- Web APIs
- Cloud Sources
- Open Source Languages
- 3rd Party Files
- Data Access
- Data Blending and Cleanup
- Data Exploration and Visualization
- Design of Experiments
- Basic Data Analysis and Modeling
- Advanced Statistical Modeling
- Predictive Modeling and Machine Learning
- Quality and Process Engineering
- Reliability Analysis
- Consumer and Market Research
- Automation and Scripting
- Mass Customization
- Content Organization
- Sharing and Communicating Results
- Business Docs
JMP®: Your singular platform for data access, analysis and sharing
Save time and effort
Easily access and import data from a variety of sources and quickly analyze and share your discoveries in one platform.
Accelerate process improvement
Boost reproducibility and get to market faster while reducing variation by automating repeat analyses – no coding required.
Remove barriers and complexity
Tackle problems of any size with our extensive suite of analytic platforms.
Get more from your investment
Increase efficiency without increasing head count and eliminate the need to have multiple tools that solve the same problems.
- Quick and easy data access no matter the data source: Excel, flat files, relational databases, APIs, R, Python, SAS and more.
- Self-service data extracts with just a few clicks -- no need for coding, your IT department, or your DBA.
- Preformatted and optimized data is imported into JMP for visualization, exploration and analysis -- saving you valuable time and effort.
Your data comes in many forms. Fortunately for you, JMP is hungry for data and has the data connectors you need to be productive, fast.
Easily read from Microsoft Excel using the Excel Import Wizard out of files such as .xml, .json, .pdf, .csv and more and pull data directly from ODBC-compliant databases using the interactive Query Builder, from data on the web or in the cloud through APIs. Whatever the format, JMP is ready.
Databases allow organizations to catalog massive amounts of data and information, but databases are usually organized for efficient storage and transactions, not for efficient analysis. This means that the data you need for analysis is scattered across multiple tables. You have to join those tables to assemble the data you need. This can make for significant work and require you to learn not only the details of the data tables but also how to use SQL or other tools to join them.
Enter Query Builder. Using this JMP platform, you specify only the primary table and one or more secondary tables, and Query Builder automatically matches foreign keys in the primary table to primary keys in the secondary tables. So joining is no longer a laborious process – it becomes automatic.
Combined with automatic matching, Query Builder has everything else you need to build your simple or complex query. Now, you can do it all with JMP.
JMP also lets you import and sample data from other sources, including:
- Many types of flat sources (e.g., text files, R code, MATLAB code, HTML files, SPSS files, Minitab portable worksheet files).
- Web pages (HTML tables).
- Data accessible through web APIs.
- Cloud data sources, .xml, .json., .pdf and more.
- Free text documents, emails, maintenance logs and survey results.
In all cases, importing data into JMP is an interactive task, and the software reacts to changes you make during the process. So a file imported into an in-memory JMP table is preformatted and is as close as possible to analysis-ready, saving you time and effort.
Imagine having confidence that your statistical analysis tool can handle your data, no matter what its original form. That’s what JMP offers. JMP tables are always fast, optimized to perform analytics, can cross physical boundaries of your production systems without changing them and are ready for you to explore, all while preserving as much of the structure and metadata of the original source as possible.
Data Blending and Cleanup
- Reduce cycle time spent preparing data for analysis to let you focus on innovation, experimentation and discovery.
- Remove complexity, particularly from data often thought to be unusable (such as text, images and functions), with visual and interactive tools.
- Transform and blend data to fit your problem, not the other way around. No need to be burdened by the way "data is" currently.
Data management, data cleanup, data wrangling, data curation. It doesn't matter what you call it, but you know you have to do a lot of it. How much time do you spend preparing your data for analysis? For many data analysts, this is a constant chore. JMP has long appreciated this fact and worked to make data preparation easier, faster and more reliable.
No matter what your data cleaning tasks, JMP automates the process. What is difficult or even impossible in other software is easy in JMP. And even if you can't clean your data directly, JMP includes methods to minimize the impact of data problems on your analysis, often eliminating the need (and effort required) to make your data pristine.
Once your data resides in a JMP table, you can take advantage of rich reshaping and restructuring tools, merge all of your data into a single file and easily perform intelligent and interactive table joins. You can also reference data in another table without joining the two tables, thus avoiding the memory and data storage issues that accompany joining large data sets.
Before you analyze your data, you should check to make sure it is clean, with values that are consistent and encoded well. JMP offers many ways to do this. One of the best methods is with the Distribution platform. If you spot outliers, simply click them and they are selected in the table, thanks to dynamic linking in JMP.
Having a visual interface to your data is a powerful advantage of JMP. You will soon wonder how much you were missing before you were able to immediately grab what you see in your data.
When many different individuals enter categories, naming can become inconsistent. To accurately tabulate categories and use them for prediction, they must match up consistently. JMP has a Recode utility to make consolidating categories easy and efficient. You can select a set of categories and choose which of them to make representative of the group. You can also tell JMP to automatically consolidate categories that look very similar to each other. This can be an incredible time-saver, especially when there are hundreds or thousands of unique entries.
Other tools for data cleanup in JMP include:
- Examining outliers.
- Screening for entry errors, error codes or missing values/missing value codes that may not have been accounted for in your data.
- Creating formula columns or derived variables (feature creation), ratio columns or response transformations.
- Cleaning up data properties.
- Binning continuous data into discrete categories.
- Splitting strings of delimited text into multiple columns.
- Making indicator variables.
- Standardizing attributes across many similar-type columns.
- Comparing similar tables to look for differences.
- Cleaning unstructured data like free text or functions.
- Exploring and finding interesting patterns in data and triaging them appropriately.
Data Exploration and Visualization
- Plot your data to spot patterns and patterns that don't fit the trend.
- Uncover hidden relationships quickly with the interactive Graph Builder.
- Use the deep and wide toolbox for building production-ready graphs to better present your findings.
- Work with your data using graphs that represent an interactive interface to linked data.
Spreadsheets don't easily reveal patterns and trends in data sets, yet seeing patterns helps you make discoveries. JMP provides rich and dynamic visualization tools, making statistical discovery easier and more effective and leading to innovation.
JMP frees you from the narrow path. Explore your data dynamically and allow it to tell you what is interesting. Move through your data quickly and with agility until you find the visualization that best communicates the story in your data. Or group, filter and subset in real time to focus on just the specific part of your data set required.
Graph Builder is the best way to begin exploring and graphing your data dynamically. Interactively build simple or complex graphical displays just by dragging and dropping. Simply drag the variables into position, choose the graph element from an icon palette and customize the display to get your final, publication-quality result. Graph Builder always gives you options that make sense for your situation.
Graph Builder is only one of the many rich and dynamic visualization tools in JMP that make statistical discovery easier and more effective.
You can also add background maps to all relevant JMP graphs using high-quality, built-in geographic images, or plot data on street-level maps that include cities, roads and bodies of water. With Bubble Plot, you can create animated data movies, showing changes in many dimensions over time.
Sometimes multiple graphs combined can be more compelling than a single graph. That’s where dashboards give you the ability to communicate the story of your data. With single-click templates, you can develop persuasive, presentation-ready visualizations. Or create your own customized dashboards and easily publish them to JMP Live to make them available to those who need to make decisions based on your discoveries.
Graphs in JMP represent an interface to your data, not just a representation of it.
Design of Experiments
- Understand cause and effect using the power of statistically designed experiments -- even when you have limited resources.
- Design efficient experiments to meet your real-world constraints, process limitations and budget with the Custom Designer.
- Use a Definitive Screening Design to help you untangle important effects when you have many factors to consider.
Many organizations rely on OFAT (One Factor at a Time) or A-B testing for experimental design, but testing one situation against another with many factors in flux is a very slow way to learn about your business.
In contrast, design of experiments (DOE) in JMP offers a proven and practical approach for exploring and exploiting the multifactor opportunities that exist in almost all real-world situations. Using multifactor experiments, you learn more quickly, at minimal cost, by teasing out not only the effect of an individual factor but also the combined impact of two or more factors. JMP offers leading-edge capabilities for design of experiments so you can design the best experiments to answer specific questions. JMP also offers a rich set of analyses tailored to your design in a form you can easily use.
Instead of fitting your problem to a textbook design, you fit the design to your problem with the budget you have. The unique Custom Designer constructs an optimal design to fit your problem, taking into account specific conditions such as time, budget and other experimental constraints.
Many analysis problems include hard-to-change variables, such as the temperature of a reaction vessel or the location of a cornfield. A completely randomized design would require such factors to be reset after each experimental run, which is clearly impractical or cost-prohibitive. The design most appropriate in such situations is a split plot, and JMP can generate I-optimal split-plot, split-split-plot and strip-plot designs. JMP also includes the correct random-effect restricted maximum likelihood (REML) model in the table that contains the experimental worksheet to make the analysis rigorous but also straightforward. No other commercial software package offers this level of flexibility with split-plot designs.
Other features include:
- Classical (textbook) full factorial, screening (fractional factorial), block and response surface designs.
- Nonlinear designs.
- Mixture designs.
- Accelerated life tests.
- Designs for computer simulation, such as cluster-based, space-filling designs.
- Covering arrays for software testing.
- Design evaluation and comparison.
Also, JMP is the first software package to implement definitive screening designs. The most important new class of designs in the past 20 years, definitive screening designs are used to efficiently and reliably separate the vital few factors that have a substantial effect from the trivial many that have negligible impact. Definitive screening designs allow you to get information about main effects, curvature effects and two-way interactions at the same cost as traditional, two-level screening designs.
Who would ever do it the old way?
Basic Data Analysis and Modeling
- Easily analyze one, two or many variables at a time in linked analyses that let you unfold the hidden story in your data.
- Explore and interpret analysis results with specific graphs designed for every statistic.
- Let JMP pick the right test and model for you, based on your data and selected variables.
- Perform just-in-time statistical analysis, in flow, to solve your problem within a single analysis window.
Employing basic tools for visual analysis is often the best way to communicate results and motivate action in an organization. And frequently, your first step in a statistical data inquiry consists of investigating variables one at a time, a process known as univariate analysis. In JMP, once you've identified the columns that interest you, the Distribution platform automatically provides graphs and statistics based on the variable's defined modeling type.
Quickly get histograms, summary statistics, box plots and quantiles for continuous data, capability analysis, distribution fitting and frequencies for nominal or ordinal values.
For bivariate analyses involving two variables, the Fit Y by X platform helps you model the relationship between a single input and a single response or outcome. This platform supports simple contingency analyses, linear regression, logistic regression, t-tests, and ANOVA and ANOM. It responds to the modeling types of the variables used, and so unifies many commonly used methods into an easy-to-use framework.
But if you need to fit more complex models with multiple inputs or multiple responses, the Fit Model offers many powerful modeling options.
Key capabilities in JMP for basic statistical analysis include:
- Histograms, box plots, and other graphical summaries.
- Descriptive statistics.
- Statistical intervals: confidence, prediction and tolerance.
- One- and two-sample t-tests, ANOVA, regression, nonparametric test.
- Distribution fitting.
- Fitting splines and curves to data.
- Statistical calculators and simulators; power and sample size calculation.
* JMP Pro only.
Advanced Statistical Modeling
- Easily deal with the diversity of modeling tasks: univariate, multivariate and multifactor.
- Use your data in the modern forms collected – text, functional and more – and transform it to data for building more useful models for better insights.
By making a separation of data into signal and noise, statistical models encapsulate trends and patterns so you can learn more about your products, your processes and your customers. This knowledge enables better decisions for the best course of action to move your business forward.
Building useful models is part science and part experience, and JMP includes an array of linear and nonlinear statistical modeling platforms to not only help you make predictions but also identify settings for factors that yield the best performance.
At the heart of the JMP model-fitting toolkit is the Fit Model platform. Fit Model lets you construct model terms and select from different methods, including standard least squares fitting, stepwise and all possible models. Build models with drag-and-drop ease using interactive model editing. Or build other models, such as logistic regression (nominal and ordinal), MANOVA, mixed models, proportional hazards, loglinear variance, generalized linear, penalized and models with different response distributions.
In addition to linear modeling, JMP includes an array of advanced modeling platforms, including:
- Fit Curve and Nonlinear.
- Text Explorer.
- *Functional Data Explorer.
- Gaussian Process.
- Time Series and Time Series Forecast.
- Structural Equation Modeling.
JMP also includes screening tools for “wide” problems, with hundreds or thousands of variables, along with a full suite of multivariate modeling techniques: principal components, multiple correspondence analysis, partial least squares, cluster, factor analysis, multidimensional scaling and more.
JMP lets you deal with the diversity of modeling tasks, from univariate to multivariate and multifactor, and allows you to extract information from data in whatever form it is collected – from unstructured text to functional and more – so you can better transform data into insights and knowledge.
Predictive Modeling and Machine Learning
- Build better and more useful models with modern predictive modeling techniques, such as regression, neural networks and decision trees.
- Automatically fit multiple predictive models and determine the best performing model with model screening.
- Avoid overfitting using cross-validation and K-fold cross-validation.
- Use machine learning methods without having to write code and tune algorithms.
Modern organizations produce vast amounts of data on everything from products and processes to customers and the competitive landscape. JMP lets you get the most out of this data with advanced analytics and modeling tools that drive deeper insights and knowledge.
With JMP you can take advantage of predictive modeling and machine learning capabilities with software built for scientists and engineers. You can easily use cutting-edge modeling techniques with built-in validation, including:
- Multiple and logistic regression.
- Generalized regression (generalized linear models and penalized regression).
- Neural networks.
- Classification and regression trees.
- Bootstrapped and boosted trees.
- K-nearest neighbors.
- Naive Bayes.
- Support vector machines.
And now, with Model Screening, you can easily access leading-edge predictive modeling techniques and validation methods – all from one platform.
With JMP as your predictive modeling engine, you add power, save time and make better decisions, all without having to write code (unless you want to). And you have access to modern analytics techniques without needing a PhD in statistics.
Quality and Process Engineering
- Monitor your processes using Shewhart charts in a whole new way with an interactive Control Chart Builder that lets you drag and drop to explore potential root causes of variation.
- Receive JMP Live notifications for out-of-control signals from control chart warnings.
- Study the capability and performance of many variables at the same time to easily identify processes that aren't meeting expectations; drill into problem processes to identify potential root causes.
The market demands continual improvement, which is why you strive to accelerate time to market, protect your brand by minimizing customer complaints and deliver products and services that consistently meet or exceed customer expectations. JMP has the necessary tools to be at the heart of your quality program, providing a wide range of relevant graphical and statistical capabilities.
You can monitor processes with the full set of control chart types in JMP or build control charts interactively with drag-and-drop tools in the unique Control Chart Builder. With a workflow analogous to Graph Builder, Control Chart Builder lets you perform what-if analyses with your process data and explore many subgroup and phase variables and their effects on your processes. You see problems in ways that are impossible using static control charts, making it easier to identify out-of-control conditions and perform root cause analysis. And, with control chart warnings in JMP Live, you can tell JMP to notify you when there is a problem.
A stable process might not meet customer expectations. Use the suite of capability tools in JMP to analyze your ability to achieve targets and specifications for many processes at once. Compute capability and performance indices, and use the Goal Plot, Capability Box Plots, Capability Index Plot and Process Performance Plots to visualize capability indices for the variables that you fit with normal or non-normal distributions. You can even explore the impact of process changes on your capability using the Interactive Capability Plot.
And when you have many dynamic processes, you need efficient tools for monitoring all of them. Process Screening lets you monitor the capability, performance and stability of all of your processes at the same time, and with Model Driven Multivariate Control Charts, you can monitor multiple processes on one interactive chart and easily drill down to study out of control processes.
For any process, it's important to verify the quality and integrity of the measurement system. The measurement systems analysis platform supports multiple analysis methods, including Donald J. Wheeler's Evaluating the Measurement Process (EMP), Gauge R&R studies, and attribute gauge studies. You can easily quantify, visualize and explore sources of variation to ensure that your measurement system is capable of detecting out-of-spec parts.
Other features include:
- Specialized control charts, including Levey Jennings, CUSUM and Three-Way charts.
- Pareto plots.
- Cause and effect diagrams.
- A utility to manage specification limits.
- Interactive OC curves.
- In any manufacturing endeavor, product reliability strongly influences your business success. Use JMP’s reliability analysis tools to prevent failure and improve warranty performance.
- Find important design vulnerabilities and pinpoint defects in materials or processes, and then determine how to reduce them with tools that identify trends and outliers in your data and model predictions.
Preventing failure and improving warranty performance are two of the most important reasons for using proven techniques to fully understand the performance of your products over time. JMP helps you pinpoint defects in materials or processes; it also helps identify design vulnerabilities so you can understand how best to correct them.
Do you need to determine the most appropriate distribution to use for making reliability lifetime predictions on your products and components? Let JMP automatically evaluate a large range of reliability distributions to find the best fit. Using Life Distribution analysis in JMP, you can specify a nonparametric distribution, as well as numerous parametric distributions, and compare fits visually.
JMP includes a rich set of capabilities for reliability analysis:
- Fitting life distributions.
- Fitting life distributions with one factor (e.g., accelerated failure models).
- Performing recurrence analysis of repairable systems.
- Modeling product degradation; destructive degradation.
- Estimating survival, parametric survival and proportional hazards models.
- Designing accelerated life test (ALT) experiments.
- Performing Crow-AMSAA analysis for reliability growth.
- Forecast warranty returns from failure data.
- Concurrent system analysis to study similar systems.
- Parallel system analysis to analyze and compare reliability growth for multiple independent systems, by system and/or phase.
- Building reliability block diagrams or repairable system simulations for when system reliability estimates are required.*
* JMP Pro only.
Consumer and Market Research
- JMP provides a comprehensive set of tools for marketing and consumer research analysis. Use these findings to release better products, react to market trends, increase profitability and build your competitive edge by understanding the voice of the customer.
- Bridge the gap between your customers, R&D teams, management and sales through objective data rather than opinions or anecdotal evidence.
No matter what role you play in the process, marketing is complex and rapidly evolving, driven by the influx of digital technologies. Yet key business issues endure: the need to find the most profitable growth opportunities, develop the best products and services, take the best marketing action and maximize cross-business impact. To stay competitive, you need to socialize your brand, listen to customer feedback and then adapt your products and services. Whether you are conducting exploratory, descriptive or causal research using primary or secondary sources, JMP provides a comprehensive repertoire of tools for quickly and easily getting value from metric and nonmetric quantitative and qualitative data.
Capabilities for performing consumer research in JMP include:
- Modern data mining techniques to build predictive models of transactional data.
- Categorical (survey) data analysis.
- Text exploration to add structure to unstructured data, derive sentiment or build topic models to enrich your predictive models with data from surveys, repair logs and more.
- Ability to import data in many external file formats, including SAS, SPSS and Triple-S.
- Single-click analysis of simple, related, multiple and structured response survey questions.
- Choice and MaxDiff experiments to optimize the design of your goods based on consumer feedback.
- Factor analysis.
- Segmentation and clustering (k-means and hierarchical).
- Multiple correspondence analysis.
- Uplift models.*
- Structural equation models.*
No matter what your level of statistical expertise, JMP helps you find new consumer insights more quickly and allows you to communicate findings to other stakeholders to drive consensus and action.
*JMP Pro only.
Automation and Scripting
- Make use of the flexibility of JMP with both a point-and-click, approachable workflow and visual interface coupled with a deep and rich scripting interface back end.
- Embrace automation at all levels of computer science skills. With code-free access to automation routines all the way up to completely customized applications, JMP can meet you where you are so you use automation to achieve your goals.
- For advanced users, empower your entire organization with applications, customized workflows and domain-specific tools, which optionally can give users code-free access to SAS, MATLAB, Python and R routines.
Buying software that cannot grow beyond your initial needs leads to early obsolescence and expensive replacements. JMP includes many basic and advanced ways to mass-customize or even extend the software to address the unique challenges that arise as usage grows and your organization evolves.
The rich JMP Scripting Language (JSL) lets you work interactively and save results for reuse. Power users can develop new functionality to solve problems that core JMP does not address. These custom scripts can even integrate capabilities from other applications, including SAS, MATLAB, Python and R. JMP can be your analytics hub and reach out to these other tools as required. With access to a JMP Live instance, you can use JSL to automate the routine publishing of up-to-date reports and dashboards in near-real time so that your entire team is making decisions on the right version of the data and with a single version of the truth.
With the automation and scripting tools in JMP you can also:
- Save scripts to regenerate analysis reports without having to write any code.
- Use saved scripts in a data table for reproducibility; save the analysis steps for yourself or to explain your workflow to others.
- Develop completely new custom applications in JMP. JMP Scripting Language and scripting tools in JMP make it possible to attain things that we haven’t even dreamed of.
- Make JMP output the standard for the unique needs of your team, your division and your organization.
- Customize everything – graphics, statistics, default views and more – to make JMP conform to the way you and your organization work, rather than the other way around.
It's easier to work productively if you can configure your software to work the way you think. Consistent settings, graphical output and even color palettes mean fewer steps to understand your data. JMP gives you a comprehensive set of preferences that enable you to control fonts, select graph options and customize detailed settings within platforms. It's analysis the way you like it. And you can choose to display only those analytic tools and menus that you use routinely. In fact, you can customize every aspect of JMP, including:
- Graph axis settings, styles, graphs and colors.
- Statistical and graphical elements presented in a JMP report.
- Import settings that can be predefined to rapidly take in new data in a form you can use immediately.
- Your scripting and application development environment.
- Translate statistical results to a simplified view of your project to communicate findings to decision makers in an approachable way.
- Empower decision makers to see into the black box, rather than simply trusting it.
- Organize, summarize and document content to better aid the accountability and reproducibility of your work.
You need to work with global teams with varying levels of expertise in your domain, in statistics and statistical understanding. While the free, online course Statistical Thinking for Industry Problem Solving (STIPS) can help expand the statistical proficiency of your teams, there still is the required step to curate, simplify and translate your experimental or model output into something accessible that decision makers can easily consume, understand and act upon.
This necessary step of content organization requires eliminating unnecessary complexity, summarizing the data as required, sharing only the minimally viable number of reports to present the change solution and documenting the results of your findings through scripts saved to the data table, annotations or editing of significant digits. Once your content is organized, it's easy to package up as a consumable report or dashboard and ready to be published to JMP Live.
And if you find yourself needing to make this same report day after day, you can develop an automation script to ensure that your content is organized in the same way as new data is available.