Likelihood to Recommend SPSS is well-suited for the following: 1) User Behavior Analysis: SPSS handles large datasets to analyze user behavior data. 2) Customer Satisfaction / Foundational Surveys: SPSS facilitates analysis of quant data from satisfaction surveys, keeping us informed about customer needs and preferences. 3) A/B test analysis: SPSS statistical tools for A/B test analysis, which helps optimize user experience of our products. Scenarios where SPSS are less appropriate: 1) Qualitative Data Analysis: I do not use SPSS for open-ended survey responses/qual data. 2) Live/in-vivo data analysis: SPSS is not ideal for real-time data processing. 3) Complex Data Integration: SPSS isn’t the best fit for complex data integration tasks
Read full review It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.
Read full review Pros SPSS has been around for quite a while and has amassed a large suite of functionality. One of its longest-running features is the ability to automate SPSS via scripting, AKA "syntax." There is a very large community of practice on the internet who can help newbies to quickly scale up their automation abilities with SPSS. And SPSS allows users to save syntax scripting directly from GUI wizards and configuration windows, which can be a real life-saver if one is not an experienced coder. Many statistics package users are doing scientific research with an eye to publish reproducible results. SPSS allows you to save datasets and syntax scripting in a common format, facilitating attempts by peer reviewers and other researchers to quickly and easily attempt to reproduce your results. It's very portable! SPSS has both legacy and modern visualization suites baked into the base software, giving users an easily mountable learning curve when it comes to outputting charts and graphs. It's very easy to start with a canned look and feel of an exported chart, and then you can tweak a saved copy to change just about everything, from colors, legends, and axis scaling, to orientation, labels, and grid lines. And when you've got a chart or graph set up the way you like, you can export it as an image file, or create a template syntax to apply to new visualizations going forward. SPSS makes it easy for even beginner-level users to create statistical coding fields to support multidimensional analysis, ensuring that you never need to destructively modify your dataset. In closing, SPSS's long and successful tenure ensures that just about any question a new user may have about it can be answered with a modicum of Google-fu. There are even several fully-fledged tutorial websites out there for newbie perusal. Read full review Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc. SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly. Enforced best-practices set up POCs for deployment in production with a minimum of re-work. Estimator validation lets data scientists test and prove different models. Read full review Cons It would be beneficial to have AMOS as part of the SPSS package instead of purchasing it separately. It would be beneficial to have other statistical tests, such as PROCESS, be part of the standard SPSS tests instead of having the need to run a syntax to have it installed. My dataset tends to be smaller, and I have never had any issues with using SPSS. I heard that SPSS may not be optimal when handling large datasets. Read full review The cost is steep and so only companies with resources can afford it It will be nice to have Chinese versions so that Chinese engineers can also use it easily It takes a while to learn how to input different kinds of skin defects for detection Read full review Likelihood to Renew Both money and time are essential for success in terms of return on investment for any kind of research based project work. Using a Likert-scale questionnaire is very easy for data entry and analysis using IBM SPSS. With the help of IBM SPSS, I found very fast and reliable data entry and data analysis for my research. Output from SPSS is very easy to interpret for data analysis and findings
Read full review because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)
Read full review Usability Probably because I have been using it for so long that I have used all of the modules, or at least almost all of the modules, and the way SPSS works is second nature to me, like fish to swimming.
Read full review The UI flawlessly merges this offering by providing a neat, minimal, responsive interface
Read full review Reliability and Availability SPSS can tend to crash when I am trying to do a lot of data. This can slow me down when I need to do a lot of data
Read full review From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected
Read full review Performance SPSS does the job, but it can be slow. I do have to plan a lot of time to get through a huge amount of data.
Read full review Never had slow response even on our very busy network
Read full review Support Rating I have not contacted IBM SPSS for support myself. However, our IT staff has for trying to get SPSS Text Analytics Module to work. The issue was never resolved, but I'm not sure if it was on the IT's end or on SPSS's end
Read full review I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path
Read full review In-Person Training The trainers on the job are very smart with solutions and very able in teaching
Read full review Online Training The Platform is very handy and suggests further steps according my previous interests
Read full review Implementation Rating Have a plan for managing the yearly upgrade cycle. Most users work in the desktop version, so there needs to be a mechanism for either pushing out new versions of the software or a key manager to deal with updated licensing keys. If you have a lot of users this needs to be planned for in advance.
Read full review It surprised us with unpredictable case of use and brand new points of view
Read full review Alternatives Considered I have used R when I didn't have access to SPSS. It takes me longer because I'm terrible at syntax but it is powerful and it can be enjoyable to only have to wrestle with syntax and not a difficult UI.
Read full review The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.
Read full review Scalability I am neutral because I have not had to look into scalability since I am using as a student.
Read full review It helped us in getting from 0 to DSX without getting lost
Read full review Return on Investment IBM SPSS has allowed me to quickly analyze data for research. IBM SPSS has allowed me to complete analyses in order to submit research findings to conferences and complete manuscripts. IBM SPSS has enabled me to meet research objectives set out in grant proposals. Read full review Could instantly show data driven insights to drive 20% incremental revenue over existing results Still don't have a real use case for unstructured data like twitter feed Some of the insights around user actions have driven new projects to automate mundane tasks Read full review ScreenShots