What will the future hold for data science and machine learning platforms? Most of you already saw Gartner’s perspective in their latest Magic Quadrant on the evolution of data science that was released at the beginning of 2018 and are familiar with the outcome. However, did you ever ask yourself why SAS, KNIME, RapidMiner, and H2O.ai became the leaders?
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I will be honest with you, I was a little surprised myself, and probably you were too, with the results for the leaders in Data Science and Machine Learning field since I expected to see Microsoft, Google, or IBM. I decided to do a quick investigation into why SAS, KNIME, RapidMiner, and H2O.ai became the leaders.
Shortly into my research, I started to notice a trend that differentiates Microsoft, Google, and IBM from Gartner’s leaders. This makes a lot of sense in a world where a good data scientist is a scarce resource, algorithms are complicated, and good coding skills are required. Companies like SAS, KNIME, RapidMiner, and H2O.ai found a more attractive approach to aid companies with their analytics by ‘solutionizing’ these challenges.
Here are the three main differentiators that I believe are the contributors to position SAS, KNIME, RapidMiner, and H2O.ai onto the leader’s quadrant.
- Error proof – SAS, KNIME, RapidMiner, and H2O.ai can be easily installed on any machine. With data channeling through the predesigned nodes, it is hard to make a mistake, which makes it error proof.
- Modeling – Most of the models are embedded into the GUI type of approach, which is simple: you select, move, connect the needed components to generate remarkably accurate results, requiring no knowledge of programming unless you need customization.
- Implementation – you are not required to be a data scientist since these tools can be easily implemented across your organization with minimal training. Keeping things simple has always been the key to success within any organization.
Gartner has solid reasons for placing SAS, KNIME, RapidMiner, and H2O.ai as leaders into their Magic Quadrant for 2018. If the trend continues, we might see other companies jumping on the analytics roller coaster and emerging as leaders in 2019.
I would love to hear your opinion on the Gartner Magic Quadrant, and what is the future holds for data science tools and platforms.
