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Executive Considerations for NLP and AI
Today, we figured we would talk a little bit, but the considerations of when you are accountable or responsible for these projects themselves, and you want to ensure that you have the best return on investments.
We touched last week about all the things of an executive’s train of thought that would go into such a project in an NLP and AI. The executive isn’t necessarily going to dig into the implementation of NLP projects and they’re not really going to source data for you, the executive wants to rationalize in their mind, do we have sufficient and the correct kind of data in this organization, or can we source it?
There are ways that you can actually look to get data and look to acquire data points that you may not have. Either you, create data and create metadata as needed, or you actually buy them from some catalog logs or databases.
If we’re talking about NLP projects, we’re often largely talking about unstructured data, largely it’s written data, it’s type data obviously, but it doesn’t have the structure of, which makes it, that you have to create the structure from it. So that’s often where the executive’s mind needs to be, are they need to ensure that the enterprise or organization has enough NLP expertise in its staff, obviously that they can properly manipulate all this text.
It’s like a mini-layer cake. It takes some work and it takes some foresight, but it’s definitely doable. And it’s possible, to think about this as an in-house project for sure.
Often, it also takes ingenuity and foresight, and thinking on the cultural level.
So, you want to be an organization that is thinking about, innovative things and also considering new kinds of algorithms and new kinds of methods to do things and not kind of just stuck in what’s working and what’s paying the bills at all times.
Let’s cut through the jargon, myths and nebulous world of data, machine learning and AI. Each week we’ll be unpacking topics related to the world of data and AI with the awarding winning founders of 1000ML. Whether you’re in the data world already or looking to learn more about it, this podcast is for you.
