16. DATA, INFORMATION AND KNOWLEDGE.

DATA, INFORMATION AND KNOWLEDGE.

Course Description: This course provides a comprehensive exploration of the fundamental concepts of data, information, and knowledge, which are the building blocks of information science and play a crucial role in the modern digital age. Understanding how data is transformed into information and, subsequently, knowledge is essential for individuals pursuing careers in data science, information management, or those who wish to harness the power of information in various domains.

Course Objectives:

  1. Define and differentiate between data, information, and knowledge.
  2. Explore the characteristics and properties of data.
  3. Examine the processes and methods for transforming data into information.
  4. Discuss the role of context and interpretation in the creation of knowledge.
  5. Investigate the ethical and privacy considerations associated with data and information.
  6. Foster critical thinking and problem-solving skills related to data and knowledge management.

 

INTRODUCTION

The first two components of information systems: hardware and software. However, those two components by themselves do not make a computer useful. Imagine if you turned on a computer, started the word processor, but could not save a document. Imagine if you opened a music player but there was no music to play. Imagine opening a web browser but there were no web pages. Without data, hardware and software are not very useful! Data is the third component of an information system.

DATA

Data are the raw bits and pieces of information with no context. If I told you, “15, 23, 14, 85,” you would not have learned anything. But I would have given you data.

There are two main types of data:

Data can be quantitative or qualitative.

  • Quantitative data is provided in numerical form, like the weight, volume, or cost of an item. Quantitative data is numeric, the result of a measurement, count, or some other mathematical calculation
  • Qualitative data is descriptive, but non-numerical, like the name, sex, or eye color of a person. Qualitative data is descriptive. “Ruby Red,” the color of a 2013 Ford Focus, is an example of qualitative data. A number can be qualitative too: if I tell you my favorite number is 5, that is qualitative data because it is descriptive, not the result of a measurement or mathematical calculation.

Data Examples

  • The number of visitors to a website in one month
  • Inventory levels in a warehouse on a specific date
  • Individual satisfaction scores on a customer service survey
The price of a competitors’ product