SPSS Statistics is a software package used for statistical analysis. It is now officially named "IBM SPSS Statistics". Companion products in the same family are used for survey authoring and deployment (IBM SPSS Data Collection), data mining (IBM SPSS Modeler), text analytics, and collaboration and deployment (batch and automated scoring services).
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Tableau Desktop
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Tableau Desktop is a data visualization product from Tableau. It connects to a variety of data sources for combining disparate data sources without coding. It provides tools for discovering patterns and insights, data calculations, forecasts, and statistical summaries and visual storytelling.
If I didn't want to code, IBM SPSS would be after JMP and Tableau, and before SAS and R. The user interface is very clunky compared to the analytics software I stated. You could definitely learn to do basic analysis faster in SAS than SPSS. I selected SPSS to test the …
Two alternatives I considered before choosing SPSS are the programming languages R and Julia Statistics. Both are freely available, open-source packages for sophisticated statistical analysis and visualization. In my case, I preferred SPSS to these somewhat newer options due …
Select IBM SPSS because it is a program that is easier to use than RStudio [which] requires the application of much more work hours to master and make the most of each of the tools it offers.
I use Stata for tasks that SPSS cannot support, but ultimately SPSS has a short learning curve, strong statistical processing, and a mature tool set. SAS is also mature, but more programming based. JMP tries to 2nd guess what I need. NOTE: R (open source) is a great option …
I have used SAS, R, Systat, Stata, Excel, Access and I continually find myself using SPSS to do the same set of tasks I would be using the previously named software to do. It's an all encompassing software that is very easy to use but can become as powerful an analytical tool …
Graphics within SPSS provide you with a general framework for understanding your data, so that you will be better able to interpret the complex inferential procedures that follow.
If you aren't comfortable programming and you need to do statistics, SPSS is a great choice. Psychology undergrads, for example, have to take statistics. Their difficulty with the programming portion cuts into their ability to learn the actual analyses, so SPSS is good for that. I would love to see IBM SPSS invest more in their UI/UX and increase the usability of this tool because it could be so much better. Their iconography is a mess, there's very little feedback, and don't even get me started on that last "big update" that basically truncated all functionality. The biggest thing I would like to see is to have the "Analyze" function broken into categories, or other buttons, or be able to customize it in some way. Having to repeatedly go through drop down menus to run analysis after analysis is a huge time suck, and very error prone (easy to click on the wrong thing). Lastly, one of the biggest challenges for beginning (and even experienced!) statisticians is interpreting output. More tool tips, feedback, integrated coaching, or something like that to help people understand more about what they are seeing could be so beneficial.
Tableau Desktop is one the finest tool available in the market with such a wide range of capabilities in its suite that makes it easy to generate insights. Further, if optimally designed, then its reports are fairly simple to understand, yet capable enough to make changes at the required levels. One can create a variety of visualizations as required by the business or the clients. The data pipelines in the backend are very robust. The tableau desktop also provides options to develop the reports in developer mode, which is one of the finest features to embed and execute even the most complex possible logic. It's easier to operate, simple to navigate, and fluent to understand by the users.
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.
An excellent tool for data visualization, it presents information in an appealing visual format—an exceptional platform for storing and analyzing data in any size organization.
Through interactive parameters, it enables real-time interaction with the user and is easy to learn and get support from the community.
For those requiring advanced procedures not on SPSS, programmable coding, like in R Statistics, may be more limited and not as robust and use of R or other programs might be needed.
Statistical Package for Social Sciences and can only be used to perform only statistical operations. Further purchases of other packages will be required.
Default graphics are far from publication quality. Generally, it’s better to use other programs for graphics.
Information about effect size and confidence intervals is missing for many techniques.
It's super easy to use for newbies and super powerful for power users! It does EVERYTHING you are usually asked to do analytically. Their Help Desk is PHENOMENAL. And I find the upgrade and renewal price to be a good deal.
Our use of Tableau Desktop is still fairly low, and will continue over time. The only real concern is around cost of the licenses, and I have mentioned this to Tableau and fully expect the development of more sensible models for our industry. This will remove any impediment to expansion of our use.
SPSS is beginner friendly and user-friendly for beginner analysts and simple statistical tests. It's "click and go" interface does take some learning, but overall this is much easier than other programs I have used and seen. Compared to SAS software, SPSS takes a great deal less familiarizing and it not a matter of learning a coding language like SAS and RStudio.
Tableau Desktop has proven to be a lifesaver in many situations. Once we've completed the initial setup, it's simple to use. It has all of the features we need to quickly and efficiently synthesize our data. Tableau Desktop has advanced capabilities to improve our company's data structure and enable self-service for our employees.
When used as a stand-alone tool, Tableau Desktop has unlimited uptime, which is always nice. When used in conjunction with Tableau Server, this tool has as much uptime as your server admins are willing to give it. All in all, I've never had an issue with Tableau's availability.
Tableau Desktop's performance is solid. You can really dig into a large dataset in the form of a spreadsheet, and it exhibits similarly good performance when accessing a moderately sized Oracle database. I noticed that with Tableau Desktop 9.3, the performance using a spreadsheet started to slow around 75K rows by about 60 columns. This was easily remedied by creating an extract and pushing it to Tableau Server, where performance went to lightning fast
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
I have never really used support much, to be honest. I think the support is not as user-friendly to search and use it. I did have an encounter with them once and it required a bit of going back and forth for licensing before reaching a resolution. They did solve my issue though
It is admittedly hard to train a group of people with disparate levels of ability coming in, but the software is so easy to use that this is not a huge problem; anyone who can follow simple instructions can catch up pretty quickly.
The training for new users are quite good because it covers topic wise training and the best part was that it also had video tutorials which are very helpful
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.
Again, training is the key and the company provides a lot of example videos that will help users discover use cases that will greatly assist their creation of original visualizations. As with any new software tool, productivity will decline for a period. In the case of Tableau, the decline period is short and the later gains are well worth it.
[IBM] SPSS is by far the best of the statistics software applications in terms of functionality and accessibility, but its biggest drawback is price. SPSS is prohibitively expensive in comparison to the other competing statistics applications such as R and SAS, making the purchase of a license for an individual very expensive if not covered by employer.
If we do not have legacy tools which have already been set up, I would switch the visualization method to open source software via PyCharm, Atom, and Visual Studio IDE. These IDEs cannot directly help you to visualize the data but you can use many python packages to do so through these IDEs.
Tableau Desktop's scaleability is really limited to the scale of your back-end data systems. If you want to pull down an extract and work quickly in-memory, in my application it scaled to a few tens of millions of rows using the in-memory engine. But it's really only limited by your back-end data store if you have or are willing to invest in an optimized SQL store or purpose-built query engine like Veritca or Netezza or something similar.
Tableau was acquired years ago, and has provided good value with the content created.
Ongoing maintenance costs for the platform, both to maintain desktop and server licensing has made the continuing value questionable when compared to other offerings in the marketplace.
Users have largely been satisfied with the content, but not with the overall performance. This is due to a combination of factors including the performance of the Tableau engines as well as development deficiencies.
