Domo: We’re Much More Than a BI Tool

Companies on the hunt for a business intelligence or analytics tool may have Domo on their short list. But it would be a mistake to think that the American Fork, Utah-based company is only a BI tool vendor. In fact, the company has a much broader portfolio that’s aimed at helping customers leverage their data for analytic, operational, and AI needs, the company recently told us.

“It’s a pretty big misconception that Domo’s just a BI tool or analytics tool,” Domo Chief Revenue Officer RJ Tracy told BigDATAwire at last month’s Snowflake Summit.

When Domo CEO Josh James (co-founder of Omniture, acquired in 2009 by Adobe for $1.8 billion) founded Domo back in 2010, the analytics technology landscape looked much different than it does today. Data warehousing was primarily an on-prem affair, and multi-million deployments often ended in failure.

That experience led James–a 2021 Datanami Person to Watch–to try his hand at building a fully integrated analytics stack. Domo developed an analytics database, a data integration tool, and an ETL layer (including a reverse ETL system to write data back into databases). The company built a no-code, low-code app development platform, and of course it also built a data visualization layer to let users interact with data.

Domo sold it all as an integrated stack, and it resonated with customers. But as technology improved, the need for a fully integrated stack lessened, and the company shifted strategy a year ago as a result.

“Part of the reason everyone thinks we’re a BI tool is because we always kind of wrapped it together and gave everyone the backend as part of our platform,” Tracy said. “But what we’ve done this last year is we’ve taken our back end out, and now we can sit all of our other technology on top of a customer’s data warehouse.”

Domo’s entire product stack today runs atop cloud analytics databases from Snowflake, Databricks, Google Cloud, and Oracle, and it’s working on supporting additional cloud data warehouses, such as those from AWS, Microsoft Azure, Teradata, and Cloudera, according to Tracy. Domo is also developing support for storing data in Apache Iceberg, which will give customers another option, he said.

Some customers will buy Domo just for data visualization layer, or just for the ETL and data integration capability. Some may license Domo to utilize its low-code/no-code development tool to build embedded apps, while some will utilize it to power chatbot or agentic AI applications via its integration with large language models (LLMs) from OpenAI, Huggingface, IBM, Snowflake, and Databricks.

Domo CEO and Founder Josh James is a 2021 Datanami Person to Watch

While it still supports an entire stack, customers can pick and choose which Domo components they want, and Domo will slide right into the existing environment, with a minimum of integration work, Tracy said. The company is counting on this strategy to help bolster its fortunes in the age of AI.

“There’s a lot of vendors that have a full stack, but you still have to figure out how to integrate all those stacks together, in a lot of cases,” he said. “When you get into agentic solutions, a lot of people want those embedded, they want them in real time, and Domo just does really well at serving, going from raw data all the way to serving out those agentic solutions, right where the users are.”

Domo strives to make its products easy to use, without requiring a lot of tedious development and integration work, Tracy said. The goal is to help customers solve business problems and get new apps into the hands of users as quickly as possible.

Sometimes there’s no data analytics or dashboards involved at all. For instance, Domo used its low-code/no-code development platform to create a scheduling application for a plumbing and heating company that can automatically find the technician who has the requisite skills and availability to solve customer problems.

Domo built another scheduling application for a security company that needed to find guards who had the proper security clearances and met other critical parameters to staff federal office buildings. That application would probably have taken the customer months to build, but it took Domo just three hours, Tracy said.

“We built the solution for them because we already have the front-end piece to build it,” he said. “If over time they decide they want to use more of Domo for more things, great. But if not, we’re happy to play in that center piece, too.”

Domo is building a reputation for development tools that are easy to work with and that customers enjoy working with. Thanks to its flat licensing model, it also helps to keep costs low for customers, delivering a good return on investment (ROI), Tracy said.

Domo now runs atop multiple platforms

“We make money around the edges and let all the compute go to these cloud warehouses that want the compute. That’s one huge advantage that we have,” the CRO said. “And we’re constantly winning for ease-of-use, for user experience. That really matters when you expect your culture to be one of curiosity, where 5,000 people have to adopt it. We’ve extended capabilities out to users that, frankly, haven’t had them.”

As the agentic AI revolution heats up, Domo is hoping that its ease-of-use mantra resonates with customers. Its Agent Catalyst offering can run atop multiple cloud warehouses and tap into the intelligence provided by multiple third-party LLMs. Domo will happily sit in the middle, coordinating the data flows among the various components and reaching out to users

The company, which went public on the NASDAQ in 2018, today has more than 2,600 customers, including companies like Disney, Yum Brands, and Emerson, and it generated $317 million in fiscal 2025 revenue. Now it’s moving beyond analytics into the world of AI, which is solving problems that customers couldn’t solve by just throwing more people at them, Tracy said.

“We’re leading first with AI and agentic solutions, and we’re seeing a pretty high success rate,” he said. “I think we’re really well positioned to help customers get value from their data.”

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