The wisdom hierarchy, also known as the Data-Information-Knowledge-Wisdom (DIKW) hierarchy, or Data-Information-Knowledge-Wisdom (DIKW) pyramid, which is a model that illustrates the relationship between data, information, knowledge, and wisdom, illustrates the progression of raw data to valuable insights. The model is considered a philosophical approach because it emphasizes the difference between categories. For example, facts alone are not information, and knowledge is not wisdom. It gives us a framework to discuss the level of meaning and utility within data. Each level of hierarchy builds on lower levels, and to effectively make data-driven decisions, you need all four levels.

  • Data: Raw, unprocessed facts and figures without context. It is the foundation for all subsequent layers but holds limited value in isolation.
  • Information: Organized, structured, and contextualized data. Information is useful for answering basic questions like “who,” “what,” “where,” and “when.”
  • Knowledge: The result of analyzing and interpreting information to uncover patterns, trends, and relationships. It provides an understanding of “how” and “why” certain phenomena occur.
  • Wisdom: The ability to make well-informed decisions and take effective action based on understanding of the underlying knowledge。

Wisdom is the top of the DIKW hierarchy and to get there, we must answer questions such as ‘why do something’ and ‘what is best’. In other words, wisdom is knowledge applied in action. We can also say that, if data and information are like a look back to the past, knowledge and wisdom are associated with what we do now and what we want to achieve in the future.

Example: App Development

Data: The raw data consists of individual user interactions with the app, such as button clicks, screen views, and time spent on each page, as well as user-submitted feedback through reviews, surveys, and support tickets.

Information: App usage and user feedback data is organized and structured, providing metrics like average session duration, feature usage frequency, user retention rates, and common feedback themes across user segments.

Knowledge: Analyzing and interpreting information from app usage and user feedback uncovers patterns such as frequently requested features, causes of user frustration, and key factors driving user engagement and loyalty.

Wisdom: Understanding user needs, preferences, and pain points to make informed decisions to prioritize feature development, enhance user experience, and allocate resources effectively to maximize user satisfaction and retention.

Mobile apps collect user data, and get additional feedback data from users, but the final goal is to make decisions to improve the app and add more fascinating features.