No SQL Skills Required: I Put Conversational AI Data Analysis Tools to the Test
In the past, understanding a report and identifying issues required knowledge of formulas, pivot tables, and even SQL. But in 2026, that barrier is being broken down - now you can simply ask questions like you're having a conversation, and AI will help you clean the data, calculate numbers, and generate charts. I've put several popular conversational data tools to the test and summarized their strengths and weaknesses for you.
Last week, a reader who works in e-commerce operations sent me a screenshot of GA4 and asked, "Is there a tool that can directly answer my questions?" I laughed because this is exactly the hottest direction for data tools in 2026.
To put it simply, most people are not unwilling to look at data, but they are blocked by the threshold of tools. Formulas, pivot tables, and SQL are all barriers. This year, a batch of "conversational" data tools has emerged, emphasizing that you can get answers just by talking. I've tried several of them and will share my experiences here.
Event Background
Data analysis has always been a contradictory field: the data is there, but few people can "ask the right questions." Traditional BI tools (like those that require setting up dashboards and relationships) have blocked many marketers and operators from accessing data. Conversational AI tools aim to turn "what I want to know" directly into "answers," leaving the technical work to AI.
These tools can be roughly divided into two categories: one that makes spreadsheets smarter and another that replaces the workflow of spreadsheets. Most of them are based on large language models, which generate SQL or Python code in the background.
This Time's Focus: Several Worth-Trying Tools
- Querri: The most "chat-like" one. You import your data, give instructions like in a conversation — unify date formats, calculate monthly transactions for each business, find the product with the highest return rate — and it will do it for you. Suitable for operators, salespeople, and accountants who deal with spreadsheets daily but are afraid of formulas. However, be aware that it may occasionally misinterpret your meaning, and you should always verify numerical data.
- Polymer: Strong in "automatically turning tables into beautiful dashboards." Connect your CSV, Google Sheets, or ad data, and AI will automatically recognize fields, generate interactive charts, and allow you to switch dimensions with a click. Suitable for marketers who need to quickly visualize ad effectiveness. However, your data needs to be clean first.
- Anomaly AI: More like "hiring an analyst." It can connect to GA4, ad accounts, and databases, run advanced SQL and Python, schedule regular analyses, and send alerts when anomalies are detected. Suitable for teams with large amounts of data that need to continuously monitor indicators; using it for small tables would be overkill.
- Zerve: For data teams with technical backgrounds, it replaces Jupyter notebooks with a graphical canvas, allowing parallel computation and good collaboration. It's not for marketers but for data scientists.
Market Impact Analysis
For Taiwanese Users: The good news is that understanding your data no longer requires taking a course first. The bad news is that tools will "give you an answer," but they won't tell you if the answer is correct. AI can make mistakes or misinterpret context, so you still need to retain basic data judgment skills.
For Enterprise Applications: For Taiwanese SMEs without data analysts, these tools are good helpers. However, my suggestion is to "divide labor" — let AI handle tedious cleaning and initial calculations, and have humans do the final interpretation and decision-making. First, clarify the calculation logic of important KPIs, then let the tools run them, so you won't be deceived by beautiful charts. For further reading, you can refer to our AI Data Analysis Guide.
For Developers: Tools like Zerve, which integrate collaboration and parallel computation, are worth evaluating by data teams, as they can save a lot of chaos from running different notebooks.
Future Development Trends
Conversational analysis will become more popular, but the division of labor between "AI providing answers" and "humans supervising" will continue to exist because data analysis can lead to entirely wrong conclusions if one assumption is incorrect, and AI is not reliable enough in this aspect. The next step to expect is the integration of these tools with agents — not only answering your questions but also proactively discovering anomalies and making suggestions.
TheAI Academy Summary and Comments
The greatest value of these tools is that they lower the threshold for "understanding data" to the ground. However, a low threshold does not mean you can trust them blindly.
Comments: Conversational tools let you "ask and get answers," but "whether the answer is correct" is still your responsibility. Treat them as very fast assistants, not infallible authorities.
Our specific suggestion for Taiwanese readers: First, use a dataset you're familiar with and know the answers to test the tools, see if they calculate correctly, and establish trust before using them on unfamiliar data. For critical numbers like revenue and KPIs, develop the habit of "verifying again yourself."
Data Sources
(This article is based on actual trials and publicly available information, with functions based on the latest versions of each tool.)
Frequently Asked Questions
Do conversational AI data tools really not require programming skills?
Yes. Tools like Querri and Polymer allow you to give instructions using natural language, and the AI generates SQL or Python code in the background to clean the data and produce charts, making it accessible to marketing and operations personnel without a technical background.
Can the analysis results from these tools be trusted?
They can be used as a reference, but AI may still misinterpret requirements or misjudge context. Especially when it comes to critical numbers like amounts or KPIs, it's essential to double-check the results yourself. It's recommended to test the tool with familiar data where you know the answers to verify its accuracy.
Which tool is suitable for small companies without a data analyst?
If you just need to quickly visualize spreadsheet or advertising data, Polymer is easy to get started with; if you want to use conversational analysis for spreadsheets, Querri is intuitive. For large datasets that require continuous monitoring of metrics, consider Anomaly AI, but for small datasets, it might be overkill.
How does Zerve differ from the other tools?
Zerve is designed for data science teams with technical expertise, using a graphical canvas to replace Jupyter notebooks, and excelling in collaboration and parallel computing. It's not a conversational tool for marketers, but rather a powerful tool for data scientists.