Great for novice users, but more experienced analysts might prefer R or Python

IBM SPSS Statistics enables my organisation to address a range of data analytics challenges. We frequently use it for interrogating datasets, generating descriptive statistics, performing a range of hypothesis tests, and creating data visualisations. In my business area we tend to use it for regression modelling, particularly using the multiple linear regression and binary logistic regression approaches.

In general, IBM SPSS Statistics is useful in our organisation, because it easily enables a range of statistical procedures and tests to be performed. It is particularly suitable for our team members who only used SPSS at university (i.e., who have no coding experience), who are very familiar and comfortable with the software. Being able to use SPSS for data analysis is a great support for our analytical efficiency and quality.

However, SPSS isn't suitable for every type of data analysis we do. Sometimes we're working with incredibly large datasets that SPSS struggles with, and sometimes we need to perform bespoke statistical tests or procedures that aren't accommodated within SPSS. For this reason, we also use different software (R or Python) just as much as we use SPSS, if not more. It's a little bit inefficient to have different software for the same sets of tasks, so I anticipate we will move away from SPSS in the future.

I work for a company which is tasked with carrying out high-quality data analysis and reporting. IBM SPSS Statistics is used by many of our analysts to support these efforts, as it enables them to easily answer complex research questions that our stakeholders are interested in. Producing high-quality work helps to strengthen our reputation as a trustworthy and insightful provider of research and statistics.