2019 Magic Quadrant: DS + ML

As Gartner puts it in its 2019 Magic Quadrant for Data Science and Machine Learning Platforms, “data science is a core discipline for the development of AI, and ML is a core enabler of AI, but this is not the whole story. ML is about creating and training models; AI is about using those models to infer conclusions under certain conditions. AI is on a different level of aggregation to data science and ML. AI is at the application level.” Gartner adds that, “Data science and ML models must be combined to work together with other capabilities, such as a UI and workflow management, to constitute an AI application. A self-driving car, for example, has ML capability, but its AI requires much more than that.”

Alteryx’s emphasis on making data science accessible to citizen data scientists and others across the end-to-end analytic pipeline is resonating in the market. Its approach provides a natural extension for a client base focused on data preparation but ready to take the next step into data science. A lack of innovation, relative to others, also contributes to Alteryx’s new position as a Challenger. Alteryx’s no-code approach is attractive to a broad spectrum of users, from business and data analysts to citizen data scientists. A focus on the ease of use and cohesiveness of its platform enables collaboration between users.

Alteryx has focused on offering a complete, end-to-end data science platform. It has added two new products to its platform. Alteryx Connect focuses on data connections, data discovery and social connections. Alteryx Promote incorporates Alteryx’s Yhat acquisition and focuses on operationalizing analytic content.

Alteryx’s focus on addressing the end-to-end analytic process easily and clearly positions it as a vendor of a comprehensive platform. Alteryx’s value proposition is clear and resonating. Alteryx scored in the top quartile for customer experience in our survey of reference customers. Scores were consistently high for overall customer experience, plans to make additional investments, inclusion of product enhancements and requested features into subsequent releases, and overall product capabilities.