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I will let Indy take it over from here. Oh, thank you. Um, I am, uh, excited to join all of you guys virtually here for New York. I heard, uh, everyone had a fantastic time last night with the Amsterdam pub crawl. Sorry to have missed it. Um, I'm sure, uh, it, it accounted for, uh, some, uh, great stories. And when I think about one of the interesting things about analytics and storytelling in particular is, and one of the reasons we started to embark in really applying kind of new, what I would consider more advanced techniques in to data is that sometimes, you know, what we record from a session and what we hear and see even with our eyes can be a bit misleading. And one of the things that's really exciting in the marketing world and in the industry at large is applying a lot more data and analytics and the data that is actually becoming more and more equally important, I would say, in addition to your enterprise data is actually the data that sits outside your enterprise. So we're seeing a tremendous interest in the market around external data. So that includes things like obviously social conversations that are happening in the marketplace. It also includes things like environmental data. So weather data is one of the key data sets that we've actually recently acquired through the weather company and the advanced forecasting capabilities of it. But the environment in terms of its unpredictability is a key element. Another external data set folks often think about is location, GPS location in particular. The fascinating aspects of when I think about the advancement of mobile in the industry is the intersection of how the person, i.e. each of us, all of a sudden actually represents a physical place. Just think about the notion, you know, for folks that have used any type of Uber service or Lyft type services, you can call a taxi to yourself. So historically, you would have had to be sitting at your desk at a terminal, know the exact address in order for you to have a delivery or a pickup. And now you could actually be on the go and intercept goods or be intercepted yourself in order to kind of transform the way you work and live. And that intersection is what's so fascinating about now the available data. The last category is actually what I would consider machine generated data. And in the world of the Internet of Things, where everything can be instrumented from the smallest of devices as well as even people, the ability to apply analytics in kind of new ways is incredibly exciting. So I've been with IBM 17 years and the bulk of the last 10 years has really been in the space of data and analytics. And when I think about the transformation that's happened in the industry long before people were talking about big data as an example, I don't think historically, if you look 10 years back, that we had the capabilities to absorb all these new types of data and also absorb it at the speed and the pace. But more importantly, as everyday users in the marketing profession, in various industries, across the globe, we probably have had to invest quite heavily in our IT systems and search and add platforms and so forth in order to get what I would consider that kind of level of knowledge to really transform the way you're thinking about your next generation of campaigns and targeting and digital engagement. And one of the things that we started to think about really creatively was to say, hey, do you have to wait until just your IT organization helps you set this up? And we started on this journey. And a lot of it is I have a very simple analogy. I mean, you have to almost think about yourself as a curator in an art museum, right? So if you think about a curator for an art museum, it's not a one time event. It's actually depending on the particular theme or showing. There may be artwork that's part of the museum. There may be artwork that sits outside. It may be part of a private collection. And curating implies you're thinking through the lens at which you want to tell the story. And often with data and analytics, it's very much the same thing. So you're thinking about pieces of data, pieces of insights, how you bring those things together. What's the quality of it? Who's the audience that you're targeting that for? And then how do you visualize it in a way that kind of stimulates not only the thinking, but the next level of conversation and decisioning that becomes really critical. So I often tell folks that are in the space of analytics that you're often the curator for knowledge for your enterprise. And so it becomes a really critical evolution of the market and the skill space. Hold on one second. So the good news about 6 a.m. is the kids are still in bed at 7 right up and early and they head off to school at 8. So that's why. So thanks for the a little interruption. One of the things that we're really transforming is the area of Watson Analytics. And it's the intersection of where we believe every enterprise will ultimately get to in terms of a cognitive business. And it starts with a digital journey and beginning to apply. Obviously, the category of artificial intelligence, but also new capabilities as well. And so when we started to brainstorm internally about what was a great way to empower every individual that understand how to use a spreadsheet, that would be pretty much everyone in the audience and in most of the education, higher universities and so forth, and even high school students. We wanted a simple way for folks to begin to do kind of four things. One was, hmm, can you quickly predict insights, whether it's around people, whether it's around campaigns, whether it's around use cases, incident reports, depending on the scenarios. So predict in a natural way without necessarily having an advanced math degree or an advanced statistics degree. So that was scenario one. The second scenario was, man, how easy would it be to visualize data very quickly? And visualizing data would be creating not only the charts and graphs, but actually creating the storyline of the message that you want to do. And historically, you would have had to have a really good, you know, guru on creative chart building or creative design or graphics. But how could every person be able to do that second piece, which is quickly visualize and create their own storyboards? The third was, can the system actually process natural language processing in the system in a way that we talk every day and not in computerized language? And the fourth is around data preparation, data quality. This is actually probably what we consider the dirtiest job in data, which is the quality and a tremendous amount of investments are spent often to figure out the quality of the data, meaning are the names correct or the address is correct? Have the cleansing process happened? What's the latest information? Have you actually matched it across master data systems? Do you understand the metadata and the surrounding data and the contextual insight around an individual or set of individuals in a given time frame? So we said, hey, instead of having to have four or five, six, seven different experts when, in fact, it's you or I just wanting to work in a more advanced way in the ways we know kind of how to use a spreadsheet, we said, hmm, could we apply Watson to spreadsheets in a kind of a new way that applies those four things? So predictive analytics, visualization, cognitive and data preparation. So what I wanted to show you very quickly was rather than show you slides, I'm going to actually just take you straight to our website and give you some examples of a couple stories and then for quickly how you can get started. And if you go to your browser of choosing, can everyone in the audience see the screen? Okay, excellent. So if you type in Watson Analytics in the search bar, you'll be able to very quickly get to the IBM Watson Analytics site and you can try it very quickly for free. We actually have three levels of pricing. So everyone in the room is clear. There's a there's a freemium version, so it's free. It has a limited set of tools and functions and data size that you can apply. Then there's the professional edition, which is thirty dollars per user, and then a more premium edition for multiple users. So you can add more people to your groups. But the couple examples of clients that have applied Watson Analytics. And I'd like to share two quick stories. One is actually from Mark at Mueller. Mark and I've had a chance to spend time together and Mark is a really fascinating guy. He when he first tried this, he went and found the resource, went in and he was thinking that he was only going to spend a couple of minutes when in fact he ended up actually spending a couple of hours uploading flat files and spreadsheets into the system and started to was just absolutely floored that within less than 30, 60 seconds that he had entire visual dashboards of what was possible around the marketing campaigns. Mark actually runs in the United States, a roofing company that looks at materials and costs in different dimensions. And so he started to put in his details around his marketing campaigns, his cost of operations. He is a very small, small company. So it's less than 20 people. And he was able to quickly visualize and analyze as well as predict a number of things that he didn't expect. Another client here you'll see is John Brett from Mears Group. Mears Group is actually based in the UK. It's a fascinating company because they are really focused on social housing and works of civil service for the community. And John and team really created the IT organization for this company and for the organization. And now they employ over 20,000 people. And one of the things that he was absolutely amazed at was once they started to kind of apply Watson Analytics to a lot of the data that they had, they started to see patterns, patterns around when accidents occurred, patterns around cost and efficiency of the people on their teams that are staffing around placement of different types of opportunities and so forth. So it was even for his own team who would have historically traditionally have had to implement more comprehensive systems in place. It was such an empowering thing for all of their everyday users to begin to analyze. These are just a couple examples, but let me show you a couple screens from the site itself. So once you load a spreadsheet, it'll quickly kind of display it in a natural way that you understand it. But more importantly, you're actually able to start to ask some business questions and answer some business questions. So in this bar here, you'll see is a quick way to type in search. And all of a sudden, these tiles will come up naturally in the language in which you want to kind of speak in and depends on kind of the language that's already in some of the materials that you'll be loading up. The second area is there's different levels of predictive scores that are automatically analyzed. Maybe it's customer churn and customer churn is detected by the quality of not only your product, but maybe the quality of the relationships and the account managers you have around specific areas. And so it gives you degrees of confidence scores in terms of different activities and actions that are worth taking. One of the things that has always taken a lot more effort is how you actually end up telling stories, even as a data analyst afterwards on and giving your teams direction on what you want to do. And so one of the interesting, compelling ways that we've actually added is this ability to actually create a dashboard in a storytelling way. So if you're a marketer with a campaign lens, you can begin to tell your story around that campaign. If you're in banking and in finance and accounting, you may want to tell your story around how expenses are applied across different groups within your organization. So this becomes a really exciting way for any user to begin to click, drag, drop different elements of the storyline, put aspects of the text in it. When you go to the site, what you'll see is at the top bar, not only the details around the product, but different kinds of add-ons. So one of the things we've actually enabled is the ability to add social media. So you could actually put in things like Twitter feeds around yourself, your company, or your competitors, and maybe topics that are trending high as part of your analysis to integrate your enterprise data with social data. Another area that we started to apply is kind of solutions by roles. So if you go into the roles category and you go under marketing, what you'll see an example is easy analytics for marketers with examples of how you do campaign opportunity analysis. So this would be an example of one of the visuals. And you'll see at the bottom different ways to look at products, types, inventory, category, sales amounts, areas for you to begin to discover and focus your campaign on. You can begin to discover trends. So a lot of these graphs that you see are naturally populated. So we really wanted something that was intuitive. It was fast. It made you feel like you had the superpower of being able to be a data scientist yourself, a great graphics person, and a storyteller all in one. And we've also added this capability around communities with resources, forums, and blogs. So this was an opportunity for us to rethink the way we do analytics for everyday citizens is what we call it, redoing analytics for everyday citizens. And when I think about also where the industry is headed, not only the marrying of external data outside the enterprise, but actually this is a reflection of my new role, which is in the area of collaboration is I'm actually focused on the intersection of analytics and communications and collaboration platforms. So one of the hottest faces today you'll see is around messaging as an example. So persistent chat and extended ways to do even these types of tools, which is real time communications and web conferencing. So we're experimenting in new ways to apply analytics to video, to real time conversations, to new forms of being able to connect people in more meaningful ways, given the social interaction patterns that we have. So this was just a quick overview for everyone there around what's possible for not only Watson Analytics, but also just a little bit of kind of what we're thinking about in terms of embedding cognitive capabilities across a number of workplace and work tools that we have. So with that, I hope you guys have a fantastic rest of the day and look forward to engaging with many of you guys virtually. So thank you.
