With organizations racing to create unique and transformative generative AI and AI agent solutions while continuing to use data for analytics and BI, we can quickly see the value of data products. They become one of the core building blocks for data-driven organizations and are essential for AI agent development as the data foundation, context, and tools necessary for effective operations. They empower AI agents with access to clean, organized, and relevant data for training, learning, and adaptation.
By integrating data from diverse sources and facilitating contextual understanding, data products enable agents to make informed decisions and interact seamlessly with their environments. They also support reinforcement learning and RAG and can tie into vectors for advanced similarity searches.
Domo, as an innovator in aAnalytics, BI, and visualization, finds itself in a unique position to expand into empowering organizations further with the tools and capabilities to build generative AI solutions and powerful AI agents. As shown in Figure 3, they offer a robust end-to-end solution. This starts with Magic ETL, which can connect to the hundreds of data sources an organization might have, wherever the data resides—including every leading data lake and application. For AI, Domo provides access to the leading large language models (LLM), followed by having the tools and knowledge to facilitate the testing and development of solutions. This leads into a core area where many organizations struggle: visualizing the data to empower the organization, employees, customers, and partners with the most effective way to use the data.
This foundation of data, tools, and knowledge forms the data product that enables the development of business solutions. For example, if you’'re building a customer experience application, the data product might consist of structured and unstructured data such as marketing content, customer records, support interactions, and more. To ensure this data product delivers accurate, actionable insights—whether in dashboards, chatbots, AI assistants, or automated agents—it must be governed by strong data quality, security, and governance practices.
