Top 7 AI Tools Used by Data Analysts

We surveyed practicing data analysts to find out which AI tools they use in their daily work. Here’s a list of the most useful solutions that help save time, improve accuracy, and speed up analytics.

Published 3 min read

Islam Temirbek, Data Analyst at Qatar Insurance Company, Instagram

As data analysts, we use AI in various areas:

— automation of manual changes to large SQL scripts
— rapid text processing
— project documentation
— drafting Python code
— automating communication with clients.

Mostly ChatGPT, and for working with corporate data, Copilot, as we have compliance approval.

Altair Bekturov, Director of the Data Analytics Department at Astana International Financial Centre, Instagram

AI has long been part of the workflow. I use:

1. ChatGPT — for code review.
2. Deep research ChatGPT — for data collection.
3. Python + Pandas — I do all the main work in them, and with the help of AI, I write code significantly faster.
4. Power BI + AI — sometimes offers interesting insights.

Arna Kairatkyzy, Data Scientist at Tele2, Instagram

I work as a data scientist at Tele2 and simultaneously teach a Data Analytics course. I encounter AI every day — not in theory, but in real projects.

In fact, AI has long been present in the arsenal of data specialists — it was just called something else before: machine learning, neural networks, forecasting models. All of these are components of AI, and data scientists have been working with them for many years.

Artificial intelligence itself became possible thanks to the development of data science as a discipline: it is in this field that methods of data analysis, statistics, and computations were developed, which laid the foundation for modern AI models.

In large companies like Tele2, data scientists do not so much use AI to automate their own work as they create and improve AI solutions for other departments and business units.

For example, we can build models that predict customer churn, optimize marketing campaigns, or automatically analyze call center inquiries. All of these are examples of applied AI, which are worked on by teams of analysts and engineers.

In essence, data science creates the "brain" of AI systems — from cleaning and preparing data to selecting and training models. AI is not a magic button, but the result of numerous engineering solutions, analytical hypotheses, and iterations. And data analysts play a key role in this process.

Here are a few examples of services that truly simplify and accelerate analytical tasks:

1. GitHub Copilot: code completion in Jupyter and VS Code. Speeds up writing functions, especially in analytical pipelines.

2. Claude by Anthropic: handles large documents well and extracts insights from long texts — useful when working with reports and presentations.

3. Power BI with AI Insights: helps identify anomalies in data, build forecasts, and find hidden dependencies.

4. Runway / Pika Labs: used to create short visual video reports that can be used in business presentations.

AI tools do not replace analysts but become reliable assistants, removing routine and allowing them to focus on the main thing — interpreting data and making decisions.