Introduction
JMP is a statistical discovery software package developed by SAS Institute. The software has a long history of providing data visualization, statistical analysis, and data mining capabilities to users. This report provides an overview of the major releases of JMP, highlighting key features and enhancements.
Early Versions (1980s-1990s)
Major Releases (2000s-2010s)
Recent Releases (2010s-present)
Current Version
Conclusion
JMP has a rich history of providing data visualization, statistical analysis, and data mining capabilities to users. From its early versions to the current release, JMP has continued to evolve and improve, adding new features and enhancements to support the needs of data analysts and scientists.
From its 1989 debut on the Macintosh to the current JMP 18, the software has evolved from a visual desktop statistics tool into a predictive analytics powerhouse featuring native Python integration and "Easy DOE" workflows. Key milestones included the introduction of Graph Builder in JMP 4, R integration in JMP 9, and the launch of JMP Pro in JMP 10. You can explore the full history and feature evolution on the JMP blog. jmp version history
A History of JMP: From Statistical Roots to Visual Analytics
Since its debut in 1989, JMP (pronounced "jump") has evolved from a niche Macintosh tool into a powerhouse of interactive statistical discovery. Developed by John Sall and a team at SAS, JMP was designed to transform data analysis from a static, batch-processed chore into a dynamic, visual exploration. The Formative Years (1989–1990s)
JMP 1 (1989): Launched exclusively for the Apple Macintosh, JMP 1 introduced the "point-and-click" interface to statistics. At a time when most software required complex coding (like parent company SAS), JMP allowed users to see their data through histograms and scatterplots instantly.
JMP 2 & 3 (1991–1994): These versions expanded the analytical toolkit, adding support for design of experiments (DOE) and basic quality control. Version 3 marked a major milestone by bringing the software to Microsoft Windows, significantly broadening its user base. Expansion and Customisation (2000–2009)
JMP 4 & 5 (2000–2002): These versions focused on integration and interactivity. JMP 4 introduced the JMP Scripting Language (JSL), a pivotal addition that allowed power users to automate workflows and create custom applications.
JMP 7 & 8 (2007–2009): These iterations embraced modern computing power. JMP 8 introduced the Graph Builder, a drag-and-drop environment that remains the centerpiece of the software’s visual discovery philosophy today. The Era of Big Data and Visualisation (2010–2019)
JMP 10 & 11 (2012–2013): As datasets grew in size, JMP refined its performance. JMP Pro was introduced around this time, offering advanced features like predictive modelling, cross-validation, and exact statistics for more rigorous research.
JMP 13 & 14 (2016–2018): These versions focused on "the messy reality of data." Improved data cleaning tools, virtual joins, and better integration with R and Python made it easier for data scientists to bridge the gap between different platforms. Modern Connectivity (2020–Present) Introduction JMP is a statistical discovery software package
JMP 16 & 17 (2021–2022): Recent updates have prioritised ease of use and logging. The "Action Recorder" was a standout feature, automatically capturing the steps of an analysis to generate JSL scripts without requiring the user to write a single line of code.
JMP 18 (2024): The latest major leap, JMP 18 features a revamped core engine and deeper integration with Python, allowing users to run Python code directly within the JMP environment while leveraging JMP’s superior graphics. Conclusion
The trajectory of JMP’s version history reflects a consistent theme: the democratization of data. By moving away from command-line syntax toward interactive, visual feedback, JMP has spent over three decades ensuring that the "discovery" in statistical discovery remains accessible to scientists, engineers, and researchers alike.
JMP, a statistical software suite originally developed as "John’s Macintosh Project" by John Sall in the late 1980s, has evolved from a niche Mac tool into a global standard for scientists and engineers. Its history is marked by a transition from a dedicated Macintosh application to a cross-platform powerhouse that became a wholly owned subsidiary of SAS in 2022. Early Origins: 1989 – 2000
JMP was first released in October 1989 to leverage the graphical user interface (GUI) of the Apple Macintosh for statistical discovery.
JMP 1.0 (1989): Designed for interactivity, allowing users to explore data visually rather than through code-heavy command lines.
JMP 2.0 (1991): Doubled in size and introduced at Macworld Expo, this version required 2MB of memory and added more interactive graphics.
JMP 3.1 (1994): A major milestone where JMP expanded beyond Apple, introducing support for Microsoft Windows. Expansion and Modernization: 2002 – 2014 Major Releases (2000s-2010s)
As the software matured, it underwent significant architectural rewrites to support more complex data sources and advanced statistical modeling.
JMP 4 (2002): Rewritten to handle a wider variety of data imports and introduced surface plots for better visualization.
JMP 11 (2014): Focused on "ease-of-use" with the introduction of the Excel Import Wizard and advanced features for Design of Experiments (DOE). The Recent Era: JMP 12 – JMP 17
The last decade has seen JMP transition toward larger datasets, better reporting, and specialized versions like JMP Pro for advanced predictive modeling.
JMP 14 focused on "explainable AI" before it was a buzzword. It added modern machine learning with transparency.
Version 14 Pro Features:
Version 6 is often forgotten because it was a short-lived, incremental update focused on bug fixes and performance. However, it introduced JMP Genomics, a separate but integrated module for analyzing microarray and DNA sequencing data, signaling SAS’s interest in the bioinformatics market.