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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Oracle Analytics
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Oracle Analytics is a solution used to visually explore data to create and share compelling stories. Oracle Analytics Cloud is a cloud native service, and Oracle Analytics Server is the on-premise option.
I described earlier that the only scenarios where I use SPSS are those where we have legacy projects that were developed in the late 90s or early 2000s using SPSS, and for some reason, the project (data set, scope, etc.) hasn't changed in 24+ years. This counts for 1-2 out of around 80 projects that I run. Whenever possible, I actively have my team move away from SPSS, even when that process is painful.
Oracle Data Visualization is very effective if used in an enterprise context with huge volumes of data coming from different systems. It supports dashboard and reporting capabilities and is easy to scale. It also allows you to leverage machine learning capabilities to extract hidden data trends. Visualization capabilities are powerful but not so various if compared to other solutions on the market. If you want to present a dashboard to an executive audience and you want to make your dashboards beautiful you must adapt them through PowerPoint.
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.
Available without of the box connectors for Salesforce and oracle Saas Cloud. This is a huge plus for our business since we don't need another middleware solution just for this sake.
We are able to connect to our on-prem SQL Server database where we have our RMA database and other applications seamlessly without writing custom APIs.
OAC writes directly into ADW which is another advantage for loading Excel files into ADW after dataflow transformations.
OAC allows replication of the database from fusion ERP and lets us create subject areas using the data modeler.
collaboration - SPSS lacks collaboration features which makes it near impossible to collaborate with my team on analysis. We have to send files back and forth, which is tedious.
integration - I wish SPSS had integration capabilities with some of the other tools that I use (e.g., Airtable, Figma, etc.)
user interface - this could definitely be modernized. In my experience, the UI is clunky and feels dated, which can negatively impact my experience using the tool.
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
Scalability and rich integration capabilities. In the future, if we go with Hyperion for the Financial Consolidation and planning purposes -BI integration with Hyperion is going to be much simpler as it has native interface connectivity and even integration capabilities with well known CRM products (Siebel) and ERP Products (Oracle EBS, Peoplesoft, SAP) is going to be easy and straight forward.
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.
Great, if you are limited to using it along with other Oracle products; sadly, not if you are integrating with other products, which can be a challenge. It is a great product with tons of functionality and great integration with other in-house platforms. Great visuals and customization for data and analytics to provide decision-making data and analysis.
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
Oracle Analytics Support team is very proactive and I have never had a situation where I had to wait for more than a day or two to get my issues resolved. This is a very big help for us and we appreciate Oracle and its team for guaranteeing that experience.
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.
A properly implemented Endeca solution performs extremely well on the largest of datasets and it positions your organization to immediately achieve your ROI.
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.
Oracle Analytics Cloud, is one of the most agile and secure data analysis platforms that according to the budget and the amount of use, you can use the resources you need under the cloud. The Oracle brand is also very well known in this field and can meet all the needs of an organization or industry in any sector.
We have seen the results of this in our initial research and are not surprised that Oracle does this like it does soo many other things in this area, so well.
I found SPSS easier to use than SAS as it's more intuitive to me.
The learning curve to use SPSS is less compared to SAS.
I used SAS, to a much lesser extent than SPSS. However, it seems that SAS may be more suitable for users who understand programming. With SPSS, users can perform many statistical tests without the need to know programming.
We've used OBIEE (or it's previous named product) for over 13 years and it's still the most used tool for BI by the business.
We moved our largest business system off of Business Object into OBI so we could gain improved performance, reliability, and easier management of metadata.
