A conversational data query tool lets you ask a question about your business data in plain English, such as “what were sales by region last month?”, and get back an answer, table or chart. Behind the scenes it translates the question into a database query, usually SQL, runs it against your systems and explains the result.
Sylus connects to systems like Microsoft Dynamics 365, Odoo, Oracle NetSuite and PostgreSQL and lets anyone ask questions in plain English, with the SQL behind every answer shown.
Key takeaways
These tools turn plain-English questions into queries, run them against your data and return answers, tables or charts.
Accuracy depends less on the AI model than on the context it gets: documented tables, agreed metric definitions and clear business rules.
Most BI vendors’ assistants work on top of a data model that someone has to build first. AI-first analysts aim to connect to business systems directly.
Pick a tool that shows the query behind each answer, so anyone can check how a number was calculated.
What is a conversational data query tool?
A data query is a structured request to a database: “give me every invoice from March over 10,000 dirhams, grouped by customer”. Databases understand those requests in a query language, almost always SQL. SQL is precise and powerful, but it has a steep learning curve, so most managers depend on an analyst to write queries for them. That is where the queue of report requests comes from.
A conversational data query tool removes the need to write the query yourself. You type the question the way you would ask a colleague, and the tool works out which tables hold the answer, writes the SQL, runs it and shows the result. You can then ask a follow-up, such as “now just for Dubai” or “compare it with last year”, and the tool keeps the context of the conversation.
These tools go by several names: natural language query (NLQ), text-to-SQL, conversational analytics, or AI data analyst. The core idea is the same. The difference between them is how much they understand about your business, how they connect to your data, and how much setup they need before the answers are reliable.
How does a conversational data query tool work?
Most tools follow the same steps between your question and the answer:
Understand the question. A large language model works out what you are asking: which metric, which time period, which filters.
Find the right data. The tool looks up which tables and columns are relevant, using the database schema and, ideally, a semantic layer of agreed business definitions.
Write the query. It generates SQL (or DAX, LookML or another query language, depending on the product).
Check permissions and run it. The query runs with the access rights of the person asking, so they only see data they are allowed to see.
Check the result. Better tools test their own output: if a query fails or returns something implausible, they revise it before answering.
Explain the answer. You get a number, table or chart, ideally with the query shown so it can be verified.
Step 2 is where most tools succeed or fail. A model that sees a table called tbl_v3 with a column called amt_2 has to guess what it means, and it won’t tell you it is guessing.
How accurate are plain-English answers from business data?
Accuracy varies widely, and the public benchmarks are sobering. On Spider 2.0, a text-to-SQL benchmark built from real enterprise databases, the best model in the Spider 2.0 paper (ICLR 2025), o1-preview, solved 21.3% of tasks, while GPT-4o solved 12.3%. Those tasks involve large, undocumented schemas, which is close to what a raw ERP database looks like.
Context makes the biggest difference. Google reports that grounding questions in Looker’s semantic layer reduces data errors in generative AI natural-language queries by as much as two-thirds, based on its own internal tests (Google Cloud blog, April 2025).
In practice, four factors decide how accurate a tool will be on your data:
How much schema context it gets. Too little and it can’t find the right table. Too much, unfiltered, and it gets confused by look-alike columns. Good tools narrow the schema to the tables that matter for each question.
Business rules. “Revenue excludes refunds” or “always filter by the active company” has to be written down somewhere the tool can read it, or it will be ignored.
Self-correction. Tools that check errors and implausible results, then retry, handle complex multi-join questions far better than tools that generate one query and stop.
Your data’s documentation. Clear table names, column descriptions and defined relationships help every tool. Messy, undocumented data hurts every tool.
The practical lesson: test any tool on your own messy data with questions whose answers you already know, not on the vendor’s demo dataset.
Which conversational data query tools should you consider?
Every major BI vendor now has a natural-language assistant, and a newer group of AI-first tools has appeared. The table summarises what each vendor’s own documentation says (checked October 2026). Where we couldn’t verify a detail, it says “See vendor docs”.
Tool | Who it’s for | Data sources it queries | Shows the SQL or logic behind answers? | Needs a modelled semantic layer or BI setup first? |
|---|---|---|---|---|
Sylus | Business teams, from executives to finance and operations managers, without a data team | Business systems and data sources: ERP, CRM, accounting, e-commerce, ad platforms, databases and spreadsheets (see integrations) | Yes: every answer shows the SQL query behind it | No: Sylus builds a semantic understanding of your business from the systems you connect, and teams can save approved metric definitions |
ThoughtSpot (Spotter) | Non-technical teams self-serving from data the data team has modelled | ThoughtSpot Models over live connections to warehouses and databases such as Snowflake, BigQuery, Redshift and Databricks | Yes: each answer shows the query in a simplified search syntax | Yes: a Model with Spotter enabled |
Power BI Copilot | Business users, report authors and data model owners on Power BI | Power BI semantic models, which are fed by Power Query connectors | Partly: shows the fields, measures and filters used, and the DAX query when Fabric IQ is on | Yes: a prepared semantic model, plus Fabric F2+ or Premium P1+ capacity |
Looker with Gemini (Conversational Analytics) | Organisations that want governed self-service on Looker | LookML Explores over databases such as BigQuery, Snowflake, Redshift, Databricks and AlloyDB | Yes: explains calculations and can show the generated Looker queries | Yes: answers are grounded in the LookML semantic model |
Tableau Pulse | People who don’t open Tableau often and want metric updates | Metrics defined on a single published Tableau data source (extract or live) | Answers cite their metric sources; no SQL view is documented | Yes: metric definitions on a published data source; Tableau Cloud only |
Zenlytic (Zoë) | Organisations that want an AI data analyst on their data warehouse | Writes and runs SQL against warehouses and databases such as Snowflake, BigQuery, Redshift, Databricks and Postgres | Yes: shows the SQL it ran | Uses a semantic layer, but its docs say a complete one isn’t needed before asking |
The main split is between assistants that sit on top of an existing BI model (Power BI, Looker, Tableau, ThoughtSpot) and tools that build their own understanding of your data. If you already have a well-maintained Power BI semantic model or LookML project, the vendor’s assistant is a natural next step. If you don’t, the modelling work comes first, and that is often the slow part. Our Power BI alternative page explains how Sylus approaches this differently, and the guide to the best BI tools covers the wider BI market.
Should you build your own text-to-SQL agent or buy a tool?
Building a basic text-to-SQL agent is now a weekend project: connect a language model to a database, pass it the schema and let it write queries. Building one that a finance team can trust is a much bigger job. It takes:
Schema context management: choosing which tables, columns, sample rows and business rules the model sees for each question, and keeping that up to date as the database changes.
Access control: read-only credentials, blocking any query that could modify data, excluding sensitive tables, and making sure each person only sees what they are allowed to see.
Accuracy testing: a set of known questions and correct answers, re-run every time you change the prompt, the model or the schema, plus checks for queries that run successfully but return the wrong number.
Security and compliance work: logging prompts and generated SQL, protecting credentials, and defending against prompt injection.
Ongoing maintenance: prompt changes, model upgrades and user support, indefinitely.
Build your own when you have unusual database setups that no tool supports, strict rules that require a self-hosted model, or a dedicated engineering team that wants full control of the pipeline.
Buy a tool when you want business users answering their own questions this quarter and would rather your engineers didn’t own prompt engineering, schema management and security for an internal analytics product.
How is this different from pasting a spreadsheet into ChatGPT?
General-purpose assistants such as ChatGPT and Claude are excellent at analysing a file you upload. But an uploaded spreadsheet is a one-off snapshot. The assistant doesn’t know your business definitions, doesn’t apply your company’s access rules, and has to be given a fresh export every time the numbers change.
A conversational data query tool connects to the systems where the data lives, so answers reflect current data. It applies the same definitions every time, so two people get the same revenue figure, and it respects who is allowed to see what. Our comparisons of Sylus vs ChatGPT and Sylus vs Claude go into the details, including where those assistants are the better choice.
Which systems can you query in plain English?
Any system the tool can connect to. For most business teams, the valuable questions sit in a few places:
ERP: stock levels, margins, purchase orders and cost of goods in systems such as Microsoft Dynamics 365, Odoo or Oracle NetSuite.
CRM: pipeline, win rates and sales by rep.
Accounting: receivables, overdue invoices, cash position and budget against actual.
Databases and warehouses: product usage, orders and anything your own applications store, for example in PostgreSQL.
The hardest questions combine sources, such as marketing spend from ad platforms compared with orders in the ERP. Our guide to data connectors explains how systems get connected in the first place. For companies in the region, see how Sylus works with the systems common in the UAE and GCC.
How do you choose a conversational data query tool?
Use this checklist when you trial tools:
Does it connect to your actual systems, or only to a data warehouse you don’t have yet?
Does it need a semantic model built first, and who on your team would build and maintain it?
Does it show the SQL or logic behind each answer, so finance and analysts can verify numbers?
Does it enforce permissions on every query, by person or team?
Can you save approved metric definitions so everyone gets the same number?
How does it handle a question it can’t answer reliably? Asking a clarifying question is better than guessing.
What does it cost as more people use it? See pricing for how Sylus plans are quoted.
Frequently asked questions
What is a conversational data query tool?
A conversational data query tool lets you ask questions about business data in plain English and get answers, tables or charts back. It translates the question into a database query, usually SQL, runs it and explains the result, so you don’t need to write code.
How accurate are plain-English answers from business data?
Accuracy depends mostly on the context the tool has: documented tables, agreed metric definitions and business rules. On raw, undocumented enterprise databases even leading models get many questions wrong, so test any tool on your own data with questions whose answers you already know.
Can I see the SQL behind an answer?
It depends on the tool. Some show the generated SQL or query logic for every answer, while others only show the result. Choose one that shows its working, so analysts and finance teams can check how each number was calculated. Sylus shows the SQL query behind every answer.
Do I need a data team or data warehouse to use one?
Not always. Assistants built into BI platforms usually need a data model built by a technical person first. AI-first tools that connect directly to business systems need less setup, though someone should still own the definitions of key metrics.
How is this different from pasting a spreadsheet into ChatGPT?
An uploaded spreadsheet is a one-off snapshot: the assistant doesn’t know your business definitions or access rules and needs a fresh export whenever the numbers change. A conversational data query tool connects to live systems, applies consistent definitions and respects who can see what.
Should I build my own text-to-SQL agent or buy a tool?
Build only if you have unusual systems, strict self-hosting requirements and engineers to maintain it long term. Production text-to-SQL needs schema management, access control, accuracy testing and security work, which most teams are better off buying.
Which systems can I query in plain English (ERP, CRM, accounting)?
Any system the tool connects to. Common examples are ERPs such as Microsoft Dynamics 365, Odoo and Oracle NetSuite, CRMs such as Salesforce and HubSpot, accounting systems such as Xero and Zoho Books, and databases such as PostgreSQL.