 Live from Berlin, Germany. It's theCUBE, covering NetApp Insight 2017. Brought to you by NetApp. Hello everyone, we are kicking off day one. Actually, it's a one day show of NetApp Insight here in Berlin, Germany. I'm your host, Rebecca Knight, along with my co-host, Peter Burris. We're going to be talking about NetApp's digital transformation. It's amidst a years long digital transformation. Set the scene for our viewers, Peter, a little bit about where NetApp is today and its evolution. Well, NetApp, like many companies in the technology industry, is trying to move from a focus where the asset's been on the hardware to a focus where the asset's more on the data that the business is using. And that's an industry-wide shift. And NetApp in particular has been especially aggressive about putting forward this proposition that increasingly companies are data-driven and that therefore they have to take care of the data to treat it differently. And that has an enormous implication for how businesses operate and certainly how technology companies are going to serve. So NetApp is not only leading the charge on its own transformation internally, but it's also helping other companies with their digital transformations. Well, it has to be. I mean, the whole notion of digital transformation is something that's very frequently misunderstood. So the way we look at it at Wikibon, and I don't think that this is at all an opposition to anything in NetApp would say. The way we look at it is that data is an asset that the business uses. And a digital business uses data assets differently than a non-digital business. In fact, we think it's a strong enough proposition. We think the difference between a business and a digital business is the digital business's use of data. So if you start from that proposition and you think about what does it mean to use data differently, then it has enormous implications in how the business institutionalizes its work, the types of people that it hires, the type of initiatives that it goes after, the way it engages its customers, et cetera. All of these are impacted by the simple proposition that if you use data as an asset, your business is going to have significant operational features that are going to transform. Well, I think that that's really what we're getting at. We heard in the keynote today, this is a real seminal moment for NetApp. And really for all businesses today, we're at a point in time with this explosion of data. And it can mean really big things for companies if you are storing that data well, managing that data, extracting value from that data. So I think that that's what we're going to hear a lot about today. Well, there are three things. If you're going to be a data-driven business, if you're going to be a business that uses data as an asset and therefore you institutionalize your work differently as a consequence, you're going to have to do three things really well. You're going to have to capture data well, you have to turn that data into value well, and then you're going to have to act on that data back in the marketplace. Increasingly that involves a degree of automation. So when we start thinking about AI or machine learning or deep learning or a lot of the other buzzwords, what that really, what those buzzwords really are about is how do we take data and then do something of consequence back in the marketplace? So every business is trying to better understand how it invests in those capabilities of capturing data, turning it into value, and then acting on it in the marketplace. And NetApp as a company is trying to provide the software and the underlying tooling as well obviously as a lot of the infrastructure to ensure that companies can do that more successfully. So it's the infrastructure and the products, but it's also this idea of best practices because we're going to hear today about a survey that NetApp executed with IDC about what the difference between the data drivers, the companies that are using data as you described, and then just the ones who are just surviving and we're really going to learn from them what it takes to do this well. Well, every company uses data to some degree and we used to spend a lot of time in the industry talking about the difference between data and information and insight. And while those debates continue to go on, there really are just a bunch of analysts and consultants talking to each other. What's really important is to better understand the role that data plays within decision making, the sources of the data and the differences in those sources, and then very importantly, the physical realities, the legal realities, and the intellectual property realities of data because those are the three things that are going to determine how your infrastructure actually gets set up, what role your applications play in business, how you can automate or not, and ultimately it's going to have an enormous impact on how the composition of your business from a people standpoint as well. Well, I want to get into that a little bit because it really does have huge implications for your workforce. There's so many different demands and pressures on companies, but then in particular on the people whose job it is to execute these strategies and they are being asked to do so much and not being given the budget perhaps that they need to do it. So I think that that's also putting a huge pressure on companies. There's a lot of pressure because of budgets but there's a lot of reasons for that. I think the fundamental issue is do people trust their data or not? We've certainly seen on many levels that people are reticent to take on a more data oriented approach to living their lives. And that's true in a social setting. It's also true inside a company as well. And one of the big transformations that has to take place inside a company is a recognition that data is crucial to informing decisions and informing actions but that it's not enough, at least not in just its raw form. There's a lot of other work that has to go on to ensure that data is presented in a way that's useful to human beings. We talk a lot about artificial intelligence and how artificial intelligence is going to disrupt a whole bunch of industries and dislocate a bunch of jobs and while there's definitely truth to that what we've also seen is that with each successive move forward with the tooling of information, we can go back a few hundred years and talking about this, that people have found ways to adjust, have found ways to incorporate that into their lives in the way the business conducted. This particular transformation is going to be especially tricky because of the intensity, of the depth, of the completeness of the data and what it promises to do. When you start introducing new types of automation driven by data, that's going to have an enormous impact on how people see themselves in the workplace. Well, I also want to unpack a little bit about what you said. You described a real reluctance or reticence to incorporate data, to believe the data, trust the data and then make actionable decisions based on that data. What accounts for this, do you think? Well, I think that partly I think it's just human nature that human beings are very tactile, we're very tactile. Our sources of information tends to be visible light, touch, listening and data is inert until it's put into a form that impacts our senses. And this is going to get very, very philosophical very quickly and I don't want to bore everybody, but what it means ultimately is that data presents models that have a consequential impact on the way the worlds work. We go through our lives with models. So for example, we can look at this impressive show floor and very quickly we have a model of how we're going to get from point A to point B. If we were looking at that just in data terms, it would remain very confusing, almost like the matrix. So people need help at ensuring that data becomes complimentary to the normal cognitive models of the way that we work and not positioned as a substitute or worse antitherical to how we generally live our lives. And that's where some of the challenges. Now there's other challenges as well. So for example, we are kind of presuming that computers are a lot smarter than they are. And in fact, computers are very, very stupid things. Now that doesn't say anything about the technology or the quality of the technology. It says something about what computers actually are. And so if we give it great software, if we give a computer or a computer system great software, it's going to behave better than if we don't. But there's a difference between a computer and a human being. A computer can be told exactly what to do and it will do it as long as the software is good. Not so with humans, particularly small humans. Yes. Exactly, for those of you who have kids. But human beings need different types of incentives. And that's going to be one of the tensions is the degree to which we can build systems utilizing tooling that is set up for technology which is precise and says do it this way. And human beings, which still need incentives and still need to be included in the process and still need to feel like they're being actuated. These are kind of highfalutin words, but they're very real words when we talk about significant system complexity and change and the designers of everything we're talking about have to consider that. Well, we're going to be discussing all of these things, all these new products and software systems as well as the change management issues today here at the NetApp Summit. Excellent, looking forward to it. This is Rebecca Knight for Peter Burris. We will have more from NetApp 2017 in just a little bit. Calling all barrier breakers status quo.