Data, information, knowledge, and insight
The terms data, information, and knowledge are used extensively in the context of computer science. There are many definitions of these terms, often conflicting and inconsistent. Before we dive into these definitions, we will understand how these terms are related to visualization. The primary objective of data visualization is to gain insight (hidden truth) into the data or information. The whole discussion about data, knowledge, and insight in this book is within the context of computer science, and not psychology or cognitive science. For the cognitive context, one may refer to https://www.ucsf.edu/news/2014/05/114321/converting-data-knowledge-insight-and-action.
Data
The term data implies a premise from which one may draw conclusions. Though data and information appear to be interrelated in a certain context, data actually refers to discrete, objective facts in a digital form. Data are the basic building blocks that, when organized and arranged in different ways, lead to information that is useful in answering some questions about the business.
Data can be something very simple, yet voluminous and unorganized. This discrete data cannot be used to make decisions on its own because it has no meaning and, more importantly, because there is no structure or relationship between them. The process by which data is collected, transmitted, and stored varies widely with the types of data and storage methods. Data comes in many forms; some notable forms are listed as follows:
- CSV files
- Database tables
- Document formats (Excel, PDF, Word, and so on)
- HTML files
- JSON files
- Text files
- XML files
Information
Information is processed data presented as an answer to a business question. Data becomes information when we add a relationship or an association. The association is accomplished by providing a context or background to the data. The background is helpful because it allows us to answer questions about the data.
For example, let us assume that the data given for a basketball player includes height, weight, position, college, date of birth, draft pick, draft round, NBA-debut, and recruiting rank. The answer to the question, "Who is the first draft pick with a height of more than six feet and plays on the point guard position?" is also the information.
Similarly, each player's score is one piece of data. The answer to the question "Who has the highest point per game this year and what is his score" is "LeBron James, 27.47", which is also information.
Knowledge
Knowledge emerges when humans interpret and organize information and use that to drive decision-making. Knowledge is the data, information, and the skills acquired through experience. Knowledge comprises the ability to make the appropriate decision as well as the skills to execute it.
The essential ingredient—connecting the data—allows us to understand the relative importance of each piece of information. By comparing results from the past and by recognizing patterns, we don't have to build a solution to a problem from scratch. The following diagram summarizes the concepts of data, information, and knowledge:
Knowledge changes in an incremental way, particularly when information is rearranged or reorganized or when some computing algorithm changes. Knowledge is like an arrow pointing to the results of an algorithm that is dependent on past information that comes from data. In many instances, knowledge is also gained by visually interacting with the results. Insight on the other hand, opens the way to the future.
Data analysis and insight
Before we dive into the definition of insight and how it relates to business, let us see how the idea of capturing insight ever began. For over a decade, organizations have been struggling to make sense of all the data and information they have, particularly with the exploding data size. They all realized the importance of data analysis (also known as data analytics or analytics) in order to arrive at an optimal or realistic business decision based on existing data and information.
Analytics hinges upon mathematical algorithms to determine the relationships between the data that can yield insight. One simple way to understand insight is by considering an analogy: when data does not have a structure and proper alignment with the business, it gives a clearer and deeper understanding by converting the data to a more structured form and aligning it more closely to the business goals. Insight is that "eureka" moment when there is a breakthrough result that comes out. One should not get confused between the terms Analytics and Business Intelligence. Analytics has predictive capabilities while Business Intelligence provides results based on the analysis of historical data.
Analytics is usually applicable to a broader spectrum of data and, for this reason, it is very common that data collaboration happens internally and/or externally. In some business paradigms, the collaboration only happens internally in an extensive collection of a dataset, but in most other cases, an external connection helps in connecting the dots or completing the puzzle. Two of the most common sources of external data connection are social media and consumer base.
Later in this chapter, we refer to real-life business stories that achieved some remarkable results by applying analytics to gain insight and drive business value, improve decision-making, and understand their customers better.
