#ConversationalAnalytics

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Google Looker Semantic Layer Facilitate Trustworthy AI In BI

Layer Looker semantics

AI enables intelligent applications and impacts corporate decisions, making accurate and trustworthy data insights more important than ever. Due to data complexity, volume, and variety of tools and teams, misinterpretations and mistakes may occur. Looker semantic layer-managed trustworthy definitions are needed. The semantic layer provides a consistent and business-friendly interpretation of your data using business knowledge and standardised references, ensuring that your analytical and Artificial Intelligence initiatives are based in reality and produce reliable results.

Looker semantic layer ensures clear, consistent language for your organisation and goods by providing as a single source of truth for business metrics and dimensions. The semantic layer eliminates ambiguity by translating vital signals to business language and user purpose, allowing generative AI technology to understand business logic rather than raw data and offer right responses. LookML (Looker Modelling Language) helps your firm create a semantic model that specifies data logic and structure, abstracts complexity, and makes it easier for users to find information.

Looker semantic layers are essential for gen AI. Gen AI used directly to ungoverned data can provide impressive but fundamentally incorrect and inconsistent results. It can miscalculate variables, arrange data, or misread definitions, especially when constructing complex SQL. Missed income and a bad strategy may result.

Every data-driven company needs accurate business data. Our internal testing shows that Looker semantic layer reduces data errors in gen AI natural language queries by two thirds. Enterprise Strategy Group determined that data consistency and quality were the main challenges for analytics and business intelligence solutions. Looker provides a single source of truth for the firm and all related apps, ensuring data accuracy and business logic.

The cornerstone of reliable Gen AI

For gen AI to be trusted, your company's data intelligence engine, the Looker semantic layer, must provide a centralised, regulated framework that describes your essential business concepts and maintains a single, consistent source of truth.

The Looker semantic layer is essential for trustworthy gen AI for BI, providing:

Trust: Ground AI responds in regulated, homogeneous data to reduce generative AI "hallucinations".

Deep business context: Data and AI bots should know your organisation like analysts. By teaching agents your KPIs, business language, and linkages, they may better understand client questions and respond appropriately.

Governance: Enforce your data security and compliance laws in gen AI to protect sensitive data and provide auditable access.

Organisational alignment: Implement data consistency throughout your company to ensure that users, reports, and AI-driven insights utilise the same ideas and vocabulary.

Gen AI semantic layer benefit

LookML, Looker's cloud-based semantic modelling language, has several crucial capabilities for integrating current AI with BI:

Experts may build metrics, dimensions, and join connections once and reuse them across Looker Agents, discussions, and users to ensure consistent replies and get everyone on the same page.

Deterministic advanced calculations: Looker eliminates chance and generates reproducible results, making it ideal for complex mathematical or logistical procedures. Dimensionalized measurements combine variables so you may operate on them together, making complicated jobs easier to execute.

Software engineering best practices: Looker uses version control and continuous integration to test and monitor code upgrades, keeping production apps running smoothly.

Built-in dimension groups enable time-based and duration-based calculations.

Deeper data drills: By evaluating one data point, drill fields allow users to go deeper. Data agents can utilise this to allow customers explore data slices.

With a Looker semantic layer, an LLM may focus on its strengths, such as searching through well-defined business objects in LookML (e.g., Orders > Total Revenue) instead of writing SQL code for raw tables with ambiguous field names. These elements can have human-friendly descriptions like “The sum of transaction amounts or total sales price”. This is vital when company clients say, “show me revenue,” rather than, “show me the sum of sales (price), not quantity.” LookML bridges the data source and decision-maker's priorities to enable LLMs find the correct fields, filters, and sorts data agents into sophisticated ad-hoc analysts.

LookML's well-organised library catalogue helps AI agents find relevant facts and summaries to answer your question. Looker must then obtain the data.

AI-BI promises intelligent, dependable, conversational insights. These changes may help their consumers across all Looker semantic layer data interaction surfaces. Google Cloud will add capabilities to conversational analytics, improve agent intelligence, and expand data source compatibility to make data interaction as easy and effective as talking to your favourite business advisor.

Conversational Analytics

Conversational Analytics uses Gemini for Google Cloud to speak with data. Conversational analytics lets non-business intelligence users ask data queries in natural language, pushing beyond static dashboards. Conversational analytics are available in Looker (Google Cloud core), Looker (original), and Looker Studio Pro.

Like the sample dialogue, Conversational Analytics allows natural, back-and-forth interaction. The user asks, “Can you plot monthly sales of hot drinks versus smoothies for 2023, and highlight the top selling month for each type of drink?” Conversational Analytics responds with a line graph showing 2023 smoothie and hot beverage sales, stressing July as the best month.

Conversational Analytics can interpret multi-part queries using common phrases like “sales” and “hot drinks,” as seen in this sample discussion. Users don't need to establish filter criteria or database column names like “Total monthly drink sales” or “type of beverage = hot.” Conversational Analytics responds with text, charts, and an explanation of its primary findings and rationale.

Main characteristics

Conversational analytics components include:

Looker (original) and Looker (Google Cloud core) users may utilise Conversational Analytics to ask natural language questions about Looker Explore data.

Conversational Analytics in Looker Studio lets you query supported data sources in natural language. needed Looker Studio Pro subscription.

Create and talk with data agents: Personalise the AI-powered data querying agent with context and instructions specific to your data to allow Conversational Analytics to offer more precise and contextually relevant responses.

Allow sophisticated analytics using Code Interpreter: The Conversational Analytics Code Interpreter converts your natural language queries into Python code and runs it. The Code Interpreter employs Python for more advanced analysis and visualisations than SQL-based searches.

Setting up and needs

Conversational Analytics in Looker Studio requires the following.

Membership in Looker Studio Pro is necessary. Looker users can get complimentary Looker Studio Pro licenses.

Administrators must activate Gemini in Looker Studio.

You and your Looker instance must meet these prerequisites to use Conversational Analytics:

Looker administrators must enable Gemini for the instance.

Your Looker admin must grant you the Gemini position. The querying model's access_data permission must also be in a role.

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