Data Explained Simply

Data is the red cells of our digital world’s veins. It flows through every system, nourishing decisions, predictions, and connections. Data is all around you, from your smartwatch counting steps to global banks assessing risk. It is invisible yet crucial. We live in a world driven by it, but few can accurately define what it is.

So, what is data in short words?

It's how we describe the world using facts and indicators. A number representing today's temperature, a pixelated image, the sound of your heartbeat, or a comment about something purchased online. Data can take many forms, including numbers, words, sounds, and images. It is the foundational material that computers and humans utilise to learn about the world. However, on their own, they are only parts of a puzzle; the true meaning is revealed only when they are connected.

In this article, we’ll peel back the layers and look at data from every angle. By the end, you’ll be able to answer three key questions:

• What is the definition of data?
• How do we compare data vs information?
• What are the 4 types of data?
• How is data collected and where does it come from?
• Why is data important in today’s world?

What Is the Definition of Data?

If you search for the word data, you’ll find many complex explanations but the idea itself is simple: data means facts that describe something. Every time you measure, count, or record an observation, you create data. The number of steps you walked today, the name of your favourite movie, or the colour of your car, all are examples of data.

In short, data is the raw material of knowledge.

It doesn’t tell us a story by itself, but it gives us the clues to discover one. Just as letters form words and words form sentences, pieces of data combine to form information and insight.

Data Vs Information: Data is “what happened.” Information is “what it means.”

When we collect and organize data, we start to see patterns, make predictions, and make better decisions, from tracking your sleep to guiding billion-dollar strategies.

What are the 4 types of data?

Not all data is the same. Some data counts things, some describes things, and some measures them with precision. To make sense of it all, researchers and analysts group data into four main types: nominal, ordinal, interval, and ratio. Understanding these types helps us choose the right way to analyse and visualize data to create information and insights.

1. Nominal Data – Naming Things:

Nominal data is used for labels or categories, not numbers that can be measured or ordered.

Think of it as “names only.”

Examples:

• Types of fruit: apple, orange, banana
• Countries: Norway, France, Iran
• Eye colours: Brown, green, blue

You can count how many of each exist, but you can’t rank them or say one is “more” than another.

2. Ordinal Data – Order Matters:

Ordinal data shows order or ranking, but the difference between the ranks isn’t exact.

Examples:

• Movie ratings: 1 star, 2 stars, 3 stars
• Education level: High school, Bachelor’s, Master’s, PhD
• Customer satisfaction: Poor, Fair, Good, Excellent

You can arrange them, but you can’t say how much better “Good” is than “Fair.”

3. Interval Data – Equal Gaps, No True Zero:

Interval data uses numbers where the distance between values matters, but there’s no absolute zero point.

Examples:

• Temperature in Celsius or Fahrenheit (0°C doesn’t mean “no temperature”)
• Calendar years (the year 2000 isn’t “twice” as much time as 1000)

This data lets you measure differences but not full ratios.

4. Ratio Data – Numbers with a True Zero:

Ratio data has all the properties of interval data, but also a meaningful zero, allowing for comparisons like twice as much or half as much.

Examples:

• Weight (0 kg means no weight)
• Age (0 years means newly born)
• Income (0 NOK means no money earned)

This is the richest form of data and supports the widest range of analysis.

These four types of data aren’t just classroom concepts, they’re everywhere in the systems that shape our daily lives. In banking and finance, data types mix constantly:

• Nominal data helps identify categories: like loan type, account type, or customer region.
• Ordinal data shows ranking or satisfaction: such as credit ratings, risk levels, or customer feedback scores.
• Interval data appears in time-based records: like interest rates over months or temperature trends used in insurance analytics.
• Ratio data powers most financial analysis: income, transaction values, debt ratios, or portfolio returns, where zero truly means “none.”

The ability to distinguish between them is more than simply technical knowledge; it enables analysts, data scientists, and decision-makers to use the appropriate tools, ask better questions, and avoid drawing incorrect conclusions. Understanding data kinds entails learning how the world measures itself.

The Lifetime of Data: From Birth to Death

Each piece of data tells a narrative. It is born, grows, travels, and eventually dies. The process from creation to deletion is called the data lifecycle.

1. Birth: Data Is Created

The moment something is captured, data begins to take shape. A customer makes a purchase, a sensor detects temperature, and a camera takes a photo. Each of these operations creates fresh data, a digital record of what happened.

2. Collection: Data Is Captured

Once created, data must be gathered. It is transferred from devices, forms, or systems to a location where it can be saved. For example, your phone collects steps from your smartwatch, and a bank gathers your transaction data at the end of the day.

3. Storage: Data Finds a Home

After being collected, data requires a safe location to reside. It is stored on databases, data warehouses, or cloud servers. Here, it's organised, labelled, and secured, ready to use when needed.

4. Use: Data Comes to Life

This is when true value starts. Data is processed, visualised, and transformed into information that assists humans and robots in decision making. Banks utilise data to detect fraud. Doctors use it to monitor heart rate. Streaming platforms utilise it to recommend the next show.

5. Sharing: Data Travels

Data frequently travels between systems or teams. Governments share demographic information with researchers, while banks exchange credit records with lenders. Every movement must follow regulations, which protect privacy and ensure data is only shared with authorisation.

6. Death: Data Is Forgotten or Deleted

Like anything else, data does not last forever. When it is no longer useful or acceptable to keep, it must be destroyed or archived. This stage is critical for privacy and security; legislation such as GDPR in Europe ensure that old or sensitive data is handled correctly.

Understanding the lifecycle of data reminds us that it is more than simply digital value; it is a living cycle that requires attention. Every stage, from birth to death, influences how trustworthy, useful, and ethical our digital world develops.

Why Data Matters?

Data is more than just numbers on a screen; it is the language of the modern world. Every piece of data offers a story about people, decisions, and outcomes. It contributes to the growth of societies, the functioning of economies, and the advancement of technology. How we use data determines how wisely, fairly, and sustainably our world runs.

Data helps scientists explore the invisible, from galaxy movements to gene patterns. In healthcare, it saves lives by detecting diseases early and improves therapy. In business, it influences decisions, reduces waste, and shapes consumer experiences. In everyday life, data discreetly simplifies everything: the map that anticipates traffic, the app that tells you to stand up, the recommendation that matches your preferences.

However, like other types of power, data entails responsibilities.

When used carelessly, it might deceive, exclude, or invade privacy.

When used wisely, it becomes a force for justice, progress, and understanding.

The difference is not in the data itself, but in how we gather, understand, and act on it.

As our world becomes increasingly digital, data serves as both a mirror and a compass. It reflects who we are and directs our paths. Every algorithm every insight has a backstory, one based on millions of tiny traces of human life. Understanding data is more than just understanding definitions and tools. It's about perceiving the world via patterns and connections, asking the proper questions and listening to what the numbers have to say.