

IBM SPSS Statistics and Dataiku are key players in the analytics and data solutions market. IBM SPSS Statistics often has the upper hand in statistical analysis features, while Dataiku excels in integration and machine learning functionalities.
Features: IBM SPSS Statistics offers powerful statistical analysis functions with regression and principal component analysis. Its intuitive point-and-click navigation makes it accessible for users looking for complex modeling. Dataiku provides strong no-code/low-code data preparation and a wide array of machine learning capabilities, ideal for data handling and feature selection.
Room for Improvement: IBM SPSS Statistics could enhance its data visualization tools and database integration. Users recommend improving its automation features and adding advanced statistical methods. Dataiku can improve its integration with platforms like GitHub, support more diverse data types, and further develop its user interface for less technical users.
Ease of Deployment and Customer Service: IBM SPSS Statistics focuses on on-premises deployment with some public cloud options, and its customer service is noted as responsive. Dataiku supports both on-premises and private cloud deployments, but users experience occasional delays in technical support responses.
Pricing and ROI: IBM SPSS Statistics is more expensive with multiple pricing tiers, but users often find the cost justified due to its comprehensive features and strong support, particularly in academic settings. Dataiku, while still costly, is seen as more affordable than some alternatives, delivering effective ROI through efficient data processing and reporting capabilities.
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.
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.
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.
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.
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.
I believe that the owners of IBM SPSS Statistics should think about improving the package itself to be able to treat unstructured data.
I'm unsure if SPSS has a commercial offering for big servers, unlike KNIME, which does.
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.
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.
Predictive analytics is the most important part of analytics.
I mainly used it for cross tabs, correlation, regression, chi-squared tests, and similar analyses often seen in published papers.
| Product | Mindshare (%) |
|---|---|
| Dataiku | 5.6% |
| IBM SPSS Statistics | 3.6% |
| Other | 90.8% |

| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 2 |
| Large Enterprise | 13 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 6 |
| Large Enterprise | 20 |
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.
IBM SPSS Statistics is renowned for its intuitive interface and robust statistical capabilities. It efficiently handles large datasets, making it essential for data analysis, quantitative research, and business decision-making.
IBM SPSS Statistics offers extensive functionality supporting both beginners and experts. It is used for data analysis across industries, accommodating advanced statistical modeling such as regression, clustering, ANOVA, and decision trees. Users benefit from its quick model building and ease of use, which are indispensable in data exploration and decision-making. Room for improvement includes charting, visualization, data preparation, AI integration, automation, multivariate analysis, and unstructured data handling. Enhancements in importing/exporting features, cost efficiency, interface improvements, and user-friendly documentation are sought after by users looking for alignment with modern data science practices.
What are IBM SPSS Statistics' most notable features?IBM SPSS Statistics is implemented broadly, including academic research for in-depth studies, business analytics for informed decision making, and in the social sciences for comprehensive data exploration. Organizations utilize its advanced features like AI integration and automated modeling across sectors to gain actionable insights, streamline data processes, and support research initiatives.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.