Data and Databases

The World of Data

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When people talk about data, they often mean many things at once. A spreadsheet, a customer name, a measurement from a sensor, a click on a webpage, and a whole video file are all examples of data. It is useful to separate a few related ideas before moving to databases more specifically.

Data, Information, and Knowledge

Data is the raw material. It can be numbers, text, timestamps, measurements, images, or many other kinds of recorded facts.

Information appears when data is organized in a way that supports interpretation. In the course exercise system, an individual submission timestamp is data. A list of all submissions for one exercise, ordered by time, already tells a story, and that story is information.

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Knowledge is what someone can conclude or understand from that information. If a teacher notices that most submissions for one exercise arrive only a few minutes before the deadline, that is already closer to knowledge than to raw data.

Figure 1 — Data becomes information when it is organized for interpretation, and information becomes knowledge when someone draws a broader conclusion from it.

The boundaries between these words are not perfectly sharp, and people use them differently in everyday speech. Still, the distinction is helpful because many information systems are built around this transition from raw stored data to useful, interpretable information.

One way to see this is to start from a single event:

• User 42 submitted Exercise 7 at 2026-03-20 22:58
• That event becomes information when it is connected to the course, deadline, and grading status
• It becomes knowledge when a teacher or course assistant notices a broader pattern, such as repeated last-minute submissions across a whole course

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Data Has a Lifecycle

Data does not simply appear and then stay unchanged forever. In most systems, data moves through a lifecycle.

Figure 2 — Data in an application moves through a lifecycle from creation to storage, use, and eventually archival or deletion.

The course exercise system is a good small example:

Lifecycle Stage	Example in the Course Exercise System
Creation	A teacher creates a course and its exercises, or a student submits work.
Storage	The application stores the submission so that it can be used later.
Processing	The system later updates grading results and status values.
Retrieval and Use	Students and teachers open pages and summaries that read the stored data.
Archival	Old course data may be retained for later reference.
Deletion or anonymization	Data that is no longer needed may later be removed or anonymized.

Thinking in terms of a lifecycle helps us ask better questions. Who creates the data? Who uses it? How long should it be kept? When should it be removed?

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Operational Use vs. Reporting Use

Some data is used to run daily activity. For example, the system must accept a submission, store it, and later show whether it has been graded. That is operational use.

Some data is used to summarize or analyze activity. For example, a teacher might ask how many submissions arrived before the deadline or what the average score for an exercise was. That is closer to reporting or analytical use.

Well-designed data systems often support both:

• Storing operational data reliably
• Answering questions about that data later

This matters throughout the course. The same stored facts must often support both everyday features, such as listing exercises, and later summary views, such as counts and averages.

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Check Your Understanding

1. Give one example of raw data that becomes information only after context is added.
2. Why can the same dataset support different decisions in different situations?
3. What is one example of knowledge that depends on repeated interpretation rather than on one isolated value?

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