As businesses seek more intuitive ways to explore their data, without their leaders needing to possess deep technical expertise, implementing conversational BI has become a hot topic.
These tools allow users to query data using natural language—text or voice—for quick business insights that are comprehensible and actionable. No more complex SQL queries, understanding data structures or pouring over out-of-context reports and dashboards. The platform and its conversational interface translate the user’s intent into queries that uncover the required information and share it back in an easy, business-friendly language.
With the growing demand of conversational BI, several competing products have emerged – each with its own unique strength. Broadly they may be grouped into two categories: those evolving from erstwhile BI or visualization tools that worked in the consumption layer, and those growing up the stack from the data management and infrastructure foundations.
Consumption Focused Platforms
BI-native tools have extended their capabilities with a natural language interface. They focus on making data visualization and insights more accessible to business users through intuitive querying, layered on top of existing analytics workflows. Prominent players in this space are:
PowerBI: A Q&A feature has been added to PowerBI mobile app that lets users tap the device’s native speech recognition feature to interact with their data, reports or dashboards. Saying, or typing questions like “What were our top-performing products last quarter?” lets them receive immediate responses with appropriate charts or graphs. Visual cues—blue underlines for recognized terms, orange for ambiguities and red for unrecognized terms—guide users in refining their questions, and a “Teach Q&A” feature enables training the tool with specific business semantics.
PowerBI premium users can also leverage Copilot to ask questions on reports and receive narrative explanations. Copilot also assists in DAX query creation, especially for situations that need complex calculations. To use Copilot, users need access to a workspace that is assigned to a paid Microsoft Fabric capacity.
Tableau has introduced a set of AI powered tools to power conversational BI. These next-gen tools, like Tableau Pulse, provide automated insights in natural language and proactively anticipate related questions, accelerating time to value. Smart suggestions and in-report questions guide business users to facets that they may not be aware of.
Formerly known as Einstein Copilot, Tableau Agent is another AI assistant that guides users through data preparation, visualization creation and analysis using conversational prompts. The agent can be embedded within dashboards, enabling users to explore data without needing to understand underlying data structures or SQL.
Users can enter queries in natural language and receive rich visual reports. It supports complex analytical expressions, including time series and spatial analysis and understands conversational phrases like “last year” or “most popular”. Users can even describe calculations in natural language, and the agent will generate the appropriate formulas.
ThoughtSpot Spotter: Leveraging agentic AI, Spotter enables users to ask questions in natural language and receive precise answers, without the need for any technical expertise. Unlike other BI tools, Spotter adapts to the organization’s unique business terminology and data structures through advanced semantic models. It learns and improves with a ‘human-in-the- loop’ feedback system.
Spotter can be embedded in any application like Salesforce, ServiceNow or Slack to deliver analytics within the app itself. It has strong security features, including row-level and RBAC. ThoughtSpot has a highly intuitive UI and customer friendly UX, with plug and play ability to work with diverse datasets and ecosystems.
PowerBI and Tableau interfaces limit deployment options, closely tying them to their native architecture. The platform costs add up, resulting in a higher cost of using conversational BI. Spotter is more flexible, supporting a range on-premises and cloud data sources.
Data Management Platforms
At the heart of effective conversational BI lies a less discernable but critical component: the semantic layer. NLP/LLM and visible interface may get all the spotlight but to get accurate, insightful answers, the system must bridge the gap between natural language and precise data logic. A robust semantic layer functions as the key to interpreting user intent, converting them to optimized data queries and ensuring answers are both accurate and relevant. For this reason, products that are designed as an enterprise-wide semantic layer have an architectural advantage when extended to a conversational BI interface.
Kyvos Dialogs leads this group with an ability to work with massive datasets using natural language queries- delivering 200x faster performance and cost efficiency. Its strength lies in deep semantic understanding and handling complex, multi-dimensional queries with the right business context—something traditional BI tools struggle to do. Kyvos boasts an unparalleled 95% accuracy and sub-second responses, even on massive datasets.
Dialogs users can expect context-aware insights in real time, with a continuity advantage. They can revise, backtrack, pin, save and resume previous conversations in an ongoing analysis. It works with a huge library of pre-defined KPIs and also supports two-way conversations for new KPI generation. Busy executives would find Dialog’s top-level summaries and visualizations uncomplicated, contextual and easy to grasp on-the-go.
Another product, AtScale claims that its natural language query system has a 92.5% accuracy rate in translations to SQL, outperforming traditional models that lack semantic context. It leverages its own semantic layer and query engine, and is integrated with generative AI. The unified layer serves as a centralized repository of business logic, KPIs and data relationships, providing the context that allows the system to interpret user queries accurately.
AtScale’s conversational interface is, however, not a native development like Dialogs. Instead, the NLQ capabilities are accessible through integrations with tools like Snowflake Cortex Analyst or Databricks Genie. Hence, AtScale is an enabler of conversational BI and not a provider.
Databricks is an integrated platform for data engineering and AI that helps organizations manage, analyze and extract insights from large-scale datasets. It has introduced Databricks’ AI/BI Genie as a conversational BI tool, translating user queries leveraging GenAI and Databricks’ Unity catalog. The catalog maintains a metadata layer that aids in understanding business-specific terminology and data relationships.
Genie uses defined SQL functions in the Unity catalog to ensure that KPIs are consistent and reliable. In comparison, Kyvos uses calculated measures in the semantic layer, yielding faster performance. Genie’s visualization capabilities are basic, and available through the UI only. Notably, querying in natural language is available, but not summarization.
Homing Onto the Right Conversational BI Platform
The choice of a conversational BI interface is not one-size-fits-all—it depends on several factors, including the organization’s current data architecture, the complexity and scale of its data, the level of data literacy among its users, governance needs and how analytics are consumed across different departments.
While BI-native tools like PowerBI, Tableau and ThoughtSpot provide conversational capabilities within their ecosystems, their tight integration and limited deployment flexibility can be a constraint. Without a universal semantic layer, consistency in definitions, metrics and KPIs across multiple tools is difficult to maintain. The same question asked in two different BI applications might yield different results due to differing interpretations of business logic.
Adding conversation capability to products that focus on robust semantics over large-scale enterprise data has an edge. Platforms that are highly intuitive and contextual in their conversations and have a flexibility of connecting with existing data ecosystems have a clear advantage over others.
