Alteryx is based in Irvine, California, U.S. It offers a data science platform geared toward citizen data scientists. The platform’s self-service data preparation capabilities and advanced analytics enable business users to blend data from internal and external sources and then to analyze it using predictive, prescriptive tools, using the same UI in a single workflow. Integration with open-source R enables expert data scientists to extend functionality by creating and running custom R scripts. Alteryx also offers a cloud-based analytics gallery for collaboration, sharing and version control of workflows.
This year, Alteryx is at the bottom right of the Challengers quadrant. Its move out of the Visionaries quadrant is due partly to solid customer growth, which has resulted in a higher score for Ability to Execute. It is also due to a lack of automated modeling features, a lack of Python or Scala programming support, and limited visual exploration and industry offerings, which together have reduced its Completeness of Vision score.
- Expansion into prescriptive analytics. Alteryx has added simulation and optimization capabilities that enable users to create models spanning predictive and prescriptive analytics. Users can now simulate alternatives, predict outcomes, and discover strategic, tactical and operational efficiencies with optimization analysis.
- Customer traction. Alteryx has strong traction in the market with its “land and expand” strategy for moving customers from self-service data preparation to predictive analytics. It does normalization and fuzzy matching very well, which makes it easier for data scientists to prepare data. It offers spatial analytics capabilities, and it is likely to start promoting these as interest in location intelligence increases.
- Customer satisfaction. Ease of learning and use is the main reason for choosing Alteryx, this being identified by over half its reference customers. Other important reasons were its ability to support a wide variety of data types, and high flexibility and control for citizen data scientists in a code-free environment. Reference customers placed Alteryx almost at the top for overall customer satisfaction and delivery of business value.
- Lack of automated modeling and key language support. Although it offers functions to help automate data preparation, Alteryx does not provide capabilities to automate feature engineering or model building. Its product does not currently support Python or Scala, which have become the programming languages of choice for expert data scientists. However, automated feature modeling and support for Python and Scala are on its product roadmap.
- Visual data exploration. Although Alteryx does offer some interactive visualization capabilities, 16% of its reference customers stated that they would like better reporting, data exploration and visualization natively. More robust capabilities are available through Alteryx’s visual data discovery vendor partners, which include Tableau, Qlik and Microsoft (Power BI).
- Scalability and performance concerns. Reference customers want greater scalability and performance, and improved online documentation and support. Challenges reported by almost 30% of its reference customers included difficulty working with extremely large datasets, inability to run complex algorithms, and lack of documentation with examples for power users. Ten percent of its reference customers also noted that the high license cost limits broader use of Alteryx’s platform.