AI and Data Analytics: Anyone Can Analyse Data If They Know How to Ask and Check
How AI helps you analyse data, examples by function, prompts to use today, cautions on accuracy and PDPA, and steps to get started.
At the end of every month, a sales coordinator spends half a day building pivot tables to answer the same questions from their manager: which products are growing, which customer groups are disappearing. In the past, deeper analysis meant knowing SQL or Python. Today you can type a question in plain language and let AI help calculate, build charts and point out what is interesting. That does not make everyone a Data Scientist, but it does make it much easier for people in every function to use data in decisions.
How AI changes data analysis
1. Ask in plain language
Instead of writing formulas, you ask: how did sales this quarter compare with last quarter, by region? AI translates the question into calculations. The same idea powers more complex work, such as Text-to-SQL that lets business teams query data in plain language.
2. Upload a file and let AI explore it
AI assistants such as ChatGPT or Claude accept Excel or CSV files and can help summarise trends, find outliers and create basic charts. This suits a first pass of exploration before you go deeper.
3. BI tools with built-in AI
Power BI has Copilot to help build reports and answer questions about data. As for the older Q&A feature, Microsoft's Power BI blog announced it will be fully retired in December 2026, with Copilot as the replacement. If your organisation still uses Q&A, start planning the move. Read more in Power BI Copilot: using AI to build reports.
Examples by function
Shops and sales teams
Upload order data and ask: which product has the highest profit per unit, which day of the week sells best, and what do repeat customers have in common? The answers help with stock and promotion timing decisions.
HR
Using data with names and identifying details removed, ask: which department has high turnover, and at what tenure do most people leave? This shows where to start fixing things.
Finance
Let AI summarise monthly revenue and expense trends and flag unusual changes, then have a person double-check the key numbers every time.
Example prompts you can use now
- Summarise this file: what columns does it have, what period does it cover, and where are there blanks or unusual values?
- Compare monthly sales of the top 5 products and show them as a line chart.
- Explain the calculation you used step by step so I can check it in Excel.
You no longer need to be a Data Scientist to analyse data, but you do need to know what to ask and how to check the answer.
Cautions
- AI can miscalculate or misread columns: double-check key numbers in Excel before reporting them.
- Personal data: do not upload identifiable customer or employee data into tools your company has not approved. Read PDPA and using AI.
- Messy data gives unreliable results: inconsistent product names or mixed date formats skew the output.
- Correlation is not causation: AI can show that two things rise and fall together, but that does not mean one causes the other.
How to start
- Refresh basic Excel or Google Sheets skills such as filtering, sorting and pivot tables.
- Choose one real work data file that contains no personal data.
- Try the example prompts above, then check the results yourself.
- Learn Power BI basics and build a dashboard from the same data.
- Present the dashboard in a meeting or add it to your portfolio.
Recommended courses
- First steps to becoming a data analyst in the AI era
- When anyone can do analytics in the Gen AI era
- Using ChatGPT Code Interpreter for data analysis
- AI for data analysis, explained simply
To build this into a career, read business data analysis with Excel and Power BI and browse all courses on Plants.work Learn.