Try our new research platform with insights from 80,000+ expert users

Dataiku vs TIBCO Data Science comparison

Sponsored
 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

IBM SPSS Statistics
Sponsored
Ranking in Data Science Platforms
10th
Average Rating
8.0
Number of Reviews
37
Ranking in other categories
Data Mining (3rd)
Dataiku
Ranking in Data Science Platforms
7th
Average Rating
8.0
Number of Reviews
8
Ranking in other categories
No ranking in other categories
TIBCO Data Science
Ranking in Data Science Platforms
25th
Average Rating
7.6
Number of Reviews
4
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.8%, up from 2.6% compared to the previous year. The mindshare of Dataiku is 11.5%, up from 7.5% compared to the previous year. The mindshare of TIBCO Data Science is 0.6%, up from 0.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

AbakarAhmat - PeerSpot reviewer
Enhancing survey analysis that provides valued insightfulness
I used traditional tools where I would prepare data, click through menus, and use SQL Server for data visualization. We switched to IBM SPSS because it offers strong certification and aligns well with the standards we prioritize in our surveys. In terms of popularity, it stands out as the top choice in the market, especially in the research and university domains. Many different organizations and institutions use SPSS for statistical analytics. While there are other tools like MCLab and similar options available, SPSS is the most renowned and widely used among them.
Sabrine Bendimerad - PeerSpot reviewer
Saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms
One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated. While it was theoretically possible to use GitHub with Dataiku, in practice, it was difficult to manage our code effectively and push it from Dataiku to GitHub. Another limitation was its ability to handle different types of data. While Dataiku is powerful for working with structured data, like regular or geospatial data, it struggled with more complex data types such as text and image. In addition to the challenges with GitHub integration, the limited support for diverse data types was another feature lacking at that time.
VS
A straightforward initial setup and good reporting but needs better documentation
It would be ideal if it could be put onto the NMP where you can make more of an analysis. Right now, people don't have enough time to go through the report and make an analysis. It should provide the information of what is on the report into some kind of a dialogue form. Then, a person can ask certain questions and it could interactively give the required report. In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues. If you are doing certain operations of RDBMS, you suffer in terms of the latency of the data. That can be improved upon. Users should be able to cross tables of the web pages they are developing on Spotfire and this needs to be really easy and convenient. Right now, you need to do a lot of tweaks. The solution should be more user-friendly and require fewer tweaks, extensions, and workarounds.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"You can quickly build models because it does the work for you."
"IBM SPSS Statistics depends on AI."
"The most valuable features mainly include factor analysis, correlation analysis, and geographic analysis."
"The SPSS interface is very accessible and user-friendly. It's really easy to get information in it. I've shared it with experts and beginners, and everyone can navigate it."
"The learning curve to using this product is not steep. The program is appropriate for those who do not have a lot of background in programming, yet have to perform basic statistical analysis."
"SPSS can handle whatever you throw at it, whether your data set contains 10,000, 100,000, or a million objects. It's like the heavy artillery of analytical tools."
"The solution is very comprehensive, especially compared to Minitabs, which is considered more for manufacturing. However, whatever data you want to analyze can be handled with SPSS."
"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"The solution is quite stable."
"Data Science Studio's data science model is very useful."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"The most valuable feature is the set of visual data preparation tools."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"The most valuable feature is the ease of setting up visualizations."
"The most valuable feature is the performance."
"The idea that you don't have to generate reports each day but they are sent automatically is great."
"We like the way we can drill down into each report to get back data on each project. From the portfolio level, I can see what is happening on it. That is a really important feature. I can look at indirect costs, for example, which are hitting each CIO portfolio. It's good to be able to see actual resources in terms of time as well as cost."
 

Cons

"The statistics should be more self-explanatory with detailed automated reports."
"The design of the experience can be improved."
"It could allow adding color to data models to make them easier to interpret."
"The product should provide more ways to import data and export results that are user-friendly for high-level executives."
"If there is any self-generation data collection plan (DCP), it would be helpful in gathering data. It would also be useful if there is a function to scale it up to, let's say, UiPath and have it consolidate and integrate into a UiPath solution."
"I think the visualization and charting should be changed and made easier and more effective."
"SPSS is a tool that's been around since the late 60s, and it's the universal worldwide standard for quantitative social science data analysis. That said, it does seem a bit strange to me that the graphical output functions are so clunky after all these years. The output of charts and graphs that SPSS produces is hideous."
"This solution is not suitable for use with Big Data."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"The ability to have charts right from the explorer would be an improvement."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
"I think it would help if Data Science Studio added some more features and improved the data model."
"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."
"The scripting for customization could be improved."
"Additional templates would help to get things moving more quickly in terms of getting the reports out."
"In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues."
"I would like the visualization for the map of countries to be more easily configurable."
 

Pricing and Cost Advice

"More affordable training for new staff members."
"While the pricing of the product may be higher, the accompanying service and features justify the investment."
"We think that IBM SPSS is expensive for this function."
"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."
"SPSS is an expensive piece of software because it's incredibly complex and has been refined over decades, but I would say it's fairly priced."
"The price of IBM SPSS Statistics could improve."
"I rate the tool's pricing a five out of ten."
"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."
"Pricing is pretty steep. Dataiku is also not that cheap."
"The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."
Information not available
report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
816,406 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
17%
University
9%
Computer Software Company
9%
Manufacturing Company
8%
Financial Services Firm
18%
Educational Organization
16%
Manufacturing Company
9%
Computer Software Company
8%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about IBM SPSS Statistics?
The software offers consistency across multiple research projects helping us with predictive analytics capabilities.
What is your experience regarding pricing and costs for IBM SPSS Statistics?
The cost of IBM SPSS Statistics is managed by organizations, not individual researchers. It is a very expensive produ...
What needs improvement with IBM SPSS Statistics?
IBM SPSS Statistics does not keep you close to your data like KNIME. In KNIME, at every stage, you can see the result...
What needs improvement with Dataiku Data Science Studio?
One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integratin...
What is your primary use case for Dataiku Data Science Studio?
We use the solution for data science and machine learning.
Ask a question
Earn 20 points
 

Also Known As

SPSS Statistics
Dataiku DSS
Alpine Data Chorus
 

Learn More

Video not available
 

Overview

 

Sample Customers

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA
BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
Havas Media, Tipping Point Community, eviCore
Find out what your peers are saying about Dataiku vs. TIBCO Data Science and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.