Hi, it's Bridget, welcome to a public channel. The purpose here is to inspire your spirit and to fill you with hope today we're going to channel Heath Ledger in the afterlife. All right, so I don't know a ton about Heath Ledger. I know that he was in the Batman movie. I know that he was dating one of the actresses, I think, from Full House, one of the twins, like Michelle, I can't think, Mary Kate or Ashley Olson. That's it. I know that he was dating, I think, one of them, at one point, that's all I know. I know he died young and tragically. I'm not sure if he committed suicide or if it was a drug overdose, I'm not sure, so we'll find out. Let's have a chat with him. Oh, and he really looks, yeah, he shows up with black makeup around his eyes. Were you a villain on the Batman movie? I was thinking, you were actually Batman, but you might not have been Batman.  Were you a Joker? He looks like you might have been Joker because he shows me green and black. He says, I was very much a character actor. I really got into my roles, the roles that I had, and he's showing me like a method actor, is that right? A method actor. He says, I was really serious about my work and I had a lot of opportunity. He's showing me living in New York and then I'm LA and he says, New York is more business, real, a lot of pressure there. There's a lot of intensity. He says a lot of intensity there in New York City. He says, LA is a lot more relaxed, but he says, you're surrounded by beautiful people and you really have to look good in order to be cast, even if you're playing a role, if you have a very specific character or a method or as an actor, it seemed as though you really had to be beautiful, one of the beautiful people. I didn't consider myself. He's saying, I'm not, I didn't consider myself that. I wasn't like a pretty boy type and he says, you might consider me a little reckless that I made some choices that were not, or impulsive, impulsive probably. A little James Dean a little bit like, he's a titch, like he's showing me fast cars driving really fast and so I don't know if he's trying to show that as a metaphor, you guys, like if he's stealing that wave or if that's literal, if he was driving fast cars. He's showing me like movie stunts too where he wanted to do his own stunts and there's a lot of danger to that and he says the insurance companies and the filming companies don't really appreciate it when you do that because it can, and your agent about has a heart attack because he can't really guarantee your safety when you do that and he's showing me big tall buildings and almost like a mission impossible vibe, he's showing me. I don't know if he's referring to how Tom Cruise did some of his own stunts or what the deal was but he's trying to show me his big tall buildings and then like a mission impossible vibe, like action movies, like the action.  Like he'd rather sleep during the day and be awake at night or he had to do that for filming or movie, like be up during the day or be up at night and wake during the day. He's like showing me like how babies have their days and nights mixed up like he felt like that. And he said, sometimes I felt really dark and he's showing me Mac Miller actually, he's making a reference Heath Ledger is to Mac Miller and I don't know if that's because he relates. Yeah, there's a mental health thing and he's showing me that there's definitely addiction and there's a balancing of getting better, like I'm getting better and then all of a sudden I go down, like I'm worse and he's showing me pushing people away. Like I don't want anybody near me, like I'm sick, I don't want anybody near me, I don't want anybody near me. He's making me feel like he was actually sick, like a cold or a flu or something like that kind of a thing. And so, and he didn't feel like he wasn't just, wasn't getting better. He's showing me a lot of things like people reaching out to him, but him pushing people away. I can see one of the twins. I don't know what your name is, I know what she looks like, she's really skinny, like super, super thin, let's say that. That one, I don't know which one it is, but her. And he says, I'm sorry I hurt you, I'm sorry I hurt you. And he's saying something about his mom. I don't see a dad, I'm saying something about his mom. And he's saying something about I didn't want to be a failure.  I don't want to be a failure.   Sometimes you win and sometimes you lose and it's big time, it's big, you know. He's showing me Las Vegas, I don't know if he actually, he's showing me Las Vegas for some reason. Because I said gambling, is that why you're showing me Las Vegas? Or is it a gamble, shooting a film in Las Vegas? I'm talking about Las Vegas, that means something. If you're a Heath Ledger fan, can you tell me what the Las Vegas connection is? Like is it a film that he had there? Is it a movie that was filmed there? Or is that a place he liked to go? Or is, did he live by it there? Or in like a Nevada someplace? We'll just put it in the comments below. So you were in the Batman movie, I know that. I'm gonna see it though. He says it came out after I died. Oh my gosh, guys, I totally forgot about that. I think it did, did it, is that true? Is that really true? I think that might be true. That came out after I died. So now it's more famous when I went after I died than I was when I was alive. He's like, no, that's sad. Now that's a sad commentary on social culture. Something about European, imagine something European, like it's European or different. Like he's specifically saying United States. But I see like the Netherlands, Holland, maybe Sweden, Denmark. I see Denmark, Netherlands, Holland. I don't know if that means anything. You guys, to him, for him, unless a director was from there or a producer from there or he liked to go there, I don't think it's that. There's some kind of connection. Maybe a film festival, I don't know, it's not that. I don't know, it's connected to the movie thing, I think. He's showing me I had a shot in a TV show or I had a cameo or a short, I had a short role or an appearance in a TV show that you would recognize, he said to me. It's not a comedy though, it's like a, could be a TV drama, it could be something simple like a CSI or something like that, where he just kind of was one of the guys that walked in and said some stuff and left. He makes me feel like bad guys. Like he liked to play really dark, kind of intense characters. And he said, because he could relate to the way they felt, like the depth of their, he says, I don't want to say brooding or foreboding, but the depth of the, they used that darkness to really make themselves powerful and they weren't swallowed up by it. They were, they thrived in it. And I, maybe I admired that because of dealing with the darkness for myself. And when you say darkness, are you talking about addiction or are you talking about like depression?  He says, he's showing me like really high energy to like speed or something like cocaine, like really high energy. And then it's almost like a bipolar, but he did, I don't think he had that. It doesn't feel like that at all. It feels like the high is like trying to get me. He says, you know, I was on medication, actually went to a shrink. He said, didn't work out so well though because it's kind of pissed me off, he says. It's like, and if I'm, he says, if I'm being on it, he's smoking, did he smoke? Could he smoke because I could see him with a cigarette? And he's kind of talking to me. God, you know what he reminds me of? He's the guy that was making me down a Sean Penn. He kind of has that vibe a little bit. The Sean Penn kind of on the bad boy kind of vibe, kind of a little bit of that. He's kind of, if I describe Heath Ledger, like I can't even think about what he looks like. I think he has light hair, like blonde hair. He, I didn't look anything up before I started this. So I'm gonna have to look up after because I need to know what it looks like. Now my brain's like, it's here to be up dark hair, kind of brownish blonde hair, I can't tell. It kind of reminds me of a Brad Pitt, Sean Penn combination is how he feels. That's a vibe of his energy.  Bad ads and I don't care. And if I, and he said, if I'm being honest, he says, I don't know that I wanted to be well. I didn't know if I would change my career. You know, would it make me not good anymore? What I, what I best at, you know, like when it changed my craft, he really feels like a character, a method after that. It's really like he took acting really seriously. He said it was easy to do that and it was a role that wasn't like, wasn't like something that other people wanted to be known for or wanted to play. I had the kind of more of the obscure role, so you guys, he would be drawn more to that than like, then like the Dylan on 90210. He wouldn't play that part. That wouldn't be what he played. He says, maybe if I was desperate for a role, maybe if I had to be on Netflix, he says, maybe I would then, you know. But no, no, I am much prefer, he says I much prefer the movies. That's, I prefer that. And he likes the stuntman stuff. So I don't know if he started off as a stuntman and then moved into the movies a little bit. But I feel a connection to like desert, like in California. But then I feel like this, almost like business mentality of New York and New York City. And there's a little bit of a contrast with those two energies for him. So make with that what you will, but. All right, you guys, this is Bridget. It's been my pleasure to be able to channel for you and to connect with Heath Ledger in the afterlife. Remember, the purpose here is always to inspire your spirit, to give you some kind of inspiration here to fill you with some hope energy, to be positive and productive in your daily life.  So make sure you are doing your best to live it. Just live it. Now, as I'm outing, getting to the end here and wrapping this up, I'm realizing that we didn't even talk about his death or what caused his death. And I'm sure that if you're looking up channels and psychic mediums, that's something you want to know because you like to know, how do they die? And I wanted to take this opportunity to reflect in, to reflect into the purpose of above-life channel is to inspire your spirit and to focus on your life. The most important part of life is to live it. To actually live it. And our death moment is one scene in the entire movie. One scene. So it doesn't really necessarily matter how he died, but I can tell you, it definitely feels like he takes ownership for his death. He wanted to go, he says, I wanted to leave. I wanted it to be over, over and done, he says, over and done. So if I had to say, I would say it was a suicide, but it may have been a suicide by medication or overdose, but it feels like that. So for those of you that have to know that, that's all it feels to me.  This is Bridget, thank you so much for watching.


And so as I'm a medieval archeologist, I will have a look on the Middle Ages, but I will not concentrate on very specific archeological excavations. I will rather go back to reflect some of our own past projects to think what this means for an understanding of dynamics of agglomerations. And so I will try to make two points. One point is the role of medieval urban archeology, and the other point is what is the meaning of urban ecology for understanding dynamics of agglomerations. So I want to emphasize that I want to understand medieval archeology as a kind of a historical archeology. So what is an important point? It gives us important background information about society in history. Many cases we have very detailed information, younger in time, the better the written sources are normally, and we need to take this as an opportunity to critically reflect our conceptions. So we heard already today that it's a problem for comparative approach that we normally compare to some classical towns. And I would like to show you that even if we have a look on the medieval towns, it's a little bit more complicated than we normally think. So the point is to get a synthesis of archeological data and written and pictorial evidence, not to take the modern example just as an analog for earlier periods, but to take this to critically reflect our definitions and to reflect our methods and our theoretical background. So quite short, I would like to refer to three case studies coming from some recent projects which are most cases not yet completely finished. So the one which is currently going on, that's some giant French, Serbian, German excavation project in Serbia, which deals with some town of the sixth century. So not the typical urban archeology of the Middle Ages. It's rather late antiquity. A town founded 525 AD and it was abandoned after three to four generations. We know about a very specific political background and specific historical situation of the foundation of this town. In fact, the emperor, Justinian I was born in that town. He had a direct neighborhood and so he had a political program to establish the city at that point. And if you have a look in the surrounding, you can learn quite fast that there is no economic reason or not yet clearly visible an economic reason, so it wasn't in the middle of nowhere. But in order to understand what happened there in terms of a process, we tried to have a look in the surrounding landscape. And so one of the quite different methods we used, one of them was the georachology and so we had them look at several spots in the close surrounding of the city. So you can see here the lighthouse scan with the main city here in the Acropolis, lower town, upper town, and we can see some more fortification surrounding this. And what is very important is to see the impact of this city, which existed a very short time on the landscape. So the one point which is, you can't clearly visible it here, so there is visible in the lighthouse a dam and there was an artificial lake here which changed the landscape at that point. And we have interesting georachological results on the opposite side of the city where we can see that we have a huge sedimentation just from the time when the town was founded. So for this very short time there was an impact on the landscape. And so we want to try to understand even if it was a founded town from political reasons, we want to understand this town as a kind of a process. And the idea that's not based on archaeological results, it's just a hypothesis which you can use to reflect what we find. So we had the idea to think about several phases of the existence of the town. So one line marks the hypothetical need for energy. We have an idea about carrying capacity which changes in time because it's not only dependent from the environment, it also depends from the social organization. And we have the idea that there may have been some external events like barbaric invasions or earthquakes which may require some repairs. So this is again a change in the need for resources. And in the final stage we can now observe by our hypothetical data that there was a kind of ruralization. So we can recognize that in the latest period there was a lot of dung heaps and a lot of animals and a lot of agrarian tools in the rather representative parts of the urban landscape. So this is... Over the time there are some periods of increased risk. We have these impacts on the landscape and we probably also have some conflicts we can recognize within the city between the inhabitants which have different ideas how to use that space. So different agents or stakeholders. So we can gain some ideas what was going on within the city. I need to emphasize we don't have a lot of written evidence from that specific city but at least we have a clear idea how an antique administration functioned, how an antique city functioned. So we can take this historical background information to understand that case study. The second example, this is... I called it now hill sites. It's normally known as cave towns in southwestern Crimea. This was a joint Ukrainian-German project up to 2009. It's not yet published but the problem is our colleagues, our corporations, various Ukrainian institutions and the colleagues are now working in Russia and they feel belonging to Russia. So it's a little bit complicated to go on. But from what we have done there was a lot of surveys in the surrounding of mainly two hill sites. The one you can see here, that's Mangup. The other one you can see here in the foreground, it's Eskikarmen. Very close together, the question how they were related. So the normal interpretation is that there is a chronological differentiation. We can't trace it by the archaeological evidence. Today the whole situation is a quite remote landscape. The geology shows that it's not a very easy usable landscape because of a cast and lack of water with very stony soils. So it's a little bit a problematic landscape. But what we have done there is also some geo-archeological research and there was already an American project in the neighborhood before. So we have here a lot of radiocarbon dates from different situations of sedimentation. So we can see several stages of increased sedimentation. And to a certain degree this corresponds with some periods we know from the written sources and we know already now in Eskikarmen and Mangup as some important phases in the development of these hillsides. But there is also a social aspect because in the time when these hillsides were established, so these triangles here and Mangup and Eskikarmen, this was also a time period when they established a lot of burial sites with imported materials. And in Russian archaeology this is called a very Gothic cemeteries which if you have a look to research history it's a little bit difficult to take this term. But nevertheless this established at that time period with a lot of imports. And we now had the idea that the establishment of these hillsides at Lomorations is something which is related to communication, to establishing groups in competing small neighborhoods. So they're trying to get access to these imports via these Byzantine cities at the coast as Kherson today, Sevastopol. So an environmental aspect and a social aspect. But now let's come to medieval towns and I'll just take one example because I've worked there for many years but you could take any other medieval town. The only specific thing here is you can see on this late 19th century photograph the medieval town in this valley. So we have the situation that this is a delimited landscape and so you can take this as a kind of direct urban surrounding and you can take this as a reference to trace settlement developments. And that is important so we have early medieval settlement here, we have starting here a late medieval village and we have the late medieval town here. So we have in this small area different stages of a settlement development and each stage can be seen as a stage in agglomeration and the town is only the latest point in that development. The latest point is the modern industrialization. Today all you can see here in the valley is just built over by an enlarged modern town. And what is interesting if we have a look on the landscape is to recognize that these medieval towns were established in a changing landscape. So what we need to have in mind if we look on medieval cities, medieval towns it's not just a process of foundation, it's a change in the whole rural landscape. So this is again the valley we have seen in the picture before. Before we have the late medieval town we have the high and late medieval village here still existing and we have the early medieval burial sites and settlements in the surrounding. So we can see in the first hand a kind of settlement concentration leading to the medieval village and then we have later on the establishment of the city we have the name Geislingen here and we have Old Geislingen at the village. So we can trace some changes here and if you have a look on the map on the right it's just a mapping of abandoned medieval rural sites in the surrounding which shows there is a lot of change of abandoned settlements in the surrounding. To see the medieval city, the medieval town as the establishment of a Neclomeration needs to have a look on the surrounding. We can think about the correlation, the interaction between the development of the villages and the development of the towns. This here is just a picture pointing on the development of the villages which in fact are the result of change from a dispersed to a nucleated settlement system where you can have an idea of several interrelations as for example increasing population or a Neclomeration of people increasing need of food and so more food production with some consequences for land use practices as for example shorter fellow periods or enlarged agrarian fields, the removal of hedges and things like this. So it's a complicated system but we need to take this as a framework to understand what happened in the settlement landscape. And now we have seen quite different examples of agglomerations just from the Middle Ages and we had other papers today already showing even more medieval agglomerations. So I think what could be an interesting point to understand these changes is really to have a look on these towns as an urban ecosystem and in several papers we heard already some ideas which are probably very close to that. So we need to see about the interaction of water, soil, plants, animals, humans, climate probably a lot of more factors involved in that. We need to see that a Neclomeration also brings a concentration of material, of energy and it has its material consequences on the landscape. So for example waste and in more modern towns we also have changes like microclimate, heat emission and things like this. So urban ecology I think has an important meaning within Historic Larcology because it helps to understand historical processes of urbanization, of village formation and it helps to integrate daily life and social practice. It helps to understand the interaction of culture and nature however we want to define them. It integrates different methods and disciplines and for sure there have been a lot of critics against this kind of urban ecological methods or approaches as there have been a lot of critics against all these kind of comparative approaches. Especially if we have a look on what we learn as historical archaeology quite often if we have the discussion with historians so they urge us to have a look on the single site and in this session we want to take a more general picture. So if we take these models from urban ecology there is often the suspect that we are too deterministic. So this is true for specific views of history but I think it's very important nevertheless. Another point of criticism is that most parameters we have in these models we can't quantify and archaeologically it's quite hard to find them. But urban ecology in this context is not in hypothesis. It's rather a background where we have to think about all these factors involved in the social development. If you want to understand acclomerations primarily as a social development. It gives an orientation about possible interrelations and I think the most important point it creates research questions. So acclomerations as an ecological and the social transformation I think if we take this as a background and to think about all these single factors which could be involved in that we could probably create an interesting perspective on our topic here. And I was quite lucky to see that these ideas were present in some of the papers we heard today already. So that's my remarks on the topic to just give a small input from a kind of historical archaeology. Thank you.


So I use magic sculpt clay to craft all of my creations. The problem with magic sculpt is that this clay, when cured, becomes very heavy. And this is a problem for specifically horse sculpture because they're on tiny frail legs, and if the body itself is too heavy, your legs are going to crack. This clay is also expensive. It's hard to mix in large quantities. This is where freeform air comes in handy. Hey everybody, it's DJB. Welcome to my channel. If you're new here, my name is Darian. I'm a model horse artist, and I've been sculpting and repainting realistic model horses since 2017. If you've seen my previous how it's made videos, you'll notice that I use a lot of freeform air. The biggest thing about this clay is it is super lightweight, and that's exactly what we want as filler for anything we're doing. If you're taking a wire armature covering it in tinfoil and then putting all epoxy sculpt onto it, it's going to be really heavy, really fast. It's hard for you to hold onto. It's hard for the legs to sustain your more prone to cracking and breaks and damage. If you take your wire armature, your tinfoil, and you apply some freeform air, being that this is so lightweight, we're going to be able to create a really thin layer of epoxy on top of it in order to create the final sculpture. Freeform air is a product by the company Smooth-On. It comes in a trial unit of 1.8 pounds, or a gallon unit of 6.8 pounds, and so the trial unit ranges around the 35-ish dollar range, whereas the gallon unit ranges around the 110-ish dollar range. I bought the gallon because I used quite a bit of this stuff. They sent me like a gallon of the Part A, and then a bunch of tiny containers of Part B, and that's because the Part B is the fast part. So the Part A is the same for all the freeform air products. The Part B is either the fast or the regular, because that's the hardener that creates the faster reaction or the slower reaction. Freeform air is a two-part epoxy dough. It's the same idea as an epoxy sculpt or a magic sculpt clay. You mix equal parts, Part A, Part B. It's a resin and a hardener. The Part A is a magical experience. This stuff is very white. It feels like a marshmallow in your hands. Part B, the hardener, is also a little bit fluffy. It's not as fluffy as the Part A. It's gray in color. This stuff is a dream to mix together. It feels like a cloud in your hands. So fun. The Part B is a little crumbly, so sometimes in general this stuff can get a little bit crumbly, so be careful not to drop it in your carpet. It will mash into your carpet and mash into your clothes. It also comes in two variations, a regular version and a fast version. And I personally really like the fast version for what I do because this is not for sculpting details. This is for the preliminary base. So I like the fast version because I can create an armature. I can cover this armature in tinfoil, tape, toilet paper rolls, whatever I need to create kind of the structure of the animal. And then I can apply Freeform air fast to that whole thing and that hardens everything up in a relatively short curing time where I'm able to make adjustments and continue working without having to wait the full 24 hours for things to cure. The regular version has a 30 minute pot life with a two and a half hour working time and the fast version has a six minute pot life with a 30 minute working time. A full cure on both of these products over 24 hours. It's a chemical reaction the same as any other epoxy putty. The two parts mixed together, the reaction starts right away. So I find with this stuff, especially the fast stuff, if you're mixing like a lot of it, it gets really hot. So you have to be careful because if you're using a tinfoil or a wire base that creates heat, you can burn yourself. So it's actually not recommended mixing it together and leaving it in a big ball because it gets really hot in the center and it starts to cure up the center. So if you flatten out your larger pieces of mixture, that helps dissipate the reaction. The smaller the mixture, you use the longer it takes to cure. So if you're using a lot, like I said, it will start to react really quickly. So you have to work fast with the fast, obviously, but if you don't want that pressure, you can get the slow version.  You can get away with it sometimes like rushing into it, but it does have like a doughy consistency still. I do not use this for any detail. This is purely base work and really basic shape work. And I find that when you sand down this clay or even just leave it as you sculpt it on, it creates a perfect base for epoxy to go on top of because it leaves a little bit of texture and then the epoxy or magic sculpt in specific really wants to stick to it. The biggest thing with this stuff, though, is it's very toxic. It smells very bad. There is no optional if, ands, or buts about not wearing a respirator and not wearing gloves. Like you must wear a respirator.  Also very messy. So the gloves are very useful because this stuff gets really crazy really fast. This stuff sands really easily as well. It turns to a crazy dust. So you 1000% also want to use a respirator with particulate filters on it when sanding. So I use 2097 particulate filters while sanding this and it sounds really well with the Dremel. It's messy. It's not super refined and even while sculpting with it, it's sticky, messy, and hard to refine. And it's not meant to refine. It doesn't love sticking to things either. So if you're working on like a foam base or plastic base or wood base, you kind of have to like mash it in there and it wants to resist a little bit. The key to success is isopropyl alcohol. You can smooth that isopropyl right on there and it will smooth out the surface. While I'm using this stuff, I honestly don't make it perfect. Like I said, it's to make the shape.  I'm going to sculpt over top of this with epoxy or magic sculpt. This is not the final form of the sculpture. This is just a filler base and it keeps everything really light. So an example here I put on the scale a sculpture that was created solidly through epoxy and a sculpture that was created with if reform air in the inside. The epoxy sculpture wasn't even complete. It has no hair on it yet. No detailing. It still would need probably another 100 grams worth of clay added to it. We save over 100 grams of weight. So it makes a big difference and just feeling those in my studio and like having to paint them, having to work with them, having lightweight is super imperative. It is Chef's Kiss. Some of the best stuff I've used and I would highly recommend checking it out, getting it for your studio. I've had a ton of questions about this so hopefully this answers it for you. If you have any questions or concerns, feel free to leave a comment down below. You can also email me at any time. Make sure to like this video if you want more content like this and subscribe to my YouTube channel. It helps more than you know. So thank you so much for watching. This has been DJB. Happy sculpting. I also now have a Patreon if you want to support me further. Link is in the description.


Wisconsin Badgers entered tonight's game against the BSU women's hockey team as the number one team in the country riding a 17 game unbeaten streak against the Beavers and the Beavers had only won one game in 38 home contests against Wisconsin. But you know, sometimes you just got to throw out the pass and drop the puck. A win would also give head coach Jim Scanlon a tie for the most wins in program history. In the first, Lauren Bench standing on her head to keep things scoreless. She had 29 saves on the night. The Badgers, they strike first. Mackenzie Steffen fires one from a bad angle and manages to go in. But then just four minutes later, Jacqueline Casa parks herself in front, bangs the rebound home. We head to the third tied where Lydia Passault steps up fires it in for the lead. Badgers with an empty net just seconds left. Clare de George lays out for the block and the Beavers topple the number one team in the country. The first win against the Badgers since 2015. Any euphoric feeling after the game? Incredible. It's an incredible feeling just to win with your team and just get that positive energy and excitement. Wow. It's it's great. It's unbelievable. Anytime you get a win, it's three points. But certainly when you're when it's against the University of Wisconsin in the ranked number one, and it's been a long time. No question. It's a little bit lecture special. BSU will look for the sweep tomorrow night or tomorrow afternoon at three o'clock. If you enjoyed this segment of Lakeland news, please consider making a tax deductible contribution to Lakeland PBS.


Welcome to the Spoken Tutorial on Vegetarian Recipes for 19 to 24-month-old Babies. In this tutorial, we will learn about calorie requirements, preparation of some nutritious vegetarian recipes.  Along with complementary food, babies should be breastfed until 2 years of age. During this period, 550 calories of energy is required every day. Babies at this age should be given 1 cup of food. A cup is about 250 milliliters or 16 tablespoons of food. It should be given 5 times a day. Let's begin with our recipes now. The first recipe is stuffed khandvi. To make this recipe, you will need 1 tablespoon roasted Bengal gram flour, 50 grams curd, 1 tablespoon crumbled paneer, 1 tablespoon steamed cauliflower, 2 teaspoons peanut powder. You will also require 1 fourth teaspoon turmeric powder, 1 fourth teaspoon cumin seeds powder, 1 fourth teaspoon chilli powder, 1 fourth teaspoon ginger garlic paste, 1 teaspoon of ghee, 2 pinches of salt. I will first tell you how to make the stuffing for khandvi. Mix crumbled paneer, steamed cauliflower and peanut powder together. Heat half teaspoon ghee in a pan. Saute the mixture on a pan for 5 to 7 minutes. Add cumin seeds powder, chilli powder and a pinch of salt in it. Mix it well and allow it to cook for 4 to 6 minutes on low flame. Our stuffing is ready. To make the rolls, take curd in a bowl. Add turmeric powder, ginger garlic paste and a little amount of water in it. Add a pinch of salt and mix it well. Add roasted Bengal gram flour in it and mix again. Make sure there are no lumps. Now pour this batter in a vessel and cook it for 5 to 7 minutes. Stir it continuously till it becomes a thick paste. There should not be any lumps in the batter. Next, apply half teaspoon ghee on the backside of a large plate. Pour a thin and even layer of the batter across the plate. We will call this khandvi. Allow the khandvi to cool for some time. Now spread a thin layer of the stuffing mixture on this khandvi. Next, cut the khandvi into equal sized strips. Then gently roll each strip tightly. Stuffed khandvi is ready. Our second recipe is Kodo millet dosa with sunflower seeds chutney. To make this, we will need 15 grams Kodo millet, 15 grams black gram, 15 grams roasted sunflower seeds, 1 tablespoon grated coconut. We will also need 1 finely chopped tomato, 1 green chilli, juice of half a lemon, half teaspoon ghee, 2 pinches of salt. Procedure, wash and soak Kodo millet and black gram in water for 7 to 8 hours. After soaking, make a fine paste of it in a mixer or stone grinder. Keep this batter aside for 8 to 9 hours for fermentation. Once the batter is fermented, add a pinch of salt and mix well. Next, heat ghee in a pan. Pour a spoonful of batter on the pan. Spread this batter in a circular shape. Let it cook from both sides for 2 to 3 minutes. Kodo millet dosa is ready. I will now tell you the procedure to make the chutney. Take roasted sunflower seeds and grated coconut. Add 1 green chilli, tomato and a pinch of salt. Grind it into a paste in a mixer or a stone grinder. Take this out in a bowl. Squeeze half lemon and mix it well. Sunflower seeds chutney is ready. Serve it with the dosa. Our third recipe is sesame seeds and drumstick leaves cutlet in curd curry. Ingredients required are 1 tablespoon sesame seeds powder, 1 tablespoon roasted Bengal gram flour, 20 grams washed drumstick leaves, 100 grams curd, 1 fourth teaspoon turmeric powder. You will also require 1 fourth teaspoon cumin seeds, 1 fourth teaspoon mustard seeds, 7 to 8 curry leaves, a pinch of salt, 1 teaspoon ghee. Procedure- Heat a pan and dry roast sesame seeds until they turn golden in colour. Allow them to cool and later make a fine powder of it using a grinder. Next, heat a pan and add drumstick leaves and saute for 5 minutes. Take this out on a plate. Add the sesame seeds powder to the sauteed leaves. Add roasted Bengal gram flour, turmeric powder and a pinch of salt. Mix it well. With clean hands, knead a dough by adding a little amount of water at a time. Then divide the dough into two balls and flatten it into cutlets. Next, heat half teaspoon ghee in a pan. Now roast the cutlets in this pan until both sides are cooked. Keep them aside for later use. We will now make the curry. Heat half teaspoon ghee in a steel pot. Add cumin seeds, mustard seeds and curry leaves to it. Once the seeds splutter, add whisked curd and turmeric powder. Cook this for 5 minutes. Once the gravy is cooked, add sesame seeds cutlets. Let it cook for 3 to 5 minutes on a low flame. Sesame seeds and drumstick leaves cutlet in curd curry is ready. Next recipe is foxtail millet pulao. Let us look at the ingredients required. One tablespoon soaked foxtail millet. One tablespoon sprouted black-eyed beans. 50 grams curd. Two teaspoons peanut powder. Four teaspoons grated carrot. One small chopped onion. The spices needed are one fourth teaspoon turmeric powder. One fourth teaspoon chili powder. One fourth teaspoon cumin powder. Other ingredients required are 7 to 8 curry leaves, a pinch of salt and one teaspoon ghee. Before we begin, I will tell you the procedure for sprouting. Wash and soak black-eyed beans in water for 8 to 9 hours. Later, put it in a strainer and rinse it thoroughly with water. Let all the water drain out and then tie it in a clean cotton cloth. Keep it aside until it sprouts. We will start making the recipe now. Heat ghee in a pressure cooker. Add onion and curry leaves. Stir it for 4 to 5 minutes on a low flame. Next, add sprouted black-eyed beans, foxtail millet, carrot and curd. Now add all the spices and salt in it. Mix it well. Add one cup of water and mix again. Pressure cook this for 4 to 5 whistles. Our foxtail millet pulao is ready. All these recipes are rich in protein and omega-3 fatty acids. These recipes are a source of vitamin B complex, phosphorus, potassium and zinc. They are also a rich source of calcium, magnesium, iron and sulphur. Include these recipes in your baby's daily diet for good health. This brings us to the end of this tutorial. Thanks for joining. Welcome to the spoken tutorial on non-vegetarian recipes for 19 to 24 month old babies. In this tutorial, we will learn about energy requirements for 19 to 24 month old babies preparation of some non-vegetarian recipes. Let us begin. During 19 to 24 months, the baby requires up to 550 calories of energy per day. This energy should come from complimentary food. In this period, one cup of food should be given each time 3 to 5 times a day. Make sure to use local and seasonal food items. Various non-vegetarian foods like fish, eggs, chicken and meat can be given. Along with complimentary food, breast milk is necessary too. Breastfeeding is recommended up to two years of age. Let's begin with our first recipe which is fish cutlet. To prepare this recipe, you can use 150 grams of any locally available fish. For example, salmon, mackerel, pomfret, cod fish, etc. I will use 150 grams of cod fish.  The spices required are 1 fourth teaspoon turmeric powder, 1 fourth teaspoon red chilli powder, half teaspoon cumin seeds powder, half teaspoon coriander powder. Procedure, take 150 grams of washed and cleaned pieces of fish. Apply salt, turmeric and lemon juice on it. Keep it aside for 25 to 30 minutes. Pressure cook the fish pieces with 1 glass of water. Cook until 3 to 4 whistles. Let the pressure release on its own and then open the lid. Take out the fish pieces and separate the bones and skin from the fish. Slightly mash the fish using a spoon or clean hands. Take the mashed fish in a bowl. Add chopped onions and chopped coriander leaves to it. Then add red chilli powder, cumin powder and coriander powder. To bind the mixture you can even add 1 tablespoon of roasted Bengal gram flour. Mix everything well and make round balls of this mixture.  Keep it aside in a cool place or refrigerate it for an hour. Next beat an egg thoroughly and add some salt. Dip the cutlets in the beaten egg and coat on all sides. Heat oil in a pan. Fry the cutlets on both sides on medium flame. Fish cutlets are ready. The second recipe is egg amaranth leaves curry. Ingredients required are 2 hard boiled eggs, 30 grams or 3 fourth cup of washed amaranth leaves, 1 tablespoon white sesame seeds, half sliced onion, half chopped tomato, 2 teaspoon ghee or oil, 1 fourth teaspoon salt. The spices required are half teaspoon red chilli powder, half teaspoon coriander powder, 1 fourth teaspoon turmeric powder, procedure heat oil or ghee in a pan. Add sliced onions and saute them until they turn light golden. Then add the chopped tomatoes. Add salt, spices and sesame seeds in it. Mix everything well and cook for 1 to 2 minutes. Then add chopped amaranth leaves in it and saute it. Add the pieces of hard boiled eggs in it. Next add half cup of water and cover it. Cook for 3 to 5 minutes on low flame. Egg and amaranth leaves curry is ready. You can also use any locally available seasonal leafy vegetable. For example spinach, fenugreek leaves, collocasia leaves, etc.  Ingredients required to make this recipe are 100 grams minced chicken, 1 cup or handful of washed coriander leaves, 1 tablespoon curd, 3 fourth tablespoon roasted flax seeds, half small onion, half inch of ginger, 2 to 3 cloves of garlic. You will need the following spices. Half teaspoon cumin seeds powder, half teaspoon coriander powder, half teaspoon garam masala powder, half teaspoon cumin seeds, 1 fourth teaspoon turmeric powder. You will also need 1 teaspoon of oil or ghee. Take salt according to your taste. Procedure Take minced chicken in a bowl and add salt and turmeric. Keep it aside for 20 to 30 minutes. Next divide this chicken into 4 parts and make round balls.  Place a stand in the vessel.  Cover the vessel and cook on medium flame for 10 minutes. Let us also prepare the paste for the gravy. Take onion, coriander leaves, ginger, garlic, roasted flax seeds in a mixer. Add curd and grind it to make a smooth paste. Next heat oil or ghee in a pan and add cumin seeds. Once it starts to crackle add the prepared paste into it. Add powder, coriander powder, garam masala powder and salt. Mix it well and let it cook for 2 to 3 minutes. Then add half cup of water and steamed chicken balls into the pan. Cover and cook on low flame for 5 to 7 minutes. Switch off the flame and garnish with chopped coriander leaves. Chicken ball curry is ready. The next recipe is barnyard millet idli stuffed with chicken liver. For this we need 4 tablespoons or 60 grams of barnyard millet. 2 tablespoons or 30 grams split black gram. 60 grams or 50 grams of chopped onion. 1 teaspoon lemon juice. 1 half teaspoon ginger garlic paste. 1 half teaspoon fenugreek seeds. The spices required are 1 half teaspoon turmeric powder 1 half teaspoon garam masala powder 1 fourth teaspoon coriander powder. We also require 1 tablespoon of oil or ghee and 1 fourth teaspoon salt. Procedure In a bowl wash and then soak the barnyard millet overnight. Likewise wash and soak split black gram and fenugreek seeds together overnight. Next morning rinse and drain the water from both the bowls. Grind them together to make a smooth batter by adding a little water in it. Add some salt and keep the batter aside to ferment for 7 to 8 hours. Next take the chicken liver on a plate. Apply lemon juice turmeric powder and ginger garlic paste on it. Wait for 20 to 30 minutes. Then heat oil in a pan and saute the chopped onions. Once it turns light golden add the chicken liver into it. Add salt, coriander powder and garam masala powder. Mix everything well. Add half cup water and cover the pan. Let it cook on low flame for 5 minutes until the water dries up. The stuffing is ready. Once it cools down chop the chicken liver into very small pieces. Next grease the idli plates using ghee or oil. Pour some fermented batter on the idli plates. Add 1 teaspoon of the chicken liver stuffing in it. Pour some more idli batter on top of it. Repeat this step to make the rest of the idlis. Place the idli stand in a pressure cooker and cook without a whistle. Cook for 10 to 12 minutes on low flame. Our barnyard millet idlis stuffed with chicken liver are ready. All these recipes are protein, omega-3 fatty acids and colon. These recipes are sources of vitamins like vitamin D, A, B2, B3, B6, B9, B12, iron, zinc, calcium and phosphorus are also present in all these recipes. Include these recipes in the baby's diet for good health. This brings us to the end of this tutorial. Thanks for joining.


Hey everyone, it's Broogly, and today, boy, do I have a creepy video for you. This video is about a brand new Backroom's Entity that just got added to the WikiDot, I think yesterday. It's terrifying, it's god-wrenching, and I think you're gonna enjoy it. This is a bonus video for the week, it's not gonna affect the upload schedule. Let's get into it. The entity in question is called Entity666, aka the Happy Files Virus. Its IETS rating is 4C, which means the entity is very likely to un-alive or wound you very badly if you don't take the proper precautions, and that this entity possesses low-level human intelligence but is still extremely dangerous. The entity itself is like a computer virus that's been causing chaos across the internet inside of the backrooms, and not just the internet, on the computers, and it's even a real life threat as well. And you'll see why I say that in a second. Meg has tried to send other web-crawling antivirus programs to destroy this Happy Files Virus, but it's been deemed impossible to destroy because of how fast it clones itself. Entity666 is very dangerous .exe file, and it also is a real manifestation that can describe itself in video game form, in executable form, in utility program forms, or even in real life form. As some of you know, there are computers on loads of different levels inside the backrooms, which makes this threat even more dangerous because it reaches almost every level. The websites that Entity666 has been targeting are Fileshare.backrooms, IIRC.backrooms, Folders.Meg, and BackroomsDrive.Meg. And this entity will code itself into the files on those websites that can be downloaded, and it will infect those files, and when you download them, well you just downloaded the entity. It will specifically disguise itself into a .zip file, so when you download it from the website, even though you thought you downloaded a game or something, it will actually be this entity. Once you extract that .zip file, you'll see a text document that says, thank you for downloading this program. I hope you enjoy it. Thank you for using Happy Files, and the program you're wanting to download will be there too, kind of, but instead of it being a normal high definition icon, it'll be a lower quality grainy picture instead of a normal one. If the program you wanted to download was like a utility program or something, like Microsoft Word, then the icon will be a picture of the entity's face instead of the normal image. As soon as you open the program, you'll notice that the windows icon in the bottom left of your screen will also change into the entity's face. There's no way to change it back, you can't close the program with Task Manager, you can't delete it, it's just completely infected, and you're probably doomed.  Once you launch this program, you are now considered entity 66A because entity 666 will now start hunting you down. If the program you downloaded was supposed to be a video game, then at around 15 minutes after you launch the game, you'll be able to see the manifestation of this entity inside of the actual gameplay. It'll start to follow your in-game character around slowly, but then it'll start to run really, really fast towards your character, and once it catches up to you, it'll jump scare on the screen by flashing a picture of its face, and it'll crash your computer, turn off the lights, make the electricity go out in your room, and everything will just be black. Then it's time for the physical manifestation of the entity. It's been described as a tall, all-black humanoid measuring around 2.1 meters in height, it wears a white mask, and it has extremely unsettling wide eyes, with a huge smile. Its facial expression does not change through its manifestation at all. It's the same on the computer, it's the same in real life, and it's always creepy. After this entity has had its fun as a virus on your computer, it will then morph into a real-life flesh and blood creature, and will try to jump scare you until you have a heart attack in real life. If that doesn't work, then it'll chase you like in the video game, slowly at first, but then extremely fast to the point where it's running around you in circles, and then it'll attack you in a gory way. After it successfully offs the person it chooses, the downloaded file that it came in and all the evidence of the entity will delete itself of any evidence from the computer, and it's like it never existed. Now to summarize this insane new entity, it's pretty much a virus that clings to a real program that people would download with the backroom's internet. It'll infect whatever program that is, once you launch it, it'll manifest into a real flesh and blood entity in the same room as you, and chase you down until it offs you. That's actually terrifying, bro, like imagine the reach of this thing, it could reach the entirety of the backroom as if it wanted to. Anyways, I hope you enjoyed this little entity spotlight video, I've never really done one like this, but if you did enjoy it, I could do another. Like I said, this is just a bonus video for the week and there'll be a brand new full length video tomorrow or the next day, I haven't decided yet, which it'll actually be part one of the backroom's iceberg, that's right, Brookly is doing a backroom's iceberg series, I think it's gonna be fun, I think you're gonna like it. Let me know if you're hyped for that. Go check out Tugli, just uploaded a video on there about me guessing all of the European countries, and I think I did pretty good. Thank you for watching the video, let me know if you enjoyed it, and I'll see you Tuesday, peace.


Okay, we're right at the top of the hour. Hello, everybody. My name is Tony Shaw. I'm the CEO of Thetaversity. Pleased to be filling in today for my colleague, Shannon Kemp, who's taking a well-earned summer break. Today's webinar is the monthly webinar series on data insights and analytics brought to you in partnership with First San Francisco partners, and today the session is sponsored by Lookup. The theme is analytics, business intelligence, and data science. What's the progression? As you join the webinar, you'll notice that your audio is muted. We'll be collecting questions throughout the presentation, however, in the Q&A section of your screen. Or if you prefer to tweet, then you can submit questions using the hashtag of DI analytics. So as we get going, there's a few other points to get us started. You're also welcome to use the chat screen on the right-hand side, but we'd prefer if you could submit specific questions in the Q&A section, please. And to anticipate the most common question we always receive, we will be sending a follow-up email within two business days, which will contain links to the slides, the recording of this session, and any additional information requested through the webinar or unanswered questions that we didn't have time to get to today. So Kelly, it's my pleasure to introduce you now. Kelly O'Neill is the founder and CEO of First San Francisco partners. Having worked with software and systems providers who've been key to the formulation of enterprise information management over recent years, Kelly has played an important role in many of the groundbreaking initiatives that confirm the value of EIM to the enterprise. She's recognized an unmet need for clear guidance and advice on the intricacies of implementing EIM solutions. And to that end, she founded First San Francisco partners in early 2007. Kelly, it's a pleasure to be with you today. I'm very happy to turn over the controls to you to get us started today. Hello and welcome. Thank you so much. And good morning, good afternoon, and good evening. Nice to be with everyone again. So analytics, business intelligence, and data science, what is the progression? So this webinar that we're going to go through today is going to define each of these capabilities and then also talk about how they relate to each other, what the overlap is, and that sort of thing. Because truly it's not, these aren't three completely separate disciplines, which I'm sure that you guys are all well aware of, but there are things that are unique about each of them and can serve different purposes. So what we're going to talk about is we'll go through the definition. So that's where we're going to start. We will talk a little bit about the differences in architectures that are driven by the reasons that you would focus on a concept like business intelligence versus analytics. We will talk a little bit about when you would use a concept like business intelligence versus the other, and we'll close with the evolution and what's coming next. And then finally we will talk about some key takeaways. So I know that we allocate 10 minutes at the end for questions, but Tony, I'm always happy to take questions in the middle if there is something that is very pertinent to a specific topic that might be good to capture the idea at that time. So please don't hesitate. Okay. I will keep an eye on that. Yeah, absolutely. Also it might be more interesting for people to hear someone else's voice other than mine just every once in a while. All right. So why is today's topic important? Well, I do think that there is still a lot of struggle around, you know, how do all of these different new terms fit together?  Does it mean that I need to replace it? Does it mean that what we used to do is no longer valid? So none of that is really true. So what we're trying to do is help to define when to use which of these core capabilities and how to put them together in a toolkit, if you will, to be able to provide to your organization different capabilities that are useful depending upon the different business purpose, different use case, and of course the different type of data. There are lots of different ways of going about doing this. The ideas and the examples that we're going to be going through today are based on our experience, our perspective. And I'm sure there are many of you in the audience that have other reference architectures that you might use or other viewpoints and perspectives. And so, again, these are ideas around how to help categorize, understand the capabilities and determine when, what is most appropriate based on a requirement within your organization. Okay. So there is no solid demarcation between these different styles of using data. And I want to just lay that out there and I might say it a few more times to make sure that you all leave today remembering that. But as we go through the definitions you will see how gray the area is. And I took the liberty to leverage definitions from the Gartner group because they do tend to be a well read, well distributed resource. And I thought that their definitions actually supported this statement that there's no solid demarcation between these styles of using data. So I'm going to start with business intelligence. Business intelligence has been around the longest. So that's why I wanted to start with this. And Gartner defines it as an umbrella term that includes the applications infrastructure's tools and best practices that enable access to and analysis of information to improve and optimize decisions and performance. So pretty general statement around what business intelligence is. It's not just technology. It's not just applications. It's not just your infrastructure stack. It's the processes, the consumptions and the best practices. How does this differ from analytics? Again, analytics is viewed as a catch-all term for a variety of different business intelligence and application-related initiatives. So I think what's kind of happening here is what we used to define as business intelligence has kind of morphed into a broader umbrella term that is now being called analytics. Analytics could include business intelligence. It could also include other sorts of more contemporary or modern ways of analyzing data based on new data types that are more accessible, the ability to leverage new data volumes that are becoming more and more accessible also. So what is data science? Well, Gartner actually groups data science underneath advanced analytics. So they define data science as the autonomous or semi-autonomous examination of data. So that's one thing that is different is it talks really about focusing on the examination of the data as opposed to the process of analyzing the information. And that is a difference is that it is really focused on the data itself. And to a certain extent, trying to have the data present the correlations versus analytics trying to use the data to mine the data to come up with some sort of answer to a business question. But advanced analytic techniques also include things like data and text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, et cetera, et cetera. So I don't need to read this to you because of course you can read that yourself. But I do think that data science has become also a catch-all phrase that is leveraging new tools, new capabilities, and advanced concepts like machine learning and as we move into more artificial intelligence and ultimately leveraging neural networks for deep learning. So if we think about these definitions, one key differentiator is that business intelligence and analytics were focused on the business questions. And then they started to hire statisticians. As they started to hire more and more statisticians, that cadre of statisticians, excuse me, my goodness, statisticians became known as the data scientists. And those data scientists applied additional rigor, mathematical analysis, and more complicated quantitative analysis to become more scientific. In this way data science focuses on the data and business intelligence and analytics tends to focus on the business requirement. But they do work together and complement each other to provide a complete picture of a, to resolve a business question or to promote business decisions. Data science can be viewed as a foundation for business intelligence or analytics. And business intelligence and analytics really is across the spectrum and data science is that capability of identifying anomalies in the data, developing the algorithms that are essentially used by business intelligence and analytics. One thing that is unique and as you can see by the definition here is that data science provides insights that are not possible with traditional descriptive business intelligence tools. So again, this is as the title of this webinar indicates what's the progression. This is a progression that leverages new capabilities, new technology, and new learnings that we've had in the market. So then the question becomes, well, which one do I use? Really the answer to this question is driven by what you are trying to achieve. Are you looking to do more operational reporting? Are you trying to communicate out to your line managers information about the efficiency of a supply chain? Are you communicating to an external regulatory body? So those sorts of use cases where you're looking at repeatable requirements or information that is based on historical trends that are presented so that that manager can make some very tactical operational decisions, those are instances where you want to leverage business intelligence. So more operational reporting, analytics and data science is leveraging more complex models ultimately to predict engagement with customers versus business intelligence can talk about how say a line manager or retail manager who's interfacing with the customer can understand their hourly, daily or weekly sales. So again, it's really driven by what you're trying to accomplish. Other considerations that you want to take into or other considerations that you want to have in mind are what are the capabilities within your organization already? So do you have business intelligence already? Have they started to do more advanced analytics? Has anyone been hired as a data scientist? How can you best leverage who you have and what you have already to progress the business requirements that you have? Now, if you don't have it within your organization already, what are you willing to invest in? Because that is another big decision. If you are transitioning from a more traditional business intelligence environment through analytics to advanced analytics and data science, those are investments in technology, in people, and the way that the organization needs to respond to the output of those exercises as well. So for example, if you brought in a predictive model that said that we recommend that an action, for example, would your management team be able to consume the output of some of those predictive recommendations? That answer of whether a management team could consume the output of a data science exercise or an advanced analytics exercise could be based on their ability to trust data as a key input into decisions or whether they are more comfortable making decisions based on their own personal experience or gut feel. Maybe those sorts of decisions that are based on an advanced analytics exercise would need to be, again, validated against other more trusted processes, for example. So you also want to make sure that if you invest in the more advanced capabilities around analytics and data science, that your organization is primed to consume the output and be able to action the recommendation that may come out of one of those more advanced exercises or be able to validate it to a certain extent and ensure that that validation process enables them to trust the recommendation in order to action it.  Now some of this has to do, of course, with the data volumes because you wouldn't necessarily want to do data science on small volumes of data because you may not have the ability to or you may not have enough of a representation to do that statistical analysis, but you can do analytics on small data. Business intelligence has traditionally been done on more small data. So there is also the size and the volume of data that comes up around your decision of which one to use or which one is going to be best tuned for your decision at hand. All right, let's look at what happens as we get into the implications of those questions in the business decisions, which means it starts to drive our infrastructure and our architecture. So we started talking about the difference between sort of business intelligence and advanced analytics back earlier in this year when we put together kind of a reference architecture. And we started using the terms vintage for traditional sort of business intelligence enterprise data warehouse categories and contemporary for more advanced analytics. So we're starting to change that nomenclature. And you can see here that on the left-hand side of this slide, we're calling this traditional. And on the right-hand side, we're calling it contemporary. So the way that we put together this example or this graphic is that you start in the middle. And depending upon your business goals and your information requirements, you either turn left to look at a more traditional approach via business intelligence, or you turn right and you look at a more contemporary approach via analytics and data science. So traditionally, business intelligence has been driven by historical or backward-looking requirements. Analytics and data science is looking forward. So whether that is just through new capabilities, whether it is through predictive analysis, prescriptive analysis, trying to determine what will happen in the future and what is the recommended action that we should take in the future. If your audience, if their goals are to make better decisions and they're looking for some operational accuracy, they're going to look more towards a business intelligence sort of infrastructure and architecture. If your audience wants to identify new insights, maybe it's to support the development of a future strategy in the organization. And maybe the audience is asking explicitly for data science. And of course you need to turn right and go towards an advanced analytics or a more contemporary sort of architecture. If you are looking at steady states of data sources, so the difference between a business intelligence architectural infrastructure tends to have known consistent sources of data, versus on the more contemporary side, the sources are extraordinarily dynamic. They might include those same sources that are used in a business intelligence approach, but they also would include other sources like unstructured data, much more purchased data, acquired data, crowd-sourced data, what have you. And those sources will be changing very dynamically. So those are other decision points. And then of course, what are your requirements from a management and a governance perspective? And if you've got very stringent requirements from a governance perspective, where you need to be able to have specific quality standards, those quality standards need to be reliable and repeatable and extraordinarily precise, that leads you more towards a traditional or business intelligence sort of architecture. So if you are looking for governance to provide enablement, meaning governance is providing data understanding through data cataloging, available consistent data definitions and business glossaries, and that sort of thing where you're not looking to control necessarily the data in the same way that you are in a traditional environment, but you want to be enabled to do your investigative analysis and that sort of thing, that would be more along the side of analytics and of course data science, where they are looking to do the experimentation and not be bound by some of the rigor that is necessary from a business intelligence perspective. So if we think about these as being the different drivers of why you would leverage business intelligence versus analytics, then how does that show up in the actual architecture? So how it shows up in the infrastructure is that essentially it's a traditional, you know, extract, transform and load, so ETL, EAI, leveraging data quality, so that the data quality can demonstrate the repeatability of the data, the thresholds are being met in areas like completeness and consistency and accuracy, etc. From an infrastructure perspective, it tends to be anything that works, and so that anything that works is a recognition that many times the infrastructure for analytics and data science is purchased by the user for the user and sometimes sits under that user's desk, quite frankly. So it is leveraging more cloud. It is leveraging the ability to buy something quickly and install it quickly and get it up and running quickly so that they can start to run more sophisticated analytics and data science and not be bound by maybe some processes or some constraints that are necessary to go through a more traditional IT-driven purchasing process. Other architectural differences, for example, data volumes. This is where a lot of people think about the main difference between, say, data science and business intelligence is all about the volume. Well, yes, of course it is. Business intelligence is much more used to leveraging the controlled volume, smaller volumes of data, etc., that enable you to have that trust from a quality perspective as opposed to the streaming data, high volume data, constantly changing data that is needed on the analytics and data science side. And then, of course, there's the usage of the information. So from a business intelligence perspective, traditional queries tend to have limitations. So they're tuned specifically to a report that you're running because you want to maximize that speed of response. So it's very fixed, it's less changeable, but it's very trusted. So that is what is in a more traditional business intelligence side versus on the contemporary side, the access process is actually tuned to consume high volumes or rather huge volumes of data. So it's tuned to run the massively parallelism that enables you to get that statistical base to come up with your predictive recommendations and that sort of thing. So again, these are differences and tradeoffs essentially that are all part of the decision making process around when to leverage business intelligence and when to look more at analytics and data science. And as you could imagine, this really is a continuum. And so the way that we've looked at, excuse me, the way that we've looked at this is kind of, you know, on the left-hand side, this is to a certain extent time-based. I don't want to say that this arrow is 100% time-based because there's a little bit of overlapping that happens. And there's, of course, the fact that people are still using enterprise data warehouses and data marks and that sort of thing. So it's not 100% time-based. But there is somewhat of a continuum where reports were really looking at what happened. Dimensional queries of your ability to more real-time slice and dice data to get a more flexible outcome to an ad hoc business question. Then we looked to find predictors and leverage predictive modeling that are identifying relationships and trends. And then that moved into the ability to understand not just what is, but if this will happen, then what decisions should we make? So thinking about forecasting based on scenarios. And then we're moving or have moved into goal-seeking models. And what I mean by this is that we're looking for the data to do some of the correlation for us and to recommend ideas that we may not have thought of, and therefore we may not have asked of a predictive model or a scenario-based forecast. So that top line is really looking at kind of what are we asking of the data and how are we analyzing it. And the bottom part of the arrow is looking at the traditional sort of data sources and data types that we were leveraging over periods of time. So going from normalized data structures and application-based analysis to combining the data from multiple applications into an enterprise data warehouse, leveraging those via more solution-specific data marks. And then, of course, as we go into the data lake world where we're able to leverage Hadoop and other technologies, the reason that I said load Hadoop here is that, you know, when we think about kind of the processing, where the processing happens, you know, you don't actually do a lot of processing as you load the data into Hadoop. And in fact, what you're doing is looking to do the processing as you read the data out of Hadoop or other sorts of data like technologies. So there is kind of a time-based analysis or a time-based component of this. There's other things that start to drive the structure and flexibility in what you're asking for. For example, as our world has become more real-time, there's also a currency aspect. So where we used to report monthly or quarterly that enabled that we could pull from normalized data structures and ultimately from enterprise data warehouses and data marks, we have gone to a much more current demand so that we want now to understand not just monthly, but we want to understand weekly, daily, or even real-time. And that sort of processing capability is much more difficult for using some of the more traditional data sources and traditional analysis capabilities, where it's easier to do based on some of the more contemporary and data-like capabilities. Kelly, before you move on, the question submitted earlier, and this might be the right context in which to bring it up. The question reads, should I normalize my data and define my questions before I select and configure a tool? Or should I throw all my data into a data lake and let the data science lose on it? It depends question I assume, but perhaps in the context of this particular diagram, it might be a good time to address it. Yeah, and I do truly think that it is and it depends because there is the appropriate nature of understanding your query first and implementing an architecture or leveraging an architecture that already exists in your organization to answer that question, right? Now, part of the reason that the data science came up anyway is what if we don't know what questions we should be asking? And so it gives you a more flexible ability to investigate based on the data and enables you to do more real-time modeling around what could happen versus what we have seen happen in the past. Now, realistically, that sort of more predictive prescriptive analytics needs to leverage some of the information that we have gathered over the years based in the past. So for example, if what you're doing is that you're trying to do fast identifications of data patterns to come up with some sort of answer to your question, somewhere you need to start to ensure that those patterns are appropriate and understood to begin with. So it's, you know, understanding your core operational and managerial reporting still has a place and it actually can help you to develop your more sophisticated analysis and can ensure that that more sophisticated analysis is based on that true understanding of your business process or your customer base in the first place. We have another question about governing unstructured data, but I think we'll come back to that at the end. Okay, yeah, not a problem. Okay. All right. Okay, good. Not a problem, not a problem. Sometimes it takes me, anyways, I moved my cursor off and finding it back, so anyway, that's my problem, not yours. So you all have seen this slide to a certain extent before. So this is our iBeam in which there's a architectural structural capability in which you need to support an infrastructure that enables data insights. And we call it an iBeam because of the thought around how an iBeam provides architectural support in a building. And the idea is that you've got kind of on the left hand side of the iBeam, the more traditional area, and on the right hand side, you have the contemporary area. And we changed this from vintage to contemporary based on some feedback that I received that vintage felt a little judgmental, which it was not intended to be judgmental to anything. So we think about it as kind of a traditional area versus the contemporary area, which is where we think about more of the big data world. Data scientists live in the contemporary area versus when we think about traditional stakeholders like managerial reporting, operational reporting, or what have you. So essentially, you cannot do traditional business intelligence without a certain infrastructure, just like you can't do data science without a certain infrastructure. So it's virtually impossible to try and accomplish what you want to do from a data science perspective in a traditional infrastructure. You have more data, more information, shorter windows in which to do the analysis, more complex querying, more sophisticated algorithms. And so the idea is that ultimately this iBeam concept enables you to marry your traditional environment, which still has very much value to your contemporary environment that's more experimental and investigative and what have you. Now, analytics, because it is such an umbrella term, spans both in the traditional and the contemporary area. Analytics in the more traditional area feels more like business intelligence. Analytics in the contemporary area feels more like data science and advanced analytics. So as you go, you can see that as you go through the left side and the right side, on the right hand side, you have the large volume ingestion. You have data being produced by edge processing or smart machine, social, etc. You have bots. So for example, as we talked about in the last webinar, bots both use or consume data and they produce data. And the data scientists want to be able to leverage all of the data that is shared from traditional sources into the contemporary area as well as all of that data that is being purchased, acquired, and pulled from other sorts of technologies. So you can move back and forth between the traditional and the contemporary depending upon your requirement and your business issue. And then the data itself leverages this iBeam or this data movement infrastructure to provide the data via a data access layer. On the traditional side, we think about data access as reports and visualization tools. In the more contemporary, it's also visualization, but it's also the ability to consume that data via more edge analytics type of use cases. Even analytics that could be potentially embedded into operational systems and applications. So again, going back to some of the decisions around your business and information requirements and whether you turn left or right, you will turn left for certain areas that are more traditional from a data source perspective. The data sources are more steady state and you will turn right when your data sources and your demand on the data is more variable and changing. So how do we decide so we came up with a few principles in terms of, you know, determining which part of the data use spectrum, it really is a series of principles. So it's not just as simple as we need a lot of data therefore it has to be a data lake. It is really thinking about what are the architectures that we need in order to deliver business intelligence and analytics to reflect the business needs. So this is what we've called in the past the, you know, where we want to recommend the best fit architecture so that you are actually creating an architecture that is truly designed to meet your business needs, understanding that those business needs do change. But creating an architecture to support the business needs not just creating an architecture that happens to be really trendy and gets a lot of recognition. So another principle supporting organizations around business intelligence and analytics that reflect true self service. This has an architectural implication to make the data available from a self service perspective, but in order to make the data available from a self service perspective there has to be some level of people in process to ensure that the data is appropriate for a self service that it is understood by the self service. And that when there are multiple people looking at the data that there's the ability to know that this data represents the same thing that that data represents so that you don't end up with conflicts. And then finally, an architectural solution must be based on the ability to support both modes so whether it's vintage and contemporary traditional and contemporary etc. So the demands of the business are to be able to do both business intelligence, as well as analytics. So just a couple of other decision factors as you're going through this so if you are answering yes to the first series of questions, that is a traditional business intelligence environment, in the sense that it the results need to be repeatable. It's meant to be operationalized and by operationalizing those repeatable results, then you're driving specific business decisions, and you can monitor progress within those business decisions, almost to the opposite of that of those questions. And then analytics and data or advanced analytics and data science group would almost answer no, in the sense that no we don't want it to be repeatable in fact we want it to be experimental. We want to be able to leverage new capabilities like artificial intelligence and machine learning algorithmic models are much more sophisticated. We want to use advanced analytics and data science versus yes of course we use somewhat of analytic models and algorithm algorithmic models and business intelligence but nowhere near to the same sophistication in advanced analytics. So would that data leave the organization either in the form of a report, or in the form of a data monetization exercise meaning that you are selling the data or you are sharing the data with a business partner for a trade for something else. So, when you do that, there's a certain amount of control that is inherent into a business intelligence environment that is absent for the most part from an advanced analytics and data science and if the data is leaving the organization, then there needs to be a bit more rigor applied that is more similar to a business intelligence environment to ensure that there is compliance around that data. Many times this is bound by regular regulatory constraints as indicated here. So other decision factors when you're trying to determine whether to use business intelligence or advanced analytics. And then this is just a process to consider that you go through and this might answer or be another way to answer the question that was posed earlier Tony where you're thinking first about business strategy and goals and so what are we really trying to accomplish. And do we have a need for operations managerial reporting, etc. Identifying those business requirements data and information needs for the planning and analysis and then of course you want to assess the organizational capabilities and the ability to support it and then develop your best fit architecture. As opposed to creating an architecture and then going back and determining whether it will actually support your business strategy or your goals. Again combinations are what's happening now not business intelligence versus analytics and data science and so it's really just what is the combination that is going to best support the business needs based on the requirements that have been articulated and sometimes the answer is both. So that may be the answer to this next question Kelly which references slides 13 but we may not need to go back there. Can we do a predictive analytics and data science under an enterprise data warehouse or is it better to do that under Hadoop and that question is based on the graphic on slide 13 which is the long arrow. The arrow, exactly the arrow slide. Well, so. Data science almost inherently means that you are looking at more of a big data infrastructure versus an enterprise data warehouse but again remember this is a continuum because enterprise data warehouses became bigger and bigger and bigger and because of the constraints associated with the requirement to have data structure around your data in the enterprise data warehouses the limitations that that requirement of structure had led to the creation of technology to support the data lake and so yes you can do predictive analysis. You can probably do a certain amount of prescriptive analysis from an enterprise data warehouse a data scientist will probably not want to use an enterprise data warehouse because they don't want to be bound by the structural constraints that are inherent by the term data warehouse and the structured schemas that are necessary within a data warehouse in order to get to the ability to predict predict you will also need to have a high volume of data so it would not be a small data warehouse might not give you the volume that you would need in order to do enough statistical analysis to be able to predict or prescribe anything so that would be another consideration of why you would want to go more towards a data lake Hadoop oriented environment versus enterprise data warehouse environment. Yeah. And it's and it is. It is again it's a continuum so it's not it's not black and white. So let's talk a little bit about organizational considerations I know that we brought them up a little bit earlier when we talked about things like the audience and the governance and that sort of thing but I did want to just explicitly call out some of the organizational changes that we have seen over time and as I was thinking about this I realized that you know what it really has kind of bounced back and forth and what this graphic is meant to represent.   Hopefully you do. But that's what I was thinking about when I was thinking about this concept of you know centralization decentralization what do organizational what do organizational units look like in that sort of thing. So business intelligence came up you know we started to to understand the value of business intelligence in order to maximize that value at the top left of this graphic in order to maximize the value of business intelligence. Many organizations created a business intelligence competency center. There are still organizations creating a new business intelligence competency center and this is kind of a centralized capability to enable efficiencies around business intelligence to enable data accuracy data reliability and best practices around business intelligence. As the users of the business intelligence tools became more and more sophisticated and independent that it shifted to a decentralized model which was more along the lines of self service business intelligence where the user wanted to have greater flexibility around the questions that they are asking. And therefore they don't want to be bound by specific queries you know more structured queries and that sort of thing. As we talked about earlier analytics is really just a progression from business intelligence. So that concept of self service business intelligence became as the tools became more sophisticated it became self service analytics or business driven analytics. So the ability to buy a cloud based analytics solution or a very lightweight based analytics solution, a simple server, load your data into it and do ad hoc personalized analysis was kind of the the grail around business driven analytics or self service analytics. Well, as that proliferation of analytics purchases around an organization became to lead to inefficiencies, then it swung back to a more centralized approach where the analytics organization provided an known reusable analytics workbench that the self service analytics group could actually leverage as a centralized toolset. So again it's swinging back to the other side. As data science came up data science, the group of data scientists tend to be a centralized group of advanced data processing and they were centralized, because they had a very specific skill set. And it was a very unique type of skill set, but now as the access to large volumes of data is easier as the ability to understand that data becomes easier based on automation and visual data processing. Now we're going back to this decentralized concept of a citizen data scientist. So, organizationally it's not either centralized or decentralized it's the understanding that there's efficiencies to be had and innovation to be had, depending upon where you are on the spectrum and what your goals are around the inquiry that you're making around analytics or business intelligence. So, I think somebody said it's called a Newton's cradle is that right. Well, thank you. I should have looked that up, right. So then if we think about kind of how do we want to consider this from an organizational perspective. So, if the two extremes are centralized and decentralized, of course we know that our world ends up being somewhere in between. We want to think about what are some of those drivers, and therefore how do we react. So, from a strategy perspective, business intelligence, and in general, the centralized sort of approach tends to be more it driven where the organization occurs based on the tools based on the tools that's available to them. And in this instance is it driven business used. Well, over time, the business has become much more technical, just like it has become much more business driven, and a decentralized capability is supported by a business driven and IT can support that business driven approach. We touched on this a bit earlier, but another consideration around centralization versus decentralization is the risk tolerance and the risk associated with the data. So, if there is risk associated with the data or if your usage of that data is for a very low risk purpose, there is value around something that is centralized where it needs to be zero risk, zero chaos. Business intelligence kind of fulfills that category there. So, this might be for regulatory purposes or when that data is published externally, and that can lead to reputational risk if the data is inaccurate and consistent, what have you. If the risk tolerance is extraordinarily low and in fact what you're looking for is to leverage chaos in order to drive innovation, then decentralized is kind of the way that you want to go so that you are enabling experimentation and you are enabling people to play around with the data if you will, but it is for internal experimentation as opposed to externally delivered information. Centralization is also something that can help when there are very specialized skills that are hard to find, hard to train, hard to maintain, and this happened with the business intelligence competency center and of course it happened with data science where the data science groups started very centralized. As the skills are more general, more cross functional or better enabled by technology, it can be decentralized more. So, again, I think I've probably positioned about three different continuums throughout this presentation, but truly that's what's happening is trying to land somewhere in between. So if we talked about the architectural principles, well what are some of the organizational principles? Well, one thing is how frequently is your business changing and therefore how frequently are the demands on the analytics capability going to change. If you are in a low volatile business, anyway, I'm running out of words, if your business doesn't change very much, then that would be a place where you could leverage centralization for efficiencies and optimum organizational coherence and that sort of thing versus the variability where you need to be able to respond quickly. As well, how adaptable are your people? So if your business is changing frequently and your organization has not developed an adaptability from a skills perspective, you might want to consider organizationally how centralization may be able to leverage folks and their traditional skill sets while at the same time being able to outsource them if you will as the business requirements change. Another decision factor is collaboration. So the more decentralized an organization is, the better they will have to collaborate in order to maintain optimizations and efficiencies. And then of course we mentioned regulatory requirements. So regulatory requirements tends to drive a level of centralization, whether it is limitations or controls around tools, processes, what have you, it tends to drive towards a level of centralization versus the decentralized organizational model. Well, with just a few slides left, I'm going to go through a couple more evolutionary slides. And this one might be something that you guys have seen in other formats before. So the evolution from descriptive or historical hindsight-based analysis, again traditionally business intelligence-based through diagnostic, which is what if analysis into predictive, prescriptive and ultimately the ability to leverage the data to provide foresight. And the idea here is that there are different requirements from an analytics perspective that drive the data solutions and the use of data solutions. And I don't know if hierarchy was necessarily the right word because it may imply to people that the top of the pyramid is more important than the bottom of the pyramid, but that wasn't what was intended. It was the ability to say that there is a build process that happens where what you have established from a descriptive analytics perspective around really monitoring your business and ensuring that your data is available to help you make decisions to operate your business creates a very solid foundation upon which you can do the more sophisticated types of analysis. So whether you're doing the what if analysis, et cetera, et cetera, as you go up to artificial intelligence, each of these layers, if you will, builds upon the previous one. And so jumping to a machine learning sort of artificial intelligence, if you don't really know how to monitor and operate your business in the first place, may not give you the results that you're looking for because it's not based on strong foundations in your business. So another way to think about the solution. So here's a summary slide in terms of when to use one versus the other. So the as you've seen in the previous slide. So what happened so this is the historical sort of descriptive analysis as we go into what will happen the predictive. And again, I probably should have shaded this to make this look like a flow but reporting in business intelligence tended to be more historical what did happen and as we move into initial analytics and advanced analytics and data science. Again, that's how we think about the more future looking foresight driven forecasting driven. And what should we do next. And these categories are supported by the ability of or the capability of the organization and depending upon the application of the organization or the requirements of that organization for whether they're just managing to survive and operational efficiency or whether they have the the opportunity to automate, invest and anticipate. So I know we're coming up on the end it just, you know, inevitably I always end up going down to the wire here so you'll hopefully we've got some repeat viewers or listeners, and you'll recognize that this slide that came from a previous webinar and which we did an overview with the data scientist. And I think the message across all of this is that data science enables the future of analytics. This is a progression. The analytics 1.0 2.0 3.0 4.0 that actually is a is an excerpt from the Tom Davenport so credit to him as well in terms of how analytics has progressed over time. The idea is that when we're looking at, you know, the the 1.0 where the user was a specific persona or role designated to do very specific things that type of specific analysis was supported by specific data set and that was supported by specific tools and technology. And then as we move into more of the 2.0 that user tends to be looking at various data sets and they want to be able to leverage data in a much more flexible environment where they're looking at all kinds of different types of data. They're looking at traditional in house data combined with external data. They're looking at sorts of technologies that can support analytics everywhere that can support the experimentation and all of this is ultimately leading into deep learning. Now, one of the outcomes of this slide in the previous webinar was the recognition that deep learning is in fact an its own and unique capability in and of itself that is leveraged that leverages data science and that that deep learning analytics and the ability to trust the machine so much to be able to predict an outcome is where this is going so consider self driving cars consider machines playing games so that the releases that are coming out from Google and IBM and what have you. But that's kind of where this this is headed so quick wrap up and key takeaways. The definitions for bi and analytics are not clearly delineated. It is a spectrum architectures will vary widely both over time within a single organization and across the spectrum from traditional bi to sophisticated advanced analytics. So focus on fit for purpose or best fit architecture. Use a formal process to determine where and how you want to support the data supply chain. This is something where sometimes that formal decision making is good so that you can have a an agreement upon how you want to support your analytics organization with a scalable architecture versus just adopting one that is available on the market. And this will continue to evolve and so those organizations that fail fast that can experiment and they can be comfortable within a cost structure that enables that will be able to take advantage of these new capabilities. And with that we're at the top of the hour. Well thank you Kelly for a great presentation today. Thank you to our friends at Looker for their sponsorship program. There was an unanswered question about governance of unstructured data. A little bit off the topic for today but there's a September 13 webinar coming up on a similar topic to that so take a look at our website for that. For those who may be interested let me remind you that the Enterprise Data World call for presentations is open until September 22. So feel free to make a speaking proposal for EDW next year. And then just to remind everybody finally again we will be posting the recorded webinar and slides to Data Diversity within two business days and you will receive an email message following up from your registration for this session. So thank you everybody for attending. Thank you again Kelly. Hope everybody has a good day and for those of you in the path of the storms we certainly send you our best. And I wish you Godspeed. Thanks very much. Take care. Thank you. Okay. Bye-bye.