

Alteryx and IBM Watson Studio compete in the data analytics solutions category. Alteryx appears to have an upper hand in data blending and ease of use, while IBM Watson Studio excels in AI capabilities and machine learning applications.
Features: Alteryx provides seamless data blending and predictive analytics with codeless access, using drag-and-drop functionality for ease of use. It efficiently manages vast datasets with integration capabilities, supporting a variety of databases. IBM Watson Studio offers robust AI features with an extensive machine learning suite, providing a comprehensive perspective on data modeling and tailored application development.
Room for Improvement: Alteryx could enhance its visualization tools, as users require more built-in graphical options. Its reliance on external tools needs to be minimized. IBM Watson Studio's interface may benefit from improved speed and usability, and more guidance on its capabilities would assist users.
Ease of Deployment and Customer Service: Alteryx offers flexibility with on-premises and cloud deployment options, supported by a proactive community and responsive technical support. IBM Watson Studio focuses on cloud deployment, offering less flexibility but benefiting from global support and a cohesive ecosystem that ensures a seamless experience.
Pricing and ROI: Alteryx is perceived as high-cost, requiring significant investment for licenses and support, yet delivering substantial ROI through increased efficiency and reduced manual tasks. IBM Watson Studio is competitively priced, with costs adjusted based on workload complexity, offering fair pricing for its comprehensive capabilities. Both solutions provide value, though pricing may influence user decisions based on budget.
Tasks that earlier took hours in Excel or SQL are now completed in minutes.
Alteryx helps familiarize managers with artificial intelligence-driven possibilities.
The product offers a significant return on investment through its scalability and integration capabilities.
My customers have seen returns on investment through increased efficiency, automated calculations, improved accuracy in pricing, and reduced staffing needs due to the automation.
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.
The support quality depends on the SLA or the contract terms.
The community access is weak, which limits the ability to engage in discussions and find documentation and examples of similar cases effectively.
I would rate the technical support of IBM Watson Studio a solid ten out of ten.
Alteryx is scalable for most enterprise analytics and data preparation workloads.
Alteryx is scalable, and I would give it eight out of ten.
Watson Studio is very scalable.
I have had a chance to communicate with the technical support of IBM Watson Studio, which has been responsive and helpful.
I rate IBM Watson Studio seven out of ten for scalability because while it scales, it requires significant resources to do so, making it expensive compared to some competitors.
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.
Expertise in optimization is necessary to manage such issues effectively.
I find IBM Watson Studio to be quite robust, with minimal downtime and great support regarding stability and reliability.
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.
The platform is associated with a complicated setup process and demands heavy hardware, making it expensive to scale.
One area that could be improved is the backup and restoration of the database and the overall database configuration.
I wish learning IBM Watson Studio could be easier and more gradual, as it is a complex task.
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.
IBM Watson Studio is considered rather expensive, with a rating of six or seven.
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 capability saves a significant amount of time by automating processes that typically involve manual work, such as data cleaning, feature engineering, and predictive analytics.
I believe the AutoAI features of IBM Watson Studio have significantly helped in my data projects by automating model selection and hyperparameter tuning.
It integrates well with other platforms and offers good scalability.
| Product | Market Share (%) |
|---|---|
| Alteryx | 4.2% |
| IBM Watson Studio | 2.2% |
| Other | 93.6% |

| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 15 |
| Large Enterprise | 53 |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 1 |
| Large Enterprise | 5 |
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
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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
IBM Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.
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