• Home  / 
  • Technology
  •  /  Alteryx Inc (NYSE:AYX) Selected As Leader in Gartner’s Magic Quadrant

Alteryx Inc (NYSE:AYX) Selected As Leader in Gartner’s Magic Quadrant

Alteryx Inc (NYSE:AYX) is transforming business through analytics and data science and recently stated that it was selected a Leader in Gartner’s Magic Quadrant for Machine-Learning and Data Science Platforms for this year. This placement is the fifth successive time that company has been positioned in the respective Magic Quadrant. It is the first time the firm has got a place in the Leaders quadrant.

The buzz

Langley Eide, the Chief Strategy Officer at Alteryx, expressed that most of the large firms understand that analytics and data can offer a powerful competitive benefit, however many are struggling to use several disparate data sources; adopt an increasing number of AI and ML services; and find enough skilled staff to get an analytic benefit. They are seeing growing demand for a common platform that allows the line-of-business to seek full benefit of their data assets, whether customers prefer a code friendly or code-free experience. They consider that their position as a pioneer in this Magic Quadrant underlines their objective to serve a growing generation of citizen data scientists.

In the preceding twelve 12 months, Alteryx has launched numerous new solutions that assist round out its comprehensive analytics platform, intending to empower analysts and data scientists alike to face data barriers and get the thrill of reaching business-changing insights quicker than ever. Alteryx Promote enables both citizen data scientists and data scientists to deploy predictive models into business systems via an API, and then monitor and manage model performance over time. It is projected to be generally available in 2018 and is the outcome of the firm’s acquisition of Yhat, reported in June 2017.

Ashley Kramer, the VP of Product Management, expressed that several firms struggle to lead business value from their developed analytic measures, whether it’s due to shortage of data scientist talent or the fact that several analytical models are never essentially deployed.

Click here to add a comment

Leave a comment: