A few days after Alteryx’s earnings release, Gartner published its updated Magic Quadrant for Data Science and Machine Learning Platforms. Alteryx recaptured the Leader position, after being moved to a Challenger in 2019. Gartner highlighted solid expansion in Alteryx’s product vision, driven by improvements in augmented machine learning and process automation. Additionally, Alteryx was rated as having the highest ability to execute on its vision amongst all companies. This recognition from Gartner validates Alteryx’s strong product position and reflects progress in 2019 to extend its offerings across the full life-cycle of data science and machine learning.
As investors will recall, in the February 2019 Magic Quadrant release, Alteryx was moved into the Challenger position, due to a reduction in perception of their completeness of vision. Gartner stated that Alteryx was demonstrating less innovation than other vendors in the prior year (period covered was 2018). Specifically from Gartner, “Alteryx’s innovation scores were low, relative to other vendors in this Magic Quadrant. Alteryx is not a standout vendor in terms of automation and augmentation, deep learning or the Internet of Things (IoT).” The perception was that Alteryx had a strong offering for data prep and prescriptive analytics, but was not evolving to address more advanced data science use cases like augmented machine learning and automation of model creation.
I discussed this situation in my Alteryx (AYX) stock recommendation in December, pointing out that over the course of 2019, Alteryx was making investments to extend its product coverage. Through internal product development, the Alteryx team worked towards supporting “predictive” analytics and automating the process of creating new models. This work was first bundled into the Promote offering, which enables teams to publish and share their analytical models. Models can be made available as an open API that can be queried by other applications, or the models can be exported into Python or R code that data scientists can utilize. In this manner, semi-automated modeling is enabled (human reviewed).
The acquisition of Feature Labs in October 2019 took this a step further. Feature Labs brings exciting new capabilities in machine learning that will be incorporated into the Alteryx platform. Feature Labs is a machine learning startup that was launched out of MIT in 2018. It focuses on automating the creation of machine learning models and offers popular open-source libraries for data scientists. Feature Labs makes machine learning model creation more practical for businesses by focusing on feature engineering, which is the process of using domain knowledge to extract new variables from available enterprise data sources to feed machine learning models.
In the updated Magic Quadrant for February 2020, Gartner picked up on these improvements. Alteryx moved further to the right on the Completeness of Vision axis and back into the Leader quadrant. The full Magic Quadrant is published below for reference.
Gartner offered the following commentary in their explanation of the results.
Alteryx has returned to its 2018 position of Leader, from being a Challenger in 2019, by demonstrating a solid company and product vision, especially in relation to augmented DSML and process automation. The vendor continues to outperform almost all other vendors in this Magic Quadrant from a revenue growth perspective and has significantly expanded its business internationally.
Alteryx has made significant progress in changing the perception that it is solely a data preparation provider. It made two strategic acquisitions (ClearStory Data and Feature Labs) in 2019 to expand its platform capabilities. ClearStory Data provides a solution that enables automation of analytics of complex data and unstructured data on large-scale data processing platforms like Apache Spark. Feature Labs automates feature engineering, the creation of AI applications and data preparation process to help improve model accuracy and overall process efficiency.
Gartner Magic Quadrant for DSML, FEb 2020
Additionally, while Gartner recognized the impact of the Feature Labs acquisition, they noted that this Magic Quadrant report did not include the full benefit in scoring of the Feature Lab capabilities, as the acquisition occurred during the report’s preparation phase. Therefore, we can expect further positive influence from this going into next year’s report. This is important as Alteryx still lags most other providers along the Vision axis. Presumably, we will see further movement in next year’s report.
Other Alteryx highlights from Gartner’s review:
- Alteryx is now perceived as having a comprehensive data analytics platform versus just data prep tools. This aligns with Alteryx’s own product positioning in the marketplace.
- Calls out the appeal of Alteryx’s no-code approach to enable data analysts and citizen data scientists to take advantage of machine learning. Further points out the large number of automation building blocks available in user libraries to facilitate rapid construction of analytics workflows.
- Recognizes the robust user community that Alteryx has amassed, including the sharing of ideas and working solutions in online forums.
- Under concerns, surfaces some customer feedback about high prices and complex licensing arrangements.
- Pointed out current Alteryx limitations with applying their solutions towards the real-time analysis of streaming data for IoT. This is valid and may be a capability Alteryx addresses in the future.
Additionally, there were notable changes in the competitive set in the Leaders quadrant between the 2019 and 2020 reports.
- Additions: Databricks, Dataiku, MathWorks
- Removals: KNIME, RapidMiner
I won’t go into a detailed review of these. Databricks is generating a lot of attention and executing well in product adoption. Their solution is rooted in Apache Spark and they are active in the open source community. We should monitor their momentum. The removal of KNIME is interesting, as they are sometimes referenced as an open source competitor to Alteryx. Gartner substantially lowered KNIME’s Ability to Execute and Vision scores in the 2020 Magic Quadrant, as compared to 2019. In the commentary, Gartner attributes this to “lower visibility and slow revenue growth relative to other vendors”, due to limited upgrades to the commercial platform. However, Gartner still acknowledges KNIME’s excellent product and connections with the data science community.
Investor Take-aways
It is encouraging to see Gartner recognize the progress that Alteryx is making in building out their full platform offering. CEO Dean Stoecker reinforced the platform approach several times on the Q4 earnings call.
Alteryx wants their solutions to address all steps in the full lifecycle of data analytics. While the majority of their current business is generated by products on the left side of the analytical maturity scale above (preparation, descriptive and diagnostic), Alteryx is rapidly building out capabilities in the predictive and prescriptive machine learning realm. Adding automation capabilities and feature engineering through the Feature Labs acquisition will further drive their product development motion on the maturity scale. Also, with nearly $975M in cash and equivalents, Alteryx is well positioned to continue to acquire or build new capabilities to extend the platform. These investments should enable larger customer deals and broader penetration of data science and machine learning use cases going forward.
