What is Data Analytics?

Data analytics is the process of examining data to find useful patterns, answer questions, and support decisions. It combines four activities:

1. Asking a clear question
2. Preparing relevant data
3. Analysing it with appropriate methods
4. Communicating what the results mean

The aim is not simply to produce charts or calculate averages. The aim is to help someone make a better-informed decision.

How it differs from related fields:

These roles overlap heavily in practice, and job titles vary between companies.

Why is it used?

Teams use analytics to:

• Understand sales – which products, regions, or customers drive revenue
• Reduce delays – where orders or support tickets get stuck
• Improve customer experience – where users struggle or drop off
• Measure marketing – which campaigns bring valuable customers
• Monitor operations – whether processes meet service targets
• Allocate resources – where to spend budget, staff, or time

Examples:

• A retail team wants to know which products sell best on weekends.
• A learning platform wants to understand where students stop progressing through a course.
• A hospital wants to know which times of day have the longest waiting times.

Example

An online shop recorded 1,200 orders in June and 1,500 in July.

Growth = (1,500 − 1,200) ÷ 1,200 = 25%.

This is a useful starting point, but it leaves important questions open:

• Did revenue also increase, or were the extra orders small?
• Which products drove the change?
• Did a discount or a one-off campaign cause the increase?
• Were returns or cancellations higher in July?

How it works

1. Clarify the question – what decision does the answer support?
2. Collect the right records – from the correct sources and time period
3. Check data quality – missing, duplicated, or incorrect values
4. Calculate relevant measures – totals, rates, averages, comparisons
5. Look for patterns – trends, differences between groups, unusual values
6. Explain the result in plain language – including limitations

A result is only as reliable as the data and method behind it, so stating limitations is part of the job, not an optional extra.

Key takeaways

• Data analytics connects data to decisions.
• A calculation is only one part of the work. Context, data quality, interpretation, and communication matter just as much.
• Always ask: "What decision will this analysis support?"

Try it: Choose a service you use regularly, such as a food delivery app. Write down one question the company could answer with data, and name two data fields it might need.