The new RapidMiner is here, and we’re excited to share how our cloud-based platform helps diverse analytics teams optimize their data in this role-based blog series. In this post, we’re focusing on domain experts—how RapidMiner gets you heavily involved in projects, facilitates your collaboration with data scientists, and brings more models into production as a result.
Every enterprise data science project requires a deep understanding of the business context to be successful. There are so many questions that need to be answered.
- What functional area are we focusing on?
- What are the key challenges that must be solved?
- What data are we using?
- How will the solution be delivered?
Answering these questions is what domain experts (those closest to the business processes) do better than any other project participant. If you’re a part of this group, you also know how to communicate modeling results to the rest of your team in an intuitive and productive way. This communication is critical to ensure solutions are actionable and projects aren’t shelved.
Here’s the snag. Naturally, many domain experts haven’t been trained in how to drive end-to-end data science projects. But, what if a lack of data skills was no longer a limiting factor?
In this post, we’ll explore why domain experts are crucial to data science success, how the new RapidMiner gets you involved in projects without code, and how we go beyond in-platform guidance to support continued data science upskilling.
How the New RapidMiner Injects Domain Expertise into AI-Driven Innovation
The truth is, your organization needs domain experts to make their AI initiatives successful—here’s how RapidMiner helps you get there.
1. Getting Involved in Data Science Projects
Why get involved?
Data science projects help you learn from the past and present to predict and optimize the future. With rich insights from machine learning, you can make existing processes more efficient, reduce risk in the supply chain, optimize customer experiences, etc.
Non-data scientists may not be aware of the CRISP-DM data science framework, because it’s decades old. But, it’s still around and relevant—because it works. Why? It makes business understanding the backbone for every project and relies on heavy involvement from domain experts, too—not just data scientists.
Data scientists’ strengths lie broadly in data preparation, modeling, and deployment. While these steps are certainly important, without a solid understanding of both the business problem you want to solve and the data that will help you solve it, they won’t lead you anywhere.
Your knowledge as a domain expert is crucial not only because you can help add the right context, but also because you’re in the best position to evaluate how well potential solutions actually work.
How we do it
To enable collaboration between users of different skillsets, it’s critical to have a platform that gets everyone involved and keeps your team on the same page. The new RapidMiner does this better than any other tool out there.
Automated data science is both a great entry point for those new to data science and a productivity tool once you’ve become more familiar. RapidMiner provides a guided and interactive experience that automates the data science lifecycle and best practices to empower non-coders like never before. Recommended actions are soundly built on telemetry from a base of over one million users, which means that you can spend more time understanding business problems and evaluating solutions—let RapidMiner handle the rest.
As we covered in our first post in this series, trained data scientists who prefer code can use RapidMiner’s built-in Python environment to do just that. The difference between coding in isolation and doing it within our platform is that we automatically log all work, regardless of which interface it was done in, to drag and drop visual workflows with plain-text explanations for each step. This creates a universal language for multi-disciplinary teams to understand both the “what” and “why” side-by-side for deeper insight into what’s happening.
The greater the shared understanding, the greater the sum of the parts, and the better the result.
2. Building Real-World Skills, Not Just Learning a New Piece of Software
Why focus on data skills?
The problem with many of the data science platforms promising to “democratize” AI is that they’re black-box solutions. Modeling is automated to a fault, the ability to explain models is limited, and online training resources and learning courses are focused on teaching which buttons to click as opposed to fundamental data science principles.
When you consider the fact that over a third of CEOs believe skill gaps in data analytics are a crucial threat to their business, this approach won’t cut it. Your company can’t just buy a fully automated platform and expect results—they also need to invest in enhancing your ability to interpret, understand, and productively work with data science.
How we do it
If you’re familiar with RapidMiner, you know that we take a comprehensive, upskilling-based approach with our customers. While the platform’s three modalities are at the heart of that, our Center of Excellence methodology and Academy are designed to take your DSML skills to the next level.
RapidMiner’s self-paced Academy builds expertise wherever a user most needs it. It helps you enhance your skills by offering role-based certifications tailored to your level of data science experience. Beyond learning data science foundations and principles, Academy enables increasingly sophisticated use of our platform, giving you a hands-on way to put your new skills to use.
RapidMiner’s CoE transforms how you use data. Our team of experts helps plan and prioritize use cases, guides you to initial success, and teaches you “how to fish” so you can scale that success across the organization. The CoE also programs usage of the platform and the Academy so you can assure you’re on the right path to success.
RapidMiner’s fine-grained upskilling path includes training for novice, experienced, and expert users so anyone can gain knowledge and skills no matter where they are in their data science journey.
This translates to more models across the business, more use cases in the pipeline, and more opportunities to drive revenue, cut costs, and manage risk.
To Wrap Up
In the not-too-distant future, machine learning is likely to be as ubiquitous in the workplace as PCs are today. With that in mind, now is the time to empower your domain experts to deliver results.
RapidMiner’s new platform gets domain experts involved early in the process, keeps them on the same page as more technical users, and upskills them to be more deeply involved in projects over time. In doing so, it removes some of the biggest hurdles to enterprise data science success and sets your organization up for a more data-driven future.
Want to get a first look at our new platform? Our on-demand webinar, How the New RapidMiner Helps You Make Smarter Decisions for Your Business, will walk you through how our data science can help you generate smarter insights, faster.