bottlenecks?

Bad case IDs can split one real case into multiple "mini-cases" or merge different cases into one, distorting handoffs and rework. Timestamp issues—missing values, inconsistent time zones, coarse granularity (e.g., date-only), or out-of-order events—can inflate wait times or create impossible loops, making normal variation look like a bottleneck. Validating these fields upfront prevents chasing the wrong constraints.

What Checks Should Precede Process Discovery?

Before mapping bottlenecks, confirm (1) case ID uniqueness and stability across the full process, (2) timestamp completeness, format consistency, time zone alignment, and monotonic ordering within each case, and (3) event completeness (no missing key steps) and sensible activity naming (no duplicates caused by spelling or system codes). We also recommend basic distribution checks (cycle time, event counts per case, rework rates) to spot anomalies early and ensure the discovered process reflects reality.

Related Articles

• Water Reduction With DOE: Lower Rinse Use While Protecting Product Quality
• QMSR Inspection Readiness: Make Improvement Evidence Traceable to Quality Records
• Invoice Exception Reduction: Use DMAIC to Fix Three-Way Match Failures