Best Data Science Platforms of 2020

It’s an exciting time for data science and technology innovations. Technology takes a front and center role in every part of our lives more every day, and the development seems to be happening so quickly that every time one innovation takes the market by storm another is on its heels. Who can keep up?

The truth is that data science and technology are more a part of our lives than we even know. Every time Facebook recommends a page, or a targeted ad shows up in your browser, or your phone announces a flood warning out of nowhere technology and data science are changing the way you engage with the world. This is one reason why it’s a good idea to stay abreast of the developments in the world of data science. Who is developing interesting software? Who is lagging behind the rest of the pack? Who has emerged as a leader in their field, whether it’s business intelligence, data analytics, or anything else?

How to Recognize the Next Best Thing in Data Science



It’s hard to know who to trust, as there are so many pundits out there claiming that this software or that service is the next big thing. Fortunately, reliable sources like the Gartner Magic Quadrant Data Science report exist and can help us understand the crucial developments in the world of data science. The most exciting data science platforms and trends are all outlined clearly in the Magic Quadrant (MQ) report, with an easily understood two-axis matrix defining who belongs in which quadrant according to two criteria: Completeness of Vision and Ability to Execute.

The Gartner Magic Quadrant report works by placing each major data science company within a certain category—leaders, visionaries, challengers, or niche players. But the MQ report does more than that. By making each designation a quadrant located upon the x and y axes of Completeness of Vision and Ability to Execute it makes it easy to see the movement in the field. For example, IBM exhibits a clear ability to execute and a complete vision when it comes to data integration tools, which is why the company seems almost like a rising star within the leader quadrant. In the same matrix, Microsoft is exhibiting a similar ability to execute, but less of a completeness of vision. Nevertheless, Microsoft’s Azure is a fan favorite when it comes to machine learning platforms, according to Gartner.

How to Use the Magic Quadrant Framework


By looking at the Gartner report, one can glean important insights, even about companies with less name recognition than Microsoft or IBM. A younger company, like SAS, MicroStrategy, or Salesforce, might be a formidable source of competition for the more established players on the field. When you’re a business market leader, you can never rest upon your laurels. You should always be on the lookout for a younger, fitter player who is looking to be a challenger in your field. The Gartner Magic Quadrant report helps business users do just that, and it helps everyday folks or investors keep an eye on those challengers and expect big things from them.

Artificial intelligence is the goal—a computer that can anticipate an individual’s or a company’s needs before needing to be told what to do—and machine learning is the way to achieve that goal. But using machine learning to train our computers to be more intuitive is only one side. We also need to be cognizant of how to work in tandem with those machines. Companies that are developing exciting technologies such as immersive visuals, fully customizable business intelligence platforms, and real-time reporting will be those that will really change how we use AI for good in our day to day lives. Those who are challengers in the data analytics and integration MQs will lead us into the future. Just wait and see.