Supported Data Formats for Event Logs
Discover supported formats for uploading event logs in ProcessMind, including Excel, CSV, TSV, and TXT, along with essential structural guidelines.
Where to Source and Structure Data for Process Mining
How to Get Data from Systems and Structure It for Process Mining
Process mining is a powerful technique that helps businesses analyze and improve their workflows by extracting data from their existing systems. However, one of the most critical steps in process mining is getting the right data and ensuring it’s properly structured for analysis. In this blog, we will walk you through how to gather data from systems like SAP, Oracle, ServiceNow, and others, and how to prepare it for effective use in process mining. We’ll also share links to additional resources where you can find more detailed information on integrating specific systems.
1. Understanding the Basics: What Data Do You Need for Process Mining?
Before you start extracting data, it’s essential to understand what type of data is required for process mining. Process mining relies on three key elements to reconstruct and analyze workflows:
- Case ID: A unique identifier for each process instance (e.g., an order number, ticket ID, or customer request ID).
- Activity: The individual steps or actions within the process (e.g., creating an order, approving a request, closing a ticket).
- Timestamp: The date and time when each activity occurred, which helps to sequence the actions and understand how the process unfolds over time.
Having these three components allows you to map out how a process operates, identify bottlenecks, and discover inefficiencies. In addition to these core data points, you may want to include other information, such as the person responsible for each action, the department, or the type of task, depending on what insights you aim to gain.
2. Extracting Data from Popular Business Systems
Most organizations use multiple software systems to manage different aspects of their business processes, such as ERP, CRM, and ticketing systems. Here’s how you can extract data from some of the most popular platforms:
SAP
SAP is a widely used enterprise resource planning (ERP) system that handles everything from finance to supply chain management. To extract data for process mining, you’ll typically use SAP’s reporting and data export features. Alternatively, you can connect to SAP databases using tools like SAP Business Connector, SAP Data Services, or SAP BW (Business Warehouse).
For more detailed instructions, check out these resources:
Oracle
Oracle’s suite of ERP and database solutions is another common source of data for process mining. You can extract data using Oracle SQL queries, Oracle Data Integrator (ODI), or Oracle Business Intelligence (BI) tools. With Oracle, you’ll often be working with relational databases, so knowing how to write SQL queries will be beneficial.
For more guidance, visit:
ServiceNow
ServiceNow is widely used for IT service management (ITSM) and can be an excellent source of data for analyzing service processes. You can use ServiceNow’s reporting tools to create datasets or use ServiceNow REST APIs to pull data directly. It’s also possible to integrate with other analytics tools to export and analyze ServiceNow data.
Learn more from:
Salesforce
Salesforce, as a leading CRM platform, offers various ways to extract data for process mining. You can use Salesforce Reports, Salesforce Data Loader, or REST APIs to pull data. Salesforce data is usually structured around objects, so it’s essential to understand how your sales and customer service processes are configured to capture the right information.
Check out:
3. Structuring Data for Process Mining: Best Practices
Once you have extracted the data, it’s crucial to structure it correctly to ensure effective process mining. Here are some best practices:
a. Clean and Format the Data Raw data often contains duplicates, missing values, or inconsistent formats. Cleaning the data involves:
- Removing duplicate entries to avoid skewing your analysis.
- Ensuring consistent formatting for dates, names, and other attributes.
- Dealing with missing values by filling them in where possible or excluding incomplete entries.
b. Create a Unified Event Log An event log is the foundation of process mining. It’s essentially a table where each row represents an event (or activity) within a process. The key fields should include:
- Case ID: To group events that belong to the same process instance.
- Activity Name: To identify the specific action taken.
- Timestamp: To sequence the activities correctly.
Depending on the complexity of your process, you can also add:
- Resource: The person or team responsible for the activity.
- Department: To understand which part of the organization is involved.
- Duration: To calculate how long each step takes.
Here’s an example of a structured event log:
| Case ID | Activity | Timestamp | Resource | Department |
|---|---|---|---|---|
| 1001 | Order Created | 2024-10-10 08:15:00 | John Doe | Sales |
| 1001 | Order Approved | 2024-10-10 09:30:00 | Jane Smith | Sales |
| 1002 | Ticket Opened | 2024-10-11 10:00:00 | Lisa Ray | IT Support |
c. Ensure Data Consistency Across Systems If you’re pulling data from multiple systems, it’s important to ensure consistency. For example, make sure that a case ID used in one system matches the identifier in another. This will enable you to combine data sources without losing context, allowing for a more comprehensive analysis. It’s also helpful to standardize how activities are named to avoid confusion.
d. Manage Data Privacy and Security When extracting and structuring data for process mining, always keep data privacy and security in mind. Make sure sensitive information is anonymized or removed, and only authorized personnel have access to the data. Consider compliance requirements like GDPR if you’re handling customer data.
4. Ingest to ProcessMind
Once your data is structured, the next step is to upload it to ProcessMind. ProcessMind allow you to ingest data in different file formats to start analyzing your workflows. Depending on the tool you are using, you may have additional integration options that can simplify the process of bringing in data from various systems.
For example, ProcessMind enables you to map data fields to ensure they align correctly with the platform’s process design canvas. If your data is incomplete, you can use the process design features to fill in the missing steps, helping you create a more comprehensive view of your processes.
5. Additional Resources for Data Extraction and Integration
Extracting and structuring data can sometimes be complex, especially if you are working with multiple systems or large datasets. Here are some additional resources that can help:
- ETL Tools (Extract, Transform, Load): Tools like Talend, Informatica, and Alteryx can help you automate data extraction and transformation.
- SQL for Beginners: Learn how to write SQL queries to pull data from relational databases.
- Guide to Process Mining: A comprehensive guide on how to approach process mining, including how to structure data.
Conclusion: Preparing Data for Effective Process Mining
Getting data from your systems and structuring it for process mining is a critical step in uncovering valuable insights about your business processes. By understanding what data is needed, using the right tools to extract it, and following best practices for structuring, you can set up your process mining projects for success. ProcessMind and other platforms make it easier to ingest, analyze, and optimize workflows, leading to more efficient and data-driven business decisions.