Data analysis is the process of examining, cleaning, organising and interpreting data to answer questions and support decisions. It turns raw numbers into clear information, such as trends, comparisons and summaries, that people can act on.
What data analysis is
Every business, government and household has data: sales records, expenses, survey responses, prices. Data analysis asks what that data says. How much did sales grow? Which product sells best in which region? Why did costs rise last quarter? Analysts answer these questions with calculations, charts and clear explanations.
Four types of analysis
Descriptive analysis explains what happened, for example total sales by month. Diagnostic analysis explores why it happened, for example comparing regions to find where sales fell. Predictive analysis estimates what is likely to happen, using past patterns. Prescriptive analysis suggests what to do, for example how much stock to order. Most everyday data analysis is descriptive and diagnostic.
The process
Analysts typically define the question, gather data, clean it (removing errors, duplicates and inconsistencies), analyse it with calculations and comparisons, visualise results in charts and dashboards, and communicate findings in plain language with clear recommendations.
Common tools
Spreadsheets such as Excel and Google Sheets remain the most widely used analysis tools. SQL is used to query databases. Business intelligence tools create interactive dashboards. Python and R handle larger or more complex analysis. Increasingly, AI assistants help analysts write formulas, queries and summaries faster.
Data analysis versus data science
The two overlap. Data analysis focuses mainly on understanding existing data and explaining what happened and why. Data science goes further into building predictive models and machine learning systems. Many data scientists start as analysts, and strong analysis skills are the foundation of both.
Data analysis in investing
Investors analyse data all the time: comparing a company's revenue and profit over years, calculating ratios, comparing companies in an industry, and tracking how a portfolio performs. Good analysis means comparing like with like, checking data sources, and not drawing big conclusions from small or unusual samples.
Common pitfalls
Common mistakes include using dirty or incomplete data, confusing correlation with causation (two things moving together does not mean one causes the other), cherry-picking periods that support a view, and presenting charts that mislead through scales or selective data.
A simple example
A small shop owner wants to know why profits fell. She exports a year of sales from her billing software into a spreadsheet, groups sales by product and month, and compares this year with last year. The analysis shows that sales of her most profitable product fell sharply after a competitor opened nearby, while costs stayed flat. That clear finding helps her decide to run a promotion and adjust her stock.
Why it matters
Data analysis helps people and organisations replace guesswork with evidence. It is one of the most useful skills in modern work, in almost every field.
Common misconceptions
"Analysis needs advanced coding." Much valuable analysis is done in spreadsheets.
"Numbers speak for themselves." Interpretation and context are essential.
"Correlation proves cause." It does not.
Frequently asked questions
What tools do analysts use most?
Spreadsheets, SQL, dashboards and sometimes Python or R.
What is data cleaning?
Fixing errors, duplicates and inconsistencies before analysis.
Is data analysis a good career?
It is in demand across industries.
Can AI do data analysis?
AI can help, but human judgement is still needed to ask good questions and check results.
How PositoAI fits in
PositoAI organises company data so you can analyse quickly: ready-made lists in Explore, custom filters on Screen, side-by-side comparison on Compare, and financial history in Go Deep.
Summary
Data analysis examines, cleans and interprets data to answer questions and support decisions. It can be descriptive, diagnostic, predictive or prescriptive, and uses tools from spreadsheets to SQL and Python. It overlaps with data science but focuses on understanding existing data. Good analysis relies on clean data, context and avoiding common pitfalls.