

Find out in this report how the two AI Data Analysis solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
In the first couple of years, I would not expect a return on investment because the initial setup will take more than a year if the process requires significant customization.
In my opinion, there's a positive return on investment.
I have seen a return on investment, especially in time saved for my clients; in the incident management process, the average cycle time for handling tickets was over ninety-eight hours, but after identifying root causes, such as tickets being held due to wrong group allocation, the cycle time reduced to approximately thirty-eight hours.
Consequently, we adjusted our processes to use Matillion Data Productivity Cloud only for extraction and ingestion, while Snowflake handled all transformations and jobs.
It took more than two weeks to receive a response.
Celonis customer support is really good; they investigate concerns thoroughly and provide solutions or troubleshooting steps, which I find helpful.
Other times I do not get much clarity on the support from the team.
They communicate effectively and respond quickly to all inquiries.
I recall that when we started using Celonis, we had a space of five terabytes and around one thousand users, and Celonis managed all of that easily.
I recommend focusing on recent data or perhaps five years of historical data along with live data for better visibility and stability in the process.
At the moment, I'd rate scalability six or seven out of ten.
Depending on the nature of data sets, volume, and mixture of different data, the scalability could be improved as manual code writing is still required.
The autoscale process works well, allowing the system to start another node automatically if the first machine reaches 80% capacity.
It's super stable.
Celonis is stable.
Ultimately, I need niche expertise, combining strong SAP knowledge with Celonis competency.
It is essential for the Celonis solution to have their services and solution models integrated with GenAI.
The most important area for improvement is the automation part.
Connections to BigQuery for extracting information are complex.
The main areas for improvement are AI features and scalability.
I think it's relatively expensive, but it's also good.
Based on client feedback, I have heard that the pricing for Celonis is considered high.
creating a data model for one process will differ in cost if you add more data models for additional processes.
Matillion Data Productivity Cloud offers discounts and special deals, especially when dealing with high-volume clients or fewer existing clients in specific regions, like Spain.
The pricing is moderate, neither expensive nor cheap.
Celonis is also beneficial for its built-in apps that streamline tasks from legacy applications, facilitating daily operations and improving efficiency.
It provides a visualization of the process itself, giving a very good synthesis of performance and helping me find improvements.
It's the first solution that combines business competence and capabilities with technological capabilities.
The predefined connectors eliminate the need to write code for connectivity.
Matillion Data Productivity Cloud is effective for ingest functions, particularly when moving information to Snowflake and performing many transformations.
| Product | Mindshare (%) |
|---|---|
| Celonis | 0.4% |
| Matillion Data Productivity Cloud | 0.6% |
| Other | 99.0% |


| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 6 |
| Large Enterprise | 46 |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 10 |
| Large Enterprise | 11 |
Celonis empowers businesses with process mining, offering automation and AI features that visualize processes, identify bottlenecks, and optimize operations. With seamless integration and user-friendly tools, Celonis adds significant value to businesses aiming for operational efficiency.
Celonis is a leading tool for process mining and optimization, seamlessly connecting with SAP and Oracle systems through pre-built connectors. Its capabilities include visualizing processes, identifying inefficiencies, and optimizing workflows with comprehensive dashboards and action flows. While Celonis scores high on functionality, integration with Microsoft, Azure connectivity, and an improved pricing model are areas for improvement. Training resources and an intuitive interface are essential for users managing frequent updates and complex programming needs. With robust process analysis and automation features, Celonis enhances decision-making and resource allocation.
What key features does Celonis offer?In finance, procurement, and supply chain, Celonis is utilized to monitor and optimize entire business processes, analyzing data from systems like SAP and Oracle to uncover inefficiencies. Organizations leverage its process analysis and automation triggers for improved performance and resource allocation.
Matillion Data Productivity Cloud offers a user-friendly platform for seamless integration and dynamic data handling, favored for simplifying ETL processes with minimal coding and ensuring robust performance in complex data tasks.
Matillion Data Productivity Cloud integrates effortlessly with platforms like AWS, Snowflake, and SQL databases, providing tools for efficient data migration, transformation, and cloud warehousing. It supports large datasets with swift management, making it valued for its graphical interface that eases ETL processes for non-technical users. Automation features ensure scalability and dynamic data handling across diverse sources, while security and cost-effectiveness enhance its appeal. Enhancements in database connectivity, interface design, and multi-environment support would refine user experience, with growing demands for real-time data capture, SAP connectivity, and frequent API updates.
What are the most important features?In industries like finance, healthcare, and retail, Matillion Data Productivity Cloud is implemented for transforming data operations. Companies leverage it for its speed in data processing and integration capability, facilitating rapid adaptation to data-driven insights crucial in these sectors.
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