KNIME Analytics Platform
The open-source standard for data science, AI, and low-code workflow orchestration.

Democratizing Data Science through an AI-powered UI for R and reproducible analytics.
Exploratory is a sophisticated data science desktop application that bridges the gap between deep-code R programming and point-and-click BI tools. Built on the Tidyverse ecosystem, it provides a UI-driven approach to data wrangling, statistical analysis, and machine learning. As of 2026, Exploratory has positioned itself as a leader in 'Explainable AI' (XAI), integrating SHAP and LIME values directly into its modeling interface to help analysts understand why models make specific predictions. Its technical architecture leverages an embedded R environment, allowing users to switch seamlessly between a GUI and custom R scripts. The platform excels in reproducibility; every action is recorded as a step in a data pipeline, which can be shared, versioned, or automated via Exploratory Server. By automating the boilerplate of data preparation and model validation, it enables analysts to focus on hypothesis testing and storytelling, making it a critical asset for organizations transitioning from descriptive to prescriptive analytics without requiring a full team of PhD data scientists.
Uses natural language processing to convert user intent into optimized dplyr (R) code for complex data reshaping.
The open-source standard for data science, AI, and low-code workflow orchestration.
The premier community-driven cloud environment for high-performance data science and machine learning.
The multi-user hub for Jupyter Notebooks, providing a centralized data science platform for teams and classrooms.
The operating system for modern AI and data science development.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A full UI implementation of Facebook's Prophet algorithm for additive time-series modeling with holiday and seasonality support.
Every action creates a reversible script step. Users can branch from any point in the history to test different hypotheses.
Built-in modules for T-tests, ANOVA, and Chi-square tests with automated p-value interpretation.
Native integration of SHAP (SHapley Additive exPlanations) for identifying feature contribution in black-box models.
Allows writing raw SQL queries that output data directly into the Tidyverse processing pipeline.
A high-fidelity GUI for ggplot2, allowing for complex layering and faceting of charts.
Identifying which behavioral traits lead to customer cancellation in a SaaS environment.
Registry Updated:2/7/2026
Export the list of high-risk customers to a CSV for the success team.
Predicting seasonal demand for 500+ SKUs to optimize stock levels.
Determining which marketing channels contribute most to conversion using multi-touch models.