This is the third part of a series of posts explaining data science in language for people who don’t know anything about it. You can go to the start here.
Data is the foundation of all good decisions.
A group of related insights gives us domain knowledge. Here, “domain” is synonymous with “field” or “topic.” Domain knowledge is the sum of general knowledge that a person has about a particular domain.
Each person has different domain knowledge depending on the area: my lawyer might be an expert in Colombian commercial law, but he has intermediate domain knowledge in chess, and very poor (I think?) knowledge of crocheting. I made an assumption there: I said that Jorge, my lawyer, didn’t know anything about crochet when, in reality, I have no idea. It’s better to avoid making assumptions, although sometimes it’s inevitable.
The greater the domain knowledge, the more intelligent the decisions are made, meaning decisions with a higher probability of being accurate or correct. Since I have domain knowledge about my wife, I have the insight that she gets home at 6 PM, and I also know that she likes me to wait for her near her bus stop so we can walk home together. So, I can make the decision to stop my activities at 5:45 PM to go pick her up. Before having this complete domain knowledge, I used to be late picking her up, which was definitely not the right choice, as she made sure to let me know.
This domain knowledge has both magnitude and quality factors. An executive with 30 years of experience in a rapidly innovating field but who continues to make decisions based on insights from 10 years ago probably has broad domain knowledge, but of low quality. A young 30-year-old entrepreneur about to launch their first company with fresh knowledge may have less domain knowledge in terms of magnitude, but of higher quality.
This leap from insight to knowledge doesn’t happen by magic: The findings of that process must be explained in a way that they are easily understood. An insight must be communicated in the most understandable way possible. The most valuable skill of the person or team responsible for measuring, storing, transforming, and communicating the data is not the manipulation of the data to create insights, but the explanation of the insight to create domain knowledge.
Once this domain knowledge has been lovingly cultivated with data, it feeds into and improves the other stages of the process. Experience is the best guide when it comes to understanding what data to look for, how to cook it perfectly for the industry and the problem at hand, and how to serve and present it in such a way that everyone in our organization can digest it well to grow big, informed, and intelligent. This is what will differentiate your decisions from mere opinions.