Nine AI tools every data analyst should know in 2026 and Obviously.ai makes the list for a specific reason

Obviously.ai earns its place specifically for no-code predictive modelling on structured business data. The category it sits in, tools that bridge the gap between data exploration and machine learning without requiring Python or R, is the category that is growing fastest in real enterprise data teams where most analysts are not data scientists.

Julius AI as an analyst sidekick for exploratory data analysis on uploaded datasets, Quadratic AI for Python code generation inside a spreadsheet and Bricks for data visualisation to chart building are the companion tools in the list that round out the workflow. None of them does what Obviously.ai does for prediction specifically.

The interesting competitive question the list raises: as general-purpose tools like Claude and ChatGPT get better at code-assisted data analysis, what is the durable advantage of specialist data analysis tools? The video makes an implicit argument that workflow integration and domain-specific reliability justify the specialist tools even as generalists improve.

For data analysts who have tested obviously.ai for predictive modelling: what specific business prediction task did you apply it to and how did the accuracy compare to a baseline model?