Our sole focus is on Applied Insights, leveraging three powerful drivers: Augmented Intelligence, Analytics Everywhere and Automation of the new Data Landscape.” I would love to hear your opinion on the Gartner Magic Quadrant, and what is the future holds for data science tools and platforms.“ It is inspiring confirmation for our team to be recognised as a L eader in the Gartner Magic Quadrant for Data and Analytics Service Providers, Worldwide, for our unwavering commitment to business innovation through insights from data, and having clients at the heart of the digital journey,” said John Brahim, Head of Insights & Data at Capgemini. If the trend continues, we might see other companies jumping on the analytics roller coaster and emerging as leaders in 2019. Gartner has solid reasons for placing SAS, KNIME, RapidMiner, and H2O.ai as leaders into their Magic Quadrant for 2018. Keeping things simple has always been the key to success within any organization.
Implementation – you are not required to be a data scientist since these tools can be easily implemented across your organization with minimal training.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.With data channeling through the predesigned nodes, it is hard to make a mistake, which makes it error proof. Error proof – SAS, KNIME, RapidMiner, and H2O.ai can be easily installed on any machine.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. Companies like SAS, KNIME, RapidMiner, and H2O.ai found a more attractive approach to aid companies with their analytics by ‘solutionizing’ these challenges. 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. Shortly into my research, I started to notice a trend that differentiates Microsoft, Google, and IBM from Gartner’s leaders. I decided to do a quick investigation into why SAS, KNIME, RapidMiner, and H2O.ai became the leaders.
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. With some guidance, you can craft a data platform that is right for your organization’s needs and gets the most return from your data capital.
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