There is a direct framework that should be utilized when we think of insights vs observations: DIKW, which stands for data, information, knowledge, and wisdom. To first discuss the difference between insights and observations, we should break down the acronym DIKW into its parts. When we think of observations, we should look at the first two letters: Data and Information. Observations are a key element to data literacy and analytics, allowing people to understand the direction of the data, see trends, and understand where data has been, as it performs. Most individuals can make observations. They can see a trend line going up or down, they can see the different sizes of pie in a pie chart, or they can observe differences in bars in the latest bar chart. In his great three-part learning series on Data Informed Decision Making, Qlik’s own Kevin Hanegan touches upon DIKW. Kevin says that Data are raw observations that are independent of each other and Information is data that you start to add meaning, understanding, relevance, and purpose to. The definition of observation is: the action or process of observing something or someone carefully or in order to gain information. In other words, observations are the what: what is happening in the data. From a data literacy perspective, observations are easier and most people can see them, but what do observations truly do for us in helping us make data-informed decisions? We need to move beyond the what.
Moving beyond the D and I, the what, Kevin goes on to describe Knowledge and Wisdom. He says Knowledge is when you start adding in your personal beliefs and values to information. Wisdom is when you take that knowledge and add in your experience. The K and W are tied directly into insights. The definition of insights is: the capacity to gain an accurate and deep intuitive understanding of a person or thing. If data and information are the what, knowledge and wisdom are the why. An insight is where individuals and organizations are able to add in their personal experience, history, etc., to move beyond the observation and what, and proceed to the insight and why; this is the human element of data and analytics. For organizations to be able to utilize and make smart data-informed decisions, insight needs to be the goal, but this is where the data literacy skills-gap is a true pain point for organizations all over the world.