Overview

Celonis Process Mining provides a platform for analyzing and optimizing business processes by extracting data from source systems to reconstruct process flows. The core functionality involves ingesting event logs from enterprise resource planning (ERP), customer relationship management (CRM), and other operational systems to create a visual representation of how processes are executed in reality, rather than how they are documented or assumed to operate. This approach allows organizations to identify deviations from standard processes, pinpoint bottlenecks, and quantify the impact of inefficiencies.

The platform is designed for technical buyers and developers involved in business process management, operational excellence, and digital transformation initiatives. It is applicable across various industries and departments, including finance, supply chain, procurement, and customer service. By providing a data-driven view of process execution, Celonis helps organizations uncover root causes of performance issues, such as unnecessary rework, compliance breaches, or delays. For example, in a procurement process, Celonis can reveal if purchase orders are frequently modified after approval, leading to extended cycle times and increased costs, or if specific vendors consistently cause delays in delivery.

Celonis offers capabilities beyond basic process visualization. Its Process Simulation feature allows users to model potential changes to a process and predict their impact before implementation. Task Mining extends the data collection to user desktop interactions, providing granular insights into human-centric tasks. The platform also includes Process Automation tools, enabling the direct automation of identified repetitive tasks or process steps, often through integration with Robotic Process Automation (RPA) solutions. Object-Centric Process Mining (OCPM) represents an evolution in process mining, moving from a purely event-log-centric view to one that models the lifecycle of business objects (e.g., a customer order or an invoice) and their interactions across multiple processes. This provides a more holistic understanding of complex, interconnected enterprise operations. According to a 2023 report by Gartner, process mining solutions are increasingly seen as foundational for achieving hyperautomation and improving operational resilience by providing visibility into execution gaps across the enterprise Gartner's view on hyperautomation.

The developer experience with Celonis is supported by a Python SDK, which facilitates data extraction, integration with external systems, and the development of custom applications. This allows technical teams to extend the platform's capabilities and embed process intelligence into existing enterprise applications or data pipelines. The platform's documentation provides guidance on API usage and programmatic interaction, enabling developers to build solutions that leverage Celonis's analytical engine for specific business needs.

Key features

  • Process Discovery and Visualization: Automatically maps actual process flows from event data, identifying variations and bottlenecks.
  • Root Cause Analysis: Utilizes machine learning to pinpoint the underlying factors contributing to process inefficiencies and deviations.
  • Process Conformance Checking: Compares actual process execution against predefined models or compliance rules to detect non-conformant activities.
  • Process Simulation: Enables modeling and testing of process changes to predict their impact on key performance indicators (KPIs) before implementation.
  • Task Mining: Captures user interaction data from desktops to analyze human activities and identify automation opportunities.
  • Process Automation: Integrates with automation tools to directly automate repetitive tasks or orchestrate process improvements.
  • Object-Centric Process Mining (OCPM): Provides a holistic view of process execution by tracking the lifecycle of business objects across interconnected processes.
  • AI-Powered Insights: Leverages artificial intelligence to provide predictive analytics and prescriptive recommendations for process improvement.
  • Action Flows: Allows users to trigger automated actions or notifications based on identified process deviations or opportunities.

Pricing

Celonis offers custom enterprise pricing, tailored to the specific needs and scale of each organization. Details are typically provided through direct engagement with their sales team.

Plan Name Description Key Features Pricing Model
Celonis EMS Free Plan Entry-level plan for exploring basic process mining capabilities. Limited data ingestion, basic process discovery, introductory analytics. Free (as of 2026-05-07)
Custom Enterprise Pricing Tailored solutions for large organizations with complex process intelligence needs. Full platform access, advanced analytics, simulation, task mining, automation, OCPM, dedicated support. Custom Quote (as of 2026-05-07)

Common integrations

Alternatives

  • UiPath Process Mining: Offers process discovery, analysis, and monitoring, often integrated with UiPath's broader RPA platform.
  • SAP Signavio Process Intelligence: Provides process mining, modeling, and workflow automation capabilities within the SAP ecosystem.
  • ABBYY Timeline: Focuses on process intelligence with capabilities for process discovery, analysis, and prediction.

Getting started

To begin using the Celonis Python SDK for data extraction, you typically install the SDK and then configure your connection to the Celonis EMS. The following example demonstrates how to establish a connection and fetch data from an existing data model. This requires authentication credentials (API key and URL) and the ID of the data pool and data model you wish to access.


import os
from celonis_ml.celonis_api import CelonisAPI

# Replace with your actual Celonis API key and EMS URL
CELONIS_API_KEY = os.environ.get("CELONIS_API_KEY", "YOUR_API_KEY")
CELONIS_EMS_URL = os.environ.get("CELONIS_EMS_URL", "https://your-team.eu-1.celonis.cloud")

# Replace with your data pool and data model IDs
DATA_POOL_ID = "YOUR_DATA_POOL_ID"
DATA_MODEL_ID = "YOUR_DATA_MODEL_ID"

def get_celonis_data():
    try:
        celonis = CelonisAPI(
            api_token=CELONIS_API_KEY,
            base_url=CELONIS_EMS_URL
        )

        # Example: Fetching data from a table within the data model
        # Replace 'YOUR_TABLE_NAME' with an actual table name from your data model
        table_name = "YOUR_TABLE_NAME"
        df = celonis.get_table_data(
            data_pool_id=DATA_POOL_ID,
            data_model_id=DATA_MODEL_ID,
            table_name=table_name,
            limit=100 # Fetching first 100 rows as an example
        )

        print(f"Successfully fetched {len(df)} rows from table '{table_name}'.")
        print(df.head())

    except Exception as e:
        print(f"An error occurred: {e}")

if __name__ == "__main__":
    get_celonis_data()

Before running this code, ensure you have the celonis_ml library installed (pip install celonis_ml). You will also need to obtain your Celonis API key and the specific IDs for your data pool and data model from your Celonis EMS instance Celonis API Keys documentation. This script connects to the Celonis platform and retrieves data from a specified table, demonstrating a foundational step for integrating Celonis data into custom applications or analytical workflows.