Natural Language BI
Oct 9, 2025
How Conversational Analytics is Changing the Game
Imagine being able to ask your data questions in plain English and get answers instantly. Conversational analytics – BI you can talk to – is changing how businesses make decisions, expanding data access beyond analysts and speeding up insights for everyone.
What Is Conversational Analytics?
Conversational analytics (or Natural Language BI) refers to the ability to interact with business intelligence systems using natural language – either through voice or text. In practice, it means you can type or speak a question like, “What were our top-selling products last quarter?” and the BI tool will generate an answer on the spot, often in the form of a chart or summary. Instead of learning a complex reporting tool or writing SQL queries, users can simply have a conversation with their data.
This concept is powered by advancements in natural language processing (NLP) and machine learning. Modern BI platforms incorporate an NLP engine that interprets the user’s question, maps it to the relevant data, and then presents the results in an understandable format. Some tools also support natural language generation (NLG) – meaning they can explain insights with a textual narrative (e.g. “Sales in the Northeast region grew 15% last month, outpacing other regions.”).
In essence, conversational analytics aims to make interacting with data as easy as doing a Google search or chatting with a colleague. According to Gartner, the goal is for analytics tools to be “as easy as a search interface or a conversation with a virtual assistant.” In fact, Gartner projected that 50% of analytical queries would be generated via search, NLP or voice by 2020[9] – a testament to how rapidly this trend has emerged.
Why It’s a Game-Changer for BI
1. Data Access for Everyone: Perhaps the biggest impact of natural language BI is the democratization of data access. In the past, only analysts or power users comfortable with BI software could directly interact with data. But conversational interfaces are as familiar as a web search, lowering the barrier dramatically. A manager, marketer, or salesperson can ask questions and get insights without waiting on an analyst. Gartner predicted that this trend would boost analytics adoption from 35% of employees to over 50%, by drawing in many new business users who previously didn’t use BI tools[10]. In other words, conversational AI is helping analytics reach a much broader audience in the company.
2. Faster Decision-Making: Natural language querying can be far more efficient than the traditional dashboard slog. If you have a follow-up question to a chart, you don’t need to dig through filters or build a new report – you just ask. This means decision-makers can get specific answers in seconds, even in the middle of a meeting or on the fly. One Google Cloud product lead noted that with these AI tools, “customers no longer need to be technical experts to gain value from business intelligence… [they] can now simply ask a question as they would ask a colleague,” which lowers the barriers to discovery[11]. When data feels like a dialogue, getting insights becomes much more natural and immediate.
3. Reduced Burden on Analysts: Conversational BI doesn’t just empower business users – it also helps data teams. In organizations without these capabilities, BI teams often field a long queue of report requests (“Can you slice this by region? What about by product line?”). With natural language self-service, many of those ad-hoc questions can be handled directly by the user. This frees up analysts to focus on more complex, high-value analyses rather than churning out basic reports. Over time, as the NLP models learn the business lingo and context, the system can answer even more questions accurately. The result is a more scalable analytics function where AI handles the simple queries and humans tackle the hard stuff.
4. A More Intuitive Experience: Traditional BI tools, with their menus, pivot tables, and chart builders, can be intimidating to the uninitiated. Conversational interfaces are more intuitive – you start with a natural question, just like you would with a colleague or an internet search. This can lead to a cultural shift where people incorporate data into their daily decision-making. Instead of relying on gut feel or static monthly reports, employees get used to asking the data a question whenever they have one. This habit can foster a truly data-driven culture. It also encourages curiosity: users tend to ask follow-up questions when it’s as easy as typing another query, leading to deeper insights than a static dashboard might surface.
Real-World Examples
Conversational analytics is no longer theoretical – it’s popping up across many popular BI and data platforms:
Microsoft Power BI: Includes a “Q&A” visual where users can type questions and get answers visualized. For example, typing “total sales by category last year” instantly creates a chart answering the query. Microsoft has also announced Copilot for Power BI, an upcoming GPT-powered assistant to further enhance natural language querying and even generate data narratives and reports automatically.
Salesforce Tableau: Tableau introduced an “Ask Data” feature that allows natural language questions within its interface. More recently (in 2023), Salesforce (Tableau’s parent company) introduced the next generation of Tableau with Einstein GPT, bringing conversational AI to analytics. With Tableau GPT and Tableau Pulse, analysts and business users can automate data analysis, anticipate needs and automatically generate actionable insights – and even pull in unified customer data for a single view of the truth[12]. Tableau GPT is designed to make it easier for everyone to tap into generative AI by simply asking questions, delivering insights in a conversational way for any user[13]. Meanwhile, Tableau Pulse provides automated, personalized analytics – surfacing insights in both natural language and visual formats – and delivers them where users work (via Slack, email, etc.) for quick, contextual decision-making[14].
ThoughtSpot: A pioneer in search-driven analytics, ThoughtSpot built its BI experience around a Google-like search box. Users type questions and get answers (with charts) from a live analytical database. This search-driven approach was one of the early proofs that conversational BI can work at scale. Many organizations using ThoughtSpot have enabled non-analysts (salespeople, finance managers, etc.) to self-serve insights simply by searching their data. ThoughtSpot’s success helped inspire larger BI vendors to develop similar capabilities.
Google Looker: Google is integrating conversational analytics into Looker, its cloud BI platform. In 2024, Google announced Looker’s Conversational Analytics (part of its new Generative AI features under the codename “Project Gemini”). This allows users to chat with a data assistant to retrieve insights. Google’s approach leverages Looker’s semantic data model to ensure the AI’s answers are based on trusted metrics. The result is that when customers aren’t confined to pre-built dashboards or complex SQL queries, the entire company can chat with data and obtain insights in seconds[15]. Looker’s conversational agent can even generate visualizations on the fly and let users drill deeper with follow-up questions, all through a chat interface.
Other Tools: Many other BI and analytics tools are adding natural language query features. Oracle Analytics Cloud has “Ask Oracle” for NLP queries. SAP Analytics Cloud offers “Search to Insight.” Qlik Sense acquired a conversational AI bot (CrunchBot) to enable Q&A in its interface[16]. Numerous startups (e.g. Outlier, Tellius, etc.) are also innovating in this space, offering chatbots or assistants that sit on top of data systems and deliver insights conversationally. The ubiquity of this trend means that asking data directly is becoming a standard expectation in analytics software.
Challenges and Best Practices
While conversational analytics is powerful, it’s not magic. Companies introducing these tools should be aware of a few challenges:
Understanding Context: Natural language systems might misinterpret queries if they lack context or if terms are ambiguous. For example, “Show me last quarter revenue in Paris” could refer to Paris, France or Paris, Texas, depending on the data. It’s important to have mechanisms to clarify and to train the NLP model on business-specific vocabulary (e.g. understanding that “Q1” means a specific date range, or that “APAC” refers to a region).
Accuracy and Trust: Users need to trust the answers they get. This means the underlying data must be clean and the logic (or semantic layer) defining metrics must be correct. It also helps if the BI tool can explain its answer – for instance, showing which data was used or what query it ran. Many conversational BI tools include features to display the underlying query or allow users to drill into the source data, which builds confidence in the results.
Privacy and Security: Opening up an easy-to-use data querying interface means you must carefully manage who can access what data. Robust security and role-based access control are essential. The system should only answer questions with data the person is allowed to see. For example, if a retail chain manager asks about “store sales,” they should only get their region’s data if that’s the policy. The conversational tool needs to enforce all the existing data governance rules.
Managing Expectations: While AI is getting smarter, it doesn’t know everything. Users may ask questions the system can’t parse or that the data doesn’t actually contain. It’s important to set expectations that the tool is there to assist with data queries, but it may not magically know the answer to complex, multi-faceted questions (at least not without proper setup). Providing user guidelines or examples of effective queries can help new users succeed faster.
Despite these considerations, the trajectory is clear: conversational analytics is poised to become a standard part of the BI toolkit. As AI language models continue to improve, the experience will only get more seamless and powerful, allowing even casual users to conduct sophisticated analysis through simple conversation.
Conclusion: Talk to Your Data – It’s Listening
Conversational BI is changing the game by making analytics truly accessible and interactive. It takes the power of data analysis out of the hands of a few experts and puts it into the hands of everyone who needs it. People can get answers in the moments that matter, without breaking stride or waiting in line for a report.
As we head into 2025, expect natural language interfaces to become commonplace in analytics workflows – embedded in dashboards, mobile apps, and chat platforms. Forward-thinking businesses are already leveraging this capability to make faster decisions and foster a data-driven culture at all levels. Those who don’t embrace it risk being stuck in the slow lane with static reports and underutilized data.
The message is simple: if you can ask a question, you can analyze your data. That’s a game-changer for business intelligence. To explore how you can implement cutting-edge AI-driven analytics (and let your team start talking to data), join the waitlist at
and be part of the next evolution in BI.