Alec Coughlin’s Post

Transcript

What's up everybody? So understanding what a semantic layer is and the implications of them within the context of the LM horsepower that we all have access to is a very big deal. If you're a CEO or a senior non-technical business leader to this post is 1000% for you dollar bet. If you get ahead of this one, you're going to put real deal points on the board. Oversimplifying it of course, as always, but here we go. Number one, what is the semantic layer? A semantic layer sits between complex databases and users, providing a simplified summary or representation or using my annoying overly used, you know, mental models that I love. A mental model almost of the most important parts of the database. Especially important when dealing with enterprise grade massive and complex datasets that are growing exponentially. Cats might include some data silos, some legacy tech, and some very old, dated and outdated code, etcetera. You know what I'm talking about. The semantic layer translates technical jargon into familiar business terms, making it easy for non-technical users to access and analyze data without needing to understand the complexity of database structures which can be quite complex. Access enabled by using universal business language that does not require the use of terms familiar to engineers that might not be so familiar to you, AKA creating an easy to use compound AI system. #2 Why should CEO's and senior non-technical business leaders care? Because his semantic layer democratizes access to the power of data and insights, AKA enabling more people to make smarter, faster, and Better Business decisions without relying on complex queries which take time and probably are difficult to figure out how to actually deploy specialized IT support. Or God forbid, bothering your Rockstar data science and data engineering teams who have plenty of other things to be focused on. This means faster insights, better alignment across departments, and a more agile organization overall, AKA speed. Innovation cycles get dialed way up and I know everybody loves that, especially on the business side of the house. One more thing. By abstracting away a lot of the data engineering complexities, semantic layers free up your Rockstar data science and engineering teams to focus on those more strategic and high impact dimensions of work. And I know a lot of people have heard a lot of those things and this that the other thing. But think about Pareto's principle in the 8020 rule and so on and so forth specific to these people. So instead of spending their time on repetitive data preparation tasks and cleaning and so on and so forth that are required and they're important, they're just tedious to do the magic. The data science work, they can dedicate their expertise to developing advanced analytics models, uncovering deeper, hard to find but very powerful insights and unlocking next level meaningful innovation AKA new revenue generating products. Like I said at the top of this dollar bet, remember that I got you. Have a good one. Happy 2025.