Data and Databases
This chapter covers the concepts of data and databases. Businesses are becoming more and more "data-driven"; understanding how data is collected, stored, and managed is essential for anyone wanting to succeed in business. Pay special attention to the sections on data warehouses and data mining, as they provide examples of how companies use data strategically.
Data, Information, and Knowledge
There have been many definitions and theories about data, information, and knowledge. The three terms are often used interchangeably, although they are distinct in nature. We define and illustrate the three terms from the perspective of information systems.
Data are the raw facts, and may be devoid of context or intent. For example, a sales order of computers is a piece of data. Data can be quantitative or qualitative. Quantitative data is numeric, the result of a measurement, count, or some other mathematical calculation. 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.
Information is processed data that possess context, relevance, and purpose. For example, monthly sales calculated from the collected daily sales data for the past year are information. Information typically involves the manipulation of raw data to obtain an indication of magnitude, trends, in patterns in the data for a purpose.
Knowledge in a certain area is human beliefs or perceptions about relationships among facts or concepts relevant to that area. For example, the conceived relationship between the quality of goods and the sales is knowledge. Knowledge can be viewed as information that facilitates action.
Once we have put our data into context, aggregated, and analyzed it, we can use it to make decisions for our organization. We can say that this consumption of information produces knowledge. This knowledge can be used to make decisions, set policies, and even spark innovation.
Explicit knowledge typically refers to knowledge that can be expressed into words or numbers. In contrast, tacit knowledge includes insights and intuitions, and is difficult to transfer to another person by means of simple communications.
Evidently, when information or explicit knowledge is captured and stored in computer, it would become data if the context or intent is devoid.
The final step up the information ladder is the step from knowledge (knowing a lot about a topic) to wisdom. We can say that someone has wisdom when they can combine their knowledge and experience to produce a deeper understanding of a topic. It often takes many years to develop wisdom on a particular topic, and requires patience.