Uploading Configuring Data Sources

Choosing Your Data Import Method

mindzie offers two primary methods for importing data into your projects:

CSV Upload

Best for:

• One-time data analysis
• Testing and proof-of-concept projects
• Smaller datasets
• Manual data refresh scenarios

mindzie Data Designer

Best for:

• Continuous monitoring and scheduled updates
• Connecting directly to databases or data warehouses
• Complex ETL transformations
• Production deployments with automated data refresh

This guide focuses on the CSV upload method, which is the quickest way to get started with mindzie Studio.

Step-by-Step: Uploading a CSV File

Step 1: Navigate to the Datasets Area

When you first enter your project in mindzie Studio, you'll be taken to the Datasets section automatically. If you're not already there:

1. Click on the Datasets tab in the top navigation bar
2. You'll see the "Welcome to mindzieStudio" screen with several options

Step 2: Select Upload CSV

On the Datasets screen, click the Upload CSV button in the top-right corner of the interface. This will open a file browser dialog.

Step 3: Select Your CSV File

1. Navigate to the location of your CSV file on your computer
2. Select the file (e.g., banking_onboarding_enhanced_event_log.csv)
3. Click Open to begin the upload

The system will display a loading indicator showing the upload progress.

Step 4: Validate and Configure Data Settings

After the file uploads, mindzie Studio will display a preview of your data and allow you to configure settings:

Encoding Settings

• The system will auto-detect the file encoding
• If your data contains special characters, you may need to adjust the encoding setting

Data Preview

• Review the preview to ensure your data loaded correctly
• Check that columns appear properly separated
• Verify that timestamps and other values display as expected

Once you've reviewed the settings, click Next to proceed to column mapping.

Configuring Key Columns

The column mapping screen is where you tell mindzie Studio which columns in your CSV correspond to the key process mining fields.

Understanding the Key Column Icons

mindzie Studio uses visual icons to help you identify and map the key columns:

• Case ID: Purple icon - Identifies unique process instances
• Activity: Yellow icon - Contains the names of process steps
• Activity Time: Orange icon - Timestamp for when each activity occurred
• Resource: Blue icon - Person or system performing the activity

Step 5: Map Your Columns with Drag-and-Drop

mindzie Studio will automatically detect and suggest mappings for common column names. To map or change column assignments:

1. Automatic Detection: The system typically auto-detects standard columns like Case ID, Activity, and Timestamp
2. Drag-and-Drop: To manually assign a column, drag it from the bottom section to one of the key column boxes at the top
3. Resource Assignment: If you have a resource column (recommended), drag it to the Resource field

In the example shown:

• Case ID is mapped to the Case ID column
• Activity is mapped to the Activity column
• Timestamp is mapped to the DateTime column
• Resource Name is mapped to the Resource Name column

Step 6: Configure Additional Column Settings

For each column in your dataset, you can:

Change Column Types

• Click on any column to modify its type
• Options include: String, Number, Date, Boolean, etc.

Modify Data Types

• Adjust how mindzie interprets the data
• Ensure dates are recognized as timestamps
• Confirm numeric values are typed correctly

Anonymize Sensitive Data

• Enable anonymization for columns containing sensitive information
• This is useful for compliance with data privacy regulations
• Names, customer IDs, and other PII can be masked

Optional Columns The "Optional" section on the right side of the screen allows you to map additional process mining attributes:

• Resource Role: Job title or role of the resource
• Cost: Associated costs per activity
• Channel: Process channel (e.g., Branch, Mobile, Online)
• Compliance Flags: Conformance or compliance indicators
• Country/Region: Geographic attributes

Step 7: Process and Save the Dataset

Once you've completed your column mapping:

1. Review all mappings to ensure accuracy
2. Click Next to begin processing

mindzie Studio will now:

• Transform your CSV data into the mindzie event log format
• Validate data quality and structure
• Create the base dataset for analysis

This processing step may take a few moments depending on the size of your dataset. You'll see a "Work in progress! Processing Dataset" dialog during this time.

Step 8: Confirm Data Import Success

After processing completes, mindzie Studio will display a confirmation dialog showing:

• The dataset name
• Total number of cases (process instances)
• Total number of events (activities)

In the example shown, the dataset contains:

• 10,000 cases (unique customer onboarding instances)
• 121,000 events (total activities across all cases)

Click Save to finalize the import.

What Happens After Data Upload

Once you save your dataset, mindzie Studio automatically:

1. Creates Two Datasets:

   • Original Dataset: Your raw event log as uploaded
   • Enriched Dataset: Enhanced version created by the mindzie pipeline (used for all analysis)

2. Builds a Data Pipeline: Prepares your data for enrichment with performance metrics, conformance rules, and other enhancements

3. Generates Default Analysis: Creates starter analysis including:

   • Process overview
   • Long case duration analysis
   • Duration between main process steps
   • Other foundational insights

These default analyses give you a head start on understanding your process and can be customized or removed as needed.

Understanding Data Transformation

During the upload and processing phase, mindzie Studio:

• Standardizes Data Format: Converts your CSV into mindzie's optimized event log structure
• Validates Data Quality: Checks for missing required fields, invalid timestamps, and data inconsistencies
• Prepares for Enrichment: Structures the data so it can be enhanced with calculated attributes, performance metrics, and conformance rules

The transformation process ensures your data is ready for powerful process mining analysis and visualization.

Tips for Successful Data Upload

Column Naming

• Use clear, consistent column names in your CSV
• Common names like "CaseID", "Activity", "Timestamp" are auto-detected
• Avoid special characters in column names

Data Quality

• Ensure every row has a Case ID, Activity, and Timestamp
• Timestamps should follow a consistent format (ISO 8601 recommended)
• Remove or fix any duplicate headers or malformed rows

File Size Considerations

• CSV upload works well for datasets up to several million events
• For very large datasets or continuous monitoring, consider using mindzie Data Designer instead
• Test with a sample of your data first to verify column mappings

Resource and Optional Columns

• While only Case ID, Activity, and Timestamp are required, adding Resource information enables deeper analysis
• Additional columns like Cost, Channel, and Region allow for richer segmentation and insights
• You can always add more optional columns later through data enrichment

Next Steps

After successfully uploading and configuring your data source:

1. Review the Generated Datasets: Check both the original and enriched datasets in the Datasets section
2. Explore Default Analysis: Navigate to Investigations to see the automatically generated insights
3. Plan Your Dashboard Structure: Decide what dashboards and metrics you want to create for your users
4. Enhance Your Data: Use the Log Enrichment Engine to add performance metrics, conformance rules, and custom attributes

Your data is now ready to be transformed into actionable process intelligence!

Related Topics

• Understanding mindzie's Dual Dataset Architecture: Learn about original vs. enriched datasets
• Mastering the Log Enrichment Engine: Add performance metrics and conformance rules to enhance your data
• Using mindzie Data Designer: Connect to live data sources for automated data refresh
• Creating Your First Analysis: Build metrics and KPIs from your uploaded data