David Menninger's Analyst Perspectives

Alteryx was founded in 1997 and initially focused on analyzing demographic and geographically organized data. In 2006, the company released its eponymous product that established its direction for what the product is today. In 2017, it went public in an IPO on the NYSE. At the time of the IPO, Alteryx was focusing much of its marketing efforts on the data preparation market, particularly to support Tableau. Throughout this time though, Alteryx offered much more than data preparation. As a result, it’s hard to pigeonhole Alteryx into an existing software category. For years, the company’s capabilities revolved around Alteryx Designer, its desktop product, and Alteryx Server, for sharing and scaling capabilities among groups of users. These products provided core data preparation and blending, but also much more, including reporting, geospatial analysis and machine learning (ML). Part of what set Alteryx apart was the combination of analytics with scheduling and orchestration capabilities included in the data pipelines it produced and managed. Alteryx could connect to many different data sources and analytics tools, so organizations could apply these capabilities as the glue between different parts of the analytics process. I have written previously about AnalyticOps and the need to provide more discipline, repeatability and governance in analytics processes. But organizations also need flexibility. Their operational processes change frequently; therefore, their data and analytics processes must be able to adapt and change easily as well. That is the key to what makes Alteryx unique and difficult to place in an existing category. We assert that by 2026, one-half of organizations will adopt an analytic operations approach similar to, and integrated with, their data operations processes to enhance responsiveness and agility. In 2022, Alteryx acquired Trifacta and shortly thereafter announced Alteryx Analytics Cloud, including Designer Cloud, Alteryx Auto Insights and Alteryx Machine Learning. In 2023, it added Alteryx Location Intelligence to its cloud offering and introduced AiDIN as the branding for its generative AI capabilities. Generative AI can produce documentation of Designer workflows to help improve governance and auditability. Alteryx also provides an Open AI connector to use in Designer workflows to transform data flowing through those pipelines. Magic Documents is a feature that uses generative AI to produce summary reports in emails and PowerPoint presentations to make it quicker and easier to gain and share insights. Generative AI is also being used to enhance other aspects of the product, including recommendations on how to prepare data and construct workflows. And most recently, the company announced Alteryx Studio to assist organizations with the customization, deployment and management of large language models (LLMs), a key component of generative AI. Analytics requires both discipline and flexibility. Organizations need to consider how they achieve this delicate balance. Software vendors have not focused enough attention on AnalyticOps and that has created an opportunity for Alteryx. There’s more work to be done to complete its transition to the cloud. It’s also likely that over time other vendors will recognize the need for these capabilities. In the meantime, I recommend that organizations that want to achieve more discipline, governance and agility in their analytic operations evaluate Alteryx. Regards, David Menninger

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Alteryx Uses Generative AI to Enhance Analytic Operations

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