Re-AI Training Programme — L4.2 AI for data analysis: finding insights without being a data scientist

Welcome to lesson 4.2, AI for data analysis: finding insights without being a data scientist. Every business has data, but few people have time to read it. Here you will learn to analyse a dataset by asking an AI tool questions in plain English. You will choose the right chart, and turn the numbers into a short story that a manager can act on. We will also cover why data matters in any job, and how to work ethically and safely.

By the end of this lesson, you will be able to do four things. First, use an AI data tool to explore and analyse a spreadsheet or a CSV file. Second, write specific questions about totals, trends, comparisons and unusual values. Third, choose a suitable chart and write a plain-language summary of what you found. Fourth, apply the basics of the GDPR, the EU data protection law. That means removing personal details from data and checking a tool's terms before you use it.

Here are some key terms for this lesson. A dataset is a collection of related data, usually in rows and columns. A CSV, which stands for comma-separated values, is a simple file format for tables that almost any program can open. An insight is a clear, useful finding that helps someone make a decision. A trend is a pattern of change over time, such as sales going up or down. An outlier is an unusual value that stands far apart from the rest. It is often worth a closer look. And a dashboard is a visual summary of key numbers and charts, all in one view.

Every spreadsheet hides decisions. A sales spreadsheet is just numbers until someone asks a question, such as: which products should we stock more of? Analysis is how you answer that. In the past, reading data needed coding or statistics training. AI tools have removed most of that barrier. So here is the promise of this lesson. If you can ask clear questions in plain language, you can produce insights that businesses will pay for.

Where does business data come from? You do not need fancy data. Most organisations already have plenty. First, there is sales and orders data. It shows what sold, when, to whom and for how much. This is the heart of most business analysis. Second, website and social media data, such as visits, clicks and likes. This shows what customers are interested in. Third, surveys and feedback, like reviews and ratings, which tell you the why behind the numbers. There is also operations data, such as stock levels, delivery times and costs. The real skill is asking the right questions of data that already exists.

Tools like Julius AI let you analyse data simply by chatting with it. First, you upload a data file. For example, a shop's sales export. Second, you ask in plain English. You might type: what were the top three products last quarter? There is no code to write. Third, the tool does the work and explains it. The AI runs the analysis, draws charts and explains the result. But remember the split of jobs. The AI does the calculation. Your job is to ask the right question and to judge whether the answer makes sense.

Here are five steps from raw file to clear findings, with no formulas. Step one, upload the data. Drop in your CSV or spreadsheet, and the tool reads the columns and rows. Step two, get to know it. Ask: what is in this data? You will see the columns, time period and size. Step three, ask focused questions, one at a time, about totals, trends, comparisons or unusual values. Step four, create charts. Ask for a bar or line chart of the key result. Step five, summarise the insights. Write a short, plain summary of the top findings for the person who makes decisions. You will do exactly this with Julius AI in exercise 4.2.

Good analysis starts with good questions. Here are four kinds. First, totals and averages. For example: how much did we sell in total, and what is the average order value? Second, trends over time. How have sales changed month by month? Third, comparisons. Which region or which product performed best? Fourth, outliers. Were there any unusual spikes or drops? Surprises often hide the real story. Tip: start broad. Ask: what stands out? Then zoom in on whatever looks interesting.

Like chatbots, data tools reward clear, specific questions. A clear question sounds like: top three products by revenue in the fourth quarter. You ask one question at a time, so each answer is easy to check. You ask the tool to show you the numbers behind a chart. And you ask for a chart with a one-line takeaway. A vague question sounds like: what sold well? Or five questions packed into one prompt, a chart accepted without checking, or simply: analyse everything. The rule is simple. Name the exact column or time period. Vague questions get vague answers. And always check the numbers, because AI can misread messy data.

The right chart makes a finding obvious, so match the chart to the question. A bar chart is best for comparing categories, such as sales by product, region or month. A line chart is best for trends over time, such as how weekly sales rise or fall across a year. A pie chart shows parts of a whole, with only a few slices, such as sales shares of three shops. And a simple table works best when exact numbers matter more than the shape, like a price list or the top ten orders. Here is a tip. If a chart needs a long explanation, choose a simpler chart.

Insights only matter if people act on them, so wrap your numbers in a story. First, lead with the finding. Start with the point, for example: three products drove most of last quarter's sales. Then show the chart. Second, say why it matters. Connect it to a decision, such as: so we should keep these in stock and feature them on the homepage. Third, keep it plain. Avoid jargon. A 150-word summary that anyone understands beats a wall of statistics. Remember, a decision-maker wants to know so what, not just see a chart.

A real insight sounds like this: sales fell 20 per cent in March, driven by one region. That is an invented example, but look at its shape. It names the number, the period and the cause. And it points to a clear next action. A vague observation sounds like this: sales were a bit lower recently. There are no numbers, no timeframe and no cause. It leaves the reader guessing. Always aim for the first kind: specific, useful and linked to a decision.

Now, analysing data ethically and safely. If data identifies real people, treat it as sensitive by default. First, anonymise personal data. That means removing names, emails and ID numbers before you analyse, whenever you can. You rarely need them. Second, respect the GDPR, the EU law on personal data. Handle data lawfully, keep it secure, and use it only for the agreed purpose. Third, keep secrets out of public tools. Never upload confidential or personal data to a tool whose terms you have not checked. Fourth, sanity-check the AI. It can misread messy data, so always verify surprising results before you report them.

Let's check your understanding. Pause the video now and think about these questions. What does an AI data tool let you do without writing any code? Can you give an example of a specific, well-phrased data question? Which chart would you use to show a trend over twelve months, and why? And name one thing you must do to handle personal data responsibly. When you have your answers, press play to carry on.

Here are some resources to help you go further. Julius AI lets you analyse data by chatting with it. Looker Studio from Google lets you build free dashboards. And you can read the full text of the GDPR. All the links are on the lesson page.

Here are the key takeaways from this lesson. AI tools let you analyse data by asking questions in plain English, with no coding. Good analysis comes from specific questions about totals, trends and comparisons. Match the chart to the question, and always double-check surprising results. Turn numbers into a story: lead with the finding and say why it matters. And handle personal data lawfully. Anonymise it, respect the GDPR and protect anything confidential.

Now it is time to practise. On the platform, begin with the activity called insight or vague observation, where you sort statements into the right group. Then do exercise 4.2. You will analyse a dataset and write up your findings, which takes about one and a half hours. Finally, take the lesson quiz. It has five questions, the pass mark is 70 per cent, and you can retake it as often as you need.

Co-funded by the European Union.