Data analysis turns existing data into useful findings: patterns, key details, trends, and decisions. Rather than only asking AI to generate something new, use it to help make sense of what you already have. That can put work once reserved for specialist analytics teams within reach of ordinary users—without pretending the machine is infallible. section 8.0 section 10.2
A practical starting point is the spreadsheet you already use. An LLM can help write formulas for calculations, data manipulation, conditional formatting, repetitive-task automation, and charts. Ask a specific question about a CSV file or describe the calculation you need; vague data produces vague answers, because apparently even robots dislike mind-reading. section 7.6
Use AI-assisted analysis to:
- Find patterns and important details across large datasets. section 8.0
- Measure public opinion across many social-media posts through sentiment analysis. section 8.0
- Identify market trends and optimize a startup’s strategy with analytics and predictive modeling. section 10.2
- Examine candidate demographics, skills, preferences, past hiring outcomes, and employee turnover factors. section 10.17
Ask the data a concrete question
Given a CSV of S&P 500 values, ask which date had the highest value. The same workflow can extend to a spreadsheet formula, a highlighted condition, or a chart: start with a defined dataset and a question whose answer can be checked against it. section 7.6 section 10.2
Analytics can support decisions, but high-stakes uses deserve extra caution. Hiring tools may surface qualifications, predicted success, or turnover patterns, yet fairness is not guaranteed; organizations also need to respect privacy, labor, and workplace laws. For business use, leave room for output errors rather than treating a polished result as a verdict. section 10.17 section 10.2
