How Looker’s semantic layer enables trusted AI for business intelligence

In the AI era, where data fuels intelligent applications and drives business decisions, demand for accurate and consistent data insights has never been higher. However, the complexity and sheer volume of data coupled with the diversity of tools and teams can lead to misunderstandings and inaccuracies. That's why trusted definitions managed by a semantic layer become indispensable. Armed with unique information about your business, with standardized references, the semantic layer provides a business-friendly and consistent interpretation of your data, so that your AI initiatives and analytical endeavors are built on a foundation of truth and can drive reliable outcomes.

Looker’s semantic layer acts as a single source of truth for business metrics and dimensions, helping to ensure that your organization and tools are leveraging consistent and well-defined terms. By doing so, the semantic layer offers a foundation for generative AI tools to interpret business logic, not simply raw data, meaning answers are accurate, thanks to critical signals that map to business language and user intent, reducing ambiguity. LookML (Looker Modeling Language) helps you create the semantic model that empowers your organization to define the structure of your data and its logic, and abstracts complexity, easily connecting your users to the information they need.

A semantic layer is particularly important in the context of gen AI. When applied directly to ungoverned data, gen AI can produce impressive, but fundamentally inaccurate and inconsistent results. It sometimes miscalculates important variables, improperly groups data, or misinterprets definitions, including when writing complex SQL. The result can be misguided strategy and missed revenue opportunities. 

In any data-driven organization, trustworthy business information is non-negotiable. Our own internal testing has shown that Looker’s semantic layer reduces data errors in gen AI natural language queries by as much as two thirds. According to a recent report by Enterprise Strategy Group, ensuring data quality and consistency proved to be the top challenge for organizations’ analytics and business intelligence platform. Looker provides a single source of truth, ensuring data accuracy and delivering trusted business logic for the entire organization and all connected applications.

The foundation of trustworthy Gen AI

To truly trust gen AI, it needs to be anchored to a robust semantic layer, which acts as your organization's data intelligence engine, providing a centralized, governed framework that defines your core business concepts and helping to ensure a single, consistent source of truth.

The semantic layer is essential to deliver on the promise of trustworthy gen AI for BI, offering:

• Trust: Reduce gen AI "hallucinations" by grounding AI responses in governed, consistently defined data. 

• Deep business context: AI and data agents should know your business as well as your analysts do. You can empower those agents with an understanding of your business language, metrics, and relationships to accurately interpret user queries and deliver relevant answers.

• Governance: Enforce your existing data security and compliance policies within the gen AI environment, protecting sensitive information and providing auditable data access.

• Organizational alignment: Deliver data consistency across your entire organization, so every user, report and AI-driven insight are using the same definitions and terms and referring to them the same way.

The semantic layer advantage in the gen AI era

LookML, Looker’s semantic modeling language, is architected for the cloud and offers a number of critical values for fully integrating gen AI in BI:

• Centralized definitions: Experts can define metrics, dimensions, and join relationships once, to be re-used across all Looker Agents, chats and users, ensuring consistent answers that get everyone on the same page.

• Deterministic advanced calculations: Ideal for complex mathematical or logistical operations, Looker eliminates randomness and provides predictable and repeatable outcomes. Additionally, our dimensionalized measures capability aggregates values so you can perform operations on them as a group, letting you perform complex actions quickly and simply.

• Software engineering best practices: With continuous integration and version control, Looker ensures code changes are frequently tested and tracked, keeping production applications running smoothly.

• Time-based analysis: Built-in dimension groups allow for time-based and duration-based calculations.

• Deeper data drills: Drill fields allow users to explore data in detail through exploration of a single data point. Data agents can tap into this capability and assist users to dive deeper into different slices of data.

With the foundation of a semantic layer, rather than asking an LLM to write SQL code against raw tables with ambiguous field names (e.g., order.sales_sku_price_US), the LLM is empowered to do what it excels at: searching through clearly defined business objects within LookML (e.g., Orders > Total Revenue). These objects can include metadata and human-friendly descriptions (e.g., "The sum of transaction amounts or total sales price"). This is critical when business users speak in the language of business — “show me revenue” — versus the language of data — ”show me sum of sales (price), not quantity.” LookML bridges the data source and what a decision-maker cares about, so an LLM can better identify the correct fields, filters, and sorts and turn data agents into intelligent ad-hoc analysts.

LookML offers you a well-structured library catalog for your data, enabling an AI agent to find relevant information and summaries, so it can accurately answer your question. Looker then handles the task of actually retrieving that information from the right place.

The coming together of AI and BI promises intelligent, trustworthy and conversational insights. Looker's semantic layer empowers our customers to gain benefit from these innovations in all the surfaces where they engage with their data. We will continue to expand support for a wide variety of data sources, enrich agent intelligence, and add functionality to conversational analytics to make data interaction as intuitive and powerful as a conversation with your most trusted business advisor.

To gain the full benefits of Looker’s semantic layer and Conversation Analytics, get started here. To learn more about the Conversational Analytics API, see our recent update from Google Cloud Next, or sign up here for preview access.