Chapter 2 - Data and Data Processing

Chapter 2 Part 1 Concepts of data, information and knowledge

Part 1: Concepts of data, information and knowledge

Structure of the knowledge pyramid

The concept of data

The term data refers to information or values that are available in a raw or processed form and serve as the basis for decisions, calculations or analyses. It can take the form of numbers, text, images, audio files or other formats and represents the building blocks of knowledge when placed in context.

Characteristics of data

Neutrality: Data is initially neutral information without interpretation or context.

Example: "25" is a data value that has no meaning without context.

Formats: Data can be available in various forms, e.g. as numbers, letters, symbols, images, videos or sounds.
Use: Data is the basis for analyses, calculations, reports and decisions.
Storage: Data can be stored digitally (e.g. in databases, files) or analog (e.g. on paper).
Data can be divided into different categories. These classifications help to better understand, organize and use data in a targeted manner.

Data classification

Data clissification

By structure

Structured data

Data that follows a fixed format or schema, e.g. tables with columns and rows.
Examples:

Tables in a database
Excel tables
Sensor values (e.g. temperature: 22°C)

Unstructured data

Data without a standardized format or schema.
Examples:

Texts in documents
Pictures, videos, audio files
Emails

Semi-structured data

Data with a partially fixed structure, often organized by tags or key-value pairs.
Examples:

XML or JSON files
Log files
HTML pages

By origin

Primary data

Data collected directly from a source or observation.
Examples:

Results of surveys
Raw data from sensors
Sales data of a POS system

Secondary data

Data that has already been processed or collected from other sources.
Examples:

Reports based on primary data
Data from public statistics
Summarized research results

By representation

Numerical data

Data represented by numbers.
Examples:

Age (e.g. 25 years)
Temperature (e.g. 22.5°C)

Categorical data

Data that can be divided into groups or categories.
Examples:

Gender (male, female, diverse)
Colors (red, blue, green)

Textual data

Data in the form of text or speech.
Examples:

Blog posts
Customer ratings
Books

Multimedia data

Data in the form of images, videos or audio files.
Examples:

Photos
Movies
Music files

By access method

Static data

Data that does not change or rarely changes.
Examples:

Archive data
Legal texts

Dynamic data

Data that changes frequently or is updated in real time.
Examples:

Share prices
Traffic data
Weather forecasts

After use

Transactional data

Data that is used in operational processes.
Examples:

Orders in an online store
Bank transactions

Analytical data

Data used for reports and analyses.
Examples:

Turnover statistics
Market analyses
User behavior

By sensitivity

Public data

Data that is accessible to everyone.
Examples:

Weather data
Wikipedia content

Confidential data

Data with restricted access.
Examples:

Company reports
Customer lists

Personal data

Data relating to individuals.
Examples:

Name, address, telephone number
Health data

Sensitive data

Data that requires special protection.
Examples:

Passwords
Bank details
Military secrets

By storage form

Local data

Data stored on a single device or server.
Examples:

Files on a hard disk
Local database

Cloud data

Data stored in a cloud environment.
Examples:

Google Drive documents
Data in Amazon Web Services (AWS)