Tonight, the DuPont Company brings you Rainfakers, starring Burgess Meredith on the Cavalcade of America. Rainfakers is a story about the weather. And in our play tonight, a Rainmaker is going to tangle with a scientist from the Weather Bureau. Now, if you don't know about the tricks that Rainmakers use, you will find out in a moment. And ladies and gentlemen, the forecast for tonight is fireworks. But first, here's Gain Whitman. You may be lucky enough to be moving into a new home. In that case, congratulations. But all of us can have new-looking homes, and at such little cost. Paint those drab, weary walls with DuPont Speed Easy. See how bright and new your home will look. Speed Easy can be used on all interior wall surfaces. It's a resin emulsion paint, but you thin it with water. Apply with a large brush or roller right over wallpaper. It dries in an hour. You'll be surprised at how little it will cost. Ask your DuPont dealer for Speed Easy. Another of the DuPont Company's better things for better living through chemistry. Tonight, we present Burgess Meredith as Walt Swanson in Rainfakers on the DuPont Cavalcade of America. Ah, the weather. Everybody talks about it, but nobody does anything about it. Wrong. I'm sorry, but you're wrong. Huh? About what? The weather. People do do things about it. As a matter of fact, I did something about the weather once myself. Yeah? And who are you? My name is Walt Swanson. I'm a weatherman, a meteorologist. If you want to be high-brow. Well, how long's this rain going to last? This rain will last about one second. Oh, come now. Is that what you mean by doing things about the weather, having our studio sound man on your side? Not at all. I weigh the weather, record its temperatures, observe the direction of the wind, chart the course of storms, measure rainfall, predict floods, warn against hurricanes, and so far as is possible, provide weather information and accurate forecast. Well, that's reporting weather, predicting it. That's not doing anything about it. Nobody can do anything about the weather. I did. I did once. Just what did you do, Mr. Weatherman? Well, it all began with Colonel Daisy and a rainmaker. It was quite a while back. Sound man? Yes, sir? A minute ago you made me some dandy weather. No, that's not what I want. Can you dig me up a hot July day, say about 102 without a breath of air stirring? How's this? Perfect. That's right. Now, I'm on my way to work. But today, you see, I don't look forward to my job much. Good morning, Walt. Hello, Ed. Hot again. Think of all the ice cream you'll sell. Well, folks ain't spend the cent. They don't have to. Things like this. Well, it's got to break sooner or later. Ain't you got some idea when? Oh, I wish I had, Ed. Well, so long. So long. Now, that was Ed Carr of Carr's drug store opening his place up. He's next door to the courthouse where the Weather Bureau is. Now, inside of the courthouse, I turn right and I start upstairs. Here's Mr. McDonnell coming down the stairs as I go up. Well, this is kind of early for you, Mr. McDonnell. You don't call these bankers hours, do you? I was just up at the Weather Bureau, Walt, looking for you. What's the matter? Am I overdrawn? I wish that's all it was. No, what's up? Well, I don't have to tell you how many farmers in this area have loans with our bank. And that's all right. That's what we're there for. We can't make money if we don't lend it. Just the same. Well? Many of those notes are due right now. More coming all the time. We'd like to extend them. Of course. But if the crop fails, they can't pay. Simple as that. So, how do you think our chances look for rain, huh? Well, I can just tell you, Mr. McDonnell, is that there's never been a drought yet that lasted forever. Well, if it lasts long enough to kill the crop, it might as well be forever, as far as the bank's concerned. Can't you give me some idea when it'll rain? Look, Mr. McDonnell, I don't control the weather. This drought isn't just a whim of mine. Now, there's no need to get excited. I merely asked if you knew when it would rain, and you don't. Oh, I'm sorry. I didn't mean to lose my temper, but this thing's been getting on my nerves. Well, something's got to be done, and I've got an idea. It's got to rain soon. I sure hope so. Oh, will you be coming over at the house for dinner, Sunday? You bet. Well, good. I'll tell Jane when I get home. Goodbye. Goodbye. Poor man. He's really upset. Now, do you hear that? That's the teletype in my office. The clearinghouse at Denver is sending us weather reports from all over the northern half of the Western Hemisphere. You know, that man learned a long time ago that weather traveled. But as long as it traveled faster than men and horses, there was no way to spread reports in time to do anyone any good. But then the telegraph and the telephone changed all that. And weather forecasting, the way that we know it now, grew up side by side with modern communications. Ah, good morning, Jenkins. Morning, Mr. Swanson. Phew. Ain't this a beaut? Sure is. What's your dry bulb reading? 102 already. Any change in the pressure? No. I think this high is going to last forever. If we don't get some indication of rain soon, I'm going into a new business.  Hello, Brownie. Why? What are you talking about? A drought. It's washed up. You can just lean back and relax. We'll be having rain any minute now. What do you mean? Oh, the Farmers Association is going to hire that eminent scientist, Colonel Daisy, and the rainmaker. And he's going to fix everything. You're kidding? They're having a big meeting tonight at the Odd Fellows Hall, and the Colonel's going to be there. Hand me that phone. What are you going to call? Well, I'm not going to sit here and let farmers pay out good money to a jip artist like the Colonel. Hello, operator. Number, please. 229, ring two, please. Oh, by the way, some English professor called up from the State University. He wants to come over here and observe for a couple of days. An English professor? He's writing a book, believe it or not, on the weather in Shakespeare's place. No kidding. I told him he could stand around and watch as long as he didn't get in the way, OK, with you? Sure. Maybe he'll give us a copy of the book. Hello. Hello, Jane. I knew it was going to be you, Walt. I could just tell away the phone rings. That's the deal I have with the phone company. Oh, I mean it, Walt. I did know it was you. I get feelings like that, and they're right most always. Coming to dinner Sunday? Haven't you got a feeling about that, too? I got a feeling we'll have roast beef. Oh, well, he couldn't keep me away. Say, Jane, is your father back yet? No, Dad's still out. Has he said anything to you about the Farmers Association hiring somebody called Colonel Daisyan? You mean the Rainmaker? Yeah, that's the guy. Oh, yes, Dad's going to talk to the folks about him this morning. Oh, thank Heaven for that. People respect your father, and they'll believe him when he tells them that this Colonel's a fake. What'd you say, Walt? That Colonel Daisyan's a crook and a fake. That's what your father thinks, isn't it? Well, I know Dad likes him. It's his idea to hire him. Jane. And I think it's wonderful, too. We just got to have rain. Jane, this man can't make it rain. Well, that's not what the thousands and thousands of people say in the places where he's brought rain. They bless him and thank him. And pay him. Well, of course they pay him. It's worth anything to get rain. But all he does is to collect for rain that would fall anyway, and I refuse to stand by and see people swindle. If you ask me you're jealous because the Colonel knows more about weather than you do. Jane, it's no good as arguing. I'll call your father later. Goodbye. Goodbye. Well, how do you like that? Well, if the people want to pay a rainmaker, what do you care? It isn't your doll. It isn't just the money, but every time one of those crooks gets away with a deal like this, the whole weather bureau looks silly. I don't like it. What are you going to do about it? Well, for one thing, you and I are going to attend that meeting tonight. And now, sound man, I want the noise of wind, a great big bag of wind. Oh, that's just what I mean.  And that old blowhards brought out a good crowd, though. And they're not all farmers either. Ed Carr's here from the drug store. There's old man McDonald and Jane. And even the little professor who's writing the book has showed up. Nobody's laughing, nobody's even smiling. Don't tell me that they believe that guy. Oh, but you was no one I could do here. Things I discharge of radiomagnetic waves into the upper regions. I doubt if I could make the process entirely clear to a layman. But I know something about meteorology. I'm head of the weather bureau here. What they're getting before they pay. What they're getting, sir, is their scent. But what are you going to make it rain? That's the point. Sooner or later, the weather's bound to change its own accord. And I feel to see why you or anybody else should cash in on it. Nor do I, sir. The whole purpose of my method is to make rain where it will not fall naturally. You guarantee to make it rain within a specific period of time? Yes. Once the radiomagnetic rainmaker has been started, rain invariably falls within three days. I did not say that, sir. But I will guarantee rain within three days from the time the radiomagnetic waves are launched. Of course, before I can turn them on, it is sometimes necessary to spend a few days softening up the elements by other means. I see. Like studying weather bureau forecast, Colonel? If I had to depend on the weather bureau for my rain, I wouldn't stay in business long. And I start three. Forecast because they aren't entirely accurate for longer than that. That's what the colonel said. But any good meteorologist can predict three or four, even five days in advance, and be right four times that he's wrong. Well, what of it? Well, don't you see, look, all a rainmaker's got to do is to get a hold of these early reports, start his mumbo-jumbo, and gamble on rain. And seven times out of ten, he'll be right, and you pay through the nose. What do you care what we pay? Sir, money ain't it? It's your money, but why do you throw it away for nothing? You call rain nothing? Look, look, I've studied weather since I was 18 years old, and there's no known scientific method of making rain. Now, rain is caused by warm, voiced air rising. That forms clouds, and when these clouds condense, why then it rains? Hey, just a moment. You said rain was caused by hot air rising. Indirect. Here is. They want rain. See what I mean? Brownie, let's get back to the office. I've got to make some long-distance calls. I'll show this pony up if it's the last thing I do. You are listening to Burgess Meredith as Walt Swanson, the weatherman in Rainfakers on the Cavalcade of America, sponsored by the DuPont Company, maker of better things for better living through chemistry. As the second part of our story opens, Swanson, with the aid of our studio soundman, is telling me about the time he tangled with Colonel Daisy and the rainmaker. How do things stand now, Mr. Weatherman? Well, very bad indeed. You see, the Colonel got his contract and I got hard feeling. Well, just a minute. At the beginning of this program, you said you were the one that did something about the weather. Yeah, I know. Well, I'm coming to that. Soundman, you suppose you could fire a distant cannon every once in a little while? Oh, that's fine. That's fine. Now that represents Colonel Daisy and softening up the elements. You see, one of the most persistent notions about weather is that gunfire or fireworks causes rain. And so many people believe it, that it's a shame that it isn't true. It doesn't rain on the 5th of July anymore. It doesn't on the 3rd, you know. But statistics show it often rains after big battles. Oh, sure it does. Commanders don't want their armies to fight on soggy ground, so they wait several days after rain before ordering an offensive. You see, and by the time the battle's over, it's usually time for rain again. That's how that old wives' tale got started. Well, let's get back to the Weather Bureau. Say, Walt, you told me to let you know when the professor came back again. He's here now. Oh, good. Now, look, I want to see him. Uh, Professor, you learning all about the weather? Well, it's so complicated. I don't know if I shall ever get it straight. That's exactly the way I feel about Shakespeare. Hey, me. You know, Professor, you really ought to see our instruments on the roof. Oh, I do. On the roof. Jenkins. Yeah, but... I want you to take Professor Raff to the roof and show him how we measure the upper air draft with the balloon and theodolite. Okay, right this way, Professor. Oh, thank you so much, Mr. Jenkins. But if Mr. Swanson wouldn't mind, I'd prefer to go later after the weather map's been made. That fascinates me more than anything. We'll still be on the map by the time you get down, Professor. Oh, well, in that case, I'll go. Oh, watch these steps, Professor. Kind of steep. Excuse me. That's right. Brownie, Brownie. Give me that map, will you? Yeah. Now, you take this map and I'll hide yours down here. What? You can get it later when the professor's gone. Is this map doesn't match today's reports? Colonel's not the only guy that can make rain, Brownie. You and I are gonna whip up some rain, too. Now, for days, I've been trying to find out where that old fraud gets his weather reports. You sure he works that way, Walt? Why, do you think he keeps that cannon going day and night? It's just to fill in time till he knows for certain that rain's on the way. Then he turns that phony apparatus on. Only this time, when he turns it on, he won't get rain. I must be dumb, but I don't get any of this. Well, I've checked all the weather services. He's not getting his information there, so he has to get it somewhere, but where? I'll bite where? Well, he's getting it from us. Who? This professor upstairs. Now, he's a fake. I called the university and they never heard of him. I've got a hunch that he's an experienced meteorologist. Do you think he reads our maps and passes on the dope to the colonel? I'm certain. I know it. I phone Springtown and some of those other places where the colonel's operated. And the professor, it so happens, has been observing in their bureaus at the same time that Daisy and was in town. Now do you believe me? Dirty weather they got in Shakespeare's place. You don't know how dirty. Look at this map that I gave you. Well, that's all wrong, Walt. The way you've got it here, we've been getting rain in a few days. Yes, if only the professor thinks that way, we're all set. He'll tell the colonel that rain is due and that old fake will turn that radiomagnetic gadget on too soon. Oh, it's a great scheme, Walt, but it's dangerous. Business issuing false reports. No, there won't be any false reports. You see, every day we'll make two maps. A regular one for the bureau and a special one for the professor. Showing a nice, fat rainstorm right around the corner. There he comes. Thank you, sir. That was fascinating. Oh, glad you liked it, professor. Ah, still at the map. Still at the map, yes. Now, there's some interesting changes here. Yes. Since yesterday. Now, look at this. You see, this week, line here, we call it a coal front. Oh, yes. Now, will you notice how it's moved south? Yes, yes, I see, I see. And you notice at this point that the barometric pressure has come down to millibars. Yes, yes, most interesting. Dear, dear, I hope someday I shall be able to what... Oh, my goodness. What's the matter? Oh, I've just remembered there's a call I must make. Use our phone right here. Thank you, just the same. This is long distance. I'll just use the booth down. Oh, sure is. Now, what did I tell you? You think he's going to phone the colonel? Well, I know it. Within five minutes, that rain machine will be going full blast. That means the colonel guarantees to bring rain within three days. He'd better be some rainmaker. Now, sound man. Yes, sir. I got something tough for you. I want the noise of one radio-magnetic rainmaking machine. How's this? Very good. That's perfect. Now, at the fairgrounds, the colonel turned the darn thing on, this machine on, the minute the professor called him. And the crowd's been collecting ever since some of them carrying umbrellas. Oh, that's quite a gadget, Walt. I like those sparks flashing out of the top. Something that certainly wows the crowd. I don't know whether I like this gag or not, Walt. What do you mean? Well, folks get their hopes up, and they don't get rain. Well, hello, Walt. I'm so surprised to see you here. Why shouldn't I be here? You were the man who thought the colonel couldn't make rain. Well, I still do. Well, just listen to that rain machine. Just listen. You can just feel it'll work. It will work, Jane. Believe me that it won't work. Well, all I have to say to you, Walt Swanson, is that I hope you get caught without a raincoat or an umbrella or anything. Well, if I do, it won't be in the colonel's rain. Howdy, colonel. Time's up. What do you mean, sir? Your contract guaranteed to produce rain within 72 hours. Three days. The three days are up. Well, something technical seems to have gone a bit wrong someplace. I find it necessary to continue. Oh, no. Why don't you give up, colonel? There's not a cloud in the sky. My contract, sir, is not with you. It's with these gentlemen of the Farm Association here. Right? That's right. It ain't his business. I'm sure another few hours will do the trick, gentlemen. Well, what do you fellas say?  Well, friends, a few days ago, a stranger showed up. At this point, Brownie and I told our story about Professor Raff and how he got the reports from the colonel. Well, you've heard it all, you know, so I see no point in repeating it. But anyway, it had a double effect on the farmers. First, they terminated their contract with the colonel. You faker, get out of town and stay out. Don't ever come back.  They expressed their gratitude to me. Morning, Ed. Pretty smart, ain't you, Mr. Swanson? Well, he must have got up on the wrong side this morning. Hello, Mr. McDonald. How are you? Well, Mr. Swanson. Sure showed that fellow up, didn't you? Yes, sir. You made us all look pretty foolish, too. Especially me. After all, Daisy and was my idea. Wait a minute. Well, if you're so all fired smart, where's the rain? You haven't done very much about that, have you? Looks like you showed yourself up, too. You'd only listen to reason. Oh, and about dinner tomorrow, forget it. Jane said to tell you not to bother coming over. I see. Thanks. Well, I sure picked the right way to win friends and influence people. Morning, Walt. Hello, Brownie. Oh, why the long face? Is Jane still sore? Jane, everybody's sore. Oh, that's what you get for showing people up. It's fatal. They really griped at the weather, you know. You're not on me. Well, you fixed the colonel's wagon anyhow. Way things are now. That's sort of cold comfort. Something special is cooking on the teletype. What's it say, Jenkins? Hey, get this. Cold front sweeping down from the north. What? Let me see that. Wonderful. Give me the phone. Brownie, get on the other phone. Will you notify the Gazette and the radio station? Operator 229, ring two, please. Here's the rest of the report. Oh, thanks, Jenkins. Look, oh, it looks like Colonel Daisy in left town just a little bit too soon. Hello? Jane, this is Walt. Really, Walt? I'd rather not talk to you. All right, don't talk to me. Just listen. I'm no Colonel Daisy or anything like that, but your reign is coming and plenty of it. You know what they say. Seeing is believing. You'll see it all right by noon tomorrow. Goodbye, Walt.  Mind it. Oh, just look at you.  Thanks, thanks. Oh, quick, take that coat off. Honestly, anyone who'd gotten it down poor like this. You know, I know that you weren't expecting me, but I... No, I wasn't. I guess that's why I set an extra place at the table and why I haven't let Dad start dinner. Oh, Jane. Oh, Walt, I think you're wonderful. I know you're a weatherman and all that, but how did you know the rain would start just when it did? Darling, it's really very simple. My coins hurt. Oh, Walt. Nowadays, you can't find one person on a whole day's travel who believes in rainmakers. Now, that's partly due, as we have seen, to cheerful young scientists who set out to disprove the quack doctrine of the rainmaker. When we, the American people, need to get a job done, we don't rub our hands against a rabbit's foot. We roll up our sleeves and go to work. That's the spirit that has given America more and better practical applications of science than any other country in the world. Our Burgess Meredith will return in just a moment, but first here's Gain Whitman speaking for Dupont. Did you ever move a zipper up and down slowly and watch the way the little teeth lock together? Try it sometime. You can marvel at the thought and planning that go into an airplane engine or an automobile, but for downright ingenuity in a small package, a zipper is a minor miracle. You find yourself wondering who was clever enough to get the idea in the first place. How the little teeth are made so accurately. How they are attached in exact alignment so that a simple, easy sliding motion closes them. Soon now, zipper fasteners of Dupont nylon plastic will appear on ready-made clothing for women. Nylon fasteners can withstand boiling water and are not damaged by the ordinary heat of ironing. They will not rust, nor will they be affected by the usual dry cleaning solvents. So light that they can be used on the sheerest marquettes and chiffons, they are strong and durable enough to give long service on heavy garments, and they can be run through a washing machine without trouble. The first to reach you will be black or ivory-colored, but manufacturers plan to add a range of colors in popular shades. When our Dupont research laboratory produced the first nylon, no one in the Dupont company would have predicted that nylon would one day be used to make zippers. The vigilant, never-failing ingenuity of people working in other businesses, however, saw in nylon plastic a better material from which to make slide fasteners. Nylon plastic is one of the Dupont company's better things for better living through chemistry. And now, our star, Burgess Meredith. Thank you. And now, if I may, I'd like to talk for a moment about traffic accidents. According to the National Safety Council, the traffic death toll during the holiday period is almost three times as great as for the same time during the rest of the year, to safeguard against accidents to reduce your speed while driving to fit the prevailing road and weather conditions. And remember, accidents don't always happen to somebody else. Each one of us is a potential accident victim, and no one of us can afford to be careless. So keep tragedy out of your home. Drive carefully. Thank you. Next week, the Dupont Cavalcade brings you Shirley Booth in The Woman on Lime Rock. It's an unusual and dramatic story of the sea of Ida Lewis, who in 1858 took her father's place as keeper of the light at Lime Rock, Rhode Island. For 52 years, Ida Lewis kept the light burning in fog and storm, good weather or bad for the men who went down to the sea and ships. Be sure and listen next Monday at this same time to The Woman on Lime Rock, starring Shirley Booth on The Cavalcade of America. The music for tonight's Dupont Cavalcade was composed by Arden Cornwall and conducted by Donald Borees. Our Cavalcade play was written by Frank Gabrielson. In the cast with Burgess Meredith tonight, we're Ted Jewett as the Colonel, Vicky Bola as Jane, Edgar Stalier as the Professor, Alan Hewitt as Brown, and Cameron Proudhon as McDonald. This is Ted Pearson wishing all Cavalcade of America listeners a very happy new year from the Dupont Company of Wilmington, Delaware. This is NBC, the national broadcasting company.


Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and it's ecosystem partners. What am I gonna spend time next, 15, 20 minutes or so talking about? I'm gonna answer three things. Our research has gone deep into where are we now in the big data community? Well, I'm sorry, where is the big data community going? Number one, number two is how are we gonna get there? And number three, what do the numbers say about where we are? So those are the three things. Now, since we wanna get out of here, I'm gonna fly through some of these slides. But again, there's a lot of opportunity for additional conversation because we're all about having conversations with the community. So let's start here. The first thing to note when we think about where this is all going is it has to be bound. It's inextricably bound up with digital transformation. Well, what is digital transformation? We've done a lot of research on this. This is Peter Drucker, who famously said many years ago that the purpose of a business is to create and keep a customer. That's what a business is. Now, what's the difference between a business and a digital business? What's the business between Sears Robot or what's the difference between Sears Robot and Amazon? It's data. A digital business uses data as an asset to create and keep customers. It infuses data in operations differently to create more automation. It infuses data in engagement differently to catalyze superior customer experiences. It reformats and restructures its concept of value proposition and product to move from a product to a services orientation. The role of data is the centerpiece of digital business transformation. And in many respects, that is where we're going is an understanding and appreciation of that. Now we think there's going to be a number of strategic capabilities that will have to be built out to make that possible. First off, we have to start thinking about what it means to put data to work. The whole notion of an asset is an asset is something that can be applied to a productive activity. Data can be applied to a productive activity. Now there's a lot of very interesting implications that we won't get into now. But essentially, if we're going to treat data as an asset and think about how we can put more data to work, we're going to focus on three core strategic capabilities about how to make that possible. One, we need to build the capability for collecting and capturing data. That's a lot of what IOT is about. It's a lot of what mobile computing is about. There's going to be a lot of implications around how to ethically and properly do some of those things, but a lot of that investment is about finding better and superior ways to capture data. Two, once we are very able to capture that data, we have to turn it into value. That, in many respects, is the essence of big data. How we turn data into data assets in the form of models, in the form of insights, in the form of any number of other approaches to thinking about how we're going to appropriate value out of data. But it's not just enough to create value out of it, to have it sit there as potential value. We have to turn it into kinetic value to actually do the work with it. And that is the last piece. We have to build new capabilities for how we're going to apply data to perform work better, to enact based on data. And we've got a concept that we're researching now that we call systems of agency, which is the idea that there's going to be a lot of new approaches, new systems, with a lot of intelligence and a lot of data that act on behalf of the brand. I'm not going to spend a lot of time going into this, but remember that word because I will come back to it. Systems of agency is about how you're going to apply data to perform work with automation, augmentation and actuation on behalf of your brand. Now all this is going to happen against a backdrop of cloud optimization. I'll explain what we mean by that right now. Very importantly, increasingly, how you create value out of data, how you create future options on the value of your data is going to drive your technology choices. For the first 10 years of the cloud, the presumption is all data was going to go to the cloud. We think that a better way of thinking about it is how is the cloud experience going to come to the data? We've done a lot of research on the cost of data movement and both in terms of the actual out-of-pocket costs, but also the potential uncertainty, the transaction costs, et cetera, associated with data movement. That's going to be one of the fundamental pieces or elements of how we think about the future of big data and how digital business works is what we think about data movement. I'll come to that in a bit. But our proposition is increasingly, we're going to see architectural approaches that focus on how we're going to move the cloud experience to the data. We've got this notion of a true private cloud, which is effectively the idea of the cloud experience on or near premise. That doesn't diminish the role that the cloud is going to play in industry or doesn't say that Amazon and AWS and Microsoft Azure and all the other options are not important. They're crucially important. But it means we have to start thinking architecturally about how we're going to create value of data out of data and recognize that that means that we have to start envisioning how our organization and infrastructure is going to be set up so that we can use data where it needs to be or where it's most valuable. And often that's close to the action. So if we think then about that very quickly, because it's a backdrop for everything, increasingly we're going to start talking about the idea of where's the workload going to go. Where's workload the dog going to be against this kind of backdrop of the divorce of infrastructure. We believe that, and our research pretty strongly shows that a lot of workloads are going to go to true private cloud. But a lot of big data is moving into the cloud. This is a prediction we made a few years ago and it's clearly happening and it's underway. And we'll get into what some of the implications are. Now again, when we say that a lot of the big data elements, a lot of the process of creating value out of data is going to move into the cloud, that doesn't mean that all the systems of agency that build or rely on that data, the inference engines, et cetera, are also in a public cloud. A lot of them are going to be distributed out to the edge, out to where the action needs to be because of latency and other types of issues. This is a fundamental proposition and I know I'm going fast but hopefully I'm being clear. All right, so let's now get to the second part. This is kind of where the industry's going. Data is an asset, invest in strategic business capabilities to appreciate, to create those data assets and appreciate the value of those assets and utilize the cloud intelligently to generate and ensure increasing returns. So the next question is, well, how will we get there? Now, right now, not too far from here, Neil Raiden, for example, was on the show for yesterday. Neil made the observation that as he wandered around he only heard the word big data two or three times. The concept of big data is not dead. Whether the term is or is not is somebody else's decision. Our perspective very simply is that the notion is bifurcating and it's bifurcating because we see different strategic comparatives happening at two different levels. On the one hand, we see infrastructure convergence. The idea that increasingly we have to think about how we're going to bring and federate data together both from a systems and a data management standpoint. And on the other hand, we're going to see infrastructure or application specialization that's going to have an enormous implication over the next few years if only because there just aren't enough people in the world that understand how to create value out of data. And there's going to be a lot of effort made over the next few years to find new ways to go from that one expertise group to billions of people, billions of devices. And those are the two dominant considerations in the industry right now. How can we converge data physically, logically? And on the other hand, how can we liberate more of the smarts associated with this very, very powerful approach so that more people get access to the capacities and the capabilities and the assets that are being generated by that process. Now, we've done at Wikibon probably, I don't know, 18, 20, 23 predictions overall on the role that are on the changes being brought by digital business. Here, I'm going to focus on four of them that are central to our big data research. We have many more, but I'm just going to focus on four. The first one, when we think about infrastructure convergence, we worry about hardware. Here's a prediction about what we think is going to happen with hardware. And our observation is we believe pretty strongly that future systems are going to be built on the concept of how do you increase the value of data assets? The technologies are all in place. Simple parts that more successfully bind specifically the role that storage and network are going to play together. Why? Because increasingly, that's the fundamental constraint. How do I make data available to other machines, actors, sources of change, sources of process within the business? Now, we envision, or we are watching before our very eyes, new technologies that allow us to take these simple piece parts and weave them together in very powerful fabrics or grids, what we call unigrid. So that there is almost no latency between data that exists within one of these, call it a molecule, and anywhere else in that grid or lattice. Now again, these are not systems that are going to be here in five years. All the piece parts are here today. And there are companies that are actually delivering them. So if you take a look at what Micron has done with Melanox and other players, that's an example of one of these true private cloud-oriented machines in place. The bottom line, though, is that there is a lot of room left in hardware, a lot of room. This is what cloud suppliers are building and are going to build. But increasingly, as we think about true private cloud, enterprises are going to look at this as well. So future systems for improving data assets. The capacity of this type of a system with low latency amongst any source's data means that we can now think about data not as a set of sources that have to be each individually, each having some control over its own data, and syncs woven together by middleware and applications, but literally as networks of data. As we start to think about distributing data and distributing control and authority associated with that data more broadly across systems, we now have to think about, what does it mean to create networks of data? Because that, in many respects, is how these assets are going to be forged. I haven't even mentioned the role that security is going to play in all of this, by the way. But fundamentally, that's how it's likely to play out. We'll have a lot of different sources, but from a business standpoint, we're going to think about how those sources come together into a persistent network that can be acted upon by the business. One of the primary drivers of this is what's going on at the edge. Mark Andres infamously said that software is eating the world. Well, our observation is great. But if software is eating in the world, it's eating it at the edge. That's where it's happening. Secondly, that this notion of agency zones, I said I'm going to bring that word up again, how systems act on behalf of a brand or act on behalf of an institution or business is very, very crucial because the time necessary to do the analysis, perform the intelligence, and then take action is a real constraint on how we do things. And our expectation is that we're going to see what we call an agency zone or a hub zone or a cloud zone defined by latency and how we architect data to get the data that's necessary to perform that piece of work into the zone where it's required. Now, the implications of this is none of this is going to happen if we don't use AI and related technologies to increasingly automate how we handle infrastructure. And technologies like blockchain have the potential to provide an interesting way of imagining how these networks of data actually get structured. It's not going to solve everything. There's some people that think the blockchain is kind of everything that's necessary. But it will be a way of describing a network of data. So we see those technologies on the Ascension. But what does it mean for DBMS? In the old way, in the old world, the old way of thinking the database manager was the control point for data. In the new world, these networks of data are going to exist beyond a single DBMS. And in fact, over time, that concept of federated data actually has a potential to become real. When we have these networks of data, we're going to need people to act upon them. And that's essentially a lot of what the data scientist is going to be doing. Identifying the outcome, identifying the data that's required, and weaving that data through the construction and management manipulation of pipelines to ensure that the data as an asset can persist for the purposes of solving a near-term problem or order whatever duration is required to solve a longer-term problem. Data scientists remain very important, but we're going to see as a consequence of improvements in tooling capable of doing these things, an increasing recognition that there's a difference between a data scientist and a data scientist. There's going to be a lot of folks that participate in the process of manipulating, maintaining, managing these networks of data just to create these business outcomes. But we're going to see specialization in those ranks as the tooling is more targeted to specific types of activities. So the data scientist is becoming to become or will remain an important job, going to lose a little bit of its luster because it's going to become clear what it means. So some data scientists will probably become more, let's call them, data network administrators or networks of data administrators. And very importantly, as I said earlier, there's just not enough of these people on the planet. And so increasingly, when we think about, again, digital business and the idea of creating data assets, a central challenge is going to be how to create the data or how to turn all the data that can be captured into assets that can be applied to a lot of different uses. And there's going to be two fundamental changes to the way we are currently conceiving of the big data world on the horizon. One is, well, it's pretty clear that Hadoop can only go so far. Hadoop is a great tool for certain types of activities and certain numbers of individuals. So Hadoop solves problems for an important but relatively limited subset of the world. Some of the new data science platforms that we just talked about that I just talked about that are going to help with a degree of specialization that hasn't been available before in the data world will certainly also help. But it also will only take it so far. The real way that we see the work that we're doing, the work that the big data community is performing, turned into sources of value that extend into virtually every single corner of humankind is going to be through these cloud services that are being built and increasingly through packaged applications. A lot of computer science is, it has to be put, it still exists between what I just said and when this actually happens. But in many respects that's the challenge of the vendor ecosystem. How to reconstruct the idea of packaged software, which has historically been built around operations and transaction processing, with a known data model and an unknown, or with a known process and some technology challenges, how do we reapply that to a world where we now are thinking about, well we don't know exactly what the process is because the data tells us at the moment that the action's going to be taking place. It's a very different way of thinking about application development, very different way of thinking about what's important in IT and very different way of thinking about how business is going to be constructed and how strategies are going to be established. Packaged applications are going to be crucially important. So in the last few minutes here, what are the numbers? So this is kind of the basis for our analysis. Digital business, role of data as an asset, having an enormous impact on how we think about hardware, how we think about database management or data management, how we think about the people involved in this and ultimately how we think about how we're going to deliver all this value out to the world. And the numbers are starting to reflect that. So I want you to think about four numbers as I go through the next two or three slides. 103 billion, 68%, 11% in 2017. So of all the numbers that you will see, those are four of the most important numbers. So let's start by looking at the total marketplace. This is the growth of the hardware, software and services pieces of the big data universe. Now we have a fair amount of additional research that breaks all these downs into tighter segments, especially in software side. But the key number here is we're talking about big numbers, 103 billion over the course of the next 10 years. And let's be clear, that 103 billion dollars actually has a dramatic amplification on the rest of the computing industry because a lot of the pricing models associated with especially the software are tied back to open source, which has its own issues. And very importantly, the fact that the services business is gonna go through an enormous amount of change over the next five years as service companies better understand how to deliver some of these big data rich applications. The second point to note here is that it was in 2017 that the software market surpassed the hardware market in big data. Again, for first number of years, we focused on buying the hardware and the system software associated with that and the software became something that we hope to discover. So I was having a conversation here in theCUBE with the CEO of Transwarp, which is a very interesting Chinese big data company. And I asked, what's the difference between how you do things in China and how we do things in the US? He said, well, in the US, you guys focus on proof of concept. You spend an enormous amount of time asking, does the hardware work? Does the software, does the database software work? Does the data management software work? In China, we focus on the outcome. That's what we focus on. Here, you have to placate the IT organization and make sure that everybody in IT is comfortable with what's about to happen. In China, we're focused on the business people. This is the first year that software is bigger than hardware and it's only going to get bigger and bigger over time. That doesn't mean, again, that hardware is dead or hardware is not important. It's gonna remain very important, but it does mean that the centerpiece of the low focus of the industry is moving. Now, when we think about what the market shares look like, it's a very fragmented market. 60%, 68% of the market is still other. This is a highly immature market that's gonna go through a number of changes over the next few years, partly catalyzed by that notion of infrastructure convergence. So in four years, our expectation is that that 68% is gonna start going down pretty fast as we see greater consolidation in how some of these numbers come together. Now, IBM is the biggest one on the basis of the fact that they operate in all of these different segments. They operate in the hardware, software, and services segment, but especially because they're very strong within the services business. The last one I wanna point your attention to is this one. I mentioned earlier on that our expectation is that the market increasingly is gonna move to a packaged application orientation or packaged services orientation as a way of delivering expertise about big data to customers. Splunk is the leading software player right now. Why? Because that's the perspective that they've taken. Now, it's perhaps for a limited subset. It's perhaps for a limited subset of individuals or of markets or of sectors, but it takes a packaged application, weaves these technologies together and applies them to an outcome. And we think this presages more of that kind of activity over the course of the next few years. Oracle, kind of different approach. And we'll see how that plays out over the course of the next five years as well. Okay, so that's where the numbers are. Again, a lot more numbers, a lot of people you can talk to. Let me give you some action items. First one, if data was a core asset, how would IT, how would your business be different? Stop and think about that. If it wasn't your buildings that were your asset, it wasn't the machines that were the asset, it wasn't your people, by themselves were the asset, but data was the asset, how would you re-institutionalize work? That's what every business is starting to ask even if they don't ask it in the same way. And our advice is then do it because that's the future of business. Not data's the only asset, but data's a recognized central asset and that's gonna have enormous impacts on a lot of things. The second point I wanna leave you with. Tens of billions of users, and I'm including people and devices, are dependent on thousands of data scientists. That's an impedance mismatch that cannot be sustained. Packaged apps and these cloud services are gonna be the way to bridge that gap. I'd love to tell you that it's all gonna be about tools that we're gonna have hundreds of thousands or millions or tens of millions or hundreds of millions of data scientists suddenly emerge out of the woodwork. It's not gonna happen. The third thing is, we think that big businesses and enterprises have to master what we call the big inflection, the big tech inflection. The first 50 years were about known process and unknown technology. How do I take an accounting package and do I put it on a mainframe or a mini computer or a client server or do I do it on the web? Unknown technology. Well increasingly today, all of us have a pretty good idea what the base technology's gonna be. Does anybody doubt it's gonna be the cloud? We got a pretty good idea what the base technology's gonna be. What we don't know is what are the new problems that we can attack, that we can address with data-rich approaches to thinking about how we turn those systems into actors on behalf of our business and customers. So I'm a couple minutes over. I apologize. I wanna make sure everybody can get over to the keynotes. If you want to, feel free to stay. The cube's gonna be live at 9.30. If I got that right? So it's actually pretty exciting if anybody wants to see how it works. Feel free to stay. George is here, Neil's here, I'm here. I mentioned Greg Tario, Dave Vellante, John Greco. I think I saw Sam Cahane back in the corner. Any questions? Come and ask us. We'll be more than happy. Thank you very much for being... Oh, David Vellante. I had a question. Yes. We had time. Yeah. All right, so the question was the most valuable companies in the world are companies that are well down the path of treating data as an asset. How does everybody else get going? Our observation is you go back to what's the value proposition? What actions are most important? What's data is necessary to perform those actions? Can changing the way the data is orchestrated and organized and put together, inform or change the cost of performing that work by changing the cost of transactions? Can it be, can you increase new service along the same lines and then architect your infrastructure and your business to make sure that the data is near the action in time for the action to be absolute genius to your customer? So it's a relatively simple thought process. That's how Amazon thought. Apple increasingly thinks like that where they design the experience and they think what data is necessary to deliver that experience. That's a simple approach, but it works. Yes, sir? I want snowflake. I don't see many of those on you to speak to, what are you seeing in terms of? What a great question. So the question was when you look at the companies that are catalyzing a lot of the change, you don't see a lot of the big companies being at the leadership and someone from Snowflake just said, well, who's gonna lead it? That's a big question that has a lot of implications but at this point in time, it's very clear that the big companies are suffering a bit from the old, trying to remember with the RCA syndrome. I think Clay Christensen talked about this, the innovative dilemma. So RCA actually was one of the first creators, they created the transistor and they held a lot of original patents on it. They put that incredible new technology back in the 40s and 50s under the control of the people who ran the vacuum tube business. When was the last time anybody bought RCA stock? The same problem is existing today. Now, how is that gonna play out? Are we gonna see a lot of, as we've always seen, a lot of new vendors emerge out of this industry, grow into big vendors with IPO related exits to try to scale their business or are we gonna see a whole bunch of gobbling up? That's what I'm not clear on but it's pretty clear at this point in time that a lot of the technology, a lot of the science is being done in smaller places. The moderating feature of that is the services side. Because there's limited groupings of expertise that the companies that today are able to attract that expertise, the Googles, the Facebooks, the AWSes, et cetera, the Amazons are doing so in support of a particular service. IBM and others are trying to attract that talent so they can apply it to customer problems. We'll see over the next two years whether the IBMs and the Accentures and the big service providers are able to attract the kind of talent necessary to diffuse that knowledge into the industry faster. That's, so it's the rate at which the idea of internet scale computing, the idea of big data being applied to business problems can diffuse into the marketplace through services. If it can diffuse faster, that will have both an accelerating impact for smaller vendors as it has in the past but it may also, again, have a moderating impact because a lot of that expertise that comes out of IBM, IBM's gonna find ways to drive in the product faster than it ever has before. So it's a complicated answer but that's our thinking at this point in time. Yeah. It wasn't a winner take all market, it was gonna be no. And to a great extent, this is a supply side from the market, I can look at the five big data that's also applying to their business. The real winners are in the five that I mentioned, the practitioners view is really clear that cloud economics are driving the economy for big data. Yeah, I would, I think that's true now but I think the real question, not to argue with Dave, this is part of what we do. The real question is, how is that knowledge gonna diffuse into the enterprise broadly? And because Airbnb, I doubt it's gonna get into the business of providing services. So I think, I think that the whole concept of community partnership ecosystem is gonna remain very important as it always has and we'll see how fast those service companies that are dedicated to diffusing knowledge, diffusing knowledge into customer problems actually occurs. Our expectation is that as the tooling gets better, we will see more people be able to present themselves truly as capable of doing this and that will accelerate the process but the next few years are gonna be really turbulent and we'll see which way it actually ends up going. Yeah, so they don't like talking about it too much but they're doing it. Exactly. And that will have an impact on ultimately how the market gets structured and who the winners end up being. Yeah, well look, at the end of the day, red hat isn't even the red hat of open source. So the bottom line is the thing to focus on is how is this knowledge going to diffuse? That's the thing to focus on and there's a lot of different ways. Some of it's gonna diffuse through tools. If it diffuses through tools, it increases the likelihood that we'll have more people capable of doing this and IBM and others can hire more, that Citibank can hire more. That's an important play. So you have something to say about that but it also says we're gonna see more of the packaged applications emerge because that facilitates the diffusion. This is not, we haven't figured out, I don't know exactly, nobody knows exactly the exact shape it's gonna take but that's the centerpiece of our big data research. How is that diffusion process gonna happen, accelerate and what's the resulting structure gonna look like and ultimately how we're gonna enterprise is going to create value with whatever results. Yes, sir. So the recap question is you see more people coming in and promising the moon but being incapable of delivering because they are, partly because the technology is uncertain and for other reasons. So here's our approach or here's our observation. We actually did a fair amount of research on this. When you take a look at what we call a approach to doing big data that's optimized for the cost of procurement, i.e. let's get the simplest combination of infrastructure, the simplest combination of open source software, the simplest contracting to create that proof of concept that you can stand things up very quickly if you have enough expertise but you can create that proof of concept but the process of turning that into actually a production system extends dramatically and that's one of the reasons why the Clouderas did not take over the universe. There are other reasons. George Gilbert's research has pointed out the Cloudera is spending 53, 55% of their money right now just integrating all the stuff that they bought into the distribution five years ago which is a real great recipe for creating customer value. The bottom line though is that if we focus on the time to value in production, we end up taking a different path. We don't focus as much on whether the hardware is gonna work and the network is gonna work and the storage can be integrated and how it's gonna impact the database and what that's gonna mean to our Oracle license pool and all the other things that people tend to think about if they're focused on the technology. And so as a consequence, you get better time to value if you focus on bringing in the domain expertise, working with the right partner, working with the appropriate approach to go from what's the value proposition, what actions are associated with the value proposition, what state is necessary to perform those actions, how can I take transaction costs out of performing those actions, where's the data need to be, what infrastructure do I require? So we have to focus on a time to value, not the time to procure. And that's not what a lot of professional IT-oriented people are doing. Because many of them, I hate to say it, but many of them still acquire new technology with a promise to helping the business but having a stronger focus on what it's gonna mean to their careers. All right, I wanna be really respectful of everybody's times. The keynote start in about five minutes, which means you just got time. If you wanna stay, feel free to stay. We'll be here, we'll be happy to talk. But I think that's pretty much gonna close our presentation broadcast. Thank you very much for being an attentive audience. And I hope you found this useful.


As the sun surely sets, dawn will see it arise. The service above self demands its own prize. You've fought the good fight, life's race has been won, and peace will reward for eternity begun. To this day, the Queensland Police Service has lost 140 fine officers. Since Constable Matthew Connolly passed away in 1861. These deaths are representative and are a reminder to us all that we should always value our time together and not take life for granted.