IBM SPSS Modeler

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.

Use of small research projects with various survey data is an excellent utility

Large data bases may result in slow performance

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.

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.

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.

For those first learning to set up a dataset, analyse data and interpret output. Quantitative analysis. 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).

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.

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.

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.

Modeler is well suited for Retail, Credit Scoring, Telcos, Government. And less suited when it comes to transactional environments.

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.

Building accurate models to predict future outcomes, identify patterns, and understand influencing factors to improve decision-making.