TrustRadius Insights for JMP are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Business Problems Solved
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.
My organization uses JMP daily for process development, DOE, yield analysis, and SPC. It is a powerful tool and the customer support is excellent. We use JSL for automating recurring analyses.
Pros
DOE creation: quickly identifying variables and trials
DOE analysis: JMP provides clear analysis of responses
Yield analysis and SPC: JMP provides insights on trends and drift before parts fail.
Cons
There is a steep learning curve for getting started, but JMP provides great customer service to get started.
JSL (Jump Scripting Language) is confusing at first, but again there are some great resources and personalized help available.
Likelihood to Recommend
The price is similar to other tools but the customer support and resources available put JMP well ahead of its competition.
I use JMP Statistical Discovery Software from SAS to analyze and trend production data, looking for process deviations. I also use JMP Statistical Discovery Software from SAS to tabulate large datasets to understand how to pareto issues and address problems. I have used the Design of Experiments function to determine primary factors in an experiment. Finally, I used JSL to automate some tasks with large tables that needed to be merged and reorganized (split, perform a function, stack based on new column), etc.
Pros
Handles large data sets
Creates the script for a graph or table, allowing you to replicate the analysis when new data is added
Provides great flexibility using the graph builder tool
Cons
Errors in formulas are rarely diagnosed with much detail
Some preferences are hard to find (are they in the platform settings or in the drop down red arrow context menus)
Likelihood to Recommend
JMP Statistical Discovery Software from SAS deals with data much better than Excel (which is the default). Recoding data is better, merging data is easier, importing multiple files into one merged file is great, and the tabulate function is one of my favorites (much more robust than a pivot table with a cleaner output). I think Excel is a little easier to learn and better for quick analysis of a small data set where you kind of already know the answer and only need the result.
VU
Verified User
Professional in Research & Development (Medical Device company, 5001-10,000 employees)
I use JMP Statistical Discovery Software to create statistics and data plots from large volumes of manufacturing data. The software helps to reveal manufacturing problems and anomalies, leading to cost reduction for our manufacturing.
Pros
Create data plots easily ( histograms, box plots, etc).
Generate statistics for large volumes of data.
Import of data into the JMP tool.
Cons
Better tutorials on how to write JSL scripts.
Need an easy way to generate a large number of statistics and plots from different variables.
Need more detailed documentation on how specific measurement systems analysis is calculated.
Likelihood to Recommend
JMP Statistical Discovery Software has an easy-to-use GUI to create data plots and statistics. Generating measurement system analysis (e.g. Gauge R&R) is also pretty straightforward. Learning the JSL scripting is a steep learning curve and can be difficult for some users to learn.
VU
Verified User
Engineer in Engineering (Electrical & Electronic Manufacturing company, 10,001+ employees)
JMP Statistical Software is used at our company by specific individuals when called upon by various departments. The software has been used for product evaluation and discovery including Design of Experiments as well as for human resources data such as turnover and salary reviews. By using JMP Software we can quickly see what the data is telling us about a given situation.
Pros
JMP Software allows for quick data visualization
JMP Software is easy to use and can be learned very quickly
JMP Software can be used for detailed statistical analysis using multiple formats and calculations.
Cons
JMP Software is continuously adding new features.
I do not have any current recommendations for changes to the software.
Likelihood to Recommend
JMP Software is well-suited for data analysis in all forms. It can be used for quickly viewing data using the chart builder or for more in-depth analysis using statistical evaluations. The only time I do not transfer my data to a JMP data table is when I have just a few data points and I can make a chart in excel just as quickly.
VU
Verified User
Professional in Human Resources (Plastics company, 501-1000 employees)
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.
Pros
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.
Cons
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.
Likelihood to Recommend
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.
I worked in student-led marketing consulting firm and I led the Analytics and Insights team, and we used JMP for data analysis. There were around 50 people in the organization, but only the ones in the Analytics and Insights department used JMP. We received requests from the account managers and we then ran some analysis for them depending on the project need.
Pros
1 - Coding is not required: I've used other tools (Python, Mathlab, and R) and coding is required for all of them. With JMP, you just load the data, see it in a table and start working right away. I see it as a statistical version of MS Excel.
2 - Powerful and easy regression: I love how easy, intuitive and powerful JMP is for running regression models. It was great for trying to fit the best regression models.
3 - Smooth OS integration: I use in both macOS and Windows and both run just fine!
Cons
1 - Not the most user friendly: In comparison to other tools (Azure ML, for example), JMP is not the most user friendly.
2 - Features are not super comprehensive: Don't get me wrong, JMP has a lot of features! But when you compare against R, which is open source - so there are a lot of people adding new libraries frequently, JMP might lack some things you might want (especially the most recent ones).
3 - Cost: In comparison to others (Azure ML is super cheap, R and Python are free), JMP can seem expensive.
Likelihood to Recommend
Well suited: - If you are using a lot of data tables, and would like the best tool to run regression;
Less appropriate: - If you want to run some of the newest machine learning models; - If you are on a budget and still want to get the best of your datasets.
JMP is being used to track a wide array of real-time production data at our biotech company. It is currently used by one department (technical operations) in order to quantify failures and track efficiencies with regard to our production processes. These include sales rates/times, creating survival curves, organizing multivariable processes, addressing multicollinearity, technician efficiencies and other aspects of our production. It mainly serves as a conduit to create quick snapshots of overall production efficiency that have replaced cumbersome Excel spreadsheets, as JMP can process basic graphs easier and faster than Excel.
Pros
JMP has "drag and drop" graph building functions that can filter variables and make adjustments instantaneously.
JMP has its own scripting language that can link to an Excel spreadsheet and instantaneously create report-outs
There is a low overall learning curve, especially if you are familiar with other statistical programs or more advanced Excel functions
Huge support network and prompt response time from SAS. Also, many third party JMP experts that can help you create report outs or write more complicated scripts.
Cons
It would be nice if JMP had multiple sheets in order to create graphs from pivot tables.
The overall aesthetic is pretty weak, compared to Excel, especially if you are trying to make a more polished presentation
Changing variables in the graph builder can cause you to lose your formatting, which can be annoying and time-consuming if you make a mistake
Likelihood to Recommend
JMP is great for crunching huge datasets, especially if you are overloading your Excel workbook. You can have JMP communicate directly with Excel or FileMaker because it has its own scripting language, so you can basically have report at the click of a button. If you are into formatting and pretty graphs, JMP does not include a ton of aesthetic functionality. The drag and drop graph building function allows you to filter out variables easily and change the look of your graph, but can be confusing at times even when you are trying to create simple graphs. Overall it is a great tool to crunch a lot of data without lag.
VU
Verified User
Analyst in Research & Development (Biotechnology company, 11-50 employees)
I use JMP as a starting point for data analysis and exploration. It is not being used across the organization, rather on a case-by-case basis.
Pros
Save scripts directly to the data table so that the user can recreate the steps to create reports, charts, when the underlying data changes.
Multiple options for graphing and plotting and flexible configuration options.
Detailed instructions and explanations in the help document.
Cons
Simple quick filters at the top of each column of the data table.
Update tutorial materials to match the layout of the newest version (13.1), or provide a quick reference guide showing what changed between the previous version and the current one.
Window arrangement could be improved and automated. When multiple windows are open (tables, charts, reports, journals) it could get confusing to get to the right place quickly.
Likelihood to Recommend
Well suited for preliminary data analysis, identifying trends and distribution of the data, finding and excluding outliers as appropriate. Since JMP is well equipped for exploratory analysis, it is not the best choice when the level of interaction with the data must be limited.
JMP is used as main statistical software to support Quality by Design implementation within generic R&D (DOE, specifications assessment, stability trending) and as main statistical tool to support Operations with product robustness initiative (SPC, Process Capability etc). It helps to optimize processes and formulations, find robust operating conditions and assess product robustness, explore interactively 'what if' scenarios..
Pros
Design of Experiments: the Custom Design platform is a great asset for R&D; it fits the design to practical problem and not the opposite.
Stability and Degradation: Platform was a great tool to assess products shelf-life and specifications within few clicks. The platform is customized to suit pharma industry based on ICH guidelines.
Monte-Carlo simulation is a great interactive platform to assess product robustness based on developed model and explore different scenarios to find the sweet spot and global optimum that is also cost effective.
Cons
Easier manipulation between multiple tables
Once in a while the software would shut-down without letting saving the file
More pharma customised features
Likelihood to Recommend
JMP Licenses procedure could be streamlined.
VU
Verified User
Director in Research & Development (Pharmaceuticals company, 10,001+ employees)
JMP is used by many departments within the organization. At the application engineer level, it is mainly used for efficient design of experiments and experimental data analysis and visualization. It is also used at all levels of engineering and R&D as a data visualization, statistical analysis, MVA analysis and model fitting tool.
Pros
JMP's fitting of complex multivariable models by use of effect screening and effect leverage techniques can often allow complex convolved responses to be understood
JMP's design of experiments (DOE) applications allows efficient experimental setup and analysis
JMP's ease of use and suite of visualization capabilities
Cons
While JMP provides scripting for automation, I have found the scripting language to be non-obvious at times and the documentation historically for scripting to be inadequate. For these situations, I often turn to Matlab instead.
Since all levels of engineers use it at some level I wish the program would, at times, better protect the user from themselves especially when it comes to determining statisical differences. While program gives all revelant metrics to user so that an educated user can know the qulity of their analysis, the attempt of program to simplify all those metrics into simple visualization can sometime lead the uneducate user into inaccurate conclusions.
With fitting model to complex data, you will often go through many variants of model effect assumptions to attempt to fit data. It would be beneficial if there was better way to coalesce these model fit attempts into a simple summary to more quickly drive to the optimum model.
Likelihood to Recommend
JMP is a powerful data visualization tool. It likewise is a powerful DOE tool. For these applications, I think it is appropriate for all. As you dive deeper into JMP capabilities, I think it becomes more appropriate for user to have at least some formal training in statistics.
