What Is Data? Big Data, Data Science, and Metadata Explained

Data is any recorded fact a computer can store: an order, a temperature reading, an email, a photo of a damaged parcel. The words around it, such as big data, data science and metadata, are used loosely. Here is what each one means, and which ones matter for a company of your size.

Three shapes of data

The same customer order can exist in very different forms. Its shape decides how easily you can store, search and analyse it.

{ "order": 10482,
  "items": [{ "sku": "TSH-M", "qty": 2 }],
  "coupon": null }

By volume, most data is unstructured, yet most business reporting still runs on structured data. Language models are shifting that balance: they make emails, contracts and support tickets much easier to search and summarise.

Big data: when size changes the rules

Big data means data too large, too fast or too varied for one ordinary database. It is usually described with three Vs:

An honest point: most small and mid-sized companies don’t have a big data problem. They have a messy data problem: a few million rows spread across tools that disagree. That calls for a tidy pipeline and a cloud warehouse, not a cluster. Distributed tools such as Spark earn their keep only when a well-sized database really can’t cope.

What a sensible setup looks like in 2026

• Raw data lands cheaply in cloud storage or a warehouse

• Transformations run as tested, version-controlled code

• Only the data people actually use is modelled for reporting

• Ownership, access and retention rules are written down

If that list sounds far away, it is exactly the kind of foundation our data engineering work puts in place.

Analytics, data science, machine learning and AI

These overlap, but each answers a different question.

Machine learning is a toolset that data scientists use, and generative AI is a branch of machine learning built on very large models. To see what happens inside one, try our interactive guide to how large language models work.

How a data science project runs

1. Define the decision you want to improve, not just the question

2. Gather and clean the data; this usually takes most of the time

3. Explore it and check your assumptions

4. Build a model

5. Test it against a simple rule of thumb; beating that is the real bar

6. Put it into use, then monitor it as the world changes

Metadata: the labels that make data usable

Metadata is data about data. A column called rev tells you nothing on its own. With metadata, it tells you everything you need to trust it:

Metadata also carries compliance weight. Under the GDPR, many organisations must keep a record of their processing activities. Knowing which fields hold personal data, where they come from and who can see them makes that record far easier to keep up to date.

Examples from Belgium and the EU

Retail

A typical Belgian retailer with shops and a webshop combines till data (structured), app events (semi-structured) and product reviews (unstructured). Analytics shows yesterday’s sales per store, a forecast sets next week’s stock, and metadata makes sure “revenue” means the same thing in both.

Manufacturing

Vibration and temperature sensors feed models that flag machines likely to fail, so maintenance happens before the breakdown. The value comes from better scheduling, not from the sheer volume of sensor data.

Public sector

Belgian and EU public bodies publish open datasets on data.gov.be and data.europa.eu, with metadata describing each source, licence and update cycle. Since September 2025, the EU Data Act also gives users of connected products more rights to access the data those products generate.

A five-minute data check

Answer these honestly:

1. Do two reports ever show different values for the same number?

2. Can you name an owner for your most important dataset?

3. Would you know if yesterday’s data didn’t arrive?

4. Does someone copy data between tools by hand every week?

5. Do you know which fields contain personal data?

Two or more uncomfortable answers usually mean the next step is pipelines and definitions, not advanced analytics. Get in touch if you’d like a second opinion.

Frequently asked questions

What is the difference between data and information?

Data is the raw record, for example 8.4 and March 2026. Information is data with context that answers a question: deliveries took 8.4 days on average in March.

Do small businesses need big data tools?

Rarely. A cloud warehouse and a few well-built pipelines handle the data volumes of most small and mid-sized companies, at a fraction of the cost and complexity.

Is metadata the same as data governance?

No. Metadata is the labels. Governance is the set of rules and people that keep those labels accurate and decide who may access what.

Where does AI fit in?

AI models are built from data and are only as good as that data. Clean, well-described data comes first; after that, AI can help you search, summarise and predict.