Analyst Perspectives

Domo is best known as a business intelligence (BI) and analytics software provider, thanks to its functionality for visualization, reporting, data science and embedded analytics. Additionally, as I recently explained, the company’s platform addresses a broad range of capabilities that includes data governance and security, data integration and application development, as well as the automation and incorporation of artificial intelligence (AI) and machine learning (ML) models into BI and analytics. Domo further expanded its purview at its Domopalooza customer event In March with the announcement of new capabilities to address agentic AI and the creation and sharing of data products.

Domo was founded in 2010 by chief executive officer Josh James, previously founder and CEO of web analytics provider Omniture. In that former role, James identified an opportunity to create a business intelligence platform designed to better enable CEOs and other senior executives to access enterprise data directly rather than relying on reports and dashboards created for them by data and analytics specialists. Expanding on that original vision over the past 15 years, Domo now provides a platform for self-service BI and AI that addresses data integration, data processing, data governance and application development, as well as data science, AI and ML. The company was rated as Exemplary in ISG Research’s 2024 Analytics and Data Buyers Guide and the associated Buyers Guides for Collaborative Analytics, Embedded Analytics, GenAI Analytics and Mobile Analytics. Domo claims more than 2,600 customers and recently reported fiscal 2025 revenue of $317 million.

Domo’s platform addresses the full breadth of an enterprise’s analytics needs. The scope spans data integration—building on more than 1,000 pre-built connectors and supported by data governance and data security—through BI and AI functionality for both data analysts and business users to application development, workflow automation and data sharing. Facilitating self-service data analytics was an early design goal for Domo, providing the company with differentiation compared to many of its rivals. Enabling self-service BI has long been a goal of many enterprises, but few have succeeded in delivering it. Only 15% of participants in ISG’s Analytics and Data Benchmark Research stated that their organization is very comfortable allowing business users to work with data that has not been integrated or prepared for them by IT.

Another earlier differentiator for Domo was its Adrenaline live cache layer, which enables the company to deliver sub-second query performance on large volumes of data. While ingesting data into Domo for analysis remains an option, the company’s customers are increasingly taking advantage of its Cloud Amplifier architecture and data federation capabilities to avoid data movement and bring Domo’s analytics and AI capabilities to cloud data warehouse and data lakehouse environments, including AWS’ Amazon Redshift, Databricks, Google BigQuery and Snowflake.

Domo made several significant announcements at its recent Domopalooza customer event in Salt Lake City. Significantly, the company announced the launch of Agent Catalyst to enable customers to create agentic AI plus new capabilities for developing and sharing data products. As my colleague Robert Kugel recently explained, agents are an important evolutionary step in the design of business software, similar to the transition from procedural programming to event-driven programming that accelerated in the late 1980s. Agentic AI enables the delivery of automated actions driven by a combination of predictive and generative AI models as well as predefined rules and heuristics. Domo’s Agent Catalyst builds on the company’s established Domo.AI capabilities to enable the creation of AI agents to analyze and automate business processes.

Domo’s Agent Catalyst provides a framework for agent creation based on a four-step process of model selection, instruction definition, knowledge provision and tool selection. Key components of Agent Catalyst include DomoGPT, which can be configured to use a variety of large language models, and a new FileSets feature to provide agents with context from unstructured data via retrieval augmented generation. Also new is Automated Insights and Metrics, a framework for identifying and monitoring business metrics pertinent to users based on their roles and interests that build on Domo’s new semantic layer for defining business terms and relationships.

As I previously explained, ISG Research defines data products as the outcome of data initiatives developed with product thinking and delivered as reusable assets discovered and consumed by others on a self-service basis. Domo has enabled collaborative development and sharing of analytics for many years, using its Buzz and Domo Everywhere functionality. At Domopalooza, the company announced new capabilities to facilitate the development of data products, including navigation enhancements and a new Workspaces feature based on folders and filters to enable users to organize, prioritize and share data products by departments and use cases.

Our research leads me to assert that by 2027, more than 3 in 5 enterprises will adopt technologies to facilitate the delivery of data as a product as they adapt their cultural and organizational approaches to domain-based data ownership. Enhancements to Domo’s App Studio enhance the delivery of data products, including new Report Builder capabilities for report scheduling and the Table Element spreadsheet interface.

While Domo Agent Catalyst is available now, many of the product enhancements announced by Domo are in the early stage of availability. For example, Automated Insights and Metrics and FileSets are in beta testing along with App Studio Report Builder and Domo’s new navigation enhancements. Workspaces is in alpha testing. Nevertheless, I recommend that enterprises evaluating analytic platforms for the ability to support the development of agents and data products include Domo in the assessments.

Regards,

Matt Aslett