Since the December 2022 launch of ChatGPT, the constant question that C-level execs ask their data and IT organizations is, “What value can GenAI bring to our business?”
To wit, the industry has gone through many iterations of GenAI approaches and applications very quickly, from basic chat interfaces to unstructured data processing to customer-facing AI-powered experiences. Throughout this rapidly evolving landscape, however, there has been one constant theme – data is critical to the success of any AI application.
Early on, IT professionals hoped that AI would allow them to skate over the issues that data professionals have been shouting from the rooftops for the last 15 years. But reality has set in – strong data quality and governance, and a consistent data strategy is the only way to truly build a data-driven organization. This is only exacerbated when trying to incorporate GenAI into your data toolbox.
The fastest path to GenAI value starts with a clear, enterprise-wide data strategy. Companies that treat data as a product – built, deployed, and reused across the organization – unlock value far faster than those that don’t. With a clear focus on how data products are being built, delivered, and engaged with across an organization, there is immense opportunity for a more consistent experience with data, whether that is through traditional BI channels, or more advanced AI/ML channels.
What is a Data Product?
The term “data product” was first brought into the mainstream at the same time the Data Mesh concept was developed in 2019. Rather than doing ad hoc or bespoke data projects that have a limited shelf life based on the needs of the analytics, the idea is that data needs to be treated as a true first-class citizen within an organization. It needs to be packaged in a way that allows every employee to collaborate on data and deliver the insights they need out of those products. This minimizes duplication of data, and puts the quality, governance, and adoption needs in the hands of the domain experts.
Data products have now evolved beyond being a data mesh-only approach and are becoming the standard which more advanced data organizations build their business upon.
The definition of a data product, at its core, is simply a collection of data assets that are brought together to provide consistent and actionable business outcomes to an organization. That could be as simple as a collection of tables all related to the same topic, or as complex as full stack applications powered by AI/ML capabilities. Whether it is data, metadata, code, or even specific platform dependencies, the packaged data product is all a business stakeholder should ever need to interact with.
Here at Presidio, we provide services that accelerate our clients’ adoption of a data product-centric strategy, encompassing all aspects of data product development, deployment, and adoption across an organization. From data architecture and platform design to data engineering and BI modernization, we can kickstart your journey towards a data product mindset.
AI Agents and Data Products
While data products have become an industry standard in the last 10 years, the AI world is still developing what its standards are. That said, one thing that has become a common practice and approach with a GenAI architecture is the introduction of AI agents to the GenAI application ecosystem.
Similar to the microservices AppDev approach, AI agents allow GenAI applications to avoid one giant LLM to run the entire application. Instead, agents stay close to the process context to allow them to do only what is needed now, and nothing more. It also minimizes data or application duplication and instead allows for more GenAI applications to be built on top of a consistent set of capabilities.
The hypothesis that many in the industry are operating under is that every data product that is developed in the next 2-5 years will have an AI agent packaged with that product. Then every AI application will be able to get an instant response for any request associated with that data product. This is what makes Snowflake Intelligence such a powerful tool in the Snowflake arsenal.
Snowflake Intelligence and the Future of Data Agents
The GA announcement of Snowflake Intelligence just accelerated this timeline. The Snowflake AI Data Cloud was already a perfect solution for building and deploying data products; creating collaborative, well-governed data products that every type of user – from engineers to analysts to end users – can easily access and drive value from. Customers know that when Snowflake introduces a new capability, it is going to immediately add value to all the data products that they have deployed on the platform. And that is no different with Snowflake Intelligence.
Snowflake Intelligence is a complete, end-to-end agentic platform, bundling the agent development stack (Cortex Analyst, Cortex Search, and Cortex Knowledge Extensions) and the orchestration stack (Cortex Agents) with the UI (Snowflake Intelligence Hub). This is bundled together inside of Snowflake’s Security and Observability boundary, providing consistent data and AI governance across all use cases.
Value is driven by direct usage of the Snowflake Intelligence UI, orchestration of agentic workflows with third-party applications and Snowflake’s managed MCP server; or the ingestion, processing, and insight generation from unstructured content. By building the agents as close as possible to the data product, the scalability of agentic applications is streamlined. Snowflake is making it easier to build data agents directly inside of each data product with an incredibly user-friendly wrapper.
Snowflake Intelligence is a game-changer for organizations ready to turn data into measurable AI outcomes – delivering quick wins, accelerating AI maturity, and building a truly data-driven ecosystem.
With Presidio as your Snowflake partner, you can translate that potential into tangible value – securely, strategically, and at scale.
