IBM SPSS a perfect tool for the beginner in data analytics
February 29, 2020

IBM SPSS a perfect tool for the beginner in data analytics

Prashant Choudhary | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User

Modules Used

  • IBM SPSS Statistics
  • IBM SPSS Data Collection
  • IBM SPSS Modeler
  • IBM Analytical Decision Management

Overall Satisfaction with IBM SPSS

We use IBM SPSS for data visualization and analysis. It is a good tool for analysis of things like regression, both linear and logistics. What sets it apart for our organization is the use we have for it creates multiple tables with varied parameters and features. It does all the required functions for data analysis, especially for our survey processes that we provide to the client; it lacks the data collection module, though.

Pros

  • Data visualization
  • Data Analysis
  • Data modeling
  • Tabular creation & modification
  • User-friendly UI
  • Compatibility with several other software and the ability to save files in a multitude of extensions

Cons

  • Random forest function is still missing
  • It needs better tools for data collection, becomes tedious with an extensive dataset collection.
  • UI could use some tweaks
  • Should derive from R, Numpy and pandas for various functions that are coming out as the new data analysis packages
  • Needs more flexibility in including new features
It has helped improve operational efficiency as well as helped in formulating models based on psychological and medical research for esteemed clients. It has been a real game-changer in upping their game from normal research to a deeply skilled and thorough one. It has helped in the observation of group trends, differences, logistic regression, etc.
IBM SPSS has improved decision making and has given out good results. That most of the models we have done on this gave high operational effectiveness and efficiency in the modeling or data analysis life cycle. It has helped draw insights from several datasets that have helped improve business decisions as well as provide support to clients with their research and analysis, thereby improving revenue and customer engagement, and in turn, overall efficiency.
Stacks up pretty well, as this is the best geospatial analysis tool next only to ArcGIS, but does almost everything that other packages do almost as well. However, there is a considerable need to improve and include new techniques like the random forest, etc. which IBM SPSS lacks as compared to several of this alternative software.
IBM SPSS is most suited for analysis of survey data, wherein you have large datasets to perform data analysis of, while deriving relationships between several variables and function, meanwhile interaction is with multiple fields and tabular on nature, i.e., uses a lot of tables, and various relationships are derived on comparison of several fields with several others

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