Insights vs. Observations

Organizations are trying to capitalize on data, but are they prepared to gain insight versus observations, and what is the difference?

Catch-phrases like “data is the new oil” are popping up everywhere, and rightfully so. Data is an extremely valuable asset, and organizations everywhere and across all industries are trying to capitalize on their own asset of data. One-way companies are trying to gain value from their data is through democratization. The democratization of data is the bringing of data to the masses. The problem this presents is most individuals do not have backgrounds that allow them to truly consume the data effectively; this is data literacy, or the lack thereof. Now, that said, the democratization of data is the absolute correct answer to gathering insight, but this presents us with a new problem: most individuals can make observations of data, but how many are comfortable gathering insight from the data? Insights are the true value, and not observations.

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.

This was just a small snippet into the world of insights and observations, but is a strong starting point for individuals to know the difference between these key elements.Overall, organizations need to empower individuals with this ability to gain insight and not just make observations.Organizations need to empower everyone with skills and data literacy learning.One key element to having this happen, is to foster the right culture at the organization, one that continually interrogates and argues with its data, and overall, fosters a world of curiosity and collaboration.It has been said that curiosity killed the cat … in the world of data and analytics, curiosity can spark data-informed decision making.

Our #DataLiteracy expert @analytics_time explains how orgs can capitalize on #data & gain insights vs. observations!


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