Top 10 Natural Language Analytics Tools
Top 10 Natural Language Analytics Tools
By Evan Shapiro, CEO, Dataline Labs
For most of business intelligence history, getting an answer from data required an intermediary. You asked someone who knew SQL. They wrote a query. They sent you a spreadsheet. You waited. By the time you got your answer, the question had often changed, or the meeting was over, or the decision had already been made without the data.
Natural language analytics changes this model entirely. Instead of relying on dashboards built in advance or analysts who can write SQL, natural language analytics lets any team member ask questions in plain English and get instant answers. The tool adapts to your questions. You do not have to adapt to the tool.
This post compares 10 natural language analytics tools. Some are genuinely conversational. Some claim to be but are more accurately described as enhanced search interfaces. The differences matter if you are trying to give your team genuine self-service analytics.
1. MIRA
MIRA is built for teams that need to ask questions of their data without waiting for a data analyst.
Natural language analytics in MIRA means you type a question in plain English and get an instant answer. You can ask follow-up questions, drill into specific numbers, break results down by any dimension, and compare time periods, all through conversation.
MIRA works across multiple data sources without requiring you to centralise your data first. Your sales data, finance data, and operations data can all be queried from a single conversation. There is no dashboard to build, no SQL to write, no developer to call on. You ask the question. You get the answer.
Conversational analytics is built into the product. You ask a question, get an answer, then ask a follow-up: break that down by region, compare it to last quarter, filter to the UK only. MIRA handles the conversation naturally, the same way a skilled data analyst would.
For teams that need self-service analytics without relying on a data analyst, MIRA is the most direct solution available today.
2. Microsoft Copilot for Power BI
Microsoft Copilot for Power BI uses AI to help users build reports and ask questions of their data within the Power BI ecosystem. For organisations already using Power BI and Microsoft 365, Copilot adds a natural language layer on top of existing dashboards.
Copilot for Power BI is useful for users who already know Power BI and want help building new reports faster. It is less useful for teams that do not have Power BI dashboards already built, since Copilot works within the existing data model rather than generating analyses from raw data.
The limitation is that Copilot for Power BI is an enhancement to an existing BI workflow, not a replacement for one. If you do not have the dashboards, Copilot does not create them from thin air.
3. Tableau GPT
Tableau GPT applies generative AI to the Tableau ecosystem, helping users build visualisations and ask questions of their data in natural language. For organisations with Tableau deployments, it adds an AI layer that makes the existing platform more accessible.
Tableau GPT is currently in preview and its capabilities are evolving. The direction is promising: natural language query generation, automated visualisation creation, and conversational data exploration within the Tableau environment.
The same limitation as Microsoft Copilot applies: Tableau GPT is an enhancement to an existing BI workflow. If your team does not have Tableau dashboards, GPT does not solve that problem.
4. Looker Studio with Gemini
Google has integrated Gemini AI into Looker Studio (formerly Data Studio), adding natural language query capabilities for users in the Google ecosystem. You can ask questions about your connected data sources and get results in natural language.
Looker Studio with Gemini is an improvement over the basic Looker Studio experience. The natural language layer helps users who are stuck on specific questions.
The limitation is the same as other enhanced search tools: it works within the data model that has been built rather than generating analyses from raw data. If your data is not already connected and modelled in Looker Studio, Gemini cannot help you.
5. Yellowfin
Yellowfin has added AI-powered natural language query capabilities to its enterprise BI platform. Users can ask questions in plain English and get results within the Yellowfin dashboard environment.
Yellowfin is an enterprise BI platform with AI features layered on top. Its natural language analytics capabilities are designed for organisations that already use Yellowfin and want a more accessible interface.
For organisations without an existing Yellowfin deployment, the natural language features are not a reason to choose Yellowfin over other options. The platform is enterprise-priced and requires meaningful onboarding investment.
6. Qlik Sense with Insight Advisor
Qlik Sense includes Insight Advisor, an AI feature that attempts to interpret natural language queries and generate analyses automatically. For Qlik users, it adds a conversational layer on top of Qlik associative analytics.
Insight Advisor is useful for Qlik users who want help exploring their data without writing explicit queries. The AI suggests analyses and visualisations based on the data model.
The limitation is that Insight Advisor still requires users to understand the associative data model to some degree, and the suggestions it makes are tied to Qlik is data model architecture. It is a helpful feature for Qlik users, not a standalone natural language analytics solution.
7. ThoughtSpot
ThoughtSpot was one of the first BI platforms to introduce search-based analytics, with its SearchIQ feature attempting to interpret natural language queries and translate them into analyses. It has invested in AI features over several years.
ThoughtSpot is an enterprise analytics platform. Its natural language search capabilities are genuine, but they work best within the framework that analysts have built. Business users can search within the metrics and relationships that analysts have defined, but they cannot step outside that framework.
ThoughtSpot requires meaningful implementation investment and is priced for large organisations. For small and mid-market teams, it is often the wrong tool despite its natural language capabilities.
8. Metabase
Metabase has added AI-powered question suggestions to its open-source BI platform. It attempts to help non-technical users build queries by suggesting questions based on your data schema.
Metabase AI is useful for users who are new to the tool and want help discovering what questions they can ask. It suggests questions that map to your existing data model.
The limitation is that Metabase AI suggests questions, it does not answer questions. You still need to accept the suggestion and run the query. If the suggestion does not match your actual question, you are back to writing SQL or asking a data analyst.
9. Sigma Computing
Sigma Computing has added AI features that help users navigate its spreadsheet-like interface and generate analyses from connected data warehouses. It uses AI to assist with formula writing and data navigation.
Sigma AI is useful for users who are comfortable with spreadsheet-style interfaces and want assistance navigating complex data models. It reduces the learning curve for the spreadsheet metaphor.
The limitation is that Sigma AI assists within the spreadsheet paradigm rather than replacing it. Users still need to understand data models, warehouse schemas, and the relationships between tables. Natural language analytics in the truest sense is not Sigma is primary mode.
10. Domo
Domo has integrated AI features across its cloud-native BI platform, including natural language query capabilities through its AI aggregations and insights features. For Domo users, this adds an accessible layer on top of an extensive connector library.
Domo AI is useful for Domo users who want to explore their data more easily. The natural language features work within Domo is existing data model and visualisation framework.
The limitation is that Domo is a complex platform with a steep learning curve and enterprise pricing. Its AI features are enhancements to an existing workflow, not a replacement for the platform is complexity. Small and mid-market teams often find Domo overwhelming.
The Bottom Line
Not all natural language analytics tools are the same. Some generate answers from raw data through genuine conversation. Others enhance existing BI platforms with AI-powered search layers that still require dashboards to be built in advance.
The difference matters enormously for teams that do not have data analysts on staff. An AI-enhanced dashboard builder still requires someone to build the dashboards. A genuine natural language analytics tool does not.
MIRA is built for teams without data analysts. Natural language analytics means the person with the question gets the answer directly. No dashboard required. No SQL required. No data analyst required. Just ask.
If you want to give your team the ability to ask questions of your data in plain English and get instant answers without waiting for an analyst, MIRA is worth trying.
Connect your data sources. Ask your first question. See the answer. The process takes days, not months.
For a full overview of the category, read What Is Natural Language Analytics.
For comparisons with specific platforms, read Top 10 Alternatives to Tableau or Top 10 Alternatives to Power BI.
Try MIRA free at searchmira.ai, or drop me a message if you want to see it in action.
About the author: Evan Shapiro is CEO of Dataline Labs, the company behind MIRA. Dataline Labs builds natural language analytics tools for the operational and commercial teams that need data access most.