Data pillars 1

Hi! Today, I will talk about the knowledge or building the knowledge for AI.

I'm taking a different approach from whatever existing today, between the structured and unstructured data. Those are two data pillars, and you know that the main or the blood of AI is the data. So, I gave six data pillars—pillars, sorry—starting from structured data, unstructured data. Everybody knows about this: structured, like databases; unstructured, like emails, documents.

And then, I separate the media data pillars because it requires a special processing when you're talking about the videos, and the images, and the voices, and how you extract knowledge, information, and data. And you know that the knowledge is after the data, and information, and then knowledge. AI can handle all those three levels of data.

And the fourth one is the dashboards and the KPIs. Data science, it's very important for AI. Actually, I consider AI as an upgrade—and somehow upgrade of the data science. So, how you can gain benefits of those dashboards and KPIs? You can build a catalog of the formulas, and KPIs, and metrics, and then AI can use that. But anyway, that the fourth one.

And the fifth one is the IoT. I consider all the devices are talking to us. So, here, all the devices, all the IoT, name it, even BMS system, they have their own data, they have their own systems, and we need to prepare this for the AI. I'm not saying you need to let AI access those, just here preparing the data for AI. For example, you can build also a catalog of all devices and what they're doing, and even access the logging, logging files that can be accessed by the AI.

The sixth area, which is very important, is networking. Networking, including switches, servers, and all related data to it. When it's extract some data from the servers, like logging files and all those, so you need to prepare also those for AI.

So, that that things are the six pillars, I call it the architecture or the AI knowledge base architecture for your organization. And why you need to do that? Because AI, every single piece of data, it's very important for AI. Even if you consider it is like an unimportant, it's not important for AI, except for, you know, duplications, or some data missing data, I mean, you know, the data filtration for for data science.

But I'm talking here when the when you when you actually read the articles about the the six pillars of the data, I'm talking about preparing the data for AI in general. Not even for LLM, not even for general AI, just preparing your data for AI. Your data is a digital assets, okay? And AI, don't underestimate any piece of data for the for the AI.

And we will talk about this in in a future, you know, videos. And I talked in details about those pillars for for data, but I will give you an example: networking. Networking today, whatever networking setup today is not ready for AI. So, you need to have those data all across your organization ready for AI, then then you think about how we can use this data to protect our organization from hacking or make it more trustworthy, trustworthy environment.

In the next videos, I will talk more about this. So, thank you, and I hope you will gain benefits out of this, and I hope, hopefully, God willing, I will publish a ebook about the six data pillars with AI. Thank you.