2.4 Data Discovery Techniques

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12 hours 48 minutes
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now one of the things that we think about with all the information that's available today
and all the entities that are collecting information
and the ways of sharing information, you know, we're almost in a time where privacy's just a thing of the past, right? Because anybody that spends any amount of time on the Web there's a footprint, at the very least and for the most part, most users. There's a massive amount of information out there.
You know what sports do you like? What's your,
um, you know, what are your preferences when you travel, hold these pieces of information now legal to for search engines to sell what you search for, so that much more information out there. So what the challenge has always been is how do we make use of this information? Because information is power.
And when they're so much
big data, what do we do with it?
Well, the idea is being able to take this data when we talk about data discovery. It's all about determining patterns and making this conglomeration of information or data meaningful.
So there are a couple of ways that we can kind of analyze data. The first is metadata, and that's one of the first things that we think about because metadata helps us make sense of that. Anywhere you go and you look up the definition for metadata, it almost always says data about data.
And that's true. But it's not particularly helpful.
So when we talk about metadata, let's say I go download a song from iTunes. Well, I get the song. That's really the data I'm after, but I also get the run time. I get the artist, I get the genre, and that's all metadata. So you can see have those additional descriptors
really helped me make sense really helped me organize,
so that if I'm driving down the road and wanna listen just to show tunes,
which I never would,
I can do that based on the metadata. So when we're looking at that in masses amounts massive amounts of data, it's that meta data may help me figure out okay, this is what I need
now. Another way sometimes is very frequently is that information is stored in the means that allows us to label the information based on its its relevance based on its significance. So when we're searching you weaken its like tagging data.
So when we're searching, we may be able to surge or we weaken frequently search based on those labels
and then, in some instances, content analysis. But when you're looking at content and all the content that's out there, it's much less likely that content analysis is really gonna be something profoundly helpful when we're talking about big data metadata, labels and content, though
some of the ways that we can use for data discovery
to make sense of big data.
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