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.
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.
In terms of ROI, the use of Dataiku simplifies the architecture of customers, which helps them to decommission some of their existing tools;
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 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.
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.
In terms of stabilization, if my data has no outlier creation in the raw data, then it is quite stable.
As for stability and reliability, so far so good; after the installation, I really had no problems.
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.
Alteryx is more cost-effective compared to Informatica licenses, offering savings.
It has a fair price when considering a larger-scale implementation.
Alteryx is expensive.
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 is user-friendly and allows easy creation of workflows compared to Informatica PowerCenter.
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 | Market Share (%) |
|---|---|
| Dataiku | 8.0% |
| Alteryx | 4.2% |
| Other | 87.8% |
| Company Size | Count |
|---|---|
| Small Business | 31 |
| Midsize Enterprise | 15 |
| Large Enterprise | 53 |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 2 |
| Large Enterprise | 9 |
Alteryx can be used to speed up or automate your business processes and enables geospatial and predictive solutions. Its platform helps organizations answer business questions quickly and efficiently, and can be used as a major building block in a digital transformation or automation initiative. With Alteryx, you can build processes in a more efficient, repeatable, and less error-prone way. Unlike other tools, Alteryx is easy to use without an IT background. The platform is very robust and can be used in virtually any industry or functional area.
With Alteryx You Can:
Alteryx Features Include:
Some of the most valuable Alteryx features include:
Scalability, stability, flexibility, fast performance, no-code analytics, data processing, business logic wrapping, scheduling, ease of use, data blending from different platforms, geo-referencing, good customization capabilities, drag and drop functionality, intuitive user interface, connectors, machine learning, macros, simple GUI, integration with Python, good data transformation, good documentation, multiple database merging, and easy deployment.
Alteryx Can Be Used For:
Alteryx Benefits
Some of the benefits of using Alteryx include
:
Reviews from Real Users
"Automation is the most valuable aspect for us. The ability to wrap business logic around the data is very helpful." - Theresa M., Senior Capacity Planner at a financial services firm
"Alteryx has made us more agile and increased the speed and effectiveness of decision making." - Richard F., Director, Digital Experience & Media at Qdoba Restaurant Corporation
"The scheduling feature for the automation is excellent." - Data Analytics Engineer at a tech services company
"The product is very stable and super fast, five-star. It's significantly more stable than its nearest competitor." - Director at a non-tech company
“A complete solution with very good user experience and a nice user interface.” - Solutions Consultant at a tech services company
"There are a lot of good customization capabilities." - Advance Analytics PO at a pharma/biotech company
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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