Alteryx Announces AiDIN for AI-Powered Features

At its Inspire conference in Las Vegas on Wednesday, long-time data integration and AI player Alteryx announced a series of new platform capabilities, many of which were focused on generative artificial intelligence, and several more of which impressively beef up its hybrid cloud capabilities. While a number of data and analytics companies have recently added large language model (LLM)-based capabilities to their platforms, the majority of these have been focused on data exploration and querying. Alteryx’s announcements do provide coverage there, but they also address the areas of data pipeline governance, and the functionality within data pipelines themselves. These are perhaps less flashy capabilities, but are arguably just as substantive, if not more so.

Also read: Alteryx Integrates with UiPath, Uniting RPA and Data Pipelines

Suresh Vittal, Alteryx’s Chief Product Officer, briefed The New Stack on six major announcements, three of which are in the generative AI realm, and three of which address cloud capabilities of the core Alteryx platform. These follow quickly on Alteryx’s 2022 acquisition of Trifacta and the momentum that it has driven. As Vittal said, Alteryx has been “hard at work integrating the Trifacta platform. We announced the Alteryx Analytics cloud with machine learning, Designer Cloud and Auto Insights built on a common unified platform. That’s getting great uptake and our cloud has been up and running for several months now.”

Check out: Alteryx Analytics Cloud Consolidates Acquisitions and Functionality

AI, above the Din

On the generative AI side, Alteryx has announced AiDIN, which serves as the umbrella brand and engine for each of Alteryx’s AI capabilities, both old and new. According to Vittal, AiDIN is the “core framework of bringing… Alteryx’s data and Alteryx’s models, combining those capabilities and powering specific use cases.” The AiDIN-related announcements include:

• A Workflow Summary Tool, which can create natural language summaries of any one or group of Alteryx workloads.  Essentially, the tool has the ability to document what workflows do, after they’ve been authored, which can help engineers understand assets that they need to become acquainted with, or proactively document their own work. The summaries can be embedded in a workflow’s Meta Info field, which ensures the generated summary attaches itself to the workflow regardless of who accesses it and when.

The Workflow Summary Tool in action
Credit: Alteryx

• A feature called Magic Documents, which is essentially a new adjunct to Alteryx’s already existing Auto Insights feature, is itself driven by AI. Now, in addition to creating Auto Insights, Alteryx customers can leverage generative AI to create a conversational email message, PowerPoint slide presentation or other document that summarizes the generated insights. So not only can AI generate a report, but it can now generate a cover letter of sorts, to accompany the report. This is a useful tool to summarize such reports for managers or executives who may not have time to review them in full, but still need to know what’s in them.

Magic Documents email generation.
Credit: Alteryx

• An OpenAI Connector, embeddable in Alteryx workflows, which can call APIs in OpenAI’s generative AI platform as an automated step in a data pipeline. This takes generative AI beyond interactive chatbot scenarios and into triggered, data-driven actions that are executed autonomously. Vittal explained that this connector is for OpenAI’s own platform, and that connectors for Azure OpenAI and for customers’ own models will be forthcoming. Google AI service connectors may be added to the mix as well.

Use Cases, Today and Tomorrow

Alteryx’s applications of large language models and generative AI are more infrastructural than many which have surfaced in the analytics space recently.  They aren’t (yet) about using natural language to generate assets like reports or workflows, but rather using the technology to extend the reach, management, power and capabilities of those assets.

These generative AI-based capabilities extend primarily to natural language use cases, but LLMs can be used for scenarios beyond natural language, especially when combined with a customer’s own data. Vittal expects that Alteryx may move to certain of these scenarios soon, stating that “…we’re trying to …decouple the foundational model and the work that happens with the foundational model from the contextualized training and find… I probably think about it as fine-tuning… that we can do [this] using the customer’s data and Alteryx’s data.”

Vittal also told The New Stack that the company has been “working with our design partners on things like metadata enrichment, things like orchestration of specific very complex operational processes. And we’re finding that there’s real value in applying generative AI to these kinds of use cases because it takes a lot of tasks out of the process.”

AI Isn’t Everything

This is all neat stuff, and there’s no disputing how cool and transformational AI is. But, as I mentioned earlier, Alteryx made a few important non-AI related announcements too. They include:

• So-called “cloud-connected experiences,” like Cloud Execution for Desktop, a hybrid cloud feature that allows customers to author Alteryx workflows in the Alteryx Designer desktop application, then save them to, and have them execute in, the cloud.
• New Enterprise Utilities for enhanced governance, including Alteryx product telemetry data to manage usage across clouds, and the ability to treat workflows as code, then curate them and manage their deployment by pushing them to Git-compatible version control repositories
• New Location Intelligence capabilities which have been rewritten for the cloud, to take advantage of the extended resources and elastic computing power provided there. This makes new use cases possible because more powerful spatial data workloads can be accommodated in the cloud. Alteryx is announcing pushdown query integration with Snowflake, and integrations with TomTom, to attain this improved performance and enable the new use cases.

Must read: Snowflake Builds out Its Data Cloud

AI Economics

That’s a lot of developments to absorb. Despite the recessionary air and austerity on both the customer and vendor sides of the data realm today, Alteryx seems to be doing quite well. Vittal put it this way: “Last quarter… we expanded 121% which is kind of best-in-class expansion. The largest of our accounts expanded 131%. So we’re continuing to see demand and durability of use cases even in this macro, some might argue in this macro even more so, because more and more the teams have to do more with less. And so automation and analytics orchestration becomes more important.”

One might argue that generative AI is creating its own economic bump for our industry. While some of that may be merely a hype-driven bubble, applications of AI like the ones Alteryx has implemented add real practicality and productivity. The latter typically drives healthy economic expansion, which is something we should all be rooting for. If we can move past the “spectacle” of AI, and focus on its down-to-earth utility, good things can result.