Every business today is data-centric and the role of semantic layers for optimizing efficiency is undeniable. Serving as the bridge between raw data sources and end-point data sources, semantic layers offer a consistent, comprehensive perspective of data across the board. This helps businesses make decisions rooted and organized in accuracy and reliability. By centralizing the definition of semantics and metrics, data teams can eliminate inconsistencies and ensure a unified understanding across all data users, be in internal, external or AI bots. Let us look into how semantic layers streamline data management and analytics, simplifying the concepts of a semantic layer into digestible and relatable terms.
How a Semantic Layer Works in the Data Stack
Semantic layers are not a new concept, but their importance has grown with the increasing complexity of modern data stacks. Today, businesses use a variety of visualization tools, customer-facing analytics, embedded analytics, and AI agents. Each time a new data visualization or data app is implemented, metrics and/or logic need to be defined. If not managed properly, this can lead to inconsistencies and discrepancies and, ultimately wasted resources. Semantic layers address this issue by centralizing these definitions, ensuring consistency across different data consumption endpoints.
The semantic layer has been around for a while, embedded in Business Intelligence (BI) tools. However, the recent shift towards standalone semantic layers is driven by the need for a universal, easy-to-use, and highly discoverable data interface. The democratization of data has led to fragmentation and silos, impeding ease of use and leading to failed analytics projects. A standalone semantic layer addresses these issues, ensuring a single source of consistent data that is easier for data teams to work with.
The implementation of semantic layers can significantly streamline data management processes. Sitting on top of data warehouses, semantic layers provide data semantics (context) to various data applications. They work seamlessly with transformation tools, allowing businesses to define metrics, prepare data models, and expose them to different BI and analytics tools. This not only enhances efficiency but also ensures that all data consumption places are working with the same, accurate data. So, no matter if the data is being used by a person looking at a dashboard, or a LLM that is giving a person answers to questions, the data is consistent. All of this makes it easier for data teams to quickly deliver data for the various data consumers they work with internally and externally.
Examples of how companies use semantic layers
Businesses like Drift and Breakthrough have successfully leveraged semantic layers to enhance their data management and analytics. Drift, a customer communication platform, uses semantic layers to ensure consistency in its metrics across different systems. This has not only improved their reporting capabilities but also enabled them to add new data in days instead of months. Breakthrough, a transportation management platform, uses semantic layers to provide consistent data across their SaaS and consulting platforms, significantly improving productivity.
Adopting a semantic layer in an organization is a step-by-step process. It begins with identifying inefficiencies and building a thoughtful semantic model. This model is then incrementally applied to different products, aligning metrics and data definitions. The process also involves identifying and fixing bugs in the old system, ensuring that the data is well-understood and well-documented. This is crucial for the success of analytics and AI projects.
How AI agents, LLMs and the semantic layers work together
The semantic layer also plays a pivotal role in the realm of generative AI. As AI agents are increasingly used for various applications, they need to access and analyze hard data. Due to their non-deterministic nature, AI agents are not good with hard numbers, they need to rely on the context training that has been done on the Large Language Model (LLM) they are fed from. Use of LLMs to generate code (like SQL) can be executed to get the hard numbers for the AI agent. This is where the semantic layer comes in, providing AI agents with the necessary data access and semantics via the LLM.
However, the use of generative AI also presents new challenges. One of these is the need for a new interface for interacting with data. Another is the need for explainability, ensuring that users can trust the data and understand how the AI is working. The semantic layer can help address these challenges, providing a well-documented and well-understood data interface for AI agents. Semantic layers provide both valuable context and necessary constraint to empower retrieval augmented generation models. While much has been written about providing context via embeddings, we are now learning that constraining LLMs via a semantic layer - choosing from a defined universe of metrics - can be very useful for improving accuracy. A recent test was performed with an AI Chatbot on data – with and without a semantic layer feeding the LLM. The results were dang clear – AI Chatbot with a semantic layer, outcomes were 100% accurate — AI Chatbot without a semantic layer, outcomes were 83% accurate.
In summing up, semantic layers are an invaluable asset for businesses keen on optimizing their data management and analytics. They centralize metric definitions and semantics, ensuring consistency across various data points, which in turn, bolster efficiency and precision. The future will bring more data sources and more data applications, creating even more chaos in the data stack. The significance of semantic layers in navigating the intricacies of the contemporary data stack is poised to increase exponentially. The semantic layer plays a crucial role in increasing confidence in data for non-technical users, fostering the incorporation of analytics and AI projects. It also paves the way for generative AI to access and scrutinize data. Looking ahead, the impact of the semantic layer on data analytics and AI is expected to be even more profound.
