I will continue writing about data analysis, visualization, and reporting. However, adding a brief explanatory note at this point would be beneficial. During this process, it is important that concepts are clearly defined and easily understood to avoid confusion.
The topic of our article is data, information, and knowledge.
Data, Information, and Knowledge
Throughout our daily lives, we rely on piles of knowledge to perceive and understand our environment. In this process, the source of knowledge and the integrity of the acquisition process are essential. So, what do these terms mean to us? Let’s first clarify these fundamental concepts.
Data
Data refers to raw (unprocessed), the most basic unit of information. It is collected through methods such as measurement, experimentation, and observation. Depending on the method of collection, data is classified as quantitative (numerical) if it consists of numbers, or qualitative if it is non-numerical and can be categorized. Quantitative data is further divided into discrete and continuous types. Discrete data is collected through counting and is often expressed in terms of count numbers, while continuous data is collected through measurement and expressed as real (real-valued) numbers. Qualitative data is grouped into nominal and ordinal categories. Nominal data is represented by symbols without any inherent order, whereas ordinal data, represented by symbols, also carries a meaningful sequence1.
Regardless of whether the data is quantitative or qualitative, raw data without transformation into information lacks meaning or functionality. Therefore, we transform the data we collect through a series of operations such as sorting and grouping into information. In this way, the data we obtain acquires meaning and functionality within a contextual framework2.
In statistics, data refers to data that has been collected, processed, summarized, and interpreted for analysis and presentation.
As an example of data, personal names, dates of birth, phone numbers, addresses, exam scores, and annual temperature values for Antalya province may be provided.
Information
Information can be described as a piece of knowledge compiled within a specific context or subject framework. Once information is gathered within a particular context, it undergoes a filtering process and is then categorized, transforming into information. It conveys a more specific and narrow meaning than raw data. It is structured toward a specific purpose. Information emerges when data is interpreted and understood3.
For instance, this year’s number of live births is higher than that of the previous year.
Thomas H. Davenport and Laurence Prusak consider the following stages in the process of transforming data into information:
- Contextualized: Understanding the purpose for which data is collected,
- Categorized: Knowledge of appropriate units and fundamental components for analysis,
- Calculated: Mathematical or statistical analysis of the data,
- Corrected: Removal of errors from the data,
- Condensed: The most concise and essential summary of the data.
Information
Information, on the other hand, is the structured and interpreted form of data that has been transformed into information. We use information during the decision-making process. According to Taylor, information is formed in individuals’ minds through experience and consists of a mixture of various elements, making it inherently complex and/or simple4.
For example, sales volume in Perakene during the first quarter of the year is three times higher than that in the last quarter.
The graph above illustrates the information hierarchy. As shown in the graph, technical and cognitive actions are also linked to each stage. The goal of this process is always to obtain the information that will be used during the decision-making phase based on data.