In today’s episode, I answer the question, “What is the difference between great data and good data or great data and data?” I explain how data is like the ingredients for cooking. Good data enables quality results, but bad data ruins everything. To find out more about the key role data quality plays in AI, be sure to watch this video!
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Mark asks, What is the difference between great data and good data or great data and data? And what are the practical applications of AI to that kind of data? That’s an interesting question.
Think about data like this.
You’re in your kitchen, you’re getting ready to cook dinner, got your appliances, you’ve got your cookbook, you’ve got your skills.
And you got data, right, you have ingredients.
If you are a skilled chef, as long as the ingredients are fundamentally okay, you can make something or you can make a dinner.
Now the better the ingredients are, the better the dinner you’re going to be able to make if the ingredients are fresh and organic, and this and that chances are you’re going to be probably able to make a pretty nice dinner.
If the ingredients are rotten, with a few notable exceptions, most rotten food is inedible, most rotten food will make you sick.
And no matter how good your appliances are, how good your recipes are, how skilled a chef you are, you are still stuck with a creative rotten food, and there’s very little you can do with it.
It’s not going to make somebody sick.
So when we talk about data, particularly in the context of AI, that’s the way that I like to think about is data is your ingredients are your raw ingredients for using AI or using anything, even using a basic spreadsheet.
If your numbers are wrong, even just doing stuff in in a spreadsheet, you’re going to get the wrong answers, you’re gonna get bad answers.
So you have to create good data or get a hold of good data so that you can use it properly.
Now, if you have great ingredients, and you have someone who is incompetent in the kitchen, then yeah, even with great ingredients, you’re still not going to get something that’s that’s necessarily all that good, right? You can give someone a Australian Wagyu marbling score seven pecania cut, which is a really, really, really nice piece of beef.
And if you hand it to the village idiot, who puts it in a blender? Yes, you’ve essentially wasted that ingredient unless you want to beef soup.
Even that is not gonna be very good beef soup.
But it’s still technically edible, because the the village idiot didn’t ruin it completely just mostly.
So your people matter, your processes matter, your platforms matter, but the quality of your data is paramount.
Because, again, you can give good ingredients to a bad cook and get a not a great result, right? You can give good ingredients to a very talented cook and get a great outcome.
But nobody’s making anything good out of rotten ingredients.
And the same thing is true with AI, with machine learning, data science, etc.
We are the chefs, our tools are the platforms.
And our data is the raw materials from which we are going to cook.
And if they’re bad, what we’re going to produce is bad too.
So good question.
Thanks for asking.
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