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What Is Conversational Analytics?

January 15, 2026

What Is Conversational Analytics?

By Andrey Avtomonov, CTO at Kaelio | 2x founder in AI + Data | ex-CERN, ex-Dataiku · Jan 15th, 2026

Conversational analytics uses natural language processing to let business users query data through plain conversation instead of SQL or complex dashboards. These platforms translate questions into database queries while maintaining security and governance controls. With traditional BI adoption stuck at 29%, conversational analytics democratizes data access by eliminating technical barriers while inheriting existing warehouse security and semantic definitions.

At a Glance

Conversational analytics transforms natural language questions into SQL queries, visualizations, and insights without requiring technical expertise
43% of organizations now use AI-powered analytics in production, with 56% citing improved decision-making as their primary goal
Success requires a governed semantic layer that ensures consistent metric definitions across all queries and prevents conflicting calculations
Enterprise adoption demands inherited security controls including row-level access policies, RBAC integration, and compliance certifications like SOC 2 and HIPAA
Leading platforms like Kaelio connect to existing data infrastructure rather than replacing it, learning from usage patterns to improve definitions over time
ROI benchmarks show 40-70% time savings for data teams and clinicians through automated compliance checks and reduced manual query requests

Conversational analytics is reshaping how organizations access and act on their data. Instead of writing SQL queries or navigating rigid dashboards, business users can now ask questions in plain English and receive instant, trustworthy answers. This shift matters because traditional BI adoption remains stuck at 29% despite years of investment, leaving most employees locked out of the insights they need.

Kaelio is built to close that gap. By connecting to existing data infrastructure and respecting established governance rules, Kaelio empowers non-technical users to go from raw data to insights faster and more reliably than legacy tools allow.

Conversational Analytics: A Plain-English Definition

"Conversational analytics software is a business intelligence tool that lets users query and analyze data using natural language instead of writing SQL or clicking through rigidly designed dashboards."

That definition, from a recent ThoughtSpot buyer's guide, captures the core idea. The software interprets your questions, queries your data sources, and returns answers as visualizations, tables, or text summaries. It uses natural language processing and AI-aided analytics to understand your intent and translate conversational queries into data operations.

ISG Research offers a complementary perspective, defining conversational AI as "a method that enables workers to engage using human language, enhancing interactions through natural language processing, LLMs, and GPT to answer questions, gain insights and take actions."

The momentum behind these tools is undeniable. According to Forrester, "Organizations are turning to conversational AI (CAI) to improve support experiences, but adoption is often harder than expected." Success depends on aligning CAI initiatives with clear business goals like reducing resolution time and improving satisfaction.

Kaelio addresses these challenges by acting as a natural language interface that sits on top of your existing data stack. Rather than replacing your warehouse, transformation layer, or BI tools, Kaelio learns from how people ask questions and helps data teams improve definitions, documentation, and governance over time. The company's mission is to empower non-technical users to go from raw data to insights easier, faster, and more reliably than they have ever experienced.

How Do Conversational Analytics Systems Turn Questions into Answers?

Understanding the pipeline from natural language input to governed results is essential for evaluating any conversational analytics platform.

Most systems follow a similar architecture. Google Cloud's documentation describes how developers can use the Conversational Analytics API to build an AI-powered chat interface, or data agent. The API uses natural language to answer questions about structured data in BigQuery, Looker, and Looker Studio, and also supports querying data from AlloyDB, Cloud SQL, and other databases. With the Conversational Analytics API, you provide your data agent with business information and data (context), as well as access to tools such as SQL, Python, and visualization libraries.

BigQuery's conversational analytics feature lets you chat with your data using conversational language. Using conversational analytics, you can create data agents to define context and query processing instructions for a set of data sources. Users can then have conversations with data agents to ask questions about BigQuery data using natural language. The chat response returned to the user provides the answer to the user's question as text and code, and also generates charts where appropriate.

AtScale delivers a universal semantic layer that bridges business logic with your data stack, enabling consistent, governed metrics across BI tools, AI models, and autonomous systems. This layer ensures that both dashboards and AI systems use the same trusted metrics and business logic.

Multi-turn 'Agentic' Workflows

Modern conversational analytics platforms support follow-up questions and maintain context across sessions. "Agentic workflows are non-deterministic, shaped by near real-time data, adaptive decisions, and evolving interactions," according to research on the PROV-AGENT provenance model.

This means you can ask "What were our Q4 sales?" and then follow up with "What about just California?" without starting over. The system remembers context and refines results accordingly.

Generating Governed SQL You Can Trust

Accuracy matters. Without governance guardrails, text-to-SQL systems can produce confident-sounding queries that return incorrect results.

Kaelio excels at translating natural language into governed SQL by inheriting existing database security controls, semantic definitions, and audit requirements while generating queries that respect row-level and column-level policies. This approach ensures that every natural-language request compiles into compliant, auditable SQL.

Business Impact: Why Are Teams Rushing to Adopt Conversational Analytics?

The business case for conversational analytics centers on three drivers: speed, accessibility, and governance.

Traditional BI adoption remains stuck at 29% despite increased availability, creating urgency for conversational AI analytics. Meanwhile, Gartner predicts that by 2027, 60% of organizations will fail to realize AI value without cohesive data governance frameworks.

Conversational AI solutions enable organizations to improve customer experience outcomes by enabling personalization through advanced natural language understanding and large language models. These solutions can also increase operational efficiency by automating routine inquiries.

Recent survey data reinforces the momentum:

43% of organizations are now using AI-powered analytics in production
56% cite improved decision-making as their top goal, closely followed by cost savings and efficiency
62% say competitive advantage is the top AI+BI outcome

Success depends on aligning CAI initiatives with clear business goals like reducing resolution time and improving satisfaction.

What ROI Should You Expect? Real-World Benchmarks

Organizations implementing conversational analytics are seeing measurable returns:

Kipu Health achieved 40% estimated time savings for clinicians using Amazon Nova to automate compliance checks
ClickHouse's internal AI assistant DWAINE reduced pressure on their three-person DWH team by 50-70%, freeing analysts to focus on strategic analysis rather than routine data requests
DWAINE is used by more than 250 internal users, handling over 200 daily messages

These examples illustrate how conversational analytics shifts data teams from reactive ticket-processing to proactive strategic work.

Why Is a Governed Semantic Layer Non-Negotiable?

The semantic layer is what separates reliable conversational analytics from glorified autocomplete.

If a metric definition changes in dbt, it is refreshed everywhere it is invoked and creates consistency across all applications. This centralization means that any changes to metric definitions are automatically updated across all platforms, maintaining consistency.

A universal semantic layer is a centralized business logic layer that sits between your data and any analytics tool or AI application. Without it, different teams calculate "monthly recurring revenue" differently, leading to conflicting reports and eroded trust.

Kaelio finds redundant, deprecated, or inconsistent metrics and surfaces where definitions have drifted. This feedback loop helps data teams keep their semantic layer clean, consistent, and up to date.

Open Semantic Standards: dbt + MetricFlow

MetricFlow is a SQL query generation tool designed to streamline metric creation across different data dimensions for diverse business needs. As part of the dbt ecosystem, MetricFlow handles SQL query construction and defines the specification for dbt semantic models and metrics.

MetricFlow supports different metric types: Conversion, Cumulative, Derived, Ratio, and Simple. It uses measures and various aggregation types, such as average, sum, and count distinct, to create metrics. MetricFlow simplifies the SQL process via metric YAML configurations, making it easier for data practitioners to define and query metrics efficiently.

Security & Governance Requirements for Enterprise Adoption

Enterprise adoption hinges on meeting strict security and compliance requirements.

"When you implement Microsoft 365 Copilot and agents, you might face new and amplified risks related to security, compliance, privacy, and governance," notes Microsoft's documentation on the Copilot Control System. The security and governance pillar focuses on three key capabilities: data security, AI security, and compliance and privacy.

Row Level Security (RLS) in Tableau refers to restricting the rows of data a certain user can see in a given workbook or data source at the time they view the data. As Tableau's whitepaper explains, "In a self-service environment, the role of data governance should be to permit access to data, enabling users to get the answers they need while ensuring data security is enforced."

Enterprise security requires fine-grained policies across SQL, BI, and APIs with RBAC, row-level security, and data masking. Kaelio inherits these capabilities from existing infrastructure, generating queries that respect the security controls already in place in your data warehouse.

Common pitfalls to avoid:

Skipping row-level security and exposing sensitive data to unauthorized users
Failing to inherit existing RBAC policies from the warehouse
Neglecting audit trails for compliance reporting
Allowing ungoverned ad-hoc queries that bypass semantic definitions
Deploying AI tools without HIPAA or SOC 2 compliance in regulated industries

Kaelio is SOC 2 and HIPAA compliant, model-agnostic, and can be deployed in a customer's own VPC, on-premises, or in Kaelio's managed cloud environment.

Evaluating Platforms: Kaelio vs. Alternatives

Selecting a conversational analytics platform requires balancing capabilities against organizational needs.

Data and analytics leaders use ABI platforms to support the needs of IT, analysts, consumers and data scientists. Gartner evaluates vendors based on two key criteria: Ability to Execute and Completeness of Vision. Informed by 2,500+ business and technology experts, 500,000+ client interactions, 27,000+ vendor briefings, and 715,000+ vetted peer reviews, Gartner expert guidance provides trusted insights for mission-critical priorities.

Julius offers transparent pricing from free to $70/month per user; Kaelio uses enterprise pricing aligned with organization-wide deployments. The key differentiator is governance depth: Julius provides SOC 2 compliance without deep security integration, while Kaelio automatically inherits warehouse-level RBAC, row access policies, and semantic definitions.

Checklist: 7 Must-Have Capabilities in 2026

Buyers must choose from a fast-evolving landscape of tools. Forrester notes that selection should reflect current needs, available resources, and plans for future expansion.

Governed semantic layer integration to ensure consistent metric definitions
Row-level security inheritance from existing warehouse policies
Multi-turn conversation support for natural follow-up questions
Explainability and lineage showing how answers were computed
Enterprise compliance certifications (SOC 2, HIPAA for regulated industries)
Flexible deployment options (cloud, VPC, on-premises)
Model-agnostic architecture to swap LLMs as capabilities evolve

Kaelio checks all seven boxes while differentiating through deep integration across the data stack and feedback loops that improve definitions over time.

Key Takeaways

Conversational analytics transforms how organizations interact with their data. By enabling natural language queries against governed semantic layers, these platforms democratize access while maintaining security and consistency.

Kaelio empowers serious data teams to reduce their backlogs and better serve business teams. Rather than replacing existing infrastructure, Kaelio sits on top of your data warehouse, transformation layer, semantic layer, and BI tools to provide a unified natural language interface.

For organizations ready to move beyond static dashboards and enable true self-service analytics, Kaelio offers a path forward that prioritizes correctness, transparency, and alignment with how you already govern your data. To see how Kaelio can work with your existing data stack, explore a demo.

About the Author

Former AI CTO with 15+ years of experience in data engineering and analytics.

Frequently Asked Questions

What is conversational analytics?

Conversational analytics is a business intelligence tool that allows users to query and analyze data using natural language instead of traditional methods like SQL or dashboards. It leverages AI and natural language processing to interpret questions and provide insights through visualizations or text summaries.

How does Kaelio enhance conversational analytics?

Kaelio enhances conversational analytics by connecting to existing data infrastructure and respecting governance rules, allowing non-technical users to access insights quickly and reliably. It acts as a natural language interface, improving data definitions and governance over time.

What are the benefits of using conversational analytics?

Conversational analytics offers speed, accessibility, and governance. It enables users to access data insights quickly without technical expertise, improves decision-making, and ensures data governance by aligning with existing data models and security protocols.

How does Kaelio ensure data security in conversational analytics?

Kaelio ensures data security by inheriting existing database security controls, such as row-level security and RBAC policies, from the data warehouse. This approach ensures that all queries respect the organization's security and compliance requirements.

Why is a governed semantic layer important in conversational analytics?

A governed semantic layer ensures consistent metric definitions across all analytics tools and AI applications. It prevents discrepancies in data interpretation and maintains trust by centralizing business logic and updating metric definitions automatically.