RapidMiner’s excited to showcase a comprehensive integration with Tableau that will change the way data analysts view the future, both literally and figuratively. The integration of the two creates an end-to-end data science & visual analytics environment that’s first in its class.
How might you use the new RapidMiner and Tableau integration? Let’s take a look at how RapidMiner can augment Tableau’s autoML capabilities.
The Future of Data Science and AutoML
RapidMiner has been obsessively focused on putting the power of data science into the hands of businesspeople for over a decade. RapidMiner was founded around the same time that Tableau was inventing data visualization. Our mantra—real data science fast and simple—is all about empowering business users with real data science, just as Tableau has supported users with advanced data visualization techniques.
That’s why we were so excited about Tableau’s recently unveiled Einstein-enhanced capabilities, described as BusinessScience: AI-powered analytics that provide data science capabilities to business people using automated AI and ML tools. Tableau’s Business Science initiative shows broad recognition of the potential that automated machine learning offers to those who have already mastered the art of business intelligence.
RapidMiner knows the power of automated machine learning—in fact, we’re proud to be recognized for having one of the best automated machine-learning solutions on the market. However, we also recognize that automated machine learning by itself can only solve a portion of business problems. For some use cases, you need all of the extra bells and whistles of a full data science platform to address a challenge.
Let’s take a look at some of the most common reasons you might need to go beyond automated solutions, as well as how using RapidMiner together with Tableau can let you harness the full power of data science and then seamlessly visualize your results.
Limitations of Automated Approaches
There are three main reasons that you might need more than just autoML for a given project:
- Needing data-science depth — Many data science projects require more advanced techniques than are available in automated solutions, in addition to intensive model customization and tuning. This requires learning a bit about how machine learning actually works (but don’t worry, we make that easy).
- Collaboration and explanation — They also often require intense collaboration across cross-functional stakeholder groups, often between coders and non-coders.
- ModelOps — Finally, once you’ve got a tuned model, you need a way to manage and monitor your models over time—something that isn’t a part of most automated solutions.
As a comprehensive data science and machine learning platform, RapidMiner has been designed to provide solutions to all of these problems. Let’s dive into each of these key areas in a bit more detail, as it should shed some light on why a dedicated Tableau user might be interested in the RapidMiner integration.
1. Needing data-science depth
Our new integration not only puts the power of market-leading autoML in the hands of business analysts to enhance data discovery and decision-making, but it also provides the extended features of RapidMiner’s end-to-end ML platform. Sometimes, you need to finely tune model parameters, or adjust how the model is treating your data beyond what is possible with automated solutions.
But how can you know what you need to do if you’re a business analyst who isn’t familiar with machine learning? Comprehensive data science platforms like RapidMiner expose you to the inner workings of complex models and help to demystify them, showing you how statistics, machine learning, and AI function. This provides you with a continuous path to upskill your abilities: once you’ve explored automated machine learning, you can experiment with our visual workflow designer. Some clients have reported that RapidMiner’s visual designer makes learning data science so easy that picking up Python isn’t as difficult as it would be otherwise. And if you make it to that level, we have a solution for you, too.
This means that you can continuously learn the art of data science and acquire new skills and gain familiarity with more advanced techniques. You can leverage the world’s most used data science and machine learning platform to continually get better and better at solving complex problems with sophisticated techniques—all without needing to know how to code.
2. Explainability and collaboration
RapidMiner has been referred to as a ‘digital campfire to gather around’ because its end-to-end visual nature makes it easy for everyone to be on the same page. There are two particular benefits this has for business analysts.
First, most problems that organizations are attempting to solve with machine learning these days are big, high-value problems, which means that there are a lot of stakeholders involved. Everyone who’s livelihood depends on solving the problem must understand models’ systems and outputs, otherwise they won’t trust the process and will actively push back against it.
Think of a problem like customer churn and imagine just how many stakeholders within a typical organization will need to be involved in predictive and prescriptive analytics that aims to solve churn. This means you need an easy way to present your data science projects to novel stakeholders so they can easily understand what you’ve done and be confident in your conclusions.
Second, by using RapidMiner, business analysts can also work side-by-side with their Python-coding colleagues, and even instantly re-use their code. This is the value of integrating your BI work with a best-in-class data science and machine learning platform.
3. ModelOps
There’s a reason why ModelOPs is the hottest term in the world of machine learning software right now. Models require monitoring, maintenance, and management over time; otherwise, they end up no longer matching the world and deliver poor predictions.
Maintenance can become tedious without the right tools, especially after you’ve put a series of machine learning models into production to address a variety of business challenges.
How RapidMiner and Tableau Work Together
Tableau users are in a uniquely good place to start using RapidMiner. As any data scientist will tell you, roughly 80% of any ML/AI project is housekeeping: preparing data, transforming data, cleansing data, etc. But if you’re already using Tableau, it’s likely that your housekeeping is in order as part of your BI program, making your data ready for machine learning with little to no extra data prep needed.
With a few clicks, your analysis can go beyond understanding what happened in the past and move into the realm of predicting what will happen in the future, letting you decide on the next best actions to take to move your business goals forward.
The integration between Tableau and RapidMiner is bi-directional and leverages the Tableau Analytics Extension, as well as the Tableau Server web API. It allows you to:
- Prepare data sets for Tableau in RapidMiner, if needed
- Explore & train machine learning models using RapidMiner
- Create real-time predictions in new Tableau dashboards
- Enrich existing dashboards with the future
Analysts have always had the ability to use machine learning to enhance their Tableau visualizations, but now you don’t need to know how to code, nor do you have to rely solely on autoML. You can integrate with a complete, flexible, and powerful data science platform that gives you the ability to work the way you want to work, build end-to-end data science pipelines without a line of code while providing unprecedented model explainability through the platform’s visual foundation. This enables better collaboration with all of your stakeholders so you can explain how models work and why they make the predictions that they do, and lets you learn more about data science and machine learning. That’s depth for data scientists, simplified for everyone else.
If you’d like to learn more about our integration with Tableau, and see it in action, check out our on-demand webinar Better Together.
