CHAPTER XXIX The savage sells or exchanges his daughter, but in civilization the man gives his away and is thankful for the opportunity. Reflections of a Bachelor Girl Dawn took a great deal of her own way. Ernest and I were privileged to make suggestions so long as we were careful to remember our insignificance, and Grandma saw to it that her lawful rights were not altogether usurped. Occasionally it fell to my lot to act in a slightly mediatorial capacity, owing to the divergence of the swell wishes of the bridegroom elect and the plebeian determination of his grandmother in law to be, regarding the wedding celebrations, but Ernest was exceptionally unselfish and therefore very long-suffering. Dawn being underage, her grandmother came forward with a project that her father should be apprised of what was transpiring, requested to give his daughter away and to bring some of his side of the house to the wedding. Dawn raised vigorous opposition. It would be like my father's presumption to interfere in any way, considering his career with my mother.  He's the very style of man I'd be ashamed to acknowledge as an acquaintance, yet alone own as a father. I'd like to see him dare to give me away. He'd have to own me first. Well, Jake there will have to give you away then, said Grandma. I'd give him away with pleasure, replied Dawn, if I must be given away like a slave or animal. You'll give me away, Grandma, or I'll stay where I am. Who giveth this woman to be married to this man, the old person will ask? Why won't he also ask, who giveth this man, and if he, too, were only a chattel belonging to someone, that she would be disposed of by no one but her grandmother, rather pleased the old lady than otherwise, so she invested in yet another black silk gown, over which she was to wear a seldom-seen cape of point lace worked by Dawn's mother. And she also purchased a wonderful bonnet, and armed herself with a new pair of lastings. Thus, Dawn was to have her way in this particular, but the old dame adhered to her original intention in the matter of the Moodaheeps. I've kept him at bay long enough now, I'll just acknowledge him this once, or it will seem as if you was a illegitimate, said she in the plenitude of her worldly wisdom, and thereupon writ a stiff, though not discourteous letter to Dawn's father, inviting any number of the bride's relatives up to six to come and spend a week before the wedding in her home for the purpose of making Dawn's acquaintance. There, I have done me duty, and they can suit themselves whether they come or go to Halifax, she remarked as she dispatched the communication. They came. Dawn's father, his second wife, and his youngest sister, Miss Moodaheep, arrived three days before the wedding and remained to grace the ceremony. When being a mere girl, perhaps it was Ernest's wealth and position induced them to meet Mrs. Martha Clay's overture, for they were thorough snobs, but if they had come prepared to patronize, their intention was killed ere it bore fruit. The hostess hired the town bus to convey them from the station and dispatched Andrew, with many injunctions to conduct his self with reason, to meet them there, while she and Dawn waited to receive them in one of the old porches. It was a bower of roses and potted plants, and further shaded by a graceful pepper tree, and made a beautiful frame for the grandmother and the maiden. The old dame so straight and vigorous, the girl as rosy it and fresh as her name. But each equally haughty and bent upon, maintaining their iron independence of the people who had discarded the girl and her mother ere the former had been born. Personal appearance was much in their favor, and no practiced belle of thirty could have held her own better than the inexperienced girl of nineteen, whose native writ and downright honesty of purpose were more than equal to all the diplomacy of thrust and parry to be gained by living in society. Her stepmother, who was apparently as good-natured as she seemed brainless, was prepared to be gushing, but that was nipped in the bud by the way Dawn extended her pretty firm hand with the dimpling wrist and knuckles and exquisitely tapering fingers. Her father and aunt, who were tall and angular with thin faces of dull expression, met a similar reception, and she presented them to me herself, explaining that I was a very dear friend with her for the wedding. I had long since risen from a border to be a guest and friend of the house, and it had devolved upon me to exhibit the presence and interview the endless collars at this time of nine days wonder. It being hot the ladies retired to doff their hats, air partaking of afternoon tea, and Dawn took her father's hat while he trumpeted in his handkerchief and attempted a few commonplace platitudes from the biggest and stiffest armchair in the parlor into which he had subsided. I left the room, but could hear him from where I stood awaiting the lady's reappearance, one from the room that had been misflips and the other from the one I had at first occupied, and Mr. George Muteheap was to occupy the third one of these apartments, which had been empty since the tragedy.  I have never seen you, and now you are sufficiently grown to be married. Yes, said the girl, will you give me a kiss? I'd rather not. You see, you are only a stranger to me. I have never heard of you only as the man who was a monster to my mother. I never saw her, but I remember to love her for what she did for me, whereas you, what do you do for her and me? I would like you to understand how I feel on the subject so that there can be no mistake, said the girl honestly. Oh, well, I didn't come here to be told that, but to give consent to your marriage. Oh, said the girl, rearing the pretty head with its wealth of bright hair. As for that, I'm going to marry. If you like to exercise your authority, I'll run away, and you can't unmarry me. It is at Grandma's wish you are here. She said to let old bitterness sleep for the time you are here, and so I will now that I have explained that I utterly refuse to recognize that a father is anything but a stranger unless he discharges the responsibilities of the office. For the sake of the race, I maintain this ground. She concluded in words that had been put into her mouth by one of the speakers at Ada Grosvenor's election league, and the appearance of the ladies put an end to further contention. Dawn's judgments were remorseless, as becoming clean-soled, fearless youth has yet unacquainted with the great gulf-twix to the ideal and real, and untainted by that charity and complacence which, like senility, come with advancing years. The aunt was elderly and unprepossessing, and the stepmother of the type bespeaking champagne and too much eating for the exercise taken. For her head was partly sunk in a huge mass of adipose substance that had once been bosom. And the other proportions of her figure were in keeping. The cups were spread in the dining room. So thither we repaired to eat and drink, while representations of Jim Clay and Jake Sorrell Sr., who had wept for the sufferings of the convicts, glowered down upon the gathering of plebeians who were half-swells and the swells who were wholly plebeian. Presently, Grandma and I excused ourselves and left Dawn with her relations. What do you think of them? Are they any better than Dawn and me? Said the old dame as we got out of hearing. How do I compare with that old sack of charcoal? Aye, how did she compare? As a slight, active, handsome woman still vigorous at seventy-six, with one who, though thirty years her junior was already almost helpless from obesity and natural clumsiness, that's how she compared. Them some of the swells for you, one of the old families who think they're made of different stuff to you and me. What do you think of Dawn, Jim Clay's granddaughter, who drove the coach, when placed beside her aunt, the granddaughter of an admiral in the army? She looks as though Jim Clay had been a general in the navy and she had done justice to her heredity, I gravely replied. Andrew, come here and tell me how you managed him, and what you think of the great bugs now you've seen him. Commanded the old lady of that individual as he emerged from the kitchen with both hands full of cake, did you walk up to him and say, Are you Mr. and Mrs. Moodheep? I'm Mrs. Clay's grandson, like I told you. No, I seen it in their luggage without asking him. And one look at him was enough for me. I didn't bother telling him who I was. I didn't care if they fell down and broke their necks, the bloom and long nose old goats. I just took hold of their things and flung them up in the bus, and the old fat one, she says, Are you Mrs. Clay's groom? And I says, Mrs. Clay is my grandma. And she says, Oh! Well you might have introduced yourself a bit better to make things more agreeable, but they really are the untakenest people I've seen for a long time. Ain't I delighted that Dawn took after my side, and now though she's my own, do you think I'm overconceited to think her fit for the king's son? Certainly not, I replied. Or it would have taken a very estimable son of a king to be meat for this princess of the break of day, appropriately christened Dawn. End of Chapter 29. Recording by Brian Berkmeyer, Ann Arbor. Find more at soundcloud slash mgo birch. Chapter 30. Of some everyday folk and dawn. This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org. Recording by Beth Thomas, Melbourne, Australia. Some everyday folk and dawn by Miles Franklin. Chapter 30. For further particulars, consult the Noonoon Advertiser of that date. That was a grand wedding celebrated in Noonoon ere the orange blossoms had turned into oranges. But for details it would be better to refer to that most reliable little journal, The Noonoon Advertiser. Only a few particulars remain in my mind, but the paper published a full account, including a minute description of the bride's gown and a careful list of the presents. It was much to the horror of Ernest that the latter was inserted, but it would have been much more horrible to Grandma Clay had the mention of so much as a jamspoon been emitted. So he consult himself with the reflection that it was only in the Noonoon Advertiser, and took care to keep the list out of the account which appeared in the Sydney dailies. The curious, by consulting a back number of the little country sheet, they learned that Mrs. L. Whitcombe, nay Carrie, the ex-lady help, gave the bride one of many pairs of shadow work pillow shams, and that Miss Grovner contributed one of the equally numerous drawn-thread table centres. Mrs. Bray presented a ribbonwork cushion, Dr. Smalley, some of the jamspoons, Andrew, a breadfork, and Mr. J. Sorrell, great-uncle of the bride, a silver cream jug, or Mr. Claude, alias Dora, U-word, kept himself in mind by an afternoon tea-set. The complete list took a column, and included dozens of magnificent articles from sporting associations and chums of the bridegroom. The bride, a glorious vision in duchess, satin, and accessories in keeping, and with real orange blossoms in hair, corsage, and train, the proud shyness of the gentle and stalwart groom standing beside her, and the brave old grandmother, drawn up a little in the rear, from the picture I shall never forget. The old lady performed her office with flashing eyes, a steady voice, and an individuality which none could despise or overlook. Accepting her grandmother, Dawn was unattended, and as the young couple came down the aisle, by previous request of the bride, I had the honour of accompanying the old lady from the church, and she said, as we drove away over the scattered rose petals to be in readiness to receive the guests, I have done it. Give me little girl away, and without misgivings, for if she's as happy as I was, she'll do. When the time was here, there was some patches of me life wasn't too soft, but looking back, I would marry Jim Clay over again if I could. The caterpillars that had been eating the grapevines, and giving Andrew exercise as destroyer, had turned into millions of white butterflies that flexed the gold sunlight like a vast flotilla of miniature aerial yachts, and enhanced the splendour of that balmy wording day. It was the month of roses, and intertwined with jasmine and mignonette, they formed the chief decorations in the roomy marquee erected for the breakfast under the big old cedars overlooking the river. All Nunu nights of any importance sat down to the repast, and their names, from that of Mrs Bray to Mrs Dr Tinker, are recorded in the Nunu advertiser. The last mentioned lady did not exhibit any of her famous characteristics at the function, further than to use a gorgeous fan she carried in wrapping her husband over the knuckles every time his attention wandered from her remarks. The toasts were many and long, and it fell to Dora Eewood to respond to that of the ladies. Since the announcement of Dawn's engagement to Ernest, Dora had frequently been seen out driving with Ada Grovner, and he paid her marked attention at the wedding, but this was private, not public information. After I had helped Dawn into her travelling dress, I had a few words apart with Ernest, while Grandma Clay paid a private goodbye to his wife. Well, he said with self-contained and pardonable triumph, I have won her in spite of that dish of water. Yes, we three have accomplished our desire. What three? Mr and Mrs Ari Breslauer and myself. Oh, was it your desire too? he said with a happy laugh. The bride now appeared, and ringing my hand she said, You'll come to us when we return. He stepped forward to place her in the carriage that took them to the railway. The paper had better be again consulted for an accurate account of the confetti pelting and other customary happenings that took place at the station. These details and the real greatness of Dawn's match, and her aristocratic relatives, who, as often suspected, had not proved to be only a myth, were the chief theme of the conversation for many days. All the engines in the sheds at the time and whose music had lulled me to sleeper nights, blew the bride a royal fanfare as she entered the first, engaged, and further cockadoodle-dood good luck as the train steamed out. Most keenly of all I remember that it was piteously lonely, and as dreary as though the sun had lost its power, when the panting engine had climbed the hill from the sleepy little town, and dropped out of hearing on the downgrade from the old valley of ripening peach and apricot, bearing the girl forever away from the slow meandering grooves of life of which her vigorous young soul was weary. A meeting of the municipal council claimed Uncle Jack that night. Andrew went over to discuss the situation with Jack Bray, and the loneliness of the old dining room was insupportable to Grandma and me. Joy and beauty seemed to have fled from the scented nights beside the river. Even the whistle and rush of the trains breathed a full-on note to my bereaved fancy, and there was a tear in Grandma's eye as she said, Well, she's really gone for all together. She that I helped into the world, and reared with my own hand, and named after the dawn in which she came. That's the order of life. It's always the same. You can't keep anyone for always. I couldn't bear it here now. It seems if everything in life was done, and there's no need for me to stay, if Ernest puts Andrew in the way of this electrical engineering he's so mad for. Jake can board somewhere. He don't care about things so much. I'll go to dawn. Thank God she wants me, and I've got plenty to take me away if she gets tired of me, as young folks often do of the old, which is only natural after all. I can sell or let the place, and when I'm gone it'll be enough for Dawn if ever she's through on the world like I was. Everything seems fair with her now, but this is a life of ups and downs, and there's no telling what may happen. LONVOY What interest can there be in a play after the night has settled affairs with the lady, or in the storybook when the heroine and hero have gone on a honeymoon, preparatory to living happily ever after? And that is what befell my tale in Noonoon. I listen no more to the splendid music of the locomotives, as they roar across the queer old bridge, nor watch the red light flashing from their coaling doors as they climb the blue mountain ascent and fire as they go. Their far-carrying rumble has been succeeded by the more thunderous voice of the sea on the rock-walled coast of my native land. Four months have elapsed since the wedding in Noonoon, yet Ernest is still content to let his athletic ambitions remain in abeyance while he squanders his time in the sweet dalliance of love. Squander, I say, but on reviewing the expired years, how sanely sweet the youthful hours we dallyed shine from amid the years we toiled, fumed, cursed, sweated, and strove to step past our brother in the bootless race for pleasure, opulence, or popularity. Being able to indulge in the insignia of wealth, even without being the good fellow he is, Ernest finds it of little significance that his hair is what fond mother's term, Orban, while dawn's triumphs were assured from the outset. As mistress of a fine town mansion, with good looks, with smart ideas of dress, and smarter ability to verbally hold her own in any set, it goes without saying that her grandmother having kept accommodation is not remembered against her to any harmful extent in everyday life, where a large percentage of folks in all cliques have to survive the knowledge of their progenitors having been worse things than irreproachable proprietors and conductors of most exemplary accommodation houses for those who travel. As Ada Grovner is not a girl in a book but in everyday life, I cannot record that she has married a man worthy of her. Such a one would have to be a leader of men, a prime minister, reformer, or other prominent worker in the cause of humanity. And as these do not abound in the quiet whirlpools of existence, I can only hope that she does not drop in for a too impossible noodle, as is frequently the fate of noble women. Dora Eeward would have done very well to discharge the clot-hopping work of her earthly journey, could have made her bread and butter and carried her parcels. But if I can depend on Andrew's letters, which breathe more heavily of generosity than of grammar and gracefulness, this eligible and strapping young member of Nounoun's society has been rejected a second time, so that Mrs. Bray's fears that he would be made over conceited by adulation from marriageable girls seems to have been unnecessary. Nounoun is enshrined in my heart as one of the pleasantest valleys on earth, so during enforcedly idle hours it has given me delight to paint its beauty, however feebly, and to put some of the doings of some of its folk in a story that others might possibly enjoy them too. But I put the manuscript aside till, as the good country doctor so much esteemed in his circle expresses it, I shall have pegged out, and the heroine and hero of the plot shall then judge whether it is fit or not for publication. It has interested me to write, but my life has crept so long on a broken wing that I come to be grateful at last for a little thing, and those whose lives are strong, fruitful and successful, may have no patience with the sentimental meanderings of an old woman who has outlived joy and usefulness. And now, may the lady of my tale, as her life progresses from dawn to noon, high noon to afternoon, dusk, evening and night, have the night of her choice and peace always beside her till new dawns break in other worlds beyond this place of fears and phantoms. The End. End of Some Everyday Folk and Dawn by Miles Franklin


The talks about what is data right and I'm going to get into why why even don't this exercise but in the interest of time why don't some of you you know we spend the whole day talking about data. What is data? Data is information. Data is information okay. Who else? Data is the raw format of the information. The raw format of the information. Anything else? Any other thoughts? Plural or panic tot? Sorry? Plural or panic tot? Plural or? Anic tot. Plural or panic tot? Plural or panic tot? Anic tot. Plural or panic tot? Okay. I haven't read that one anywhere. Any other thoughts? I want to quickly kind of pen right down. So one was the information right. I just tried info. The other one was you can get it on this thing. How do I do that? Pull it down and look. This is the automated one. This is probably like a remote or something. So info, right that's one. Plural or panic tot? I would just pick panic tot. Or panic tot. No there's a point in there. Okay. Who else? You can take black color marker. There you go. So we have information. We have plural or panic totes. Somebody else please come on we talk the whole day about data. It's quantitative not qualitative. It's quantitative not qualitative okay. It's a quantitative point. Point not qualitative okay. What else? Relevant information except in numbers. Info in numbers. Relevant info in numbers. In numbers okay. Anybody else? This is just set of everything. Set of anything is data okay. That's what we're going to do. Alright so I'm a programmer okay. And I spent the last 10 years or so doing programming. And actually 10 years or so doing serious programming. And before that like fun stuff right. So as a programmer very early on you get used to this idea of abstraction right. What we'll do believe me this is exactly what every programmer does right. We'll go right 10 lines of code. Then we'll say this is a duck right. We'll say this is a duck. Then we believe in it. We'll be like so tied to the idea of like this is a duck. It quacks like a duck. So it must be a duck right. Like example for like I mean to take a non programmer parallel and write quack on a paper. And I say hey quacks like a duck it's a duck right. I will send it to my customer. I'm going to tell you. I'm going to give you.  It has a really nice duck. Go play with it. What's going to happen though. And this is also another lesson you learn as a programmer very quickly. Is that the kid's going to do something you didn't expect. He's going to say you know what ducks can swim. He's going to take that cheered paper and put it in water. It won't swim.  So that's one scenario. Another scenario could be. So that's the scenario where your customer had a problem. Another scenario could be that somebody else who's building around your duck right. Somebody decides to build a zoo. And they build I don't know. Somebody decides to build a jungle. And then they build a lion. And the lion comes to the duck. Lion comes to eat a duck. And he says this is a paper duck. This doesn't work for me right. So this some other guy complains that you said this was a duck. But this isn't a duck. But I'm so passionately sold to the belief that this is a duck. Because I built a duck. I built a duck. Come on. How can I have a duck? Right. So when you are on as programmers, you learn this idea of abstraction. And you get tired of it. But slowly you realize that abstractions leak. Right. And there's a very nice article by those folks.  But abstractions. So you might simplify an idea and say hey anything that can coact is a duck. But that's not entirely your situation. And in some cases that might work. But in other cases it will fail. Right. Abstractions leak. Now all programmers have realized this idea. They realize that you know stuff must be defined properly. Why? Because we take a small abstraction and we build bigger abstractions around it. And all of software is fiction frankly. All of it is just signals of 1s and 0s. So all of it is fiction. And just because I believe that the stuff underneath means a database and I build stuff on it. Doesn't mean it really is a database. Right. So abstractions allow us to think like this. So we become, our mind gets set to it. But we also realize that abstractions break. And that's why as programmers we realize that defining stuff is very very important. So much so that if you go to standard bodies like the W3C. Or you know the Apache foundation which is doing like open source stuff. You'll notice people having pages and pages and pages of discussion around the definition of a small idea. Why? Because those small ideas or the small definitions are used to build bigger things. And it's easier to think at a large scale than you have good abstractions. But if your basic abstractions are wrong, you have a problem thinking at a large scale. Because things have to break. Right. So the word data. Data. We've built so many abstractions around data. Right. We've got data analysis. We've got data mining. We've got databases. We've got data quality. Metadata, master data. Information. So the terms before this was stuff that BI people and data analysis people deal with. But information is something everybody deals with. Can somebody here define information? Meaningful data. Meaningful data. Cool. Cool. So philosophers have spent a lot of time on this idea of information and knowledge. Even people as far back as Plato and Socrates have spent a lot of good time thinking about what is knowledge and what is information and how do we learn and stuff like that. So I mentioned knowledge. Knowledge is another abstraction. It's based on data. Kind of sounds the same. Then you have things like facts. That's also true data. Maybe something like that. I don't know. There's truth. What is truth? So philosophers have also spent a lot of time thinking about this stuff. Wisdom. And believe me I'm not kidding you. I have read at least 10 papers in the last two days since I proposed the talk. Which correlate data to wisdom. Wisdom is the stuff Zen masters think about.  If so much of our world is built around fundamental concepts in philosophy, fundamental concepts in the view of science, fundamental concepts. In this new science we invented information science. But actually these days trying to invent a brand new science or data science. I'm not sure what that means. So what is data? Sorry there is a decrease in data science. There is a decrease in data science already. I will debate that degree already. Anyways, so what is data? And I was planning to ask that question at this point. But some of you have already done that. So we said, first one was information is data. So what's the difference between the two? What is information and what is data? Are they the same thing? She said meaningful data is information. Fair enough. I will not go on that one. Quantitative and qualitative information is data. Cool. Information in numbers is data. Something like that. We've got a bunch of definitions. And in this room we have so many definitions. I'm sure there are others who have thought of other stuff. And I'm not said it in the morning. Then I'll say something about variables. That's also one of the theories. Turns out, because I'm so passionate about this idea of having strong basic abstractions, four or five years ago when I started working with data analysis problems, I became a little worried of what the hell is this thing. It didn't make a lot of sense to me because if you go to a dictionary, it's the first place you go to find a meaning of a word. You go to a dictionary and if you go to different dictionaries, they will define data in terms of information. They define information in terms of data. They define knowledge in terms of data. They define data in terms of knowledge. So it's basically cross-link. What is down to it if you really do an analysis of what means what, you get nothing out of dictionaries. But you get one basic idea of what is data. You may not talk about that side. So that's one specification. Data as the given. So the Latin root of the word data is there, which means to give. And hence the past participle data or datum means something that is given, one item that is given. The plural of that is data, which is several things that are given. Now what does it mean in this, what does given mean in this sense? Given it's the starting point or something, I'm tempted to think of math problems. Math problems are things like given A is 2, B is 5, what is A multiplied by B? So given in this sense, something that you start out with before you go anything. It's the starting point. If you're a programmer, it's the input to your program essentially. This example, or you could have a slightly better example, given the length of a rectangle is 2 feet and its breadth is 5 feet, what is A? So my question is, did we add more data? What did we do? What is the difference between the statement given A is 2, B is 5, what is A multiplied by 5? Given the length of a rectangle is 2 feet and its breadth is 5 feet, what is A? What is the difference between the two statements? It's the context, very important thing, right? So as we mentioned, let's remember that context point. So as we mentioned that if you do some initial looking into things, you find that data information and knowledge seem to be related. We're not sure which is which. We don't know whether data is made up of knowledge or knowledge is made up of data because it's just a little weird, at least in dictionaries. But it seems that these three things are linked, really. Now, if you start digging deep into what these three things are, then you come up with two main, actually three main subjects, which have looked at these three words. The first one, which is the oldest of them all, is the study of knowledge, it's called epistemology. It's a sub-branch of philosophy. And that's where scholars have spent a lot of time trying to define what is knowledge, right? And this stuff predates computers because people have been thinking about what is knowledge for a very, very long time. And in that paradigm, there are several explanations and I'm not going to attempt and define all of them. There is one that makes a lot of sense and that definition is knowledge is justified through belief. So I found this, I saw that there seems to be a lot of consensus on knowledge is justified through belief, right? What does that mean? So some brief look into epistemology research looks like, something, if you want to say p is knowledge, then p must be true. You must believe that p is true and you must have a justification of why p is true. If you can do these three things, then you have knowledge that p is true, right? So this is how they try to define it. And for now what we'll do is, since we're trying to define data, we just get this context and forget about the term knowledge, right? Let's look at data and information because they seem to overlap way too much. So I start talking about subjects, right? So I'll go back to my slide, actually. So knowledge is defined mostly in epistemology. There's this other subject called information science, which is fairly recent.  And there they try to define all these three things. Most of the research on the topic seems to deal with all, but their definitions of knowledge in my opinion are incredibly narrow. They're force-fitted into the idea that everything's knowledge is all about computers, right? It isn't about information systems. Knowledge is an experience. It's something that happens in our mind. It's very hard to define. It's when we know something, we have knowledge, right? And how do you define this knowing? It's a pretty hard thing to deal with. So I don't think that the information science definitions of knowledge do justice to knowledge, but the philosophy definitions are very different. So what we'll do is we'll look at data and information in the information science papers, right? And we forget about knowledge. So I start digging into several papers. One of the first basic definitions that I came up with was data has no meaning or value because it is without context and interpretation, right? So this is trying to say that data is raw, right? It's raw in your context. Whatever your setup is, data there has no context and hence you don't have any way of understanding that data because you don't have any way of understanding it. It means nothing, right? And because it means nothing, it has no value. But you know what? As somebody who's trying to make a career out of data, well, data has no value, are you kidding me? So another definition. So the first one was from Jennifer Rowley in this book called The Wisdom Hierarchy Representation of Data, Information, Knowledge, and Wisdom. The second one is also... Actually, it's from a different paper. This one says data are discrete objective facts or observations which are unorganized and unprocessed and do not convey any specific meaning. All right, so there's some more things added in there. So he's saying that one, they are discrete. They're not continuous things, right? There's one piece of data, there's another piece of data, right? There's a third piece of data, they're discrete things. Why? Because data are datums, right? So they must be discrete rules or something. Objective facts or observations, or objective facts or observations. So discrete observations which are unorganized and unprocessed and do not have any meaning. This is a little more information, it says it's unprocessed. It's the given in whatever you're trying to do because you've not done any processing on it yet. This makes sense because it goes in line with the original root of the word which is data is the given value, right? So unorganized and unprocessed conveys no meaning. This is the whole map. So the same paper when they try to define information, they say information is data that has, the data that have been given meaning by way of context. Now which is what I assume is absolutely right, data is information in a certain context. Sorry, information is data given data plus context, right? So if you have data and you have the context to understand the data, then you have information, right? You have something that makes sense, right? And you have to think of this context very, very deeply. What is this context? This context is several things, right? Just let's take this English sentence for a minute. This is conveying some information. How are you able to understand? Okay, like you said five minutes. Okay, how are you able to understand this sentence? First, how come I have written something, some white stuff on the board and you can understand it? What is the context? How can you understand it? Data plus metadata equals information. Data plus metadata, okay, find data plus context. Same thing, I think context and metadata are similar things. But what is the metadata thing? Because we have the open data thing. The context is that you know the English alphabet. You are the context, right? You know you have in the past learned a different piece of information, which is the English alphabet. And that's why you can understand what is written on this board. So the context is deeper than what you might think of. Context is about who you are, what you've learned in the past, and what you have now, right? It's also about how I write it and stuff like that, who I am, who is sending the information, how is he sending it. So context is a really broad concept. But once you have data plus context, you have information. This slightly broader definition says, information is data that have been organized so that they have meaning and value to the recipient. So information, the root of the word is inform, right? It's about informing somebody. It's about communication. So if it is about communication, the other person, so I might be sending this text. You may or may not receive it. If you do not know English, you will not receive it. So if you don't have the right context, this information is just data. It means nothing to you, right? So we look back at our example. Given A is 2 and B is 5, what is A multiplied by 5? This was our first example. The second example was given the length of a rectangle is 2 feet and its breadth is 5 feet. What is the area? This thing, what he said was we added more context. We added more information. We did not change the data. I'm rushing through it because I want to get to more and more. So far, we looked at one definition of data, which comes from the root of the word itself, which is data is something that is pre-given to you. Another definition that is explored in a lot of information science papers is data as a signal. This stuff is really very, very interesting. Why? Because it helps us explain all the data analysis abstractions we do. So data as a signal. Here I'll try to define it. I'm not going to bore who these people are. So data are the sensory stimuli which we perceive through our senses. So data, very simply you can think of it as data signals coming through your senses. You can hear data, you can see data.   But basically, data is stuff coming in as signal. Information is the meaning of that signal. But to have, to understand the meaning, you must have the right context. So it's the same definition, slightly twisted. But it says that data is constantly coming at you. I heard something flap here, that is data. It just doesn't mean anything to me. That is why it's not something that I care about as information. But it is data. Somebody's moving around. It is data. I just don't care about it. I just don't try to comprehend it. So this is an example for one of the papers. The noises that are here are data. The meaning of these noises. Example, a car, a running car engine is information. So if you have the context of having heard what is the sound that a car makes when it's running, then you can interpret the noises to be the sound of a car. I don't like this definition. But I also at the same time like it. Because it solves all the problems we face in the world of dealing data inside computer systems. And he's not talking about computer systems at all. But there are three important things to look at. Data is one of more kinds of energy waves of particles. So data is a signal for everything else. Actually think of it as light. So data is light. If data is light, it says data is light selected by a conscious organism or intelligent agent on the basis of a pre-existing frame of inferential mechanism in the organism or agent. So basically what he's saying is data is light. The signal you're dealing with is light. So data is light. And I am the organism seeing the light. Then I have some context. I have the context that something that looks green is green. I have that context. I look at an apple because that light reflects at me. I see that this is an apple because I've seen the apple before. So I'm the person who has the context in my head. So I'm the intelligent agent in this case. And I have an existing frame of my state or existing context. So basically there's a signal coming at me and I'm the context. I decide given my context what to keep, what to throw it. Because there's always signal coming at us. And this is true in computer systems alone. There are computer systems that are perceiving data from sensors, computer systems perceiving data from several different things. Now, what is a database? And this is where it all ties into what we do. A database is a record of this constantly coming in signal. So there's signal coming in, coming in, coming in, coming in. We've just chosen to keep the record of a certain part of it given our context. So let's imagine a business. A business has customers coming in, customers buying stuff, customers just browsing. The business chooses that in my context it is important when a customer buys something. And in that context it chooses to record whenever a buy transaction happens. So a signal in the recorded state. Now, let's think of our light analogy. So what is recorded light? Photograph. Right, exactly. So imagine data as a photograph of what happened in your business. So data as a photograph of what happened in your business. Now, what if it was continuously several photographs? You would imagine a video. I would choose instead of imagining a video to imagine a reflection. What if my computer system was a mirror? There was light coming in. So now this guy. So my computer system is a mirror. Whatever business process system you want to imagine. My ERP system, my whatever system. So I have signal coming in, light. I have a mirror. This is my system. And this system has a context. It knows that it must record buy transactions. And this here is my database. So my DB is a reflection of everything that happens in my business. So the data I have in my enterprise data warehouse is a reflection of everything that happened in my business. Now, why is this word reflection very interesting? It's interesting because light displays all the properties of a signal we might want to imagine. If, for example, this mirror is bad. If this mirror is bad, my reflection is bad. So this is like last two minutes. So if the mirror is bad, my reflection is bad. So the agent that is creating the reflection is that agent is bad. The reflection is bad. And this is the stuff that we deal with in the entire, all the books that are all about data quality. They are talking about the flag that if you recorded badly, you have bad data. And who's recording the bad data? The context. So it's not just the system. In the context, there's also the person doing the manual recording. So in the context, there's also the medium of how I got the information. So if it was coming over a phone call, the phone call was jarred and I did not get the right information. So if the mirror is bad, I get bad reflection. Another thing that happens is that you can have multiple mirrors reflecting the same light. If you have multiple mirrors reflecting the same light, you end up in this other data problem that we deal with is where we get multiple reflections. And these reflections might be out of sync. So there's a mirror in India. There's a mirror in US. And as light passes, for some reason straight line, I don't know. But let's say there's a mirror here in time. There's a mirror in bomb, right? And I shine light from here. The reflection first reaches back here, then reaches bomb. And there's a time detail. So the reflection in Bombay is out of sync from the reflection in Bangalore. And this can be a serious problem. It could also be that the mirror in Bangalore is more sophisticated. So the system in Bangalore is better at recording stuff than the system in Bombay. So again, the reflections are out of sync. The moment the reflections are out of sync with even another common data problem is which one wins, right? Which one is the correct information? And this again is a data quality issue. It can be an issue of which one is master data and things like that. So what I've realized is that thinking of data as a reflection of whatever happened in your business really works in the context we are in, right? Of data analysis. Why? Because then you can factor in this, this, the problem, all the problems reflections can have in your analysis, right? And then I want to make a final point and then I will stop. I'm going to stick to all of this, right? Yeah. This is the idea that hasn't been explored. Actually isn't even in a lot of, actually it's nowhere. This stuff that I've been thinking about. Data is a social entity, right? Social objects are something that a lot of people in social sciences have defined as topics of conversation, right? So all of us, why are we here? We're talking about data, right? So the reason we are here is data. There have been a lot of smaller conversations around toilet data and particular numbers and we talked about insurance a little while ago. So, singular data points are possible topics of conversation provided the right people are involved. In fact, the main reason we do data analysis is to help with conversations. We have business problems where we want to make arguments and we do data analysis to help our arguments. So, if conversations happen around data, why don't these conversations live with the data? What happens today is that you get a data point, you generate a report, you go to a meeting, discuss that data point, right? Or you go through a chain of emails discussing that data point. Or you post it on a blog and you get policy makers to discuss that data point. You post it on a newspaper post and then several people discuss that point. So there's conversations happening around data. There's even better conversation happening around higher levels of data, right? Stuff that is visualized, for example, into more useful information. Somebody's statistics, things like that. There's conversation happening around data. Data is a social object. Data can trigger conversations that the right people are involved. Then we must think about bringing these conversations closer together because today they're silent. I have an email conversation with somebody, somebody else has an email conversation with somebody. There is value lost here because there is information that doesn't build back into the data. And that's why I think thinking of the social impact of data is very important. And this is where, in fact, what open data is about, right? The whole idea is you open source data so that there can be conversations that will trigger better governance, right? So if that's the goal, we need to bring these conversations together somehow. That's kind of my question. So I have a question. In the end of your talk, focus around making information, right? Joining different pieces of information, what is your take on information science versus knowledge management? Well, actually information science is a subfield which is contradictory to the definitions in their papers because they say that information, when understood, makes some knowledge, right? So if that's the case, information science is left behind. Knowledge is a higher level entity than information science. But still information science is studied management of knowledge. Now the question becomes, and this is also there in a lot of information science papers, is knowledge something that you can manage? The philosophy side of things believes that there are two kinds of knowledge. There's knowledge that happens inside us. And then there's universal knowledge. And they say that universal knowledge may be something you can manage. So maybe at the organization level you can manage certain knowledge, but you will still end up with knowledge that is inside people, which is why a lot of information science knowledge management theory deals with managing people and training them and improving their quality of knowledge. But it still lives inside people that knowledge, only a percentage of that knowledge ends up in computer systems, even though the subject is called information science, knowledge still remains an experience you and I have individually. Thank you.