Good afternoon. My name is Harriet Chan and I'm a volunteer with the San Francisco Bay Area Chapter of Better Investing. And I'm here this afternoon to assist better investing and the San Francisco Public Library in the campaign to design to encourage individual of all background to start a lifetime investing program. In stocks. Or mutual funds. Next slide. Again, my name is Harriet Chan. This is my email SKCOTS at yahoo.com the SKCOTS is the word stocks fell backwards. Okay. Next green is the disclaimer. So, as mentioned by Doreen, this presentation is for education purposes. I would advocate that people become active investors, and you should always do your own research piece of mine is what you get when you do your own research. Okay, for today's agenda. Next screen. We're going to talk a little bit about who is is better investing, how to access value line, and what is value line. And we'll talk a little bit about value line section one and three and the company reports and a summary and question. The goal of this class is to show you how to access value line at the San Francisco main library. We're going to highlight and find some parts of the value line share brief description of some of the key value line items. Now due to time, we will not go into details, many of the definitions and sections. We do cover more of this in the classes offered by better investing. I want to say that there's no magic behind good investing. Virtually, anyone can be successful. We will add however that even the stock picking wizard of Peter Lynch would be impressive how much more easily you can put into the stock market to work as a member of better investing. I have volunteer for five years to teach third grade math. And basically that's all you really need that to, you just need to know how to add subtract multiply division and percentages. I just, I do want to mention that when I taught this class in person, this class usually takes three hours today we only have one hour. So I'm going to introduce you the online version of value line and how you can log into the San Francisco library and get your value line online and do some studying at your own pace. If you have questions, feel free to email me. Next screen. Who we are better investing is a nonprofit. We are volunteer base so everybody in the San Francisco Bay Area chapter of better investing or volunteers. Basically, a lot of our directors, we're all successful investors and we just want to share our information. We are member driven. We make no commissions, and we make no stock recommendation and WWW dot better investing that org is our website. As we go through the presentation today please make a note of any questions you have. And we can go over at some stopping point. We're doing better investing. Basically, we just want to create long term investors through education. Through the resources we have and the tools, and to give you support. We do want to encourage you to be active investors to get the reward but more importantly peace of mind. It really reduces your stress. In terms of academic we all need to find ways multiple ways of streams of income, whether by investing in stock dividend investing real estate have multiple streams of income makes us less vulnerable. This is Peter Litch. He was saying that all you need is fifth grade math, but I've been investing for 40 years and I would say, you only need third grade math. So, but you actually need to do the math. Okay. So I want to say that, you know, when you do the math do the dollar sign and do the percentages. Now, you need a library card. So, when I did this last year you have to be a resident of San Francisco. So they have just updated me and said you don't to get a San Francisco library card. All you need to be is a resident of California. So that's great, but you do have to go into a library. So, I, I have a question for the librarian. It used to be that they can apply online. Is that not true. Or has that changed. I can answer that. You can fill out the application online, but you have to visit any of our locations so the main or any branch to present your ID so that we can verify that it's you and then if necessary if your ID has your old address bring a piece of mail with you like a utility bill. And once we verify your ID and your address then we'll issue the card. So you do have to make that one appearance that one of our branch libraries or the main library. I would say it's worth the trip. Okay, next slide. So the following on San Francisco branch libraries have hard copies of the value line. So in the main library. On Larkin Street, it's on the fourth floor behind what they call the page desk. Okay, I started out many, many years ago, using the hard copy, and I, I tend to just because I'm familiar with it. I still tend to prefer that. Okay. Is there any questions before we dive in here is one question. I see one, and I don't know if JP sees others. See. Okay, there is a question about getting a library card is the same is the same when I'm a resident of San Francisco and I can get a South San Francisco library card. That's San Mateo County so it's a different system. I think that one time had a card at one of the Peninsula libraries so I think they extend it to all Californians but I'm not sure. And that was years ago so I would contact the south south San Francisco library to see if you're eligible to get one of their cards as well. But I don't know if it works both ways, you know, as far as getting cards with different systems but usually they'll, they'll open it up to Bay Area residents so I would just contact the south San Francisco or, you know, the peninsula library system to see what they need to get from you to get a card but for San Francisco, that one I can confidently answer. If you live in California, you can get one of our cards, you just have to visit us. Next slide. So this is the front page so you type in Sam SFPL.org. Okay. And where you want to go is the learn the research and learn. Okay, next screen, you want to go to the articles and database. And the first thing that pops up is the value line. So you want to click on value line. And it comes up with my user ID and my password. Okay, and so all you need to do is to press the and submit that button. And it will become your first screen of the value line. Where you will be going to browse and search everything I'm sharing with you is in the investment education, the red box there, see the tab, investment education so everything that I'm saying everything that I'm teaching is in this section. It's a very simple resource. So allocate some time. We only have an hour so if you go once you sign on you can browse that and poke around as long as you would like. Okay, the other thing that I want to mention on the screen is that there is the dial 30 listing, the dial 30 listing is free. You can just log on to the library or you just log on value line. The Dow Jones 30. If you don't know is the 30 blue chips us companies. Okay, so it's a good place to start. If you're new to investing large companies that make money. Another place for education is sites are like large brokerage firm more most brokerage firms like fidelity and Charles Schwab does not charge commission to buy or sell stocks anymore. Decades ago, it used to cost $155 for a transaction and a few years ago. That's been dropped down to zero. If you open up a brokerage account. The best way is to automate your investing transfer a set set amount of dollars over to your brokerage firm on regular intervals. Okay, so you're going to over here you're going to type in in that little box, J and J, which stands for Johnson and Johnson, and you press enter. Next screen. So this is what comes up. So on the online format of value line. Value line is just a report card on a company. They do fundamental analysis, which means they will look at the company books and see how well they are doing and fundamental analysis, P is very important. Now what is P, P stands for price of the stock and he stands for earnings. So you're looking at the ratio between the price of the stock and the earnings. The other way, another way of looking at it that it could be current earnings and future earning is also very important. If you look at the yellow box in there. This was dated 1020 2020 and the stock price was $143 and 97 cents. The trailing PE is 16.7. What we mean by trailing is that it's the last 12 months. But it's also very important to figure out what the forward PE what it's going to earn the next 12 months. And so you have something to compare it to the now. So if it compares Johnson and Johnson's against value line 1700 stocks. So Johnson Johnson is not a hot stock. It's dull and boring, but it's a steady Eddie stop and dull and boring has its appeal. The current yield of the dividend is currently 2.78% at this time. Better. It's paying a lot better than money market fund, J and J Johnson Johnson has an increase each year the dividend since 1963. We are a member of the aristocrat dividend companies companies that have increased the dividends for a minimum of 25 years. Tomorrow, I will be teaching a class called dividend reinvesting tomorrow at three o'clock. Okay, at the library so. And what I did was why I developed the classes. I hear a lot of people said they don't have time and they don't have money. So dividend investing is a passive strategy, but you still are in the stock market, and I'll show you historical data for 42 years. And so the average returns, much, much better than tertiary bills savings account or CDs and looking forward to sharing that data with you. Okay, so I had created for $50 a month on Johnson and Johnson you could have invested $600 a year. And in 18 years you went at over $100,000 and in 24 years 190,000. So that's pretty a good return for $50 a month and not much research.  Like yesterday when I presented about how to start a investment club. It's in the stock market where you can really make your money grow. Okay, now I want to go back to that yellow squared their box and over on the right hand side. There's the blue. Okay, if you click on that. I will tell you. Sorry Harriet am I on the right slide back back one up back. Okay. Okay, like that. Right. Yeah, that explains to you what the 18 month price line and so this is this slide was created in October of 2020. And it was at 143 today to stop Johnson and Johnson is at 177 I believe. So if you figure out the math, 177 minus the 143. It's about 25% return. And so it's been 18 months. It's a pretty good return 25% I think. Okay. Next slide. Now, the red box safety. Johnson Johnson is ranked number one. Okay, there are 100 stocks that have a ranking of number one. The average stability index and their financial ratings. Okay, it rates from one to five one is the highest. And five is the lowest. So if you're a new investor, you want to stick with the ratings of one and two. Okay, there are 1700 stocks in value line. And there's 100 are rated number one, 300 are rated number two, 900 are rated number three, and 300 rated four and 100 is five. Okay. The green box timeliness. Johnson Johnson is rated not ranked number two. It was upgraded on 1023. So what does it mean. When you're ranked number two, it means that there's another 299 stocks that is also rank. Number two. Okay, the purple box. We're going to talk about the financial strength. The financial strength for Johnson Johnson's a plus plus. Now there are nine grades. There's a plus plus a plus a B plus plus B plus B C plus plus C plus and C. The lowest grade is reserved for company with financial difficulties. The lowest grade is the C. Okay. If you look here. If you look at the total sales in that purple box, you will see that the sales are increasing, which is good. The cash flow is increasing and the debt is going down. The return on capital is going up and the return to the shareholders equity is going up operation margin is up cash flow is very, very important, and you can compare that to your own personal situation. You know, good cash flow really could can make you be able to maneuver whatever financial situation you're in. Okay, and also I want to mention that Johnson Johnson is one. There are two companies that are triple A rated in the United States. Johnson Johnson is one of them. The other one is Microsoft. Next slide. Okay, so what I want to show on this screen is that if you click on the show. And then it's going to show the definition. Okay, the rating and definition. Okay, next screen. And you can see the next slide if you want to hide it. You click on the high button, and it goes away. Next screen. Okay, so we're going to scroll down a little bit and talk about the commentary and the three to five year projections. What you want to look at is those those numbers. Do you like those numbers do you like what the numbers are telling you, you need to form your own opinion. So in this commentary, there's about 400 words in here. And you can click on more. Now that's that green arrow, and it'll expand. Okay, now that blue arrow records the ranking and the, and the rank and rating changes forms the technical to timeliness and earnings predictability. Next screen. Okay. So here is the supplementary report. People says what should I read I said read it all. You will get a better view of the company. The last report was a very positive report. Now mid year. It was not so positive. Now, what's nice about the online version, it gives you access to the historical reports, the hard copy you only have the most current report. Okay, next screen devaluation. I want to share data, review the data on compare the data. And I wanted to point out to you that the italic means that it is a projected estimate. So you can see it bold and italic. It is an estimate. So when you look at these numbers you want to compare it and say, do you see things that the company is doing right. It's been increasing since 1963. What does that mean to you, are you are you a person who was looking for income. So, giving dividend data of $50 a month investment for 2524 years is 14,000 1400, but it generated $22,000 of dividend. Investing the dividend in your J and J stocks gives you a value of 190,000. Okay. Now, I also want to say that if you don't understand everything, go to the investment education, and there's a glossary get to so if there's a definition that you don't understand you can always use that to help you to give you a better understanding. Okay. Now, the one thing that I noticed in here is that the long term is growing. Okay. And why is it growing. It's growing because the company sees that to borrow money to interest rate is very low so they're going to take advantage of using borrow money. Next screen. That this is sales and graph. Okay. And a reminder that the italics are estimated projection. Total return with the competitors check them out over here on the right hand side. You want to verify you want to check those companies out to if they look interesting. Verify that you're buying the best company in the industry. I'm the person that's more interested in future growth. Past performance is already priced into the stock. So Johnson and Johnson is a large company. And, and it's actually like a mega company. And so general exception, expectation is about 7% for a large company, small companies are more agile, but the finances are not as robust. So JJ is a global company. It operates in over 120 companies have a strong, having a strong or weak dollars will have an effect on their bottom line. Okay. Now, what I want to point out to you here in the screen is that in the orange box you see this great spike in the adjust the earnings. We went from 685 to 8, 868. And this was because of a tax issue. And they made the correction, but value line also chose not to go back and correct it so going forward, they will use the new way of calculating the income. And this looks like an anomaly there. Okay, so let's look at the dividends of six to eight and a half percent in dividends. So the question is, if you can get your money to increase six to 8% you need to compare. The raises that you're getting that is in the six to 8% raises. So this is about using your money and making it work hard for you. Last year so security increase was about 2%. So, a average of seven and a half percent increase looks pretty good with the history they can and they can sustain their increases. So this is the score rating here in this red box. Add up the numbers add up the scores, and if it's 200 or more. You can go forward and do some more research. If it doesn't add up to 200.  I got this tip reading from the Better Investing magazine, and it was a letter written to the editor. It was a great idea. And there's a time saver for screening stocks. If you find earning predictability is near 100 or 100. If the price stability is lower. It means it will be buying opportunities for me. So that's the idea of buying on the dip. So we scroll down a little bit more. We're talking about industry data. Click on that this says we more. And this is a must read. We're talking about the industry that J&J is in. Okay, the teal box inside transactions. This informed you who is buying and selling in upper management. This data used to take 30 days, but now with technology, it only takes a couple of days before we know. You can also see that over in the red box that the that many mutual funds and pension funds owns J&J. It's about 60 to 70%. There's only a problem if the market crash. People call up for redemption. And this may cause tax issues and valuation flux is in the short term. Okay. Next screen. Okay, the business description. So you have the business description. And right next to us the business history, and there isn't any history. So therefore you have to go to the Johnson and Johnson website to and they have a big section on their history. The technical ranking. Okay, if you click on the show, it'll display this history of the technical ranking. Okay, and over here on the right hand side, the value line report value line PDF report. Okay. It lists out two years of reports. We were going to stop at this juncture, and then we're going to review the hard copy. Is there any questions? Yes, there's questions, Harry. We've got a few one from the very beginning. You had mentioned a few brokerage firms Fidelity and what was the other one? Charles Schwab. Charles Schwab. Okay. And then someone also asked on the J&J page, where's the future PE listed? Yeah, so we saw the trailing and the patron wondered where the future one was listed. Okay, could I hold that off until we get to the hard copy? Of course. Next question is a couple more. What is the definition of timeliness? Timeliness. They had the, it is a value line ranking. And they, timeliness means that, you know, there's a lot of interest in the stock, there's only 100. Okay. But the thing about the timeliness, if you go to the education tab and read about timeliness, you'll find that the timeliness says that the stock is usually volatile. And more volatile than usual. So, and if it's timely, and it's ranked number one, that means a lot of people have been buying the stock. So it tends to be on the high side. You know, so, and you have to decide, do I want to go in now or do I want to watch it? And so a lot of times I says, if you think it's too high, watch it, watch it for a couple of weeks and see what happens. One more Harriet, and then I'm sure there'll be more later, but what is considered a good PE ratio? What range should we look for? Okay. If you look at the S&P 500 to 500 largest company in the United States, the PE ratio right now is about 16. Okay. And so we're talking about growth companies, the largest companies, they're about 15 or 16. It's different if they're cyclical companies. You know, when they're cyclical companies, they go up and down and you have to take that into consideration. Now, growth companies, I would say, okay, the other thing about what's a good PE ratio, if they have a PE ratio, it is a good thing. Right now in the stock market, a lot of people are investing or speculating in stocks that does not make earnings. So that's, you need to be careful of. So the criteria was, is that they at least are making money. It used to be that a company would not IPO until they have proven that they could have made a profit for five years in a row. And that was back in the 1980s. With the onset of the internet, there was this company called Netscape, and they were competing and they felt the pressure so they IPO'd without proving that they had made money. What was surprising was that it was so successful. And that opened the floodgates for all the companies. And so a lot of companies they will do the initial public offering without proving that they, they can make money. So a little, it's a little bit more dangerous now, I think. So, fundamental analysis always looks at the books to say how well you're doing so I think that's important. That's an answered question. Hey, we're just about to proceed with the print version. There was a question about the slides we will send those. It's a large file. So, I hope everybody's able to receive them, but we will do our best with our, we always send the follow up email we will attempt to send them out. Thank you everyone for your questions. Oh there's one more question. Where would one look to determine the type of company it is growth or cyclical, etc. Let me. We're going to get to the hard copy. Okay, at the next screen and I'll talk about that. Okay. Okay, so here we go. Okay, so next screen. This is the hard copy is of the value line on a company. One page fits on an eight and a half by 11 piece of paper. This is what. So the new format the online format is much easier on the eye. The hard copy I have to say format in the beginning was very, very intimidating. It was very stressful. But in time, I think I figured out this page. Discipline, perseverance and patience. Okay. Next screen. So this is the top half. And so there was a question about the PE. Okay, so on the orange square there there's just the PE. So it gives you the P of 18.7 and it says trailing and median. Okay. So the value line does something unique that how they calculate the, the PE, they use six months of historical data and six months projected earnings data. So that's unique to value line. But so you can see the trailing is. I remember right. It's like last 10 years and the medium is, it's also 10 years. So, what was the question on the PE about how you could figure that out. The question was, I think they're wondering where the future PE was listed. Okay, okay. Hold on. Okay. So then in the red square, you have the timeliness, the safety. Okay, and then what's really interesting is the orange box under that. So we do a 18 month target range. So the 18 target range, it says that the midpoint is 171. So I find it very interesting that the Johnson and Johnson today, which is 18 months later. Since October of 2020, we're at 177. So they're pretty much on target. Okay. Beneath that is what they're projecting for 2023. They think the high price will be 230 and the low price will be at 190. So we're looking at a 9% or, or a 15% increase. Okay. So, in the yellow box. This is the per share information. And down on the lower half of that yellow is the company data. Now the online version kind of mesh them together. So the hard copy breaks them out per share on top, and the company data down on the bottom. Okay. So, next screen. So, here the annual rates. So this annual rates estimated to 2023. Many years ago value line was the only company that gave you an estimate of what the future sales and earnings and dividend was today that's very different today. So, this company has it, and for better investing we have a tool, and we get data from Zach from Yahoo for Mike from morning stars that give you estimate and of course, your brokerage firm gave it to you. So right off the bat, you can get an estimate from six different resources. Okay. So, in that orange box here, where it says annual rates that first box. Okay, so sales are going to grow 5% cash flows going to grow 8.5% earnings they think they were going to grow 10% dividend is going to grow 7.5%. Okay. So how do you figure out that PE. So if you give the earnings. Okay, and it's going to grow by 10%. So this is where you have to do the math. Okay. So, and then if you look at the earnings in that same orange box is the third one down. So in 2021 they're estimating that the sales will be $9. Okay. And so, if you multiply that by, if we go back to a previous screen. And look at the P ratio 18.7 up on top. Okay. So if you times that 18.7 times nine, you get a number. And you say do I like that number. So again, like I said, third grade math is really all you need to figure out the numbers. Okay, let's go back to the next page. So now over here on the right hand side, the company financial strength. It's a plus plus. This is where I talked about, you know, you add up the numbers. And if it's 200 do more research, if it does not add up to 200, you should pass, especially if you're a new beginner, because what you want is a quality that makes money. Okay. And also to the left of that financial strength numbers, it's footnotes, the smaller the print, the more important you should read it. It's very interesting. So, you'd be amazed. Okay. So everything that I had talked about the online version. Okay, is on the hard copy on one page. And it really does take a lot of time to digest it. So I really recommend that you go back to the investment education and go through that. Like I said before, when I was teaching this class usually took three to four hours to teach this class. So, okay, so I want to go back and I mentioned the Dow Jones 30. It's available for free. And when you sign on on the front page, you see it Dow Jones 30. This is the listing of 30 stock large company that usually have liquidity and is probable. It is a really good place to start looking for a quality company. Okay. Now, next screen. Okay. So this is the Dow dirty. Okay, so let's go back to the front page. Next screen. Okay. So a lot of people have asked just like, how do I use value line. So when you long on here. What I usually use is the dashboard and find idea tab. Okay, so you can go there and look at preset screens. Next slide. So when you go to the fine ideas over to the left hand side you're going to see a thing called screeners. Okay, so when you click on that. You'll get to this stock screener page. So value line has a whole bunch of screens that you can look at different kinds of stuff. If you're looking for growth stock or you're looking at banking stocks. So there's all kinds of things that you can select and poke around with. Now what I kind of did was I scroll down. And I clicked on the conservative stocks ranked number one for relative safety. So you can poke around here. And so actually give yourself some time, allocate some time so you can go in and take a look. So the reason why I pick the conservative stocks ranked number one for relative safety is that I think that a good way of looking at it when you're new in the investing. You might say you want to buy a stock for your grandmother, you want it to be safe, you want to earn some money, but you want it to be safe. Okay, protecting the principle. So here, if you look at the stock screener, there's the safety ranking. Right. So you can click on one, and it'll give you a list of stocks. Next screen. Okay. Okay. So, and then if I wanted to see the ranking, I can click on that. There's a different kind version. There's a lot of data on the screen so you kind of have to scroll to the left or scroll to the right. But there is a lot of data. And simply because there's just a lot more white space. So it's not crammed all crammed in. So it's kind of nice as visually is easier on the eyes. Okay. So here is the ranking rating and ranks. Next screen. Okay, so you can see in the red box, you know this data in there, and you need to scroll to the left, if you want to see it. The technical ranking their earnings predictability, the price growth persistent and the price stability information is presented. Next screen. So the estimate now here I decided to click on the estimate and projection. Okay, so that kind of gives you an idea of what the estimate and projection category looks like. So it needs you need to allocate some time so you can digest all of this. If you have a touch screen, you can touch your computer to slide the data over. If not, you have to go to the bottom of the page and use the scroll bar. Okay. So safety ranking. Here we go. The estimate and projection. Okay.  So, you know, if you find something interesting. If you find one, you know, like automatic data processing, the dividend growth is going to be 10 and a half percent. Apple is projecting the dividend growth rate to be 15 and a half percent. So that's very interesting. Again, it's interesting to me because I am looking for dividend income. So it may not appeal to you if you're not. Okay, but nevertheless the data is there. So you can check that out. Next screen, the profitability. So the old, the function of a business is to make money. Okay. So this kind of gives you the information about the operating margins, the percentage, the percentage of the profit margins. So you can see here for Apple and for Abbott labs what they are. Okay. Right now in the beginning you may not know whether or have an opinion, but in time after you look at these things again and again. And then you see different companies and different industry sector, you will start to have an opinion. Okay. Next screen. The next category is the annual rates. So just give you the sales growth rate, the sales for the five year growth rate, and the estimate for one year growth rate, and the estimate for five years. So if you're a long term investor, you might be looking at the five years, much more than you were looking at the one year. So here, the last category is industry. Okay. So, information listed in the, in the company name, the ticker, the industry, the inventory turnover, the percent change and retail sales. There's a lot of information so you need to be patient with yourself and give yourself time to absorb the data. So you really need to allocate and develop a schedule or find families and friends to make it easy and okay to talk about investing. Okay. So any, do we have any more question here. I don't see any for now, Harriet. Okay, good. Okay. Try to match up where you are. Okay. So here is next screen. Okay, next screen. Next slide. Okay, this is the, I want to talk a little bit about the hard copy. So this is what they call the summary and index page. So when you check it out at the library from the reference desk. It's a different page. And so you could see right there on the top half, it says screens. So there's things that if you're interested in timely stocks or high dividend stock if you're looking for companies, or you're going to looking for stocks from three to five year price potential. So the best performing stocks in the last 13 weeks, or the worst performing stock in the last 13 weeks stocks will high PE stocks will low PE stocks that has three to five year dividend yield. So there's a lot of screens that you can take a look at. So the question you need to ask is, what are you interested. And so, once you figure that out. I use it a lot, especially if I'm thinking looking for a dividend stock, or I'm looking for growth stock. This screens really appeal to me. It will take some time for you to understand what's on this page because there's a lot of data. Like here on the right hand side, where's is the median estimated 18 month appreciation potential is 15%. So right in there, when you look at this 15% that tells me that the market at that time is pretty high. So it only has a 15% potential of appreciation. So all these numbers eventually when you get through with them will start to make sense. Now the bottom half. Okay, next screen. The bottom half is a listing of the industry. There are 96 industry. And so what what we recommend is that you just stay in the top 30s. Okay. It's just that there will be enough company out of 1700 and the top third that it would you'll be, it will be enough materials to do research. Okay. Now the online version, with you scroll down, you will get to the next slide. You could see that it's on page two. So what it is, this is what they did online was it just become a long, long file, and you can scroll. So here on page two, you would get the index to the stocks and then listed in alphabetical order. Okay. So, sorry, having a little bit of difficulty here. Can you still hear me. Yes. Okay. Because I think I lost. Okay, here we go. Sorry about that. Okay, so then the next screen. I wanted to show you the bottom of the page. And again, there are footnotes. Take the time to read them. Okay. Next slide. Same thing. Read the read the different footnotes that they have in there. Next screen. So this is the last page. So sir, over on the right hand side, it says page 23, w y to z y. So this is the ending of the list. Okay. Next screen. I want to show you the three to five year potential that's on page 36. So you can go through that. So, again, that would be the online version to get to this, you would go into the fine ideas. Okay, there's a lot of information. Just give yourself some time. We're almost done. I want to tell you something about value line value line was it began in 1931. Next slide. During your depression. It's a was developed as a method for evaluating stocks in 1965. Next screen. Okay. They developed the timeliness ranking system. They have 70 analysts, and all the numbers and value lines are developed by the analysts. Arnold Barnhard started the company and he wanted to help the small investor gain access to financial information that previously was only available to institution and wealthy investor. So thank you, Arnold. Okay, so value line is published weekly, and it covers 1700 stocks and 96 industry. Okay. They are categorized into industry, and each page is revised every 13 weeks. And then the cycle begins with their updated data in between they also will issue supplemental reports, if needed. Okay, so in summary, okay, you can get the San Francisco public library you can apply online but you must go in. What time does it, and then you have access to value line. So, that's it. Thank you for attending. If you're interested, better investing do have a 90 day free membership. No credit card information is required. And tomorrow I will be teaching a dividend reinvesting class. So I hope you can attend that. Any questions. I made it an hour. That was excellent I don't see any questions on my side. Okay. Yeah I don't see anything and so everybody look for an email from us later this afternoon. It's a big file all these slides so hopefully it'll go out there to everybody without any problems you can always send us an email at thissidetechsfpl.org, if you don't receive it will will try and fix it. Tomorrow at 3pm dividends investing for beginners Harriet Chan will be teaching that and I will include a link in the email for those of you that haven't already registered. I want to thank everyone for coming today. And as always, you know where your library we're here for you. So join us anytime. Thank you everyone thank you Harriet thank you JP. Okay bye bye.


I'm the Chief Technology Officer from Teradata. And I'll be speaking today with Jesse Lynch, who's the Director of Business Solutions at T-Mobile. And what we're going to do is talk to you about actual use of big data solutions and the technologies that we believe are going to be important for sort of maximizing the value in these solutions going forward. So what I'm going to do is talk a bit about sort of give the overall context in terms of sort of what's going on in the industry from our perspective and identify, you know, three key technologies that need to be delivered to the marketplace in order to sort of bring these capabilities to fruition. And then I'll turn it over to Jesse, and he's going to talk about the T-Mobile strategy and deployment using some of the technologies that they've had working in their labs and environment to deliver that value. So when we look at the growth of volume and data, we talk a lot about this concept of big data. And people focus on the bigness of the data. And I'm going to kind of take us away from that focus a bit, because I think the bigness of the data is actually the easy part. I think the interesting part is it's a different kind of data. That's the interesting part. Because historically, what we've done in analytics is analyze the transaction level detail, we called it. But in fact, transactions are not really the detail. We want to go to subatomic level detail. So for example, if I have a call detail record, we call it a call detail record. But a call detail record is actually a summary record for the billing system of lots and lots of things that happened on the network in order to get you there. And we look at interaction data, the nature of the interaction data is quite different than the stuff that comes out of billing systems and ERP systems and those kinds of things. The structure of the data is fundamentally different as well. And so the new vocabulary word that I want you to take away from today's discussion is spime. Now, those of you who read a lot of science fiction, you may know this term already. It's an object aware of space and time. And in science fiction, you talked a lot about this concept. And I would argue that today, it's not science fiction anymore, it's reality. In fact, I can see by looking at you that everybody in this room is a spime. Because you are all holding mobile devices that allow me to track your location as of any point in time. They're not all T-mobile devices, I noticed, so we need to change that. We're working on that. They should be. They should be. The ability to track parts in the supply chain, items in the retail store shelf, assets like trucks and trains and so on, this is going to proliferate data everywhere. And this is really what I would call the third phase of big data analytics. And we haven't really fully reached that yet. But that's where we're going. And so when we look at evolution of big data analytics, first wave was web analytics. Get the click stream data. Get the search data. Do lots of interesting analysis and so on. That's a pretty old story here in the valley. Web 2.0, that's mostly where people are today.  So to get the text, do the natural language processing, extract facts, dimension, sentiment scoring, do the analysis on that. People are trying to figure out how to do it and how to monetize it and so on. Pretty interesting. That's kind of where we are now. And some of the examples Jesse's going to talk about are sort of use of text in a kind of semi-structured form for doing interesting analysis. That next phase, SPIME says that we're going to be tracking all objects. Everything is a SPIME. Every vehicle, every person, every part in the discipline, all SPIMES. Some of you may already know this. I know I have some friends here in the valley who chip their pet, put a little chip back behind the ear of their dogs and go online. You can see where your dog is at any point in time. When in Hong Kong, they actually do that to their kids. Now, not with a chip behind the ear, but they actually put a bracelet on and you can go online and you can track where your kids are. Junior was supposed to be out of school at 3 PM. Why isn't he home yet? So a huge amount of data, orders of magnitude higher volume, but it's also a different kind of data. So what I want to do is focus on three key technologies, which I think are going to be critical for big data exploitation. The first is multi-temperature data management. If we're talking about big data, we're talking about high volumes of data of many different types, and we need to make it economic to exploit this data. I hear a lot in the industry right now about in-memory databases. Put all your data in memory and performance is done. That's great if you're selling hardware, but I would argue that anybody who says big data and in-memory databases in the same sentence doesn't understand what they're talking about. Economically, it's not reasonable. So yes, we like high performance, but we also want rational economics. And multi-temperature data management is a very important technology for delivering that. So part one. Part two is the idea of a polymorphic file system. Polymorphism, it's a funny word. It means that you can change shapes to adapt to new situations. Big data is not always square data. It doesn't always fit in a table. It doesn't always fit in a relational database. It's not always in record form. And so a polymorphic file system allows you to store objects in a form that is appropriate for that object in terms of access, in terms of the computation, in terms of the framework for programming to get access to the information. Key technology. There's a lot of vendors just over in the other world that are doing a lot of interesting things in this area of non-relational data. Key concept of this conference. And the third one, which I think is not nearly as well understood as it needs to be in this big data space, is the idea of late binding. So in the big data, we talk a lot about high-volume data. That's actually less interesting to me than this late binding idea. So in traditional data warehousing, we put the data into the database using these ETL processes. We extract from the source systems. We transform the data. And then we load it into a relational database. We are defining the structure of the data at load time. But when we think about big data and the rate of change of the structure of the data, that structure is changing all the time. And so very often, for a data scientist, I don't want to be bound by the structure determined at load time for the data. I want to add the structure at query time. So the idea of late binding is we defer overlaying the structure of the data at the time of query. And this is going to cause a radical shift of power in the BI space in the marketplace. And it hasn't happened yet because the vendors haven't really fully figured it out. So the BI tools that are popular in the marketplace, most of them kind of depend on the data being highly structured in these relational forms. They do all this mapping. They generate SQL and so on. That model is going to be blown up. And the ETL vendors who generally know how to transform data, they're going to have to move upstream and be part of the data access, not just part of the data loading. But I don't think they've all figured it out yet. Some have sort of gotten the idea. But they don't really know how to build the tools to provide end users access to the data. So the BI tools know how to present the data and generate SQL, but they don't know how to sort of do this late binding thing. And the ETL tools know how to present data. There's going to be a very interesting land grab that goes on between these vendors over the next several years as they figure that out. So these are the three concepts that I want to focus on here. So this multi-temperature data, we can talk about the trends very clearly around data acquisition. Want more data and want it faster. But the important thing here is the appetite for data outpaces Moore's law. You'll often hear the discussion that memory is getting cheaper by roughly 30% every 18 months. And that compounds, it builds on itself, right? Cheaper and cheaper, cheaper. And therefore, it'll be cheap enough to put all your data in memory and performance will be great. This is what I call marketing math. See, marketing math, you have only one side of the equation. That's what they tell you. I'm an engineer. In engineering, there's always two sides of the equation. You've got to solve for the variable. What's the other side of the equation? How fast is your data growing? So do you believe that your data is growing faster than 30% every 18 months? Now if you only understand ERP data, if you only understand how many customers, how many accounts, how many orders I got, probably that's not growing faster than 30%. But if you look at the interaction data, if you look at spying data, even if you look at clickstream data, if you look at social media, if you look at all these things, any sort of reasonable analyst, Gartner, Forrester, IDC, any of the guys that study this stuff, they will tell you 50% every 12 months. So the point is, data grows faster than memory gets cheaper. So it's economically irrational to say, I'm going to put all my data in memory unless you're going to restrict yourself only to the boring relational data that we've been working on forever. You're trying to get analytic excellence, that's really not what you want to do. And moreover, if you look at access patterns to the data, so this is captured from an analytic environment for a company that has over a petabyte of data, so this is on some toy academic benchmark thing. This is real in production, excess of a petabyte of data, very complex workload with 1,000 users hitting it. Notice, over 90% of the IOs is to less than 20% of the data. And this happens to be in telecommunications, but the reality is almost any industry will have similar types of characteristics. So this is what I'm going to call the hot data. The data we access very, very frequently, that's the hot data. This data, absolutely. I want it all in memory. At least it should be in flash memory if not DRAM. This data over here, memory prices at 50 times the price per terabyte versus electromechanical disk drive doesn't make any sense at all. I want the lowest cost per terabyte over here. So the idea here is with multi-temperature data management that you've got a range of storage devices at different price performance characteristics. So for the hot data, I want the lowest price per terabyte, but actually that's going to be less than 20% of my storage requirement. So I'm willing to pay a higher price per terabyte because I get a lower price per IO. But this data over here, I want the lowest price per terabyte. And some of the interesting open source projects like Hadoop understand that very well. Yes, we have memory built into those servers, but we also have a vast disk farm so we can store the data in a very cost-effective way. And so the key point here is not can you configure hardware that way. Any competent hardware vendor or engineer can configure hardware with a storage hierarchy. The hard part is the multi-temperature data management should not have to be done by a human. It should not be an army of DBAs doing this. Now when you look at the performance characteristics and the advantages and price and performance of doing this, it's very, very high. So you might be willing to send your best DBA away for two weeks. And she would analyze the access patterns and understand all the data and how it's being done. And then come back and map it to the devices. And it would be worth it, except by the time she gets back, she's wrong. Because the data changes temperature all the time. It's not static. It's very dynamic. And it's not as simple as the older data is colder. That's kind of obvious analogy people make. But it's actually much more complicated than that when you look at the real world. So multi-temperature data management means automatically no hands, no humans. Software is measuring the access patterns, predicting access patterns, and getting the data to the right place. So that's the first key technology. Second key technology is around this idea of the polymorphic file system. When companies build traditional data warehouses, they tend to focus on this stuff up here. Getting the data from a relational ERP system or a comma-delimited file or a fixed file format may be a legacy database like IDMS or IMS. But even those, we can force our way into a record-oriented format. But as soon as we get into this model of getting XML data, or weblog days, or rich media, they say, oh, I don't know what to do. Too hard.  There is no such thing as unstructured data. Unstructured data is a random series of zeros and ones that nobody cares about. Unstructured data is a term that relational database bigots use to describe data they don't understand. And there's a lot of value in this data down here that's going to waste. So there's a bank that I work with in North America. And when you call up the bank's service center, there's a voice that says, this call will be recorded for training and other purposes. Other is a huge word. So what does the average bank do? They store the voice call in case the customer complains later on. I said to transfer $1,000. You transferred $10,000. We can go back and listen to it and figure out who was right and so on. That's not really very high value. It's certainly not analytically interesting. This bank, they take that voice recording. They do voice-to-text translation. This technology is easily available today. And by the way, multiple languages. And then they do fact extraction, dimension extraction, sentiment scoring out of this voice. And they can predict defection. They can predict cross-selling opportunities and so on. All from these voice recordings, this rich media that the traditional relational database biggest, they have no idea what to do with it. Huge value if you can harness that. Just as an example. Here's another example. Social network analysis, more relevant to the T-Mobile case, where I want to be able to understand, within all my subscribers, who are the most valuable subscribers and list valuable subscribers, not by their revenue, but their power of influence within the social network. So yeah, you hear, of course, LinkedIn and Facebook. They do this kind of stuff as well. But it's not just for the social media companies. Any company where you can understand the relationship of your customers and use that to predict behavior and influence it is very, very powerful. But this is graph processing. All these metrics over here are probably terms that most of you haven't heard of, things like centrality and betweenness and closeness. These are standard metrics that you can compute using graph theory very powerfully. Now, can I compute these metrics using SQL processing on a relational database? Well, sure I can. Everything's touring complete. If I'm good enough SQL programmer, I can sort of wedge the data in and wedge the SQL and do the right thing. But it's hard. Imagine that I have the native ability to store a graph and do graph processing on it, completely in parallel, no hands. So the idea of the polymorphic file system is, sure, we can store relational data. We can put it in a column format and a row format. That's easy stuff. Any reasonable relational database can do that. But the ability to store graphs or key value pairs or text or XML or JSON or whatever it is that you need, we should be able to store that in and coexist multiple of those data types, because it's not like you store all graph and nothing else. You want to be able to store different kinds of data types each in the form that makes sense for the kind of processing that you want to do and the representation that makes sense for that data. The natural representation gives you more efficiency and more productivity in doing your analytics. Now once I've got all this non-traditional data so I can do my click streams and key value pairs and my social network connections and a graph, et cetera, et cetera, now I want to process it. And I don't want to be limited to SQL. Sure, SQL is interesting. This is a no SQL conference. There's a reason that you're all here. SQL doesn't solve all the problems. It solves some interesting problems, but certainly not all of them. And so we need to go beyond that. MapReduce is clearly the most well-known of the beyond SQL paradigms, but graph processing and all kinds of other models are very interesting. Time series analysis, one of the most common kinds of analyses that you want to do is not very well suited for SQL processing. SQL is a set processing language. Mathematically sets have no ordering, so it's really hard to do time series analysis without some pretty ugly self-joins and multiple paths of the data and really ugly SQL coding. In MapReduce, I can, in a single pass of the data, do this kind of time series analysis very efficiently and very easy to express. The kind of graph processing we're talking about for social network analysis, deep data mining, text analysis, we need to go beyond the constraints of traditional programming languages. And yet we need to have them fully parallelized. And we do not want to assume that you're sucking everything into memory because that's not a scalable, from an economic point of view, solution. And I want to make these capabilities available to data scientists who do not have to be computer scientists. It's very important. A data scientist should not have to have a PhD in computer science from Stanford University to get the value out of the data. The fact that in the valley, there are so many data scientists who are computer scientists, this is a shortcoming of the tools that they have to work with. When you get outside the valley, when you go to companies that are not .com companies, banks and retailers and telcos and so on, they don't hire Stanford University PhDs for data scientists, especially not from computer science department. So we need to change the paradigm there. And so when we look at what we need, clearly a parallel data store with very good scalability. And we need to be able to store relational data and non-relational data, not be constrained just to square data in rows and columns. We need to be able to process that data. We need to be constrained not by SQL but use multiple programming languages, multiple models of access to data. Now, oftentimes a conference like NoSQL conference, it's NoSQL, SQL, that's for dinosaurs, relational databases, put that over there in the corner with the cobalt programmers. We're doing new fun stuff now, NoSQL, Hadoop, et cetera. This kind of, I'll say, religious extremism is actually not very smart. I'm an engineer by training.  Good engineer, no pride, no shame. We steal good ideas from whoever has them. So I want to use the right tool for the problem that I'm solving. That means I want the choice to use SQL when it's the right thing. But when it's not the right thing, I want the choice to use other stuff. So, antsy standard SQL? Absolutely. But graph capability built in, text capability built in, map-produced capability built in, I want all those things in addition. So no SQL doesn't mean never SQL. It means not only SQL. Sometimes yes, sometimes no, depending on the problem that I'm solving. Up here is the idea that I shouldn't have to reinvent everything myself. If I'm a data scientist, I don't want a solution because a solution implies that you know the question. Data scientist's job is to find the questions. We're looking for patterns or relationships in the data. So I don't want a solution. But I want a toolkit. I shouldn't have to reinvent the Levenstein minimum edit distance algorithm. Just give it to me. Just give me the Bayesian statistical models. Just give me the text parsing and give me those things and let me put them together in interesting ways. And allow leverage of the open source community to build these things out and add to that library that we can use and share and increase the value and decrease the time it takes to get that value. So that's the vision from a technology perspective, what we need to do. Now I'm going to turn it over to Jesse, he's going to talk to you about the journey that we're on at T-Mobile. Thanks a lot, Steven. So that's a tough act to follow right there. There's a lot of good information. I'm going to talk to Jesse Lynch. I actually am in the IT organization at T-Mobile responsible for all the solutions that we deliver for information management, BI, predictive analytics, you name it, we do it. And a lot of the things that Steven talked about we are struggling with or we're working through as our business starts to look at different ways they want to see data, different sorts of business models we need to tackle. And from an IT standpoint, we're trying to figure out what the right tools are. And I'm going to talk a little bit about some of our strategy and a POC that we've done to start us along our way in this journey. And I'll say right now, we're actually not the most tech savvy folks in the world. There have been a lot of people I've talked to here, very tech savvy. We're trying to find tool sets for us that can help us get into this space, but not have to have every detailed skill of Java and all the other tool sets that one needs to utilize. And so we did a POC here with a tool set that allowed us to use SQL and also get some value out of what we were doing, because we will probably never be in that space as a big telco. So real quickly, and I think people see this picture a lot and the term big data is a term that bothers me a little bit. But when I look at this, I do agree that our world is changing from we have more data than we had 10 years ago. We've got more variety of that data. I'm dealing with it every day in a telco space trying to figure out what to do with this data. It's coming at us faster if we so choose to ingest it. But we can't forget the fact that we've got to get value out of it. That's the fourth V that we sometimes don't see on these pictures. And we've got to have a goal that says we have even more data, in my opinion, to make a mistakes on or to misread than to get actual value out of. So as we think about this, when we look at our technology stacks and we think about how we deal with all this data, we're dipping our toe in that water and trying to figure out to make sure we can get some value along the way and don't lose the force for the trees as we get caught up in everything that's coming at us and doubling every two years, I think, is where that's going. So T-Mobile in general right now. So I don't know if people follow the news. We're certainly trying to shake things up from a telecom environment. I think we're doing a pretty good job. This year we've done quite a few things to make life hopefully easier for our customers. We've said, hey, we're dropping contracts. We're no longer going to make people have a contract. You can bring your own device. If it works on our network, great. We'll charge you a pretty low rate for that.  And if we're not good by you, you can walk away. There's some of these sorts of things that we're doing out there. And what it's also driving us to do, though, is it's creating a little less sticky environment for our customers. And it's a little nerve-racking for us. We're taking some chances. And we think it's better for customers. But we better be pretty good than at understanding our customers and the experience that they're having with us because we may not have some of those hooks that keep people with us for a while in a contracted form. And if anybody's seen our CEO, he's very strong in this space. But he also is saying it's very important for us to make sure our experience for our customers, both from a network, a customer care, et cetera, is very strong. So there are pressures on us to start bringing data together in ways we haven't looked at it before, and make sure that we are creating the right experience for the customers. So we are embarking on journeys to bring data like this together into single places so that we can run analytics across those touch points that we have with our customers. And many of us, some of this data is stuff we've all dealt with for a long time. Some of it's pretty new, social. Some of these different channels we have. It's bringing new types of data to us and new ways we need to think about using it. And we do a very good job at siloed looking at this data, not a real good job looking at it holistically. And that's what we're trying to move toward as we go into the coming years. So along these ways, we did a POC where we started to say, we've got to start somewhere in this journey. And we've got some data that we've already tried to work with for years. And we thought, let's do a POC on this and start to look at some of the new technology that can help us. And what we started with was something called our account memo data. And if people have similar systems, do we do out there?  And it's pretty ugly. And it's hard to work in that in the traditional tool sets that we have today. It's multiple terabytes worth of data. It has all sorts of information in it. And with this POC, we said we've got to figure out a better way to get nuggets of value out of this piece of data and see if we can prove some tool conversations out. This is sort of what it looks like. This is not all of the fields that are involved. There's also account information. There's some other screens we'll show that. But it's a classic. It's some structured data. It's some semi-structured data. It's got some stuff that our care reps can type into. A treasure trove of information. But for years, we have really struggled to look at it holistically and pull nuggets out of there to either say, our operations are having some real problems here, everybody. We should be changing what we're doing. Because some of this data tells us that. That's what we believe is within this data. And we just haven't been able to get to that. Or we've got some indicators in here of some things that could be happening to our customers that can be pretty bad situations. Or very positive. We're doing some good stuff and the data can tell us this. So this is what the data looks like. And when we went through this POC, we wanted to look at cases where there were actions that happened. Certain actions. Somebody activated. Somebody deactivated. Somebody poured it out. What kinds of steps and what kinds of data within this semi-structured piece of information can we pull out to make meaningful for our business and start to make some change? Once again, a POC, and it's something that we're developing more into a production state, but an area that we think was going to provide us a lot of value. As I said, we're really an informatic SQL, Teradata, business objects type of a shop. Nobody on my team knows Java. We're in our infancy in some of the new tool sets that exist out there that we know can help us in this case. And we turn to Teradata. We said, guys, what do you have for us? What can we use to help us, from an IT standpoint, a business standpoint, get at some of this data, access some of this data, and get some value out of it in a fairly easy way? And we started looking at their Aster platform. And it made sense for us. We've got information in Teradata already. We could pull some stuff over there. And we've got, from a business standpoint, quite a few SQL savvy folks on our business side. In fact, a lot, which is a good and bad thing from an IT standpoint. But a very smart group of folks out there. And so it was a good tool for us to work with and with some business partners to say, hey, you just have to know SQL. We'll walk you through what we need to do. Let's do a POSG together. So we did a couple of things. So we started to look at this data and go, how can we once again winnow down the data that's important? And all of our goals, as I said earlier, was there's a lot to need with a lot of data to make some mistakes. How do we narrow in on the data that's important? So one of the things we started talking about was creating sessions out of the data. Because this data comes in a time series format. And stamped into this thing. And we didn't know what kind of experience our customers having, or what kind of sessions they were having with our care apps or with our sales partners. And we decided to say, let's take a look at that. And let's do some work to say, let's take a look at sessions and those records that correlate for, say, a 60 minute time period and start to say, are we seeing anything in the data that can help us understand? Are we having challenges? Are we doing good things about it? And the nice part for us was we could call functions within this. That would take care of some of the map-reduced work and make it much more simple for us to work with this data and use this data and sessionize this data. If we try to do this with SQL, it'd be a much harder task. And we tried to do it in the past with much smaller amounts of data. It's much easier to do in this type of a format. And our business partners and our IT folks understand it pretty simply. We could just say, give us a session. Go through the IDs. Give us a time range. And let's start to see what comes out. Let's start to see how that data starts to prep more. So here's an example before we did some sessionization. Once again, plenty of good scrub detail here. But this is a customer who had some interactions with over the course of a June time period. And after we sessionized it, we realized, all right, this person's had four, what we would call, interactions with us. One could say there's 15 interactions. But really, there's been four interactions they've had with us, and one took quite a few steps to go through. And this is just an example. And we see it hundreds and hundreds of times over our database. And these are the types of things that we want to be able to narrow in and go, why did it take this many steps in order to do some sort of function? Luckily, a positive came out of this. I believe we added some new service to this person. But we had to take some steps in here. It took a while. We had to verify their information twice. We had to do some things in there that maybe not can be hurtful or bad from a customer standpoint. And we need to be able to analyze that and understand, is there anything operationally we should be doing different than we do today? And we've never really had the ability to look at this in a wide swap. And potentially, on an individual customer, not that big a deal. But if you're seeing that across thousands of customers, it starts to become worth it to go, hey, we're seeing a pattern. Let's make some changes here. And ultimately, something like this, and I like this kind of statement, it starts to give us what the effort of a customer is with us. I had to think about that for a little bit. Because we often have customers that come to us to go, I've been all day working with you guys, trying to figure this out and trying to do this, that. And our care reps or sales reps are going, I don't see it in my system. And I don't know what's going on. I see you called care a couple of times. But the reality is there are some steps through there. And it took 20 minutes to add a single sock to the account or to add a new product. And we're just looking at the memo data. It could be that if we brought web data into this or handset data or IVR, it could have been that they did a lot more things out there than just these 20 minutes. And it'd be really nice to know about it and really nice to know where we may have had some shortcomings or where we did something really well within that session. Like I said, this turned out well. Some of the sessions may not turn out so well. And we want to be able to hit through those. We then started to talk about, well, we've got sessions. So we've got cases where we can start to pick out certain sessions that may be anomalous or have certain characteristics that we want to dive deeper into. We can also then talk about end pathing. And this may be a very common thing for folks out here. But right away that you can start to look at, I have an action and I want to see what happened after that action. And what's the common paths that people took after that action? Or something happened. And I want to see the steps that happened before that action that led up to this situation. And it allows us to start to look at patterns once again and define what are we doing things right? Are we not doing things right? Are there things that we can improve in this process to make our experience better for our customers? And if people try this in SQL, and I think it was mentioned earlier, this is a pretty hard thing to do in SQL, to recurse back through the data and to create a pathing sequence across a large set of data. Using this function, using a MapReduce function, it was a lot simpler for us to have that end pathing done and to start to highlight where are some common paths or steps that our customers are taking, depending on how we're looking at the actions at the end or the beginning. And these are some of the things that we started to look at, right? Hey, you're about to go over your data usage. We send out messages to people when that happens. Is that a good experience? Do they not like the message? Do they call a care and complain? What happens when that happens? Hopefully they do what we want, which is they top up. Account closure and somebody porting out. This is churn. This is the big pain for all of us telcos. People leave us. What are the steps that led up to somebody leaving? And can we look at this data in a different way to find patterns than we've been able to look at before? And some of these people may be looking at it and going, well, this is the next common sense, Jesse. What are you talking about? It's pretty big data for us to deal with, and we haven't always done it on a large scale. And now that we're having different tools, I think we're going to have a better ability to be able to look into this and really start to understand our churn patterns better. And finally, new accounts. What happens when somebody signs up for a new account? How good are onboarding? Phones are getting complicated, if people have noticed. And when you get a new phone, there can be some challenges. So we have to do a really good job to onboard folks, let them understand what their bill is going to be, help them set up their phone. And if we don't do a good job at that, what's happening? Are there some steps people are taking after we are putting somebody, a new person on an account, that we need to address operationally? And we get it in bits and pieces. But if we can do predictive analytics on that, we can find patterns that we can take care of in a much more cost-effective way. And in the new accounts stuff, I thought I'd put a couple slides just in detail here. So this is a Sankey Graph. I learned that through the process, the Sankey Graph, kind of nice visual representation of what comes out of this end pathing function and starts to show visually, where do I need to look at where people, if I say a new account opened, what are the steps that people generally take after that? And lo and behold, there's some fat lines up here.  Is it good that they call care? Is it bad? They're excited. They want to go add some more services to their account, or they're calling care because they're confused. And they don't know what's going on. It's a great opportunity for us to winnow into the data and just dig into that line. Ignore other stuff for now. Let's dig into the stuff that's the big line that is meaning that there's multiple customers taking a step. And why in the world is that happening? And it allows us to start to dig into the data. And I've got a couple slides like this. But basically what it allows us to start to do is to start to dig into that information, both the different memo codes that get stamped into that data, plus the manual text information. And we can start to massage that data to make some more sense of it. And I've got kind of a picture after this. So you can start to take those memo path codes, start to narrow in on, hey, when I've got activations, I can create some session actions. And I can start to say, hey, when I end up taking a certain action, and I take these sort of paths through my data, which are these stamps that roll down, how can I get into this manual text and start to do some work to dig into that? Can I go mine it? Can I narrow it down? Because I don't want to mine all this stuff. It's 3 billion rows worth of stuff. But if I can narrow down into this, I can start to see some better patterns. And I might start to see some things that I've got third parties that are selling that are causing problems. Or I've got certain call centers that are causing me challenges. And there really isn't better ways to figure that out sometimes rather than getting into this data. And so the goal is to narrow it down as quickly as we can. And a quick final example here. This, for example, with some logic we're able to build in that said, hey, for new activations that actually turn into a problem, i.e. somebody leaves, they take something off their account, there's an issue, what's the first action that started that process? And I can start to prepare that data and then start to go. And then what are the paths that were taken after that? And I can make some decisions, whether I want to go mine through that text further, whether I want to do some more analysis on that on an aggregate level. But I can really start to shape this data into better ways to do analysis. Or simply, as one of our business partners said, we got to a point she was able to start to look at this by, it was small enough that she could look at it physically over a couple of pages and go, oh my goodness, I'm seeing some crazy stuff in here. So it was an eye-opening thing for our business partners that this is wonderful, because I've never been able to really do this before from a SQL standpoint. And by the way, you've got all the data in here, which is great. I've always worked on little bits of subsets of this stuff. And some boring code here, but it's something that is hard to do if we didn't have this. It allows us to write this and use the end-pathing functions some of the map, reduce it more of a SQL way that our business understands and our IT folks understand in a simpler manner. And we can start to parse that data out in a much more efficient manner than we've been able to do before. And I know it's fascinating, but it was really helpful for us to be able to make it this simple to get through this data. A final picture here real quick, just for curiosity. This was the synchi diagram for our upsell messages. The great messages, people tend to just go top up their data. They don't get offended by our message. They don't call care. If we tell people you're about to go over your data plan, people are usually pretty receptive to that. It was nice to confirm that, not see any issues out of that. But something visual like this was very powerful for our businesses to go, cool, I don't really have to worry about that. That's kind of nice. That's working well and not have to worry about it. And I would say from kind of wrapping up from my standpoint, and Stephen showed a picture of this, and this is kind of a teradata picture. But for us, this is an architecture that we're starting to look for as a company that already has some EDW components in place. We're certainly looking at saying there is a piece of architecture that we need to drive. And we need to have a cost to value conversation with our business around, hey, landing all the data for you to look at is something we know that you want to do. And we can do that. And we're going to give you some tool sets to be able to get to that data, say, in Hadoop, in some of our exploratory environments. But you business are going to have to put a lot of the leg work into this.  But you're kind of on your own to make this work for you and decide, is this data valuable enough to move farther along the chain and make more available to others across the organization? Because when we go to put in our EDW, it's a little more work upfront for us to model it and make it available to other people across the organization. But it can be very well worth it, and it very well makes sense, but I'm going to make you prove it to me business. Because in our old days, we wouldn't have had this discovery kind of platform concept. We would have taken the data, modeled it in the EDW, and they would have gone, that's not what I wanted. The world changed yesterday. This didn't work. So we're saying, hey, this is great. You're stopping here. And either you need new skill stats business, or we can bring people in and third parties to help you with some of the elbow grease, or some of the tools for you advanced SQL folks. You'll be able to use that. As we move along, though, and you want to make this more widely available, we will certainly help you productionize that and codify that. And our holy grail gets to a point where we have this picture, and we take the predictive analytics that I was talking about earlier, and we turn that into actions for the front lines. So if we identify in there these patterns that go, hey, we are doing something majorly wrong when we go and have an IVR conversation and in care, and there's a couple steps, guys, you need to know about, let's get something in the front line so they know that this customer is coming in with an issue, and there's a next best action that you need to take to help save that person. And that's ultimately what we want to head with this from these analytics into our front line impacting situations. This unified data architecture, I think the key point here, which is a theme to the whole conversation this afternoon, is use the right technology for the problem that you're solving. No one technology solves all the problems. So what you have here is Hadoop distributed file system, store all my data forever at the lowest price per terabyte. Very, very interesting. But if you put a data scientist on top of HDFS, they're going to really struggle to get the value out in a productive, cost-effective way. So having this discovery platform, this is kind of like a data R&D platform. There's actually not a lot of data in here. It's like your lab. You bring some data in. You experiment with the data. And if it doesn't have value, it's OK. You throw it away. You do another experiment. All the data lives for here forever. So I've got this storeroom of data. I can go get it. We store it very effectively. I can bring it in here. But I'm not constrained by SQL. I can do map reduce. I can do graph processing. I can text processing, all those kinds of things here. And if I find something really valuable, then I'm going to promote it into the enterprise data warehouse. So the core data that has value, we put it here. We move the data from Hadoop to the discovery platform. We use a no ETL strategy. Just bring the data in pretty raw. And let's work with it with map reduce and other things. And we have some technology built on top of the open source H catalog to allow us to use SQL H to go in and grab that data. I don't have to be, as a data scientist, a sort of flat file program on top of Hadoop. I can just reach in with a SQL type of interface. But I have access to advanced capabilities like map reduce and time series analysis and text analysis and graph analysis. I'm not constrained by the SQL model. Now, when I promote the data, then I'm going to add the structure. I'm going to use ETL here because I'm managing for efficiency and repeatability and auditability and all those kinds of things. So data R&D is very different than data manufacturing. So we use different technologies for those. So with that, we thank you very much for spending the last session of your afternoon before the beer and wine happens. We've got a couple of minutes for questions. If you have them, other than that, thank you very much. So the question is, in the architecture, we are using the statistical modeling. So the way that I think about it is there's kind of the model building, and then there's the model scoring. And those are different things. So the model building, training sets, finding the right data, doing the discovery, that's largely happening here. Now, the scoring, that's actually a kind of interesting question because it depends on your skill sets a little bit. So if I had really good skill sets on HDFS, I might actually decide to score down here. But the commercial products aren't as mature here as they are here. So it depends on your skill sets and the tools that you're using, but the tools are getting more mature down here. And we believe our vision is a lot of the data transformation scoring preparation will happen down here. The reality is there's still a lot of it happening up here because the tools and the skill sets are maturing. So you have to kind of decide what the right combination is. And then you bring the scores here in order to produce the value. And I'll just say from a practical standpoint, for example, with T-Mobile, we still use SAS. So SAS is still a tool set that's there that we will probably still use for some of that. There's production models that kick out of there. And there's a skill set, a wide variety of people who have those skill sets. And we'll still do things like push down into Teradata, put data in there, and run that over full data sets within Teradata. But that is after you've discovered that makes sense. It is a predictive model. And it's one that we want to use on a regular basis for predicting upsell, cross-sell chances. So that still fits in this picture. And by the way, SAS Institute is making huge investment in order to get their stuff to run on HDFS effectively. And until they're there, we can do that. So those are the types of things. And we actually also have folks who will be using R in other sorts of systems as well as in other statistical pattern or tool set that we can feed Hadoop into those kind of discovery platforms as well and use R. So yeah, our goal, it's coming from what we would lucid clumar source systems, which are some internal systems, all of our OLTP systems, our engineering system off of the network, third-party data out of from a third-party standpoint. We had a lot of third-party data. So our goal, one of our challenges is within our company, our source systems have 10 or 15 different feeds that come off them to various groups in the business organization wanting a little bit of information here and a little here. We're saying, no, we're going to send it to one spot and we're going to do, we're going to have some metadata so you know what the world it is. And then if anybody wants to use that for analytics and BI purposes, then we shoot it out from that one spot. So our goal is eventually to have all of the interesting and reasonable data in that one landing zone. Some people call it data lake or common landing zone. That's the goal. Thank you very much. We appreciate it. And if you see any of us in the conference afterwards, just come grab us if you have more questions. Thank you. Thanks a lot.


Stevenson falls 57-55 in the season opener to Catholic Glen Clark with head coach Gary Stewart. Coach I know it's pretty good effort from your team today and you were playing without a couple of important guys. I also know that you probably would have liked to have come away with a victory today. How do you measure the performance as a whole from your team? Well I was really proud of our guys. Obviously they're a really talented group. They've got two Abadabas that are hard to get a tourniquet on and I thought our kids did well to battle up against all the ball screens that they saw and we had different coverages in different zones so to be able to recognize that and we got to be straight line drives in the second half and a little bit of that was fatigue a little bit of communication and a little bit of that was Catholic scheme so I don't fault our guys the effort the concentration was there and then the ball stuck in the second half and so we didn't have great ball movement we didn't move very well without the ball and we're 18 practices in so into a new system trying to integrate everybody so we knew it was gonna be a challenge and then you you throw in having KJ and and McCall out of there you know that that made things even more difficult for us. Nate Arendt in the second half you got a big body in there and he really went to work offensively and gave you guys a huge boost. Yeah he finds a way to score he's a little slippery inside and a little bit unorthodox and finish as well it came down to us in Catholic for him so he was a highly recruited kid and and at the end it was it was us too and so we're we're glad we got him. He's an impressive player coach just being here and having the crowd out here today and people into it a lot of people coming over after the football game after everything you guys have been through to be playing basketball in November in front of fans what did it mean to your entire team today? Oh it's so important you know it's it's trying to get back to normality and you know it was so good to just yell at the officials and hear the crowd and be in this environment again and play a meaningful basketball game and onward and upward you know I'm really excited about the team but I'm also excited about where our COVID numbers are trending and so the combination of those two things bodes well for for not only our basketball program but the community and then the country at large. We got a big weekend coming up next weekend too it's exciting to have these types of events back here at Stevenson University I know how much it means to your program. Yeah thanks Glenn for that we we are excited to get back to practice and obviously we've got two heading into next weekend and that'll be very important for us so we can learn some things from today and and hopefully apply those lessons and play a little bit better next weekend. Coach we'll see you then appreciate it thanks for taking the time for us as always. Thank you Glenn. Coach Gary Stewart I'm Glenn Clark go Mustang Sports.com


So my question to you is how can you ethically justify allowing capitalistic healthcare when the system inherently dictates that people with less money are somehow less deserving of health than those with more?   You can buy better stuff, buy safer stuff, and have a higher quality, including in the healthcare. So I'm not I'm not for equality of outcome, not equality of outcome in healthcare, not equality of outcome in what kind of food you eat or what equality of outcome in anything. You get to produce what you own. Now I was a student here at UT and my wife got pregnant unexpectedly and we had student insurance that didn't cover pregnancy.


Welcome back to the session on latex. In this session, we will learn to format the text in a document by using different font styles and increasing and decreasing the size of the text. Let us switch to text studio. I have some text already written here in the article document class. Well, now let us make some text bold, we will make what you see is what you get editors bold. So, we write slash text b f open curly brace, we come here and we close our curly brace. Now, let us emphasize on text content is written in plain text. So, we write slash e m p h open curly brace, we come here and we close our curly brace and let us underline the word concentrate. So, we write slash underline open curly brace, concentrate and close curly brace. Let us build and run as you can see what you see is what you get editors is highlighted in bold. The content is written in plain text is emphasized and the word concentrate is underlined. Now, let us switch back to our slides. Here is a list of some text formatting commands which you can use to format the text. We have seen text behave, emphasize and underline. Well, feel free to practice these which are given here. So, now that we have learnt to format some text, let us explore how we can increase or decrease the size of the text. These are some commands which do the needful, let us explore them. So, let us say we want to make this word tech large. So, we write slash large tech and we build and run it. The word tech has indeed become larger, but the text following it has also become large. I know that you will say that we did not put a curly brace, well let us do it. So, we write slash large open curly brace tech and close curly brace. We build and run well no difference. There is a slight variation in the way we write curly brace for defining the font size. The difference is that we write the curly brace before we define the large command. We build and run, only the word tech has become large whereas, others are in normal size. This means that while defining or changing the font size, one must first open the curly brace, write the later command and close the curly brace after the text we want to format. Let us look at another example. Let us make slash footnote size, material with less efforts, open curly brace and close curly brace. We build and run, there you go. So, we have seen bold, emphasize, underline, tech with a larger font size and material with less efforts with the font size as footnote size. Now, let us assume that you have lot of text and keeping track of a curly brace of a tech size is a bit difficult. So, we write the command without the curly brace. Let us write some random text slash lipsem 1 dash 2 slash lipsem 3 dash 4. We include our package slash use package lipsem, we build and run, we do have some random text over here. Let us resize it. Now, let us write tiny font size ti ny and we build and run without the curly brace. As you can see, all the paragraphs have become tiny. What if we want this paragraph to be in the normal size? It would be great to revert back to the default size and the command is slash normal size. We build and run, there you go. So, these two paragraphs are in tiny size whereas, the other two paragraphs are in the normal size. Let us switch back to our slides. You can try these commands as well. So, now you can change the font size and use different font styles in the document. Thank you. Have a nice day.


As you, Pereira, thank you for your time. We might begin by having you tell us a little bit about your background. Well, my whole life has been 24 years of struggle for our national liberation. When Indonesia invaded East Timor, I was a student in Portugal. And subsequent to that, we all made a decision to pursue the freedom of our country first and the degree later. And we never realised it would last 24 years. We thought at that time, 18 years of age, I thought it was just a couple of months. But it went on and on for a quarter of a century. And we lost our youth in the struggle for freedom. And thanks God we achieved that. Has it got any easier? Has it got any better? Well, the achievement of independence is almost a miracle for us because we had the conviction that justice had to prevail. But we always knew that it's a small, tiny island between two giants, Indonesia and Australia, and both agree with our integration. So we really had a high mountain to climb. But achieving the summit was close to a miracle. One of the powerful things that you bring to this conference is that you sit on the other side of the fence from the military and the police, from the policy makers. What do you think are the powerful messages that need to be imparted in a forum like this? Well, first it's good because we are speaking about success. So it's not failure. And building peace in East Timor so far, eight years of age, a nation state, two years transition from the United Nations in 2002, but it has been successful. And therefore it's very important to reflect on the reasons for the success so we can sustain the future without failures. And being here and reflecting from the perspective also of the Australian experience, it's actually a double value in a way because Australian intervention since 1999 has been crucial in terms of stabilization of the country, also overall international community's intervention. And second point is that after the quarter of the century of war, our people really reached the war fatigue mode. We don't want conflicts in war anymore. And that's the underlying factor of success that our people want peace. But building peace is not a static thing. It's a progressive and aggressive process because people want peace but also wants prosperity, wants national development, wants equity, wants social justice, wants jobs. So it's a piece that has to be conceived as a very dynamic and aggressive process. You need to really deal with the consequences of policies, consequences of development. You have what some call development syndrome that the more the country grows, the more demand you create. And therefore that's really a very serious responsibility for the government and I'm in government so it's part of my responsibility. Do you think in the case of East Timor there was enough attention given to the people on the ground? Well it has been, since this government took over, this is the fourth constitutional government in eight years. Dialogue has been a prerogative. Our Prime Minister has just completed a tour to the whole country, to all the sub districts of the country. It took him months and months meeting with the people on the ground, sitting on the ground with the people, listening to them to produce 2011 budget which we have submitted to the parliament. And therefore consultation is really of paramount importance because pre-empting conflict is about inclusionary policies, is about bringing people truly into the process of development, not just ideological approach to people but it's really listening to the people, understanding the expectations and managing it. And in that sense in the last three years have been very successful. It appears then that managing expectations is such an enormous challenge, would you agree? Yes, first of all, our situation is very unique. When I think about South Africa for instance, when we started our national reconciliation process, South Africa, the former enemy still is within. East Timor, the enemy left the country. So we have the hot potato if you like. In our hands we need to manage and flourish it. And therefore managing the expectations of the people is also understanding how much our people have given so far to the country, to free the country. And being a country that is majority living in poverty, less than one dollar a day, in spite of this poverty, our people gave the best they have to free the country. And therefore we need to nurture this as an asset. And we build into our politics, national politics this in-depth ingredient of honesty with our people. Don't promise things that you cannot deliver. And never promise things that our people don't want it for now. But listen to them what they need right now and invest as much energy and resources into it to make sure that our people feel that the core of the centre of sacrifice has been worthwhile. Well we have a forum here now where you have the decision makers, the policy makers. What is your advice to them? Is there a pearl of wisdom from someone like yourself? Well I think it's taken people seriously because when you make policies you affect the livelihood of people. And you have to really be conscious that unless you affect it positively you can actually deliver conflict. And that's very important. In a state like ours, eight years old, very embryonic, that law and order, the force of coercion and suppression of people's divergence from the government policy are not there. The richness of the country is dialogue, is communication, is interaction. It's politicians and the community merging together so that it constantly provides the necessary legitimacy to the government, to the leaders, to make decisions on their behalf that are not necessarily legally enshrined but politically legitimate because people see that the decisions have really been made on our behalf because it really affects our life positively. What would you like East Timor to have achieved in three years time? In the next three years we want to make sure that the phasing out of United Nations is as successful as possible. As the SRG spoke eloquently this morning and I share her optimism, I think we will manage that very successfully. We'll be an example through the region about building democracy. But a democracy that is not belligerent, not adversarial, but is inclusionary. It's one that looks at people's and political parties as a very important asset of democracy, not as instruments to control power. Ajú Pereira, thank you so much for your time.