A sales spreadsheet is just numbers until someone asks 'which products should we stock more of?' Analysis answers that.

Reading data once needed coding or statistics training. AI tools have removed most of that barrier.

You don't need fancy data. Most organisations already sit on plenty, including operations data like stock and costs.

• Sales and orders. What sold, when, to whom and for how much: the heart of most business analysis.
• Website and social. Visits, clicks and engagement that show what customers are interested in.
• Surveys and feedback. Reviews, ratings and questionnaires: the 'why' behind the numbers.

Tools like Julius AI let you analyse data by chatting with it.

• You upload a data file. A spreadsheet or CSV, for example a retail sales export, is loaded into the tool.
• You ask in plain English. Type 'What were the top three products last quarter?' instead of writing any code.
• It works and explains. The AI runs the analysis, draws charts and explains the result. You decide which questions to ask.

The AI does the calculation. Your job is the question and the judgement.

From raw file to clear findings in five steps, with no spreadsheet formulas.

1. Upload the data. Drop in your CSV or spreadsheet. The tool reads the columns and rows.
2. Get to know it. Ask 'what's in this data?' to see the columns, time range and size.
3. Ask focused questions. One clear question at a time: totals, trends, comparisons or unusual values.
4. Create charts. Ask for a bar or line chart of the key result to make it easy to see.
5. Summarise the insights. Write a short plain-language summary of the top findings for a decision-maker.

Tool: Julius AI. This mirrors exactly what you'll do in EX 4.2.

Good questions

• Totals and averages. 'How much did we sell in total? What's the average order value?'
• Trends over time. 'How have sales changed month by month?'
• Comparisons. 'Which region or product performed best?'
• Outliers. 'Were there any unusual spikes or drops?' Surprises often hide the real story.

Like chatbots, data tools reward clear, specific questions.

Name the exact column or period: vagueness gets vague results.

The right chart makes a finding obvious. Match the chart to the question.

Tip: if a chart needs a long explanation, pick a simpler chart.

Insights only matter if people act on them. Wrap your numbers in a clear story.

1. Lead with the finding. Start with the point, for example 'Three products drove most of last quarter's sales'. Then show the chart.
2. Say why it matters. Connect it to a decision: 'so we should keep these in stock and feature them on the homepage'.
3. Keep it plain. Avoid jargon. A 150-word summary anyone understands beats a wall of statistics.

A decision-maker wants 'so what?' more than 'here's a chart'.

The same data can produce a useful insight or a useless one. Spot the difference.

Aim for the left column: specific, useful and tied to a decision.

If data identifies real people, treat it as sensitive by default.

• Anonymise personal data. Remove names, emails and IDs before analysis whenever you can. You rarely need them.
• Respect the GDPR. Personal data must be handled lawfully, kept secure and used only for the purpose agreed.
• No secrets in public tools. Never upload confidential or personal data to a tool whose terms you haven't checked.
• Sanity-check the AI. AI can misread messy data. Always verify surprising results before you report them.

Reference: GDPR, Regulation (EU) 2016/679.