Troubleshooting Data Issues

Common Data Issues and Solutions

When preparing data for process mining, several common data-related issues can occur that can impact the accuracy and quality of the analysis. Below is a troubleshooting guide to help you identify and resolve these common problems.

1. Duplicate Records in Event Logs

Symptoms:

• The same event appears multiple times for the same process instance (same Case ID, Activity, and Timestamp).
• Unusually high counts of certain activities or events in the process map.

Possible Causes:

• Data was recorded multiple times due to system integration issues or logging errors.
• Data ingestion process repeated events unintentionally.

Solution:

• Remove Duplicates: Use data cleaning tools to identify and remove duplicate entries. In Excel or Google Sheets, use the “Remove Duplicates” function, or if using a database, write SQL queries that remove repeated entries based on Case ID, Activity, and Timestamp.
• Filter During Ingestion: When ingesting data, configure filters to ensure only unique events are imported into the process mining tool.

2. Missing Timestamps

Symptoms:

• Incomplete or missing timestamps prevent accurate sequencing of events.
• The process map shows gaps or missing connections between activities.

Possible Causes:

• Some systems do not log timestamps for every activity.
• Manual processes or non-digital tasks that are not tracked with a timestamp.

Solution:

• Estimate Missing Timestamps: If possible, estimate the missing timestamps based on known data points (e.g., assume a task took the average time between previous and subsequent tasks).
• Supplement with Manual Data: For manual or non-digital tasks, manually input timestamps based on estimates or logs from other sources.
• Data Imputation: Use data imputation techniques, such as predicting missing timestamps based on other events in the sequence or average process duration.

3. Inconsistent Case IDs

Symptoms:

• Events that belong to the same process instance are split across different Case IDs, leading to fragmentation of the process model.
• Multiple representations of the same process instance, causing confusion and inaccurate analysis.

Possible Causes:

• Different systems or departments use varying naming conventions or structures for Case IDs.
• Data entry errors or inconsistent formatting across systems.

Solution:

• Case ID Mapping: Develop a case ID mapping strategy to unify case identifiers across systems. Use tools like ETL (Extract, Transform, Load) platforms or SQL to merge and standardize Case IDs.
• Use Data Transformation Tools: If Case IDs have different formats, use transformation tools to convert them into a consistent format before ingesting the data.

4. Incorrect Activity Sequencing

Symptoms:

• Events appear out of order, with later activities showing up before earlier ones (e.g., “Order Completed” before “Order Placed”).
• The process map displays nonsensical flows or loops.

Possible Causes:

• Timestamps were entered incorrectly or are missing.
• Data was ingested without proper ordering.

Solution:

• Sort by Timestamp: Ensure that events are sorted by their timestamps in ascending order for each Case ID. Use tools like Excel, SQL, or Pandas (Python) to sort the data correctly.
• Check Timestamp Formats: Verify that all timestamps are in the same format and time zone. Convert all timestamps to a common format, such as ISO 8601 (YYYY-MM-DD HH:MM:SS).
• Validate Data Quality: Spot-check a few cases manually to ensure events are in the correct order and that no sequencing errors occurred during data entry or ingestion.

5. Data Inconsistency Across Systems

Symptoms:

• Mismatched data across different systems that contribute to the same process.
• Events appear in one system’s data but are missing from another, leading to gaps in the process map.

Possible Causes:

• Different systems use different metrics, naming conventions, or formats for the same events.
• Incomplete data extraction or partial system integration.

Solution:

• Standardize Data: Before ingestion, standardize how key fields (e.g., Case ID, Activity Name, and Timestamps) are represented across different systems. Use data transformation tools to ensure consistency in field names and formats.
• Combine Datasets Carefully: Use ETL tools to merge data from multiple systems and ensure that the combined dataset has a coherent structure. Ensure consistency in event names, timestamps, and case IDs before combining datasets.

6. Large Volumes of Data Leading to Performance Issues

Symptoms:

• Slow performance when loading or analyzing large datasets in the process mining tool.
• System crashes or timeouts during data ingestion.

Possible Causes:

• The dataset contains too many records for the system to handle efficiently.
• The process mining tool cannot process high volumes of data in one go.

Solution:

• Data Sampling: Instead of processing the entire dataset, use a representative sample of the data. This can reduce the size while still providing valuable insights.
• Filter Unnecessary Events: Remove low-value or irrelevant events (such as system log entries) before loading the data into the process mining tool.
• Incremental Data Loading: Instead of ingesting all data at once, load smaller chunks of data incrementally and analyze them separately.

7. Irrelevant or Noisy Data

Symptoms:

• The process map is cluttered with events that are not related to the core process.
• Too many insignificant variations make it difficult to focus on key insights.

Possible Causes:

• Background system events, system logs, or unrelated tasks are captured in the dataset.
• Noise from low-priority tasks or system processes.

Solution:

• Filter Unnecessary Events: Exclude irrelevant events that do not contribute to the process being analyzed. For example, remove system log events or activities that are not part of the business workflow.
• Group Low-Level Events: If necessary, group or aggregate low-level system events into higher-level activities to simplify the process model and focus on core activities.

8. Handling Outliers

Symptoms:

• The process map shows extreme variations in task duration or resource allocation that do not align with typical performance.
• The analysis is skewed by rare or exceptional cases.

Possible Causes:

• Outlier data points (e.g., tasks that took an unusually long time or cases with abnormal patterns) are present in the dataset.
• Edge cases or rare incidents disproportionately affect the process map.

Solution:

• Identify Outliers: Use statistical analysis to detect and flag outliers based on task duration, resource usage, or other metrics.
• Decide Whether to Include or Exclude: Evaluate whether these outliers provide useful insights (e.g., identifying rare but critical issues) or should be excluded to focus on standard processes. If excluding, document the decision to ensure clarity.

9. Unaligned Time Zones in Data

Symptoms:

• Events that occur in sequence appear to be misaligned due to different time zone settings.
• Process duration calculations are incorrect because of time zone inconsistencies.

Possible Causes:

• Data from different systems or departments might use different time zones, leading to inconsistent timestamp data.
• Time zones were not standardized before data ingestion.

Solution:

• Convert to a Common Time Zone: Before importing data, convert all timestamps to a consistent time zone (e.g., UTC). Many tools, including Excel and Python, offer time zone conversion functions.
• Document Time Zone Adjustments: Keep a record of the original time zone for each dataset and document any conversions performed.

10. Unbalanced Event Logs

Symptoms:

• Some cases have too few events, while others have too many, leading to an unbalanced process map.
• Certain activities or cases dominate the analysis due to uneven data distribution.

Possible Causes:

• Inconsistent data logging or incomplete capture of events for certain cases.
• A skew in the data where some process instances are over-represented.

Solution:

• Normalize the Event Log: Ensure that each process instance has a similar level of detail. If certain cases are missing key events, investigate the cause and attempt to fill in the gaps manually or remove those cases from analysis.
• Weight the Data: If necessary, weight the events or cases to ensure that over-represented cases do not disproportionately affect the analysis.

Conclusion

Data quality is essential to successful process mining. By identifying and addressing these common data issues, you can ensure that your analysis yields accurate, actionable insights. Implementing best practices in data cleaning, preparation, and validation will help avoid common pitfalls and enable you to get the most out of your process mining efforts.