

Alteryx and Dataiku are contenders in the field of data analytics and machine learning platforms. Based on feature effectiveness and user feedback, Alteryx appears to have the upper hand due to its user-friendly interface and successful integration capabilities.
Features: Alteryx's intuitive drag-and-drop functionality allows users to perform data blending and predictive analytics without requiring coding skills. It integrates effectively with Tableau and supports in-database analytics with SQL Server and Oracle. The solution also supports geographic analysis through its spatial tools. Dataiku facilitates a variety of data science tasks, focusing on machine learning and feature creation, catering to those with some coding ability, and aids comprehensive data preparation.
Room for Improvement: Alteryx users recommend enhancements in data visualization capabilities and better integration with in-database tools. It lacks advanced visualization options compared to some competitors. Dataiku shows difficulty in handling complex data types like text and images and is noted for cumbersome integration with collaboration tools such as GitHub.
Ease of Deployment and Customer Service: Alteryx offers multiple deployment options across on-premises and cloud configurations but sees predominant use in on-premises setups. Customer service reviews highlight an active community, though official support can be slow. Dataiku's limited cloud deployment contrasts with robust on-premises support and an efficient but less detailed support system compared to Alteryx's extensive community resources.
Pricing and ROI: Alteryx's pricing is perceived as high, with noteworthy expenses for licensing and servers, yet users report meaningful ROI due to operational efficiency. Dataiku is less costly than some alternatives but still relatively expensive, delivering substantial ROI for comprehensive data projects.
Tasks that earlier took hours in Excel or SQL are now completed in minutes.
Alteryx would actually save time and a lot of money and effort for the team and increase efficiency.
Alteryx helps familiarize managers with artificial intelligence-driven possibilities.
The market is competitive, and Dataiku must adopt a consumption-based model instead of the current monthly model.
I consider the return on investment with Dataiku valuable because for us, it is one single platform where all our data scientists come together and work on any model building, so it is collaboration, plus having everything in one place, organized, having proper project management, and then built-in capabilities which help to facilitate model building.
It is a good return on investment since it helps save a lot of time, and it's easy for my teammates to work cross-functionally on the same project.
I contacted customer support once or twice, and they were quick to respond.
The customer service was not good because we weren't premium support users.
Customer support is good since I've had no issues and can easily contact representatives who respond promptly.
Dataiku partners with local industry experts who understand the business better and provide support.
The support team does not provide adequate assistance.
They should not take the complaints so lightly.
Alteryx can be scaled to different machines or scaled up with different servers and deployed in the cloud.
Alteryx is scalable for most enterprise analytics and data preparation workloads.
Alteryx is scalable, and I would give it eight out of ten.
Dataiku is quite scalable, as long as I can pay for more licenses, there is no technical limitation.
Dataiku's scalability is pretty good; I can scale the projects very easily, and clear guidance is given as well.
I didn't need to reach out to Alteryx for support because available documents usually provide enough information to resolve issues.
I have not encountered any lagging, crashing, or instability in the system during these three months of usage.
I have not noticed anything with the product itself, but with some of the connectors they have provided, there are some issues.
It would help if there was a backup proposition in place to avoid hampering our work due to updates.
For around ten percent of the day, it is usually down, and we are unable to do work on it.
In terms of stabilization, if my data has no outlier creation in the raw data, then it is quite stable.
The tool could include more native connectors, such as for global ERPs, instead of requiring additional fees for these connections.
The support structure changed; initially, we received great support, however, it later became less reliable due to licensing issues and a tiered support system.
The additional features that Alteryx needs to work on to make it more competitive include better collaboration and easier integration through API.
Someone who needs to do coding can do it, and someone who does not know coding can also build solutions.
The license is very expensive.
I would love for Dataiku to allow more flexibility with code-based components and provide the possibility to extend it by developing and integrating custom components easily with existing ones.
The price is very high, with licensing typically starting around five thousand dollars plus user per year.
Alteryx is more cost-effective compared to Informatica licenses, offering savings.
It has a fair price when considering a larger-scale implementation.
There are no extra expenses beyond the existing licensing cost.
I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies.
The pricing for Dataiku is very high, which is its biggest downside.
Alteryx not only represents data but also supports decision-making by suggesting the next steps.
Analysts who do not have any coding experience can still work on the transformation and preparation of data, which is quite useful.
Alteryx includes built-in tools such as drive time analysis and linear regression, which are much harder to achieve in standard BI tools such as Power BI or Tableau.
This feature is useful because it simplifies tasks and eliminates the need for a data scientist.
Dataiku primarily enhances the speed at which our customers can develop or train their machine learning models because it is a drag-and-drop platform.
It offers most of the capabilities required for data science, MLOps, and LLMOps.
| Product | Mindshare (%) |
|---|---|
| Dataiku | 5.6% |
| Alteryx | 3.8% |
| Other | 90.6% |
| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 16 |
| Large Enterprise | 54 |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 2 |
| Large Enterprise | 13 |
Alteryx provides user-friendly, no-code tools for data blending, preparation, and analysis. Its drag-and-drop interface and in-database capabilities simplify integration with data sources while maintaining data integrity.
Alteryx offers a comprehensive suite for automation of data workflows, reducing manual tasks and enhancing processing efficiency. Known for robust predictive and spatial analytics, it effectively handles large datasets. The platform's flexibility allows for custom script deployments, supported by a strong community. However, Alteryx faces challenges with high pricing, lack of cloud support, and limited data visualization tools. Users express a need for more in-built data science functionalities, improved API integration, and a smoother learning curve. Connectivity and documentation gaps, along with complex workflows, are noted concerns, suggesting areas for enhancement. Alteryx is widely used for tasks like ETL processes, data preparation, predictive modeling, and report generation, supporting functions like financial projections and spatial analysis.
What features define Alteryx?Alteryx is implemented across industries for diverse needs such as anomaly detection in finance, customer segmentation in marketing, and tax automation in auditing. Teams leverage its capabilities for data blending and predictive modeling to enhance operational efficiency and address specific business needs effectively.
Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.
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