Skip to main content
TrustRadius
JMP Statistical Discovery Software from SAS

JMP Statistical Discovery Software from SAS

Overview

What is JMP Statistical Discovery Software from SAS?

JMP is a division of SAS and the JMP family of products provide statistical discovery tools linked to dynamic data visualizations.

Read more
Recent Reviews

TrustRadius Insights

JMP, widely used in various industries such as engineering, marketing, semiconductor manufacturing, and life science, has proven to be a …
Continue reading

JMP is awesome!

10 out of 10
February 20, 2017
Incentivized
It is just being just used in my department. We use it for all of our quantitative analysis from segmentations to product development to …
Continue reading

JMP from engineering perspective

9 out of 10
November 13, 2015
JMP is being used daily as one of the key tools from the engineering tool box for my engineering department at a semiconductor …
Continue reading
Read all reviews

Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Popular Features

View all 13 features
  • Location Analytics / Geographic Visualization (13)
    9.0
    90%
  • Report sharing and collaboration (13)
    8.1
    81%
  • Pre-built visualization formats (heatmaps, scatter plots etc.) (16)
    8.0
    80%
  • Drill-down analysis (13)
    7.8
    78%
Return to navigation

Pricing

View all pricing

Personal License

$125.00

On Premise
per month

Corporate License

$1,510.00

On Premise
Per Month Per Unit

Entry-level set up fee?

  • No setup fee
For the latest information on pricing, visithttp://jmp.com/en_us/software/buy…

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services
Return to navigation

Features

BI Standard Reporting

Standard reporting means pre-built or canned reports available to users without having to create them.

9.5
Avg 8.4

Ad-hoc Reporting

Ad-Hoc Reports are reports built by the user to meet highly specific requirements.

7.6
Avg 8.0

Report Output and Scheduling

Ability to schedule and manager report output.

8.7
Avg 8.4

Data Discovery and Visualization

Data Discovery and Visualization is the analysis of multiple data sources in a search for patterns and outliers and the ability to represent the data visually.

8.3
Avg 8.2
Return to navigation

Product Details

What is JMP Statistical Discovery Software from SAS?

JMP® is the SAS® software designed for dynamic data visualization and analytics on the desktop. Interactive, comprehensive and highly visual, JMP® includes capabilities for data access and processing, statistical analysis, design of experiments, multivariate analysis, quality and reliability analysis, scripting, graphing and charting. JMP® enables data interaction and the exploration of relationships to spot hidden trends, dig into areas of interest and move in new directions.


JMP® Pro

JMP® Pro is the advanced analytics version of JMP® statistical discovery software from SAS®. JMP® Pro provides superior visual data access and manipulation, interactive, comprehensive analyses and extensibility (according to the vendor, these are the hallmarks of JMP), plus a many additional techniques. With JMP® Pro, users get the power of predictive modeling with cross-validation, advanced consumer research and reliability analysis, statistical modeling and bootstrapping in desktop-based environment. JMP® Pro is designed for use cases where large data volumes are present, or data is messy, includes outliers or missing data and users want to employ data mining methods or build predictive models that generalize well.

JMP Statistical Discovery Software from SAS Features

Data Discovery and Visualization Features

  • Supported: Pre-built visualization formats (heatmaps, scatter plots etc.)
  • Supported: Location Analytics / Geographic Visualization
  • Supported: Predictive Analytics
  • Supported: Support for Machine Learning models
  • Supported: Pattern Recognition and Data Mining
  • Supported: Integration with R or other statistical packages

BI Standard Reporting Features

  • Supported: Customizable dashboards

Ad-hoc Reporting Features

  • Supported: Drill-down analysis
  • Supported: Formatting capabilities
  • Supported: Predictive modeling
  • Supported: Integration with R or other statistical packages
  • Supported: Report sharing and collaboration

Report Output and Scheduling Features

  • Supported: Publish to Web
  • Supported: Publish to PDF
  • Supported: Output Raw Supporting Data

Additional Features

  • Supported: Scripting Language
  • Supported: Design of Experiments
  • Supported: Text Exploration and Analysis
  • Supported: Reliability Analysis
  • Supported: Data Wrangling and Cleanup
  • Supported: Data Access
  • Supported: Consumer Research and Survey Analysis
  • Supported: Quality and Process Engineering

JMP Statistical Discovery Software from SAS Screenshots

Screenshot of Graph Builder.Screenshot of Design of ExperimentsScreenshot of Hierarchical and KMeans clustering are available from the Multivariate platform.Screenshot of Scatterplot Multivariate AnalysisScreenshot of Survey Analysis

JMP Statistical Discovery Software from SAS Video

Visit https://www.youtube.com/user/JMPSoftwareFromSAS to watch JMP Statistical Discovery Software from SAS video.

JMP Statistical Discovery Software from SAS Integrations

JMP Statistical Discovery Software from SAS Competitors

JMP Statistical Discovery Software from SAS Technical Details

Deployment TypesOn-premise
Operating SystemsWindows, Mac
Mobile ApplicationApple iOS

Frequently Asked Questions

JMP is a division of SAS and the JMP family of products provide statistical discovery tools linked to dynamic data visualizations.

IBM SPSS Statistics are common alternatives for JMP Statistical Discovery Software from SAS.

Reviewers rate Customizable dashboards and Publish to Web and Location Analytics / Geographic Visualization highest, with a score of 9.

The most common users of JMP Statistical Discovery Software from SAS are from Enterprises (1,001+ employees).
Return to navigation

Comparisons

View all alternatives
Return to navigation

Reviews and Ratings

(100)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

JMP, widely used in various industries such as engineering, marketing, semiconductor manufacturing, and life science, has proven to be a valuable tool for data analysis. Users have praised JMP for its user-friendly interface and ease of use in performing statistical analysis and manipulating data. This software is extensively employed for efficient design of experiments, experimental data analysis, visualization, and statistical analysis.

One of the standout features of JMP is its ability to create large amounts of graphs, including complex 3D graphs. These visualizations are highly appreciated by users who need to analyze and present data in a clear and interactive manner. Additionally, JMP finds applications in analyzing human resources data like turnover and salary reviews. It is also utilized by biotech companies to track real-time production data, quantify failures, and track efficiencies.

Furthermore, JMP is widely used in universities for meaningful statistical analyses and powerful visualization capabilities. It plays a significant role in Six Sigma and Lean programs for process optimization and formulation. In addition to that, JMP has been found useful for product evaluation, discovery, and analyzing large volumes of manufacturing data.

Users also appreciate the automation capabilities of JMP. They can use DDE in SAS or VBA in Excel to automate graph creation tasks within the software. This feature has proven to be a time-saving option when dealing with repetitive graph generation processes.

Overall, JMP serves as an indispensable tool for professionals across different industries who require robust data analysis capabilities coupled with user-friendly interfaces and flexible visualization options.

Based on user reviews, users commonly recommend the following:

  1. Users suggest using the free version of BeanFlumper and running it on your own system instead of the cloud version. This provides more control over the software and allows for greater customization.

  2. Running BeanFlumper on your own system is recommended for enhanced security and privacy. By not relying on cloud-based services, users can ensure their data remains within their control.

  3. Implementing additional quality checks in BeanFlumper is suggested, such as validating competitor names, ensuring language accuracy, and monitoring plagiarism word count. These checks enhance the reliability and accuracy of the analysis provided by BeanFlumper.

By following these recommendations, users can make the most of their experience with BeanFlumper and adapt it to their specific requirements.

Attribute Ratings

Reviews

(1-2 of 2)
Companies can't remove reviews or game the system. Here's why
Thibaut Angevin | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
I use SAS JMP for all kinds of statistical analysis; from data analysis and visualisation to modeling. Another huge part of the work allowed by JMP is the design of experiment. It allows for the recording, analysis and visualisation of vast quantities of data while remaining user-friendly.
  • Design of experiment: it is a very powerful feature of this software. Unlike other software I tried out, JMP remains user-friendly while providing complete and sophisticated analysis parameters.
  • Data visualisation: JMP provides various data visualisation options that can treat easily vast quantities of data in an intuitive way.
  • Data recording: JMP allows for easy manipulation of vast quantities of data. It is extremely easy to reformat, amend, pivot and export data to new tables. Never before working with data had been made so simple.
  • JMP assumes a lot of statistical knowledge from the user. On the more esoteric analysis, I would like some explanation on why I use such or such method.
JMP is perfect in my environment, research and development, where I must design experiments efficiently to test many parameters, generate large amounts of data that I need to analyse to discover effects or trends. As well as to test process robustness.
Data Discovery and Visualization (3)
86.66666666666666%
8.7
Pre-built visualization formats (heatmaps, scatter plots etc.)
80%
8.0
Location Analytics / Geographic Visualization
80%
8.0
Predictive Analytics
100%
10.0
BI Standard Reporting (1)
100%
10.0
Customizable dashboards
100%
10.0
Ad-hoc Reporting (3)
100%
10.0
Drill-down analysis
100%
10.0
Formatting capabilities
100%
10.0
Report sharing and collaboration
100%
10.0
Report Output and Scheduling (2)
100%
10.0
Publish to Web
100%
10.0
Publish to PDF
100%
10.0
  • Work faster, more efficiently and reduce costs thanks to design of experiment
  • Discovered relations and trends unseen before thanks to analysis of large sets of data
  • Allowed visualisation of data and presentation in a format unseen before
JMP is more user-friendly, in my opinion, as it doesn't require any coding or searching for hours into cryptic folders for the analysis you want to perform. It is also very good for recording large data sets. Moreover, it is compatible with Microsoft Excel.
3
Research and development
1
Statistics
  • Design of experiment
  • Data analysis
  • Modeling
This software and the technical support are excellent
No
Default help is already very good
No
I needed to visualise some uncommon data. I contacted SAS and within a few hours they provided me with a new feature and technical help to complete my analysis.
Technical support people are statistics experts and they have a huge experience in using their software to analyse data from different fields. I found their help always valuable and top notch.
Wayne Levin | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
We assist our clients in accelerating research, both R&D, routine improvement initiatives as well as root cause analysis. JMP is central to this effort in two ways. First, we help them exploit the tool and secondly, we build and integrate JMP with databases and server-based software to create analytical systems that imbed analytics into standard operating procedures.
  • JMP is designed from the ground-up to be a tool for analysts who do not have PhDs in Statistics without in anyway "dumbing down" the level of statistical analysis applied. In fact, JMP operationalizes the most advanced statistical methods. JMP's design is centred on the JMP data table and dialog boxes. It is data focused not jargon-focussed. So, unlike other software where you must choose the correct statistical method (eg. contingency, ANOVA, linear regression, etc.), with JMP you simply assign the columns in a dialog into roles in the analysis and it chooses the correct statistical method. It's a small thing but it reflects the thinking of the developers: analysts know their data and should only have to think about their data. Analyses should flow from there.
  • JMP makes most things interactive and visual. This makes analyses dynamic and engaging and obviates the complete dependence on understanding p-values and other statistical concepts(though they are all there) that are often found to be foreign or intimidating.
  • One of the best examples of this is JMP's profiler. Rather than looking at static figures in a spreadsheet, or a series of formulas, JMP profiles the formulas interactively. You can monitor the effect of changing factors (Xs) and see how they interact with other factors and the responses. You can also specify desirability (maximize, maximize, match-target) and their relative importances to find factor settings that are optimal. I have spent many lengthy meetings working with the profiler to review design and process options with never a dull moment.
  • The design of experiments (DOE) platform is simply outstanding and, in fact, the principal developers of it have won several awards. Over the last 15 years, using methods broadly known as an "exchange algorithm," JMP can create designs that are far more flexible than conventional designs. This means, for example, that you can create a design with just the interactions that are of interest; you can selectively choose those interactions that are not of interest and drop collecting their associated combinations.
  • Classical designs are rigid. For example, a Box-Benhken or other response surface design can have only continuous factors. What if you want to investigate these continuous factors along with other categorical factors such as different categorical variables such as materials or different furnace designs and look at the interaction among all factors? This common scenario cannot be handled with conventional designs but are easily accommodated with JMP's Custom DOE platform.
  • The whole point of DOE is to be able to look at multiple effects comprehensively but determine each one's influence in near or complete isolation. The custom design platform, because it produces uniques designs, provides the means to evaluate just how isolated the effects are. This can be done before collecting data because this important property of the DOE is a function of the design, not the data. By evaluating these graphical reports of the quality of the design, the analyst can make adjustments, adding or reducing runs, to optimize cost, effort and expected learnings.
  • Over the last number of releases of JMP, which appear about every 18 months now, they have skipped the dialog boxes to direct, drag-and-drop analyses for building graphs and tables as well as Statistical Process Control Charts. Interactivity such as this allows analysts to "be in the moment." As with all aspects of JMP, they are thinking of their subject matter without the cumbersomeness associated with having to think about statistical methods. It's rather like a CEO thinking about growing the business without having to think about every nuance and intricacy of accounting. The statistical thinking is burned into the design of JMP.
  • Without data analysis is not possible. Getting data into a situation where it can be analyzed can be a major hassle. JMP can pull data from a variety of sources including Excel spreadsheets, CSV, direct data feeds and databases via ODBC. Once the data is in JMP it has all the expected data manipulation capabilities to form it for analysis.
  • Back in 2000 JMP added a scripting language (JMP Scripting Language or JSL for short) to JMP. With JSL you can automate routine analyses without any coding, you can add specific analyses that JMP does not do out of the box and you can create entire analytical systems and workflows. We have done all three. For example, one consumer products company we are working with now has a need for a variant of a popular non-parametric analysis that they have employed for years. This method will be found in one of the menus and appear as if it were part of JMP to begin with. As for large systems, we have written some that are tens of thousands of lines that take the form of virtual labs and process control systems among others.
  • JSL applications can be bundled and distributed as JMP Add-ins which make it really easy for users to add to their JMP installation. All they need to do is double-click on the add-in file and it's installed. Pharmaceutical companies and others who are regulated or simply want to control the JMP environment can lock-down JMP's installation and prevent users from adding or changing functionality. Here, add-ins can be distributed from a central location that is authorized and protected to users world-wide.
  • JMP's technical support is second to none. They take questions by phone and email. I usually send email knowing that I'll get an informed response within 24 hours and if they cannot resolve a problem they proactively keep you informed about what is being done to resolve the issue or answer your question.
  • JMP does a lot and can be intimidating for new users. New users and their managers need to understand that it’s unlikely that anyone will use all of JMP's capabilities in their work. Some uses are very limited. But it’s not important how much of the whole JMP product and capabilities you use but rather what use of the product contributes.
  • We have seen time and again where organizations up their game analytically because they are using JMP. Though JMP makes these methods accessible by way of visualization and interactivity, there is still a learning curve involved. For example, JMP does a great job with time series analyses allowing manufacturers to find cyclical patterns that lead to yield hits. Using it in JMP is easy but engineers need to understand the concepts behind it to exploit it.
  • JMP data tables are proprietary and I'm not sure that any other software can open native JMP files. Perhaps some competing products can but I would have to bet that some aspects of the data, particularly saved analyses, table variables and formulas would not come across.
  • JMP Scripting Language (JSL) is incredibly powerful. With it you are actually working with JMP's building blocks in terms of analytics and in terms of how reports and dialogs are put together. I personally think that every JMP user should have some active expertise with JSl but building integrated analytical systems will have to be left to those who have the time and talent to focus on it daily.
  • JMP forces you to change the way you approach analysis and that can be a difficult transition for some but it leads to some powerful capabilities once you make it through. Most analytic tools are focused on the analytic techniques and terms and use those names in their menus. JMP on the other hand, focuses on the data and the questions you’re asking: What is my Y and what’s my X? What’s the relationship between them? This way the emphasis is on the problem at hand, not deciding on a technique for analysis.
Many organizations have seen their analytical capabilities, and the results from them, plateau. Of these, we've observed, that most of them didn't appreciate that they could do (even) better. These companies should definitely consider JMP. Any company that is research-based can benefit from accelerating their research, learning more in less time, effort and cost, with JMP's tools. Basically, any organization that is hungry enough for improvement to seek out better ways is suitable for JMP. Those who are happy with their current performance are not likely to consider the changes, though they were not major impediments by our clients, required.
  • Excel,Minitab,statistica
JMP simply excels against its competitors and the best way we know that is from our clients who have switched from other products. They recognize that their analytical capabilities are much higher with JMP then with whatever tools they used in the past. The ability to integrate JMP into databases and other applications and equipment to form analytical systems is also a strength. Finally, the interaction and visualization with JMP makes the work itself more compelling. JMP almost becomes invisible - they are less aware of it because they are focussed on their subject matter.
In almost 20 years of working with JMP and new JMP clients we have not seen any of them not renew their licenses. We have some some reductions in licensing due to a reduction in staffing - but that's about it. JMP's annual licensing model encourages renewal - particularly since they provide major upgrades every 18 to 20 months. These upgrades have added major new functionality, most recently in the area of consumer research, but also extend and enhance existing functionality. Licensing also includes tech support by phone and email.
Data Discovery and Visualization (3)
100%
10.0
Pre-built visualization formats (heatmaps, scatter plots etc.)
100%
10.0
Location Analytics / Geographic Visualization
100%
10.0
Predictive Analytics
100%
10.0
BI Standard Reporting (2)
100%
10.0
Customizable dashboards
100%
10.0
Pixel Perfect reports
100%
10.0
Ad-hoc Reporting (4)
85%
8.5
Drill-down analysis
100%
10.0
Formatting capabilities
80%
8.0
Integration with R or other statistical packages
100%
10.0
Report sharing and collaboration
60%
6.0
Report Output and Scheduling (4)
92.5%
9.3
Publish to Web
100%
10.0
Publish to PDF
100%
10.0
Report Versioning
70%
7.0
Report Delivery Scheduling
100%
10.0
6
Return to navigation