Without those things, you just have a shiny object and not necessarily an outcome. That's why these building blocks are fundamental. And the clients, they get to this point, and they're the ones who try to jump to the shiny object and they don't have the data to support that. >> And then you've got companies going on digital transformation, which is basically saying all their data legacy, trying to modernize it. The modern companies like Uber, and we saw the first fatality of an Uber car this week, again, points out the reality that realtime is realtime, and the importance of having data, whether it's sensing data.
We're not, it's coming there, you can start to see it happening. Realtime data is key. That means data mobility is critical, and you mentioned private, public. Storing the data and moving the data around, having data intelligence, is the most important thing. Realtime data in motion, intelligence, you know, where are we? Is that a setback with the Uber incident? Is it a step forward, is it learning? What's your view of the data quality of movement in realtime? >> I think data ingestion is one of the least talked about topics that is one of the most important. With IBM Cloud Private for data, we can ingest 250 billion events a day. Let me give you some context for that. 2016, the entire credit card industry, everywhere in the world, did 250 billion transactions. So what credit cards do in a year, we can do in a day. Biggest stock trading day ever on the New York Stock Exchange, what got done in that entire day, we can do in the first 40 minutes of trading. But that value there is, how fast can you bring data in to be analyzed, and can you do a decent bit of that pre-processing, or analytics, on the way in? That's how you start to solve some of the problems that you're describing, because it's instant >> John: Yeah. >> And it's unsurpassed amounts of data. >> So ingestion's a key part of the value chain, if you will, on data management. The new kind of data management. Ingesting it, understanding context, then is that where AI kicks in? Where does the AI kick in? Because the ingestion speaks to the information architecture, IA. >> Rob: Yes. >> Now I got to put AI on top of that data, so is the data different? Talk about the dynamic between, okay I'm ingesting data for the sake of ingesting, where does the AI connect? >> So you got the data, yep. So you go the data, AI starts where you're saying, all right, now we want to automate this. We're going to build models, we're going to use the data that we've got in here to train those models. As we get more data, the models are going to get better. Now we're going to connect it to how humans want to interact. Maybe it's natural language processing, maybe it's visualizing data. That's the whole lineage of how somebody gets toward this AI idea. >> What are some of the conversations you're having with customers, and how have they changed? And give some color, I mean, only a few years ago we're talking about data lakes. >> Right. >> Okay, what is the conversation now, and give some context of how far that conversation has gone down the road toward advancement. >> I think we're going from data lakes to an idea of a fluid data layer, which is all your data assets managed as a single system, even if they sit in different architectures. Because there's no one, we all know this. We've been around this industry forever. There's no one way to support or manage data that's going to support every use case. So this idea of a fluid data layer becomes critical for every organization. That's one big change. Other big change is containers. What we're doing with Cloud Private for Data is based on Kubernetes, that's how people want to consume applications, but nobody's really solved that for data. I think we're solving that for data. >> Let's dig into that. It was one of my topics I wanted to drill down on. Containers have been great for moving workloads around, certainly Kubernetes has been a great orchestration tool.
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