By now, you’ve hopefully seen that RapidMiner was identified as a Leader in both of the major analyst reports that cover the data science space: Gartner’s 2017 Magic Quadrant for Data Science Platforms and The Forrester Wave™: Predictive Analytics and Machine Learning Solutions, Q1 2017.
It’s a great accomplishment for RapidMiner to be recognized as a Leader in both analyst reports, especially when we compete in the large data science market against much bigger companies like IBM and SAS. It’s a testament to the investments we’ve made over the past couple of years at RapidMiner in areas like engineering, customer success, and new user onboarding.
What follows are my thoughts and opinions on the state of data science / machine learning platforms formed by speaking with customers, prospects, and partners and working with analysts like Gartner and Forrester. I’m speaking for myself not any analyst firm, and you should read their reports and the disclaimers which I included at the bottom of this post.
With that, let’s get started. I believe that the recent success of RapidMiner in the Gartner and Forrester reports reflects our laser focus on the needs of data scientists who want to deliver real data science—fast and simple. That’s our mission statement, and we choose those words carefully.
First and foremost, we stand for real data science, something both Garter and Forrester identified as a strength for RapidMiner. Gartner highlights our “large selection of algorithms, flexible modeling capabilities, data source integration and consequent data preparation” and notes that “the platform’s strength lies not just in particular areas, but also in its all-around consistency.” Forrester called out that RapidMiner “has a comprehensive set of operators that encapsulate a wide range of data prep, analytical, and modeling functionality to increase productivity of data scientists.”
We deliver on the real data science mission through extreme focus, here are some of the many ways we stand out by:
- Providing a massive library of machine learning algorithms to provide data scientists with a wide variety of choices for different datasets.
- Offering correct model validation to ensure that a model is validated in the most accurate way possible.
- Letting developers reuse R and Python code to extend RapidMiner and so that data science teams can incorporate RapidMiner into their workflow.
- Constantly innovating the platform through cutting edge machine learning algorithms from companies like H20.
Our real data science mission makes it easy to contrast RapidMiner with competitors attempting to capitalize on the momentum around machine learning and data science with products that simply miss the mark.
For example, Alteryx provides a limited set of lightweight machine learning functions for their mostly business analyst audience so Alteryx can check the “we do machine learning” box. But when evaluated against a robust data science platform like RapidMiner, the deficiencies become crystal clear. Forrester didn’t even include Alteryx in their evaluation, which I believe reflects their lack of product depth for data science use-cases. That’s why we talk about real data science at RapidMiner: we build our products to solve the hard problems.
I think this is the same reason DataRobot and others were excluded from both the Forrester and Gartner reports. Data scientists are wary (to say the least) of automated “you give us the data, we’ll give you a model back” approaches. Real data science requires transparency to understand the approach that was used to create and validate the model. Would you trust a mission-critical business decision to a black box that simply spits out number with no insight into how it got there? Neither would I.
Of course, there are other real data science platforms covered by Forrester and Gartner, most notably IBM and SAS. While both continue to be up and to the right in analyst reports, RapidMiner has closed the gap significantly this year. But neither IBM nor SAS are particularly fast or simple, the other important piece of our mission.
To RapidMiner, fast means that a data scientist can be more productive using RapidMiner than they are using other data science approaches and simple means that our product should be approachable for a wide range of users with a diverse set of skills.
Forrester eloquently captured the spirit of fast and simple in their Wave report: “RapidMiner wraps breadth and depth in a beautiful package,” going on to say that “RapidMiner invested heavily to revamp its visual interface, making it the most concise and fluid that we have seen in this evaluation.”
Concise (synonym: fast) and fluid (synonym: simple). Sound familiar? J
SAS continues to be widely used and widely disliked by customers who struggle with both their pricing model and the need to purchase a seemingly infinite set of add-ons and overlapping products. We often see organizations with large investments in SAS use RapidMiner for new projects, steadily increasing their RapidMiner usage while decreasing their dependence on SAS licenses. By the way, it only takes about a week for an experienced SAS user to learn RapidMiner. Contact us if you want to learn more about moving from SAS to RapidMiner.
Finally, I compare IBM’s data science offerings to a buffet in Las Vegas: lots of choices, none of them particularly great. And worse you still must hire the chef (i.e. IBM Services) to cook the food for you.
To me, the primary takeaway from the 2017 editions of the Gartner Magic Quadrant for Data Science Platforms and the Forrester Wave™: Predictive Analytics and Machine Learning Solutions, Q1 2017 was that our placement validates our mission to provide real data science, fast and simple. Our visual approach lets data scientists work faster without compromising the quality of the underlying data science, and RapidMiner frees organizations from the complexity and cost of the legacy approaches that have historically dominated this market.
(Thanks to Darren Guarnaccia of Sitecore for the inspiration behind the title)
*Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
The Forrester Wave™ is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of Forrester Research, Inc. The Forrester Wave is a graphical representation of Forrester’s call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.