The point is to understand

 📖 "Any fool can know. The point is to understand." - Albert Einstein

The siren call to action can be impossible to ignore. In fact, the pressure to "do" can be so great, that we'll look at data- make an assumption and then act without ever looking back. Albert Einstein seemed to be warning us not to confuse knowledge (data) with understanding... something worthy of exploring.

I got curious and tried a CHATGPT prompt. (Data vs. information vs. insight vs. understanding.) I was presented with following response-

💻 Data –Raw, unprocessed facts or figures without context.

💻 Information –Data that has been organized, structured, or contextualized to make it meaningful.

💻 Insight –A meaningful interpretation or discovery derived from information, often leading to action.

💻 Understanding –A deep, holistic grasp of patterns, causes, and implications that allows for better decision-making.

💻 Each level builds upon the previous one, moving from raw data to actionable wisdom.

Not a bad summary. Raw numbers (data) need to be organized and contextualized (information) before consumption, or we run the risk of drawing incorrect assumptions. Analyzing this information can lead to a meaningful discovery (insight) from which we may hypothesize an action.

Sadly, this is often where we stop. We decide and then we do. Most organizations review the results of their actions and conduct another cycle of the data- information- insight... but skip the final step to understanding.

The grasp of patterns, causes and implications really required a thoughtful review, not only of the results of the action, but what it was in the initial analysis (decision making) that led to the outcome from the initial action. Think of it this way...

A product team reviews their KPIs and sees that interaction with a feature is lagging, so they make adjustments to the feature, wait until the next read of the data, and decide if the the feature need more tweaking. They will most likely go several iterations of this cycle until they the desired results.

This is a great practice - it's often the most effective way to achieve outcomes efficiently.

Mr. Einstein advocates for the team to review the results of their iterations, but also at what led them to those decisions.

❔ What did they learn about their customer and their market with each iteration?

❔ What patterns did they observe across those iterations?

❔ What insights were wrong, or missed as they iterated?

Answers to these types of questions lead to a deeper understanding (or a deep, holistic grasp of patterns, causes, and implications) that allows for better, future decision-making. As they continue to evolve their products, they can iterate even more effectively!

Iteration, and the data- information- insight- understanding cycle leads to improvements to not just your product, but also how your team performs!

What's stopping you?