Data Cleaning and Preparation for Process Mining

Data Cleaning and Preparation for Process Mining

Effective process mining starts with good-quality data, and a crucial part of this is data cleaning and preparation. Poor data quality can lead to inaccurate or incomplete insights, making it harder to improve processes. In this document, we’ll cover the essential steps of data cleaning and preparation to ensure that your datasets are ready for successful process mining.

Why Is Data Cleaning and Preparation Important?

Process mining relies on event logs—datasets that contain the detailed sequence of activities within a business process. If these datasets are incomplete, inconsistent, or contain errors, the insights you derive from process mining will be unreliable. Clean and properly structured data ensures that your process mining tool can accurately map out workflows, detect bottlenecks, and highlight areas for improvement.

Key Steps in Data Cleaning and Preparation

1. Data Collection and Integration

The first step in the cleaning process is ensuring that all relevant data is collected from various systems involved in your process. Data may come from different sources, such as ERP, CRM, or other operational systems. This is where data integration comes into play.

• Consolidate data sources: Collect data from all systems that contribute to the process. For example, if you are analyzing an order-to-cash process, you may need to collect data from both your sales system (e.g., Salesforce) and financial system (e.g., SAP).
• Ensure consistent formats: Before moving forward, standardize how the data is exported and formatted. For example, ensure that all timestamps, currency, and IDs have a uniform format.

Once you have your data, it’s time to clean and prepare it for process mining.

2. Remove Duplicates

Duplicate records can severely distort process mining analysis by inflating activity counts or showing multiple instances of the same event. Identifying and removing these duplicates is essential to creating accurate event logs.

• Identify duplicates: Check for records where the case ID, activity, and timestamp are identical, as these are likely duplicates.
• Remove or merge: In cases where duplicates are identified, either remove them or merge similar records as needed.

3. Handle Missing Data

Missing values are another common issue that can impact your process mining results. Missing timestamps, activities, or case IDs can disrupt the sequence of events and create incomplete process models.

• Identify missing values: Use tools or scripts to detect missing fields (e.g., blank timestamps, empty activity names, or null case IDs).
• Fill in the gaps: If feasible, fill in missing data using external sources, domain knowledge, or by estimating based on other data points. For example, if a specific activity’s timestamp is missing, use surrounding event times to approximate it.
• Imputation strategies: For critical missing data like timestamps or case IDs, use imputation techniques (e.g., mean substitution or regression models) to predict values, or remove cases where data cannot be recovered.

4. Normalize Data Formats

Consistent data formatting is critical to ensuring that the process mining tool can interpret the event log correctly. Data normalization includes formatting timestamps, standardizing activity names, and ensuring uniform case ID structures.

• Timestamps: Ensure all dates and times follow the same format (e.g., YYYY-MM-DD HH:MM:SS). If your data contains time zones, convert them into a consistent one or use UTC to avoid misinterpretation. More Info on our supported date formats can be found here
• Activity names: Activities might be recorded differently across various systems. Standardize names to ensure consistency (e.g., “Approve Order” and “Order Approval” should be merged).
• Case IDs: Make sure the case ID is consistent across systems and that each process instance is correctly identified by a unique ID.

5. Remove Irrelevant Data

Not every activity or event in your system will be relevant to your process mining analysis. For example, certain background tasks or non-process-related events can clutter the dataset.

• Filter out irrelevant events: Identify and remove activities that do not contribute to the process you’re analyzing. For example, system logins or unrelated administrative tasks can be excluded to avoid cluttering the process map.
• Focus on key activities: Use domain knowledge to identify which events are critical for understanding the process and focus the dataset around those.

6. Handle Outliers and Noise

Outliers or “noise” in your dataset can distort your process mining results by giving an inaccurate picture of how the process normally functions. For example, a task that took an unusually long time due to a rare event can mislead your analysis.

• Identify outliers: Use statistical methods to detect outliers in your dataset. For example, tasks that take significantly longer than average might be considered outliers.
• Determine whether to keep or remove: Assess whether the outliers provide valuable information (e.g., representing rare but critical process failures) or if they should be removed to focus on the standard process flow.

7. Consistent Case and Activity Sequencing

One of the most important aspects of process mining is ensuring the proper sequencing of events. If the data is out of order, the tool may interpret the process flow incorrectly.

• Check activity sequence: Ensure that activities follow a logical sequence based on timestamps. For example, an “Order Approved” event should never appear before an “Order Created” event in the same process instance.
• Sort events by timestamp: Sort the data for each case by the timestamp field to ensure that events are in the correct order.

8. Create an Event Log

Once your data is cleaned, formatted, and consistent, it’s time to create an event log—the primary dataset for process mining. The event log should contain:

• Case ID: A unique identifier for each process instance.
• Activity name: The name of each process step.
• Timestamp: The exact time when each activity occurred, ensuring the order of events.
• Optional fields: Depending on the analysis, you might include additional fields like the resource responsible for the activity, department, or process category.

9. Validate the Dataset

After the data is cleaned and structured, it’s essential to validate the dataset to ensure it accurately represents the process and is ready for analysis.

• Spot-check cases: Manually review a few process instances to ensure the data makes sense, and the event sequences are logical.
• Run test analysis: If possible, run a test analysis in your process mining tool to see if any errors or inconsistencies arise.
• Feedback loop: Work with business experts to confirm the dataset reflects actual process behavior.

Tools for Data Cleaning and Preparation

Several tools can help automate the data cleaning and preparation process. Here are a few common options:

• Python/Pandas: A powerful programming language and library for data manipulation and cleaning. You can script custom data cleaning workflows to remove duplicates, normalize formats, and more.
• Excel/Google Sheets: Useful for smaller datasets, these tools offer various data-cleaning features like removing duplicates, filtering irrelevant rows, and formatting columns.
• ETL Tools (Extract, Transform, Load): Tools like Talend, Informatica, or Apache Nifi can help automate data extraction, transformation, and loading from different systems into a process mining tool.
• OpenRefine: A free, open-source tool for data cleaning that allows you to clean messy data, remove duplicates, and standardize formats.

Conclusion

Data cleaning and preparation are critical steps in the process mining lifecycle. By ensuring your datasets are complete, consistent, and accurate, you can avoid misleading analysis and gain actionable insights into how your processes work. By following the steps outlined in this document—removing duplicates, filling missing data, standardizing formats, and creating a clean event log—you’ll be well-prepared to extract maximum value from your process mining initiatives.