Skip to content
← Back to blog

What Is Data Analysis? Meaning, Uses and a Simple Worked Example

Gulab Chand Tejwani

Gulab Chand Tejwani

PMP · PL-300 · Microsoft Certified Trainer

· 7 min read

Data AnalysisData AnalyticsBeginnersExcelCareer
What Is Data Analysis? Meaning, Uses and a Simple Worked Example

Data analysis is the process of collecting, cleaning, organising and examining data to answer a question or support a decision. You start with a clear question, gather the data that can answer it, fix errors, summarise and compare the numbers, and then explain what the results mean and what someone should do about them. Businesses use it to understand sales, customers, costs and performance; the tools range from Excel and SQL to Power BI and Python.

I'm Gulab Chand Tejwani. I teach data analysis, SQL and Power BI at TechNet Consultancy in Vallabh Vidyanagar, Anand, and online, and I hold the Microsoft Certified: Power BI Data Analyst Associate (PL-300) certification. Most beginners I meet think data analysis is mostly about tools or advanced maths. It's mostly about asking a good question and checking your numbers carefully. The example below shows what I mean.

What does data analysis mean?

In plain terms, data analysis means looking at recorded facts (sales, enquiries, marks, website visits, costs) to find out what happened, why it happened, and what is likely to happen next. The output isn't a spreadsheet or a chart for its own sake. It's an answer someone can act on: "enquiries are up but enrolments aren't, so look at what happens after someone enquires before spending more on marketing."

Data analysis and data analytics are often used to mean the same thing. When people separate them, "analytics" usually means the wider field, including the tools, reporting and prediction, while "analysis" is the act of examining a particular dataset.

A simple worked example

Here's the kind of question a training institute might ask: "Our marketing is bringing in more enquiries. Is it working?"

The data (illustrative numbers):

MonthEnquiriesEnrolmentsConversion
July401025%
August521223%
September611220%
October751317%
Course enquiries and enrolments by month (illustrative example)
Bar chart of enquiries and enrolments from July to October: enquiries rise from 40 to 75 while enrolments go from 10 to 13
Enquiries are rising faster than enrolments: something between enquiry and enrolment needs a closer look.

What the analysis shows:

  1. Enquiries grew 88%, from 40 in July to 75 in October (75 ÷ 40 = 1.875).
  2. Enrolments grew only 30%, from 10 to 13.
  3. Conversion fell from 25% (10 ÷ 40) to about 17% (13 ÷ 75).

So the answer to the question is "marketing is bringing more people in, but a smaller share of them are enrolling." The numbers don't say why. Slow follow-up is one likely explanation; others are a change in the type of people enquiring, a price change, or batches filling up. The next step is to check those: how quickly enquiries get a call back, which source each enquiry came from, and what changed in September and October. That's data analysis: a question, the right numbers, a careful calculation, and a recommendation about what to check or change next. Nothing in it needed more than division.

What is data analysis used for?

  • Sales and marketing: which products, cities or campaigns bring in the most revenue or enquiries.
  • Finance: where costs are rising and which customers pay late.
  • Operations: delivery times, stock levels, waiting times and bottlenecks.
  • HR: hiring time, attrition and training results.
  • Healthcare and education: patient or student outcomes, attendance and resources.
  • Government and research: surveys, census data and the effect of policies.

Wherever decisions are made, someone benefits from checking the numbers first.

What are the main types of data?

There are several ways to classify data, which is why you'll see different answers to "what are the types of data?". The two you'll meet most often:

Qualitative vs quantitative. Qualitative data describes qualities or categories, such as city, course or feedback comments. Quantitative data is numbers you can measure or count, such as sales, age or marks.

The four levels of measurement, which is what "the four types of data" usually refers to:

Four types of data: nominal (named categories such as city), ordinal (ordered categories such as ratings), interval (equal gaps with no true zero, such as temperature in Celsius) and ratio (equal gaps with a true zero, such as sales or age)
The type of data decides which calculations make sense: you can average sales, but not cities.

In tools like Excel, SQL and Python you'll also see data types such as text, numbers and dates. Those are storage types, a different classification again. The practical point is the same: know what kind of values a column holds before you calculate with it.

What are the steps of data analysis?

Different sources list four, five, seven or more steps, but they describe the same work:

  1. Define the question and what a useful answer looks like.
  2. Collect the data that can answer it.
  3. Clean it: fix duplicates, blanks, typos and wrong formats.
  4. Explore and analyse: summarise, compare and look for patterns.
  5. Communicate: charts, a short summary and a recommendation.

Cleaning usually takes longer than beginners expect. If you work in Excel or Power BI, Power Query data cleaning steps shows how to do it in a repeatable way.

Is data analysis maths?

Partly, but less than most people fear. Everyday analysis uses arithmetic, percentages, ratios, averages, and an understanding of things like median and spread. The example above used only division. Statistics becomes important for things like testing whether a difference is real or predicting future values, and you can learn that as you go. If you're comfortable with school-level maths and a calculator, you can start.

Is data analysis easy to learn?

The basics are learnable in a few weeks, especially if you already use Excel. What takes longer is judgement: asking the right question, spotting data that looks wrong, and explaining results simply. Most people find the tools easier than they expected and the thinking harder. Practising on real or realistic datasets is what closes that gap.

Which tools are used for data analysis?

The order I recommend, and why, is in the data analyst roadmap for 2026.

Learn data analysis with a trainer

If you'd like to learn this with guidance, our data analysis training covers Excel, SQL, Power BI and Python with practical projects, in Vallabh Vidyanagar, Anand, or online. You can send an enquiry from the course page to ask about the next batch and fees.

FAQ

What is meant by data analysis?

Data analysis is the process of collecting, cleaning and examining data to answer a question or support a decision, and then explaining what the results mean.

What is data analysis used for?

To make better decisions: understanding sales and customers, controlling costs, improving operations, tracking performance, and measuring the results of changes.

What are the 4 types of data?

Usually the four levels of measurement: nominal (named categories), ordinal (ordered categories), interval (equal gaps without a true zero) and ratio (equal gaps with a true zero). Data is also commonly split into qualitative and quantitative.

Is data analysis a math?

It uses maths, mostly arithmetic, percentages and averages for everyday work. More advanced statistics helps later, but you don't need it to start.

Is data analysis easy?

The basics are approachable, especially with some Excel experience. Asking good questions and checking results carefully takes practice.

What are the steps of data analysis?

Define the question, collect the data, clean it, explore and analyse it, then communicate the findings and a recommendation.

Choose optional cookies

We use necessary cookies for account security. With your permission, we also use analytics and advertising cookies on marketing pages. See our Privacy Policy.