Working with dedicated predictive analytics tools is often relatively easy, at least compared to programming your own from scratch. Most tools offer visual programming interfaces that enable users to drag and drop icons optimized for data analysis. It helps to understand coding and to think like a programmer, but the tools make it possible to generate sophisticated predictions with a few mouse clicks. If you need more, adding custom code can solve many common issues.
The best place to begin is to look for a product that works with your data. All predictive analytics tools can analyze data in generic formats such as CSV, but many tools get along better with those from the same vendor. IBM’s SPSS, for instance, can work directly with the company’s db2 database. Cloud tools such as those from Amazon Web Services tend to be integrated with AWS’s many data storage solutions, like S3 or RDS.
Beyond the data, another key differentiator is the types of questions you intend to ask. Some tools are better at analyzing certain questions than others. Make sure the tool can compute the statistical measures needed to answer the questions your business needs to address.
Users must also be honest about their need for artificial intelligence. The area is exciting and new, but not every stack needs AI. A company that’s just asking for a simple number to predict demand for widgets next quarter doesn’t need a generative AI solution that may even hallucinate.
Another important question: Who will be using the tool? Some enterprises maintain teams of data scientists who want to develop new algorithms and work with open-source tools. They’ll want more accessible stacks with the ability to integrate new code written in Python or R.