The data is the new currency. Not sure where I heard that term but I liked it and then incorporated it into my material. So, whoever is out there where I got that from, I'm sorry, I didn't quote your source but I thank you because I like this notion, data is the new currency. Remember back when in the first half of the course, the importance of being the most important provider of information as a company and you're offering services to your customers, you want to be the most important provider of information. So, it's this information that we want to extract it's key information, it's key insights from these huge datasets being able to provide that to our customers. There's trade-offs involved in this. It takes times computationally expensive to analyze huge datasets. So, there's this notion of given it's a set of data, how quickly can we get to some kind of key insight or make some kind of a decision based on what the data is telling us? Then, the other dimension is depth of insight. How deep is it? Is this just a superficial insight, or is this is a very very deep insight with that causes of business owner or board of directors of a company and say, well, there's a tremendous opportunity for us here to do X whatever. Whatever X is. Characteristics of big data. Used to be volume, there were three Vs. Now there's more Vs. There's seven. I have six here. Volume. How much data do we actually have? Velocity. How fast this is data being produced? In the Pratt and Whitney jet engine, we saw that data was being produced at 10 gigabytes a second as one example. That's pretty fast. Variety of data. How many types of data do we have? There can be many types. Structured data like database entries are a message in an embedded system like in a published subscribe scheme we talked about. There's lots and lots of unstructured data. Text, video, audio, think of all those videos out on YouTube. There's terabytes and petabytes worth of data out there. Value. How valuable is this data? Data might be valuable, might not be valuable. We want to save and analyze all the data, or are there we just want to pick out the important data and work on that? How do we do that? How do we identify that? Veracity. Just because you've got a bunch of data, may contain inaccuracies. We'll look at that a little more. There might be data missing, a center might be offline, or there could be errant values, and there were no errors involved in the sensor reading and so it's reported some value that's out of range for instance. Or the data is old, it's stale, it's not relevant anymore. Visibility. How accessible is it? Is it only accessible at a corporate data center site, or is it accessible for my cell phone on the other side of the planet? So, those are my six, but it used to just be volume, veracity, and variety, and there's a video at the end of this set of slide decks that the gentleman from HP, and he'll talk about this in more detail. I think he gives us seven things and then seven things, and then seven more things. He gives seven Vs that he starts his presentation with. I'd watched it and I thought it was interesting and I wanted to share with you guys. So, here's the the traditional picture of the original three Vs; volume, velocity, and variety, and this makes up big data. In character, this is just another way, this is from predictive analytics for dummies. Another way of visualizing these three Vs, is you've got velocity in one axis, and you got variety on another axis. So, this is every millisecond, every hour, every day, this is the speed at which data is being produced. The variety, we've got tweets, we've got medical data, we've got search engine queries, cell phone data, and then the volume goes up megabytes, terabytes, petabytes, exabytes.