data scientist coding

01 August 2022


3 Ways the New RapidMiner Helps Data Scientists Create Transformational Change

If you’re a real data scientist (maybe you even have a PhD), there’s a good chance that you’re skeptical of “democratizing” AI using a code-optional tool. This is understandable. So many vendors preaching democratization make it seem like enterprise AI is as easy as plugging in your ready-made dataset and sitting back as business-changing insight is generated. As we all know, the reality is far removed from that.  

First, business data is messy—in fact, it’s so messy that data scientists often spend more time prepping it than selecting or training models. If you’re able to get over that hurdle and create a promising model, there’s a good chance that decision-makers will put it on the (eternal) back-burner because they don’t understand how it makes predictions.

These challenges make it extremely difficult to successfully implement even a single use case—let alone the hundreds or even thousands that your organization could potentially take on.  

In this post, we’ll break down how RapidMiner’s new cloud platform helps data scientists overcome those challenges so you can focus on high-value work, build trust in predictions, and scale your impact across the enterprise. 

Focus on High-Value Work 

Depending on where you look, it’s estimated that data scientists spend between 50-80% of their time on data prep. Consider this alongside the fact that data science teams are usually juggling requests from several business units, and it becomes clear that enterprises have an analytics efficiency problem.  

Not to mention, the tools that many companies have in place to support data science projects exacerbate that problem. Data scientists are asked to download their own IDEs and given no instruction or support for how to publish their work.  

To maximize your impact, you need to speed up the amount of time it takes to get data into a usable state while enabling non-coders to get involved in projects—not just so they can provide critical context on what data means, but so that over time, they’re empowered to take on simple or more common use cases without relying on you as heavily.  

How we do it 

First things first: coding is not an afterthought in RapidMiner’s platform. We’ve invested heavily in incorporating an embedded, centrally governed, notebook experience that ensures data scientists can leverage the power of their preferred coding languages and open-source libraries. But sometimes, code isn’t the most efficient way to get something done—as is commonly the case with data prep. 

Because the new RapidMiner’s three user interfaces (code, automated data science, and visual workflows) are all part of the same browser-based experience, it’s extremely easy to work interchangeably between them.

So, while you may not want to rely too much on autoML, you can use it to expedite traditionally time-consuming tasks like data prep and prototyping. By leaning on the platform to automatically identify and correct quality issues like missing values, outliers, and duplicates, you can get to the meat of your projects faster. 

It’s not just data prep that gets a productivity boost. Data scientists can also use RapidMiner to streamline data pipeline development and CI/CD best practices while knowing that security and governance are built into the fabric of the platform.  

Our team has also focused on simplifying ML Ops, which can be a huge stumbling block if you’re trying to force-fit work you’ve done in your own IDE. RapidMiner’s cloud-native platform helps you avoid deployment friction and embed models where they’ll make their most useful predictions. Models are automatically monitored to protect against drift and degradation, so your team’s work will stand the test of time. 

Another benefit of being able to complete an end-to-end project in any of the three interfaces is that non-coding domain experts can contribute at any point in the data science lifecycle. And, because RapidMiner takes an upskilling-centric approach, they’ll eventually develop the knowledge and skills they need to take on common or simple use cases without much assistance—allowing you to spend more time on novel problems. 

Build Trust Through Engagement 

Today’s enterprises are at a crossroads. On one hand, business leaders embrace the value of becoming more data-driven and using advanced techniques to make smarter decisions. On the other hand, they often don’t trust the models their teams bring to them.  

Given that many companies require data scientists to manually package insights generated by complex models using PowerPoint presentations, this isn’t exactly a surprise. 

The good news is the right tools can help to quickly bridge the gap between where organizations are and where they want to be by making results more intuitive and easier to understand.  

How we do it 

Before model consumers can make decisions based on predictions, they need to be able to audit the data those predictions are based on. RapidMiner’s project-based framework stores all models and data assets associated with a given use case in one place, making it easy to evaluate where predictions are coming from.

And, regardless of whether a model is created using code, automation, or visual workflows, every step of your processes will automatically be logged in visual workflows for full visibility and transparency. 

That brings us to the insights themselves. Data scientists can use RapidMiner’s powerful, no-code AI apps to break down predictions to decision makers in an intuitive way—without involving developers—and include any relevant project content within those apps. You can make results more approachable by showing which features are weighted most heavily and running “what-if” scenarios to compare against expectations.  

Presenting results and recommendations in this way doesn’t just make it easier for businesspeople to consume—it saves you the time and effort of having to manually craft a convincing story around your work. 

Scale Your Impact Across the Enterprise 

As you know better than anyone, your organization isn’t limited to a handful of use cases. Any process that generates or relies on a high volume of data can potentially be sharpened through data science, which means that your company likely has enough projects to keep you busy for…well, a long time. 

When you’re running into the challenges described above, it’s hard enough to get a single model deployed, let alone transform your business. But once you start overcoming them, you can help more areas of the business put differentiated solutions in place, faster. 

How we do it 

When your work lives exclusively in your coding notebook, you’re instantly creating a barrier between you and the non-coders who may want to learn from and/or repurpose it. But because RapidMiner allows you to package coded data transformation steps and models as operators, non-coders can easily pick up where you leave off—freeing you up to solve other problems throughout your organization. 

And as mentioned earlier, RapidMiner’s project-based framework makes it easy to organize and share any datasets, models, apps, or features that are associated with a use case so that other teams can learn from them when handling similar problems. This ensures that best practices are reused and helps to avoid duplicating efforts.  

To Wrap Up 

Today’s data scientists are burdened by overly time-consuming tasks, lack of trust in predictions, and a skills gap that makes it difficult for others to understand and repurpose their work. 

By simplifying tedious parts of the data science lifecycle, making it easier to build inherently transparent models, and helping to scale the impact of data science projects, the new RapidMiner enables data scientists fully deliver on the promise of enterprise AI.  

If you’d like to learn more and see some of the functionality described in this post in action, be sure to check out our on-demand webinar “How the New RapidMiner Helps Data Scientists be Even More Impactful.” 

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