balloons for celebrating

07 June 2022

Blog

Announcing RapidMiner’s Next Gen Platform: How We Got Here and Where We’re Going Next

We’re celebrating at RapidMiner today. Why? Our new, next gen cloud platform is now officially available to the public! 

What really fueled this transformation to an enterprise-focused platform? We saw far too many prospects and customers facing challenges that prevented them from fulfilling the promise of AI at their organizations. As a data science platform, we’ve always wanted to make AI more accessible so that anyone can positively impact the future—we want the new RapidMiner to embody that more than ever. 

As we roll out our new platform and our new brand, I’d like to take a moment to look back at where we started, how we’ve grown over the years, and why we’ve made the choices that we made. As RapidMiner’s founder, I’m hoping you’ll indulge my nostalgia. 

RapidMiner’s Journey to Enterprise 

Like Dorothy following the yellow brick road to Oz, our path to an enterprise data science platform hasn’t always been easy. But, unlike Dorothy, we didn’t land here by chance.  

A Walk Down Memory Lane 

Picture this: young Ingo, fresh out of school at TU Dortmund, ready to use data science as a tool to save the world. I’d landed a gig creating a churn prediction model for the largest telecommunications company in Italy at the time, and I worked tirelessly for six months on a model that would have saved them tons of money. 

The only catch? The model never made it into production. 

Why? Because I failed to communicate what I was doing and how I was doing it. Who wanted to believe a young data scientist who could only show them 25,000 lines of code as proof that his model would work? No one

For me, it’s easy to trust mathematics, but for others, code is its own kind of black box—people know there are algorithms and math swirling around, but it’s hidden within the models. For many businesspeople, that’s no better than throwing the dice or asking a crystal ball for an answer. 

Data Science Evolved, and We Evolved with It 

Shortly after my failed project, I met RapidMiner’s future co-founder Ralf Klinkenberg, another aspiring data scientist who wanted to demystify AI. At that point, data science was just for data scientists (read: nerds in basements). We wanted to change that and help bridge the gap between data scientists and businesspeople. How? By providing a tool that supplemented coding with higher level, easier to understand modalities that brought together people with different skill levels—from that idea, RapidMiner was born. 

RapidMiner Evolved Ingo
What’s changed more over the years—RapidMiner or me?

RapidMiner started primarily as an academic tool for students, professors, instructors, and researchers. Entire classrooms full of soon-to-be data scientists learned how to do hands-on AI on our platform. Today, 4,000 universities around the world use RapidMiner every day. 

We’ve always been rooted in a solution- and industry-oriented branch of academia. The reason I left academia was because I wanted to be closer to real-world problems, and it’s always been a priority for me that our platform could handle real-life situations and provide feasible solutions for them. This capability and drive to solve real-world challenges on an organizational level helped us gain more recognition from enterprise users outside the academic community. 

RapidMiner is used to teach data science, but it’s also used to show executives and analysts that AI is another great tool in their toolbox. In 2013, we were first named a leader in the Gartner Magic Quadrant, and in 2017, Forrester named us a leader in their Wave report as well. Today, we have nearly one million users—even if many of them started using RapidMiner for learning data science, they now rely on our platform to support all the analytics work that powers their business every day. 

As we turned our attention to helping enterprises deliver on the promise of AI, data science continued to change. Becoming “data-driven” was a key initiative for most organizations (including our customers), and many leaders began to understand the types of problems they could solve with predictive analytics. But, while people throughout the world better understand what AI is and is not capable of, there’s a lingering gap between data science and business knowledge. 

RapidMiner is on a mission to help our customers broaden their horizons on the data science side—if you understand analytics and statistics, you can use our platform to make smarter decisions. But, we also want to upskill data scientists—they need to know enough about the business problems they’re working on to understand how data science can solve them, and how to best communicate their results. 

It’s a tall order, and no software on this planet, not even RapidMiner, can solve it alone. It requires a willingness among people to learn and change the status quo. To help along the way, RapidMiner can be the glue that holds everything together and allows everyone involved to take those necessary steps toward each other. 

Our Future is Bright 

Now that we’ve journeyed from being a student-friendly teaching platform to a platform that supports some of the world’s leading enterprises, we’ve gotten a much closer look at the challenges that prevent businesses from extracting real value from their data.  

In addition to the knowledge gap between data and business experts, there’s a lack of trust in solutions that prevents even the promising models from being deployed. And, given the complex architectures, IT infrastructure, and policies that most enterprises have in place, it’s extremely challenging to embed models where they can make their most useful predictions. 

Our new platform has been re-architected from the ground-up to address these problems—it’s designed to help build trust in models and predictions, and it really scales in an enterprise environment by connecting to common data sources and allowing for code-free deployment. 

And of course, our new platform is accessible to everyone, and allows people with unique backgrounds and expertise to break down existing silos and collaboratively build solutions by contributing to the areas they know best. There are data scientists of all shapes and forms—coding data scientists, citizen data scientists, business stakeholders, data engineers, IT people, the list goes on and on. I firmly believe that our new platform enables these different groups better than anything else out there. 

We’ve also come to realize that, for all the benefits our current product has (and will continue to have), software administration for an on-prem product will always be a point of friction. When a vendor has an impactful release, the last thing a customer should think about is how much of a pain it is to update their instance. At best, this causes a disruption and drain on IT resources—at worst, it gets in the way of innovation.  

That’s why I’m most excited about the fact that the new RapidMiner is fully cloud-based. For customers who prefer a cloud-first environment, it’ll reduce so much friction in enterprise deployments and help organizations get to work faster. Not to mention, it’ll scale more easily and help organizations seamlessly adapt to changes in their architecture, policies, and personnel. Boom

Looking Ahead 

So, what does the future of data science look like? The beauty is that no one knows for certain, though I believe there’s something more flexible than code but as simple as AutoML out there, waiting to be discovered. I envision a future where these different modalities no longer exist and are instead replaced by something even more powerful and more user-friendly than what we have today. That’s what RapidMiner is working toward. 

I’m so proud of how far we’ve come in the past 20+ years, and I can’t wait to hear what the Community thinks of the new RapidMiner! 

Take a look around our platform page to see how we’ve upgraded RapidMiner to further support our nearly one million users across the globe. 

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