For an analytics leader, the decision is which tool your team will trust and what must be true in the data layer underneath it. A fluent answer built on the wrong metric definition only makes an error easier to act on.
Evaluating these tools means testing whether a product can interpret intent, surface anomalies, support correction, and fit your permissions and administration. It also needs prepared data with stable schemas, current records, and shared definitions, because faster access does not make an answer more trustworthy.
What is a natural language query?
Natural language query (NLQ) lets a business user ask a typed or spoken question and receive an answer, explanation, or visualization. The difficult part is not the search box. It is mapping the user's words to the right tables, joins, filters, and business definitions.
Adoption is already substantial. A Gartner survey of 403 analytics and AI leaders, conducted from October through December 2024, found that over 50% already use AI tools for automated insights and natural language queries.
The semantic problem appears when a user asks for Revenue. Does that mean Net Recognized Revenue or Gross Bookings? If finance and sales use different definitions, an NLQ system can generate technically valid queries that return conflicting answers. This is why last-mile analysis cannot compensate for inconsistent definitions upstream.
Four criteria show how well a tool manages this risk: intent handling, anomaly detection, human correction, and enterprise readiness.
How we compared these AI BI tools
We evaluated the tools against the same four criteria. Teams comparing data preparation tools should apply the same discipline to the layer that creates the data being queried.
- NLQ intent handling: How clearly the tool translates a question into fields, filters, calculations, and visualizations
- Anomaly detection: Whether it can identify unusual values or trends, either proactively or when prompted
- Human-in-the-loop learning: Whether users can correct, refine, or preserve an interpretation
- Enterprise readiness: Whether the product documents appropriate connections, permissions, security, and administration
Feature comparison: ThoughtSpot vs. Julius AI vs. Polymer vs. Explo vs. Google Sheets
| Criterion | ThoughtSpot | Julius AI | Polymer | Explo | Google Sheets |
|---|
| NLQ intent handling | Spotter converts questions into analytical searches | Generates editable Python, R, or SQL | Polymer AI Chat answers plain-English questions | Report Builder AI creates reports conversationally | Ask Gemini creates analysis, formulas, and charts |
| Anomaly detection | SpotIQ surfaces trends and outliers proactively | No documented proactive detector | No documented proactive anomaly detector | No documented proactive anomaly feature | Prompt-driven analysis; Connected Sheets has a separate anomaly feature |
| Human-in-the-loop learning | Saves verified questions and business terms | Supports multi-turn refinement and editable code | No documented persistent correction workflow | Context can guide conversations, but user-learning behavior is not documented | Users refine prompts and formulas; persistent answer coaching is not documented |
| Enterprise readiness | Role-based access control (RBAC) and live cloud data connections | SOC 2 Type II, with SSO, RBAC, and audit logs on enterprise plans | Documents SSO and access controls; confirm specifics with the vendor | SOC 2 Type II and HIPAA, with customer access controls and embedded permissions | Workspace administration plus optional BigQuery governance |
The five AI tools for data visualization, compared
Each product fits a different buyer.
ThoughtSpot: enterprise search at scale
ThoughtSpot is built for analytics leaders providing governed search across large organizations. Its current AI analyst is Spotter, which replaced the earlier Sage experience. Users can inspect interpreted search tokens, while SpotIQ proactively surfaces trends, correlations, and outliers. Verified questions and business terms provide a documented correction path, and role-based access control supports enterprise administration.
Its limitation is that a single search cannot join data across separate connections. Shortlist it when an executive needs to explore governed warehouse data during a live discussion.
With Prophecy, an analyst defines and validates the revenue metric once in a governed workflow that lands prepared data in the warehouse. ThoughtSpot then points at that table, so an executive asking Spotter for revenue by region gets a chart built on the definition finance already approved.
Julius AI: conversational data science for analysts
Julius AI suits analysts, consultants, and small data teams that need ad-hoc modeling rather than a managed enterprise BI layer. It writes and runs Python or R in a per-user sandbox, and users can inspect or edit the generated code. It supports files on all plans and live data connectors on qualifying plans.
Julius does not document a proactive anomaly detector. Refinement works within a conversation, but persistent memory across conversations is not documented. Enterprise controls include SOC 2 Type II, while enterprise plans add SSO, RBAC, and audit logging. Use it for iterative work such as testing relationships between warehouse temperature and equipment failures.
Prophecy handles the preparation Julius assumes is already done. An analyst can build a governed workflow that joins and cleans sensor and maintenance tables into a modeling-ready data set, then point Julius at that output to explore correlations, rather than re-cleaning raw extracts inside each ad-hoc session.
Polymer: fast structured analysis for spreadsheet teams
Polymer fits departments that work mainly in CSV files, spreadsheets, and marketing applications. Its conversational interface, PolyAI, accepts plain-English questions, while generated dashboards reduce setup work for spreadsheet-centric teams.
Polymer's documentation does not describe proactive anomaly detection or persistent learning from corrected answers. It documents single sign-on and access controls, though teams should confirm the current security, file-size, and column limits against Polymer's own documentation before committing, since those details change. Use it when a marketing team needs to inspect a partner CSV without first building a production data model.
Prophecy closes the gap when a Polymer dashboard needs to graduate from a one-off CSV to a governed source. An analyst can rebuild the same logic as a Prophecy workflow that refreshes on the cloud platform, so the marketing dashboard reads from a maintained table instead of a spreadsheet someone re-uploads each week.
Explo: embedded NLQ for customer-facing products
Explo targets product and engineering teams embedding analytics into SaaS applications. Its AI capability is Report Builder AI, where end users request visualizations and reports in natural language. Its edge is embedded, multitenant reporting with customer access controls, visibility groups, and supported database connections, backed by SOC 2 Type II and HIPAA compliance.
Explo does not document proactive anomaly detection or a persistent end-user correction loop. Use it when customers request custom report fields or filters inside a SaaS product.
Prophecy prepares the multitenant data Explo exposes to those customers. An analyst can build a governed workflow that shapes and row-scopes each tenant's data on the cloud platform, so the tables Explo embeds already enforce which customer sees which rows, rather than pushing that logic into the reporting layer.
Google Sheets with Gemini: the Workspace-native baseline
Google Sheets is the familiar option for teams already using Google Workspace. The entry point is Ask Gemini, which can create formulas, tables, charts, filters, pivot tables, and contextual analysis. Users can prompt it to identify anomalies, but this is request-driven rather than an always-on alerting system.
Gemini supports iterative prompting and formula correction, while Workspace provides administrative controls. Generated charts are static images that do not update with the original data, and Google says complex analysis is most consistent below 1M cells. Connected Sheets is an optional BigQuery connection layer, not the NLQ interface itself. Use this combination when a Workspace team wants familiar analysis with governed BigQuery access.
With Prophecy, the BigQuery tables a Workspace team reaches through Connected Sheets are prepared in a governed workflow first. An analyst re-grains and validates the data in Prophecy, publishes it to BigQuery, and Gemini then works against a current, defined table rather than a manually maintained tab.
Why NLQ fails at the data layer, not the visualization layer
Conflicting metric definitions, broken schema assumptions, or stale records can make the same question return different answers. Buying better BI tools does not resolve those conditions.
Gartner predicts that by 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases because of incohesive data governance frameworks.
Three failure modes matter most:
- Metric consistency: Revenue, churn, or active customer must have one governed definition
- Schema stability: Changes to table grain, fields, or joins must not silently alter answers
- Data freshness: Current questions require current, reliably refreshed data
A BI tool can interpret a question, but it should not be expected to repair those problems at query time.
Building the governed data foundation NLQ depends on
NLQ trust is a data preparation problem. The underlying workflows must standardize business logic, absorb schema changes, refresh reliably, and operate under the data platform team's controls.
One definition per metric before the question is asked
Business terms should be standardized where prepared data is created, not redefined in each dashboard. At a regional insurer, if finance counts an active policy at month-end while operations counts any policy active during the month, the same NLQ prompt produces different totals.
A governed data workflow can define active policy once, validate the logic, and publish prepared data in the cloud data platform. Prophecy and other approved consumers can then query that data without recreating the calculation.
Schema stability so the AI's map of the data doesn't break
NLQ depends on predictable fields, joins, and grain. An AI-ready workflow must make schema changes visible and test their downstream effect.
For example, a claims team may re-grain a table from one row per claim to one row per claim line. Without validation, totals multiply silently. A governed workflow tests the new grain and updates dependent logic before users receive altered NLQ results.
Freshness without an engineering ticket
Fresh data requires a repeatable path from a business request to production. In the Generate → Refine → Deploy lifecycle, an analyst describes the requirement, AI generates a first-draft visual workflow, and the analyst refines and validates it before deployment.
The workflow then runs natively on Databricks, BigQuery, or, in a Snowflake data preparation workflow, as native Snowflake SQL. A retail analyst can update a daily inventory transformation without creating an ungoverned spreadsheet or waiting for a separate engine.
Governed self-service, not a second class of ungoverned workflows
Self-service works only when analyst-created workflows follow the platform team's access, testing, versioning, deployment, and audit requirements. This model gives analysts autonomy without creating a separate production path.
A platform owner at a regional bank can approve governed self-service because workflows deploy to the company's cloud data platform under its controls. The same architecture helps data platform teams reduce routine requests while retaining oversight.
Which AI data visualization tool should you choose?
Choose ThoughtSpot for enterprise search, Julius AI for ad-hoc analytical work, Polymer for spreadsheet-led teams, Explo for embedded customer reporting, and Google Sheets with Gemini for Workspace-native analysis. Whichever you shortlist, test it against your real metric definitions, schemas, permissions, and refresh patterns, because the governed data layer decides whether users trust the answers.
Request a demo to see how Prophecy builds governed visual data workflows that run natively on your cloud data platform.
Frequently asked questions
What is a natural language query (NLQ) in business intelligence?
NLQ is the ability to interact with data using typed or spoken language and receive answers or visualizations. Gartner's NLQ definition also includes grouping, filtering, ranking, comparisons, trend analysis, and temporal or geographic understanding.
Are AI data visualization tools accurate?
They can be accurate, but accuracy depends on question clarity, schema mapping, joins, metric definitions, and user validation. Enterprise text-to-SQL remains difficult: the 2025 Spider 2.0 study reported that its agent framework solved 21.3% of complex enterprise tasks.
Do you need a data warehouse to use AI BI tools?
No. Julius AI, Polymer, and Google Sheets can work with files or spreadsheet data. ThoughtSpot Cloud and Explo rely on supported database or warehouse connections, while Connected Sheets lets Google Sheets connect to BigQuery as an included integration.
What's the difference between conversational BI and augmented analytics?
Conversational BI is the natural-language interaction layer within the broader field of augmented analytics. Gartner defines augmented analytics as using machine learning and AI to assist data preparation, insight generation, and explanation. Conversational BI is usually user-initiated; augmented analytics can also surface insights proactively.
