Conversational Analytics for dbt + BI Workflows

By Andrey Avtomonov, CTO at Kaelio | 2x founder in AI + Data | ex-CERN, ex-Dataiku · Feb 3rd, 2026

Conversational analytics transforms dbt and BI workflows by enabling business users to query data using natural language instead of SQL or dashboards. This approach eliminates duplicate coding while ensuring consistent self-service access to metrics through the dbt Semantic Layer, reducing analyst workload by 40-60% and accelerating time-to-insight by up to 70%.

Key Facts

• Analyst efficiency: Teams spend 40-60% less time on ad hoc reporting requests when conversational analytics is implemented

• User adoption: Only 15-25% of employees regularly use traditional BI tools, while conversational interfaces see higher engagement rates

• Accuracy improvement: LLM accuracy increases by up to 300% when integrated with semantic layers versus raw tables

• Speed gains: Organizations report 40-70% faster time-to-insight with natural language analytics

• ROI metrics: Forrester study shows 337% ROI with payback period under 6 months for conversational AI implementations

• Governance controls: The Semantic Layer implements robust access permissions ensuring security across all query interfaces

Conversational analytics is reshaping how data teams and business users interact with metrics, turning static dashboards into real-time dialogue. In this article we show why that matters for any company running dbt and BI tools, and how Kaelio makes the shift painless.

From Dashboards to Dialogue: Why Conversational Analytics Matters Now

"Conversational analytics is a capability within modern business intelligence (BI) or analytics platforms that lets you explore your data by asking questions in natural language." That single sentence from ThoughtSpot captures a paradigm shift that every data team should understand.

Traditional BI workflows force business users to navigate complex dashboards or wait for analysts to run queries. Conversational analytics flips that model. Users type a question like "What was pipeline conversion last quarter?" and receive a governed answer instantly, no SQL or dashboard navigation required.

This matters because "Conversational analytics, powered by contextual semantic layers, makes it possible for anyone, not just analysts, to ask questions in natural language and get trusted, contextual answers instantly," according to Codd AI. The combination of natural language processing, intent recognition, and context awareness allows even non-technical users to explore data conversationally.

For organizations running dbt, this shift creates a significant opportunity. Your semantic models and metric definitions already exist. The question is whether your business users can access them without learning SQL or waiting in a ticket queue.

Where Traditional dbt + BI Workflows Struggle

Every analytics engineer knows the pattern. A Slack message arrives: "Hi, can you send last quarter's revenue numbers by city? Need it ASAP for a QBR deck." What follows is a familiar cycle of clarifying questions, ticket creation, and context switching.

The numbers are striking. "Most analytics teams spend a staggering 40-60% of their time on ad hoc reporting, fulfilling one-off requests from business users rather than building scalable models or predictive insights" according to research cited by Codd AI. That time could go toward building better data products, improving data quality, or developing predictive models.

Meanwhile, BI tool adoption remains stubbornly low. "Despite billions spent on BI, only 15-25% of employees regularly use BI tools" based on industry research. The tools exist, the data is modeled, but the friction between question and answer prevents widespread adoption.

The root cause is a workflow problem. "We believe that analytics teams have a workflow problem. Too often, analysts operate in isolation, and this creates suboptimal outcomes. Knowledge is siloed. We too often rewrite analyses that a colleague had already written. We fail to grasp the nuances of datasets that we're less familiar with. We differ in our calculations of a shared metric," as stated in dbt's viewpoint.

These challenges compound when generative AI enters the picture. AI tools that bypass your dbt models and query raw tables create inconsistent answers and governance risks. The semantic layer you carefully built becomes irrelevant if users can circumvent it with a chatbot that guesses at business logic.

How Does the dbt Semantic Layer Unlock Trusted Conversations?

The dbt Semantic Layer solves a fundamental problem in analytics: ensuring everyone uses the same metric definitions regardless of which tool they use to access data.

"The dbt Semantic Layer eliminates duplicate coding by allowing data teams to define metrics on top of existing models and automatically handling data joins," according to dbt documentation. When you define a metric like "monthly active users" or "net revenue" once in your dbt project, that definition propagates everywhere.

The architecture centers on MetricFlow, which translates natural language requests into SQL based on the semantics defined in your dbt project. As the dbt Semantic Layer with Cortex blog explains, "MetricFlow is the underlying piece of technology in the semantic layer that will translate that request to SQL based on the semantics you've defined in your dbt project."

This translation layer is critical for conversational analytics. When a user asks "What is total revenue by month in 2026?", the system needs to understand which table contains revenue data, how to calculate the metric, and which time dimension to use. MetricFlow handles this translation without requiring the LLM to generate SQL from scratch.

Best practices for exposing metrics through the Semantic Layer fall into five themes:

• Governance: Control who can access which metrics and prevent unauthorized modifications
• Discoverability: Make metrics easy to find and understand
• Organization: Structure metrics so non-technical users can navigate without extensive context
• Query flexibility: Allow users to query metrics with or without dimensions
• Context and interpretation: Expose both business definitions and logical definitions of metrics

The Semantic Layer also implements robust access permissions, ensuring that conversational interfaces respect the same security controls as your dashboards.

Key takeaway: The dbt Semantic Layer provides the governed foundation that makes conversational analytics trustworthy rather than risky.

What Does a Kaelio + dbt Reference Architecture Look Like?

Integrating conversational analytics with your dbt stack requires thoughtful architecture. The goal is to add natural language capabilities without disrupting existing workflows or creating governance gaps.

"Kaelio integrates seamlessly with your warehouse and data transformation layer to ensure everyone's working from consistent, governed metrics when accessing insights through natural language or dashboards, no rebuilds required," as described on Kaelio's site.

A typical reference architecture includes:

1. Data Warehouse (Snowflake, BigQuery, Databricks, etc.): Your single source of truth for raw and transformed data

2. dbt Transformation Layer: Models, tests, and documentation that transform raw data into analytics-ready datasets

3. dbt Semantic Layer / MetricFlow: Centralized metric definitions that ensure consistent calculations across all downstream tools

4. Kaelio AI Layer: Natural language interface that queries the Semantic Layer and respects governance controls

5. Consumption Interfaces: Slack, web dashboards, embedded analytics, and BI tools

The Model Context Protocol (MCP) server provides a standardized framework for AI applications to access dbt-managed data assets. This ensures that conversational interfaces can leverage your existing models, tests, and documentation without duplicating effort.

"MetricFlow translates natural language requests to SQL based on your dbt project semantics, eliminating guesswork about business logic," according to Kaelio's documentation. This means Kaelio can answer questions using the exact metric definitions your data team has validated.

The impact of this integration is measurable. "LLM accuracy increases by up to 300% when integrated with semantic layers versus raw tables," according to semantic layer research. Organizations also report "93% of users rated governance-focused platforms highly, confirming that enterprises prioritize both AI capabilities and data governance," per Kaelio.

Kaelio shows the reasoning, lineage, and data sources behind each calculation. This transparency builds trust with users who need to understand where their numbers come from.

How Do You Prove the ROI of Conversational Analytics?

Building a business case for conversational analytics requires quantifying both direct savings and productivity gains.

The most immediate benefit is analyst time reclaimed. If your team spends 40-60% of time on ad hoc requests, even a 50% reduction in that workload translates to significant capacity for higher-value work. "Organizations using natural language or AI-driven analytics report 40-70% faster time-to-insight" according to industry research.

A Forrester study on conversational AI found measurable returns across multiple dimensions. The composite organization analyzed achieved "ROI 337%" with a "payback period: <6 months," according to the Forrester Total Economic Impact study.

User experience research on natural language interfaces shows concrete improvements. "SQL-LLM significantly reduced query completion times by 10-30% (mean: 418 s vs. 629 s, p = 0.036) and improved overall accuracy from 50% to 75% (p = 0.002)," according to academic research.

ROI calculation categories include:

• Analyst time savings: Reduced ad hoc request volume multiplied by average analyst cost per hour
• Business user productivity: Faster time-to-insight multiplied by decision value
• BI tool consolidation: Reduced licensing and maintenance costs from streamlined tooling
• Reduced training costs: Lower barrier to data access means less time spent teaching SQL or dashboard navigation

The key is measuring baseline metrics before implementation so you can demonstrate improvement. Track ad hoc request volume, time-to-resolution, and BI tool adoption rates.

How Do You Tackle LLM Hallucination and Compliance?

LLM hallucination is a legitimate concern for any enterprise deploying conversational analytics. "A major flaw that prevents widespread deployment of LLMs is factual inconsistencies, also referred to as hallucinations, which impair trust in AI systems," as noted in academic research.

The risk is real: "Enterprises building GenAI applications that incorporate large language models are experiencing problems with hallucinations, grounding, poor user experience and inappropriate data stores for use with LLMs," according to a Gartner report.

Mitigation strategies fall into several categories:

Semantic Layer Grounding: By routing queries through the dbt Semantic Layer, you constrain the LLM to work with validated metric definitions rather than guessing at business logic. The LLM's job becomes intent recognition and query construction, not SQL generation from scratch.

Transparency and Lineage: Users should see the SQL generated, the sources queried, and the calculation logic applied. This allows validation and builds trust.

Knowledge Graph Integration: "Knowledge Graphs (KGs) provide a structured collection of interconnected facts represented as entities (nodes) and their relationships (edges)." These can provide factual grounding that reduces hallucination risk.

Compliance Controls: For regulated industries, conversational analytics platforms must meet standards like HIPAA and SOC 2. "Dialogflow CX is compliant with the following: HIPAA, ISO 27001, ISO 27017, ISO 27018, ISO 27701, SOC 1, SOC 2, SOC 3," according to Google Cloud documentation.

Kaelio addresses these concerns through its architecture. It is SOC 2 and HIPAA compliant, can be deployed in your own VPC or on premises, and never accesses row-level data. The platform relies on your existing semantic and modeling tools as the source of truth, adding a natural language interface without introducing governance gaps.

Implementation Checklist for Data & Analytics Teams

Rolling out conversational analytics on top of dbt models requires coordination across data engineering, analytics, and business teams.

Phase 1: Foundation Assessment

• Audit existing dbt models for documentation completeness
• Review Semantic Layer metric definitions for accuracy and coverage
• Identify high-frequency ad hoc requests that would benefit from self-service
• Document current BI tool adoption rates as baseline

Phase 2: Semantic Layer Optimization

"We recommend organizing metrics and dimensions in ways that a non-technical user can understand the data model, without needing much context," according to dbt integration best practices. This means:

• Use clear, business-friendly naming conventions
• Add descriptions that explain what metrics measure and when to use them
• Expose both business definitions and logical definitions
• Test metrics across common query patterns

Phase 3: Integration and Testing

"With automatic code generation and using natural language prompts, Copilot can generate code, documentation, data tests, metrics, and semantic models for you with the click of a button," per dbt Copilot documentation. Leverage AI assistance to accelerate documentation and testing.

• Connect Kaelio to your data stack
• Configure permissions to match existing governance controls
• Test common queries against known correct answers
• Validate lineage and transparency features

Phase 4: Rollout and Iteration

"The ADLC is heavily informed by a single guiding principle: analytical systems are software systems," according to dbt's Analytics Development Lifecycle. Apply software engineering practices to your rollout:

• Start with a pilot team to gather feedback
• Monitor query patterns to identify gaps in metric coverage
• Use feedback loops to improve Semantic Layer definitions
• Expand access based on demonstrated value

Key takeaway: Treat conversational analytics implementation as a product launch, not a tool installation. Success depends on semantic layer quality, user training, and continuous iteration.

Conversational Analytics is the New Control Plane for Insights

Conversational analytics represents a fundamental shift in how organizations interact with data. Instead of navigating dashboards or waiting for analyst time, business users can ask questions and receive governed, trustworthy answers instantly.

The technology works best when built on a solid foundation. The dbt Semantic Layer provides centralized metric definitions. MetricFlow handles translation from natural language to SQL. Governance controls ensure security and compliance.

"Kaelio finds redundant, deprecated, or inconsistent metrics and surfaces where definitions have drifted. It helps standardize naming, ownership, and documentation, so your metrics stay accurate, not duplicated," as Kaelio describes. Beyond answering questions, the platform helps improve the underlying data infrastructure.

Kaelio is SOC 2 Type II Certified and HIPAA Certified, meeting the compliance requirements of regulated industries. It integrates with your existing data stack without requiring rebuilds, and it provides the transparency and lineage visibility that builds user trust.

For Series A and B SaaS companies where founders and business teams need insights to move fast, conversational analytics eliminates the bottleneck between question and answer. Your data team built the models. Now everyone can use them.

Ready to see conversational analytics in action? Reach out to Kaelio for a demo and start answering your team's toughest questions in minutes.

Frequently Asked Questions

What is conversational analytics?

Conversational analytics is a feature in modern BI platforms that allows users to explore data by asking questions in natural language, providing instant, governed answers without needing SQL or dashboard navigation.

How does Kaelio integrate with dbt and BI tools?

Kaelio integrates seamlessly with your data warehouse and transformation layers, ensuring consistent, governed metrics are used when accessing insights through natural language or dashboards, without requiring rebuilds.

What challenges do traditional dbt and BI workflows face?

Traditional workflows often involve complex dashboard navigation and reliance on analysts for queries, leading to inefficiencies and low BI tool adoption. Conversational analytics addresses these by allowing direct, natural language queries.

How does the dbt Semantic Layer support conversational analytics?

The dbt Semantic Layer ensures consistent metric definitions across tools, allowing conversational analytics to provide trustworthy answers by translating natural language requests into SQL based on predefined semantics.

What are the benefits of implementing conversational analytics?

Conversational analytics can significantly reduce ad hoc reporting time, improve BI tool adoption, and enhance user productivity by providing faster, more accessible insights through natural language queries.

Sources

1. https://docs.getdbt.com/docs/use-dbt-semantic-layer/dbt-semantic-layer
2. https://kaelio.com
3. https://www.thoughtspot.com/data-trends/analytics/conversational-analytics
4. https://codd.ai/blog/roi-of-conversational-analytics-unlocking-value
5. https://cloud.google.com/looker/docs/conversational-analytics-overview
6. https://www.getdbt.com/blog/analysts-autonomy-data-governance
7. https://next.docs.getdbt.com/community/resources/viewpoint
8. https://docs.getdbt.com/blog/semantic-layer-cortex
9. https://next.docs.getdbt.com/guides/sl-partner-integration-guide
10. https://www.ibm.com/watson/assets/duo/pdf/watsonassistant/TheTotalEconomicImpactofIBMWatsonAssistant-March2020v3.pdf
11. https://arxiv.org/pdf/2511.14718
12. https://www.sciencedirect.com/science/article/pii/S1570826824000301
13. https://www.k2view.com/gartner-early-lessons-in-building-llm-based-generative-ai-solutions-halluciantions
14. https://docs.cloud.google.com/dialogflow/docs/compliance-security-controls
15. https://docs.getdbt.com/docs/cloud/dbt-copilot
16. https://www.getdbt.com/resources/guides/the-analytics-development-lifecycle