Knowing the product themes is critical to understanding why JMP is the way it is today and how it will evolve and improve tomorrow.
From the start, it’s been our mission to develop great software. High-level product themes guide us to do the rights things, rather than to simply do things right.
Our product themes provide a framework to:
- Prioritize development projects and initiatives.
- Craft the JMP Product roadmap for JMP 18 and beyond.
- Help us communicate the value of new capabilities to our customer base – moving past feature and function conversations to value-based conversations about solving business problems, improving engineering efficiency and driving conversations about the impact of JMP, statistics and making decisions with data as a standard practice throughout an organization.
Now that the product themes are a major section of the new JMP Statistical Discovery LLC – Goals 2022 (Section 4: Develop great software) and that we are about ready to kick off the JMP 17 sales training, it’s important for our internal teams to understand how these themes shape the future of JMP.
So, what are the product themes and why do they matter?
Stay true to our roots and design principles.
JMP empowers scientists and engineers to rapidly draw insights and make statistical discoveries from data. One of my favorite presentations is John Sall’s Seven Steps Make a JMP. This presentation describes why JMP is the software of choice for hundreds of thousands of scientists and engineers worldwide.These are our roots. Times change. Technology changes. However, the seven habits endure.
These habits aren’t about software or features. They are a direction and a set of principles by which to discover patterns in data and things that don’t fit the pattern. To focus on practical significance and the “so what?” To always be learning – especially about statistics and sound statistical techniques for understanding data. So, when adding something to the product we need to ask ourselves: Does this enable us to stay true to our roots?
Reduce the time it takes for new users to be productive in JMP.
We are adding new JMP users every day. Accounts are growing and expanding, and we need to quickly enable new JMP users to be productive in JMP. JMP facilitates engineering efficiency and lets engineering teams become more productive with data without increasing headcount – so important in a business world where retention is on everyone’s minds.
Bryan Fricke focuses on the new user experience. (He’s also leading the UX Workstream initiative in 2022.) JMP 17 will support new users with search capabilities, letting them easily find a specific technique or example, help, the scripting index and the JMP community for results.
Make it easier to access data regardless of the data source.
If you can’t easily get to the data you need to solve the problem at hand, you can’t move your way through the analytic workflow. It’s true in some analytic workflows, like in DOE, you create data as you go. But often before running a DOE you need to look at historical or observational data to help identify factors and factor ranges. Those data you need may be locked away in databases or only accessible through an API. We’ve been making progress on this in recent versions of JMP. The Query Builder was a huge step forward in making joins, writing SQL queries automatically, and getting at data in relational databases. But other examples include the Excel Import Wizard, the HTTP Request functionality (which opens the doors to the nearly infinite world of data sources through API calls), Google Sheets, the OSI Pi data connector in JMP 17 and the interfaces to SAS (including CAS and Viya), just to name a few. We’ll continue to monitor the landscape of where data is being stored and prioritize data connects in every subsequent version of JMP to expand the library of data connectors available. We are working towards an unequivocal “Yes” to the question: “Can JMP access data in…?”
Reduce the time for users to prepare data.
Ahh, yes. The perils of dirty data. It’s everywhere. It’s almost a running joke now. In many customer interactions I pose the question: “Your data is always perfect and ready for analysis when you receive it, right?” I usually get a few eye rolls and some chuckles. Data are messy. And messy in different ways.
JMP can help wrangle this messy data and we’ve been incrementally working on this theme for many versions. A couple notable mentions here are the Recode and Text Explorer platforms for dealing with messy categories, misspellings (and hundreds of other things) and the value locked away in free text data. Formula columns and the one-click “new formula column,” Explore Missing Values and Outliers platforms, and the addition of previews in the tables platforms. There are so many more good examples. We want users to stay in the flow of data analysis. When met with the inevitable “this looks odd,” we want them to be able to triage and fix or work around the issue easily.
Improve real-world performance under the stress of real-world problems.
We’ve got customers around the world solving huge problems with JMP. We’ve made efforts to expand the scope of the data sets used to validate our statistical algorithms with our “big” test framework and we’ve scaled our import routines to open data sets fast. We use multithreading when it makes sense, and performance under stress also means storing data as efficiently as possible. The whole table compression works wonders for reducing file size and the efforts made for scaling performance for wide data problems for genomics data sets in JMP Pro 17 continues to expand what is possible for desktop data analysis. Desktop computers are getting ever more powerful, and we want JMP to be able to tackle whatever problems or data our users throw at our JMP platforms.
More “point and click” automation of routine tasks, more workflows.
Historically, it has been a burden to get started with scripting or move from a point-and-click JMP user to one who automates routine tasks. But it is much less so with the addition of the action recorder, which captures interactive tasks in the Enhanced Log in well-formed JSL for building scripts without having to write a line of code. When you invest upfront to automate what you do every day, you have more time to solve novel problems, your routine analysis tasks are less error-prone and, again, you can become more efficient with your time to focus on problem solving, innovation and driving change in your organization. We are taking this even further in JMP 17 with the Workflow Builder, which should make the step from point-and-click JMP user to scripter even easier. A beneficial byproduct? Upskilling other employees and new JMP users by letting them work in JMP directly while learning a workflow vs. training through screen captured videos. It removes one more level of abstraction between the new JMP user and the software interface and provides a seamless way to share and scale learning between a training organization within a company and the users of the software.
Strive for continuous improvement and innovation in JMP for analytics, EDA and visualization, DOE, and scripting.
JMP has world-class capabilities for data analytics, statistical analysis, exploratory data analysis, data visualization, design of experiments and a robust scripting language. All these capabilities are mature parts of the product. We have internal thought leaders, research statisticians and scholars on our R&D staff who publish cutting-edge research and develop new techniques; they are pioneers in their fields. Our workflow for getting these new techniques into the product in a way that is approachable and manageable to a user without a PhD in statistics is of tremendous value to our JMP user base.
Improve JMP as analytic hub for SAS, R, MATLAB, Python, etc.
Why? Because scientists and engineers have these tools on their desktops. We want to interface with the other software that our core audience uses to perform their jobs. Our goal is to compliment and not displace these products. They have their own place in the JMP analytic workflow. By supporting robust connections between JMP and these products you can move data in and out, visualize the results of simulation models run in MATLAB, or even publish models built in JMP to a production system running in Python or SAS environments. The goal here is agility and interoperability. As part of the roadmap, we are also monitoring the competitive landscape and seeing what other tools scientists and engineers have on their desktop or what environments productive systems are running in. Like many things in JMP, if we hear it enough times, from enough different customers in our target industries, we will get serious about adding additional interfaces within the product.
Continue to invest in tools to make JMP discoveries shareable.
JMP Live, the newest product in the JMP family of products, aims to solve the problem “How do I take what I did in JMP and use the discovery to drive organizational change?” JMP Live puts discoveries made with the sound statistical techniques in JMP at the fingertips of decision makers and anyone with a web browser. Data in the hands of a few experts can drive improvements but data and in the hands of everyone can drive organizational change.
We will continue to invest in the technology behind JMP Live. We are excited about how far the product has come in just a few short years and look forward to future of JMP Live where it will be the standard for dramatically increasing the visibility of the value that JMP provides to an organization. Other examples include JMP Projects, Dashboard Builder, Workflow Builder, and the Action Recorder. For sharing models, the work in Model Screening and Formula Depot/Score code generation is also important.
Pursue opportunities to deliver JMP remotely.
You may have seen the JMP for the Cloud training. The remote and hybrid work environment changes that have accelerated in the last several years have us thinking about new options for delivering JMP software in the next 3-5 years.
This theme can take many forms. It means modernizing the way we deliver desktop software, both major versions and updates. It means delivering the software using new technology to lift desktop software to the cloud (for example with Amazon AppStream). And it means extending the JMP Live platform to warehouse pre-cleaned and organized JMP data for all JMP users and making basic platforms to solve problems available from within a browser, right from JMP Live. We are excited about this theme because there are so many opportunities for solving problems differently in the future.
Into the future.
The role of the product team is to set the vision and roadmap of JMP by listening to the voice of the customer and working with our global sales teams to better understand how customers are using JMP today to solve problems and how they would like to use it better in the future. We also monitor the competitive landscape, understand industry trends and are all longtime JMP users ourselves. All of this goes into efforts to prioritize, think about the future and be proactive in how we grow and expand our software to solve new problems, increase engineering efficiency and make JMP the standard tool for all scientists and engineers worldwide who need to speed innovation and make better decisions with data.
The product themes are a major section of the new JMP Statistical Discovery LLC – Goals 2022 (Section 4: Develop great software) and were shared at the JMP kickoff in January.
We will continue to carry out these themes in subsequent versions of the software we develop. Even though JMP 17 isn’t out the door, we’ve already started to plan for JMP 18 and subsequent versions.
