IBM SPSS Modeler provides us with a no-code data science and predictive analytics platform for developing and deploying machine learning models. It give us the ability to model out a idea to test the hypotheses.
Pros
Predictive Modeling
Data Preparation
Text Mining
Cons
User Interface could improve
More statt capablities
Likelihood to Recommend
Building accurate models to predict future outcomes, identify patterns, and understand influencing factors to improve decision-making.
IBM SPSS Modeler is used mainly in the world of social sciences and psychology and other research. This product is user friendly and a new user can use for statistical analyses without much practice. This tool provides a range of statistical techniques (descriptive statistics, regression and other tests) and can be used for a variety of data sets with good outcomes. You can also use with Microsoft Excel which makes it very practical. There are good support links available to assist in the use of this tool. The only negative aspects of this tool are cost, and it may not be as flexible as some programs.
Pros
Wide range of statistical techniques
Good tables and charts
Integrates with Excel
Cons
Appears slow with very large data sets
Lack of frequent updates
cost reduction
Likelihood to Recommend
Use of small research projects with various survey data is an excellent utility
Modeler is used to analyze large amounts of data and to develop and deploy predictive models. The software will look for an appropriate model, assess the quality of models, and create predictions for new data. Modeler can be used in the analysis of numeric data and also with text data. Additionally, text and numeric data can be combined in models if appropriate.
Pros
Combine text and data
Provide facilities for all phases of the data mining process.
Use a node and stream paradigm to easily and quickly create models.
Cons
The graphics are weak.
Likelihood to Recommend
Modeler is well suited for understanding consumer data. The ability to create a prediction and then to understand what is driving that prediction is strong in Modeler. Modeler is closely aligned with the CRISP-DM data mining approach meaning it is not just the 'doing' but also the theoretical background behind the development of data mining models.
IBM SPSS Modeler is the on-premise version of Modeler Flow, which we use in the Cloud Pak for Data as a Service application in IBM's cloud. Mainly, it is an interface based application with nodes that replaces the need to write code. In several instances, it can be a quicker method to designing a data science project. We use it for the Text Analytics node to assist with ad hoc NLP requests to turn over NLP in a quicker fashion than typical.
Pros
Fast code builder.
No need to maintain software versioning.
Visual code, easy to see what's happening.
Cons
Details of models and nodes requires some "digging", "clicking".
Customization of versions is hard to implement.
Latest open source code packages take time to integrate into the application.
Likelihood to Recommend
Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.
As an online psychology and statistics tutor, research consultant, and uni student, I use SPSS daily to support undergraduate, postgraduate and lecturer learning of how to use the SPSS program and interpret findings; as well as using the program for in-house research, my assessments, and as a consultant for NGOs, small business and as a research assistant for academic staff. Problems addressed include: descriptive analyses, assumption checking, NHST, effect sizes, and building models such as with path analysis, SEM, moderation and mediation (using PROCESS), and visual representation of data; for lecture and tutorial reviews, research proposals and reports including theses, and to manage research projects from outside of academia.
Pros
Inputting data
Test selection
Data manipulation
Providing sandbox data sets
Two week trial for students considering purchase
Reasonable rent prices
Cons
Including MCAR in basic packages
Standardise language across dialogue windows
Enable 'undo' after sorting cases
Include Bayesian options
Likelihood to Recommend
For those first learning to set up a dataset, analyse data and interpret output. <span style="letter-spacing: -0.05px; word-spacing: -0.85px;">Quantitative analysis. </span><span style="letter-spacing: -0.05px; word-spacing: -0.85px;">Not well suited to correlations which don't meet the assumptions of tests on SPSS, creating CIs for some regression analyses, qualitative analysis (I use NVivo).</span>
IBM SPSS Modeler is used by our analytics department in order to perform management analysis and reporting, and also to perform financial control testing within the company. With the various statistical models and analytical tools, we can use this product for data discovery and exploration, then analyze the raw data and find patterns in it that help decision makers in their business decisions.
Pros
A very nice and easy to use interface.
A great variety of analytics, from statistical calculation to data validation and predictive statistics.
Has a steep learning curve.
Cons
Has very old style graphs, with lots of limitations.
Some advanced statistical functions cannot be done through the menu.
The data connectivity is not that extensive.
It's an expensive tool.
Likelihood to Recommend
SPSS Modeler is very well suited for researchers (especially in the social area) or for those who want to find patterns in a big dataset. It has very colorful statistical and analytical features, so SPSS is perfect to analyze data and find patterns in it. Unfortunately, it has very limited and outdated visualization capabilities, so IBM SPSS can be used to produce data, but not to visualize it. It is recommended to use other tools for visualization.
IBM SPSS Modeler is extensively used by our data analytics team. It is used for customer analytics to formulate and understand retention and engagement metrics, and used to analyze our CRM. It gives us a good amount of insights that help us to with easy and quick decision making. The business people use it for business problems and they don't have to have technical understanding.
Pros
The model formulation from large amount of structured and unstructured data is commendable.
It has a beautiful and intuitive new UI.
The manual is very easy to understand. Hence, reduces the on boarding.
Cons
The natural language processing (NLP) needs improvement.
Already present integrations to other IBM products is poor.
There are many things like Text analytics which are only available in gold edition.
It is hard for a non-tech person to implement advanced modelling which requires Python, R.
Likelihood to Recommend
If your analytics is research oriented, coming with cases and hypothesis, and you have the sound technical knowledge of Programming which is used in data mining (Python, R), then this tool will give you a huge advantage irrespective of whether you have structured or unstructured data. However, if your organization doesn't have the data to work upon, this product won't be able to help you further.
Our company has 20+ years of developing solutions based on SPSS tools, one of them is IBM SPSS Modeler. In our experience, we have seen companies using Modeler across the whole organization to support critical business process. The versatility of the tool and easy deployment make it a first choice in organizations from Government to Retail, Finance, and Academics. Being able to reduce churn, to retain customers, forecast sales and inventory stock all within a friendly and powerful user interface is something that customers are really looking forward when it comes to accelerating ROI.
Pros
GUI is really well accomplished and friendly, almost everyone with little investment in training can take advantage of the tool.
Escalability, you can grow your investment in licensing according to your actual needs, from an annual authorized user, to perpetual concurrent and Big Data and Machine Learning capabilities.
Open Sorce Ready: take leverage of all your developments made in R or Python and deployment all over the organization even with the user who isn´t used to code.
Cons
Some Analyses aren't there out of the box but can be added through open languages like R and Python.
Graphs could be better.
Unable to read data stored in OLAP databases
Likelihood to Recommend
Modeler is well suited for Retail, Credit Scoring, Telcos, Government. And less suited when it comes to transactional environments.
