Exploratory AI

What is Exploratory?

Exploratory is a visual analytics platform founded in 2016 by Kan Augustus and Hideaki Hayashi. Developed with the goal of democratizing Data Science, the tool wraps the power of the R programming language (specifically tidyverse packages and statistics algorithms) in an intuitive drag-and-drop user interface, allowing business analysts to clean data, run statistical models, and build predictive analysis without writing complex code scripts.

How it works

The software runs as a desktop application where users import data from databases, files, or cloud services. Exploratory guides the user through an iterative cleanup process (e.g., handling null values, parsing dates, filtering records). Every action generates a visual step in a history panel. Users can then build interactive charts, run statistical models (such as Linear Regression, Random Forest, or K-Means), and generate forecasting models in a few clicks.

Key features

• Visual Data Wrangling: Step-by-step interactive cleanup operations without writing code.
• Advanced Statistical Models: Drag-and-drop statistical testing (Anova, Chi-Square) and Machine Learning models (XGBoost, Decision Trees).
• Time Series Forecasting: Integrated Prophet algorithm (developed by Meta) to create future forecasts with a few clicks.
• Editable Notes and Dashboards: Integrated publishing tool to create dynamic reports and presentations based on R Markdown.
• One-Click Reproducibility: All data manipulation history is saved structurally for auditing and batch execution.

Available integrations

Exploratory has native support for connecting to a wide variety of data sources:

• Databases: PostgreSQL, MySQL, Redshift, Google BigQuery, Snowflake, Oracle, SQL Server.
• Local files: Excel, CSV, JSON, Parquet, statistics files (SPSS, SAS, Stata).
• Web APIs: Google Analytics, Google Sheets, Salesforce, HubSpot, Stripe.

Who it is for

• Business analysts (BI) who want to move beyond Excel spreadsheets and create complex predictive or statistical analyses.
• Academic researchers who need to conduct rigorous analysis in R but prefer not to waste time writing complex code syntax.
• Senior data scientists who want to accelerate the initial phase of exploratory data analysis.

Real use cases

1. Sales Demand Forecasting: Commercial departments use Exploratory's integrated time series algorithms to forecast sales volumes for upcoming quarters based on historical billing data.
2. Churn Analysis: Customer Success teams import product usage data and use decision tree models to identify which customer behaviors are associated with subscription cancellations.

Pricing

Pros and cons

Pros:

• Ease of executing extremely complex statistical analyses without writing code.
• Visual history of cleanup steps makes debugging processing errors easy.
• Generates professional interactive reports natively.

Cons:

• Requires installing a heavy desktop application.
• Commercial license can be expensive for small freelancers.

Alternatives to Exploratory AI

Key alternatives in the visual and statistical data science market are:

• Alteryx: Leading enterprise platform for data preparation and predictive analysis based on drag-and-drop workflows.
• KNIME: Open-source data analysis platform based on nodes and visual workflows.
• RStudio / Posit: The standard IDE for R development, suitable for those who prefer to write raw code.
• Tableau: Focused mainly on BI and advanced interactive dashboards, with fewer pure statistical features.