Hello and good morning once again. Today is Tuesday Entrepreneurship. My name is Joy Mochache. My social media handle is joy underscore mochache. This is why in the morning, remember you can reach us on our social media handles. That is hashtag why in the morning, have Tuesday Entrepreneurship. And the way you can do that is through Facebook, on Y254 channel, on Twitter, that is Y254 channel and Instagram, Y254 underscore channel. Welcome once again. We have a beautiful show today. We actually have two people that we're going to be talking to. We're going to be listening to their story and listening to how they put, you know, how they, I guess put food on the table every single day and how they're able to run life. And first of all, let me state that this is a couple and they strictly want to be introduced as a couple. And we are starting with Mr. Steve Kawa. He is the CEO of House of Steve. He's also into photography. He does training for runway models, commercial models. He's also an image brander and as well as teaching salsa dances. And his mate in his relationship is Valentine Anyango. They're in the same business. She's actually the managing director of House of Steve. And she's actually the current Miss University of Nairobi, the Kikuyu campus. And yeah, like once again, they are a couple, Karibuni Sana, Steve and Valentine. Thank you. Thank you, Karibuni. Thank you. Yes, so as you've seen today, we talked to people who are business-minded and you own a business that is called House of Steve. I can start by asking, when did House of Steve begin operating? House of Steve, a modeling agency. It started as a project that we wanted to handle as a couple after I won my title, Miss University of Nairobi, Kikuyu campus. So it was a project that I did to help nature talents, the models out there doesn't have a platform to do their thing, show their works and everything. So we started it early January. Okay. I see early January. And this is right after you won the title. I won the title last year, around May. Around May. Congratulations. Thank you. I'm sure it feels good. Yeah.  Thank you so much. Congratulations, Sana. And next question I'd like to ask is, what is House of Steve all about? And what do you strive to achieve as a company? What does your company do and what do you strive to achieve on a day-to-day basis? Okay, House of Steve is not only an agency, it's made me view it, but it's also a family. Okay. Which will help me as their father in the world, as their mother. Okay. So we train models, we do branding, and we also give them the photography, since you know I'm a photographer, a professional photographer, who has done shoots for over two years. Okay. So these are familiar with nature models. Those who want to be branded, we do that. And we offer them also an environment where they feel loved, they feel trained in many other services. Ah, okay. So you just snatched people and offer services of photography and modeling of all kinds of sorts, on a day-to-day basis. And how did the idea come along? I know that you are basically in this, being a miss, you are basically in this line of fashion already that is stamped out for you. But as a man, what made you enter into this idea? I've been a model before, by the way. Oh really? Yeah, I was Mr. Chiromo, 2016, 2017. Congrats. That's two years ago. So when I met Val, yeah, we've been dating for over a year. Oh okay, congratulations. So she was also interested in modeling. She did modeling early in high school. So I was her manager, I encouraged her. I did a much-trading and introduced her to more people. So after she won the title, we felt it was to start something big as a couple. Something, at least, our modeling could not end just from the title, as many would win the title and go back home. Right. So being a photographer since I quit runway, I ventured into photography. We came together and drafted an idea. That's when we came about to also stay. That's interesting. So you were both modeling and then... Ah, we are both modeling. Mukaunana. Was it love at first sight? Or did it first start as a friendship? Apparently, I'd been single for a year. And maybe she was also facing our problems in relationships previously. We knew each other then, started dating after one week. Oh, really? So that's love then. All right, I see that this is your logo for House of Steve. Is that right? Okay. Is there any particular meaning behind it, or is it just... Okay, indeed. Is there any secret hide something you've hidden there? Not so much, in the public. In the house, as you can see. Okay. That's a roof. Yes. Significantly implying that it is somewhere which you are roofed. You feel at home. Yeah, we don't only trade for commercial purposes, but we give you the sense of belonging. Right. Yeah, it's something I'd like to emphasize. The most agencies, they are so specific. Like, you have to be slain, tall, you understand, be a model. But we want to bring a different perspective in modeling. Well, even if you are crippled, you can be a model. Wow. Because modeling is not how people take it, that you have to be tall, you have to be cute. Modeling is all about, it's either selling clothes, selling someone, or you can sell yourself as an individual. Selling a brand. You could be so good in dressing, but you are not that good in working on runway. So you can do glamour modeling. For peaks, a crippled person could be so good in peaks. Yeah, we also invite and welcome plus-sized models. Okay. Yeah, so that's why the roof is purposeful for that we house anybody willing. Anybody willing? Anybody willing who has the passion to be a model, even if they happen to be a cripple or plus-sized. This is the place where they can feel like home. And this is where they can get the right nurturing. House of Steve. That's amazing. Shall we continue? How do you normally source your models on a day-to-day basis?  Because I have the title. And I want to reach, sometimes I reach out to models because I want to nurture them. I want to provide the platform and give them that opportunity. So most of the times we do shoots like this. That's pretty. And we market ourselves. We have posters and we have everything. Actually in my campus, there are a lot of models coming from there. So they all have dreams. I only provide the platform, yeah. Yeah, okay, I see. So all these people actually just, I love this idea. Do you actually have cripples who have come and they have an interest in modeling? Not yet. Not yet. I wish you could put a word out for that because I think I've seen something to do with that in the international realm where they don't, I've even seen albino models. Oh my gosh, that's amazing. I like the way House of Steve is going. And I'd like to ask, what are some of the things you learned as a Miss University of Nairobi? What are some of the things you learned that you feel are helping you in your venture with Steve? First of all, as a model, you have to be confident. What matters most clearly in modeling? It's not actually the beauty, the face. You have to be confident, elegant. You have to have the poise, the elegance, everything. So being confident as you are training for Miss and Miss University of Nairobi campus, you have to go with the mindset that there are only two people in this world. Those who want to be me and those who want to marry me. Like you have to be confident, be you, be yourself, and everything is just good. So the main thing is confidence. Confidence takes you places. Repeat that phrase you said so I can tell myself that when I'm lacking confidence, I can tell myself. There are only two people in this world. There are only two people in this world. Those who want to be me. Those who want to be me. And those who want to marry me. And those who want to marry me. I love it, I love it. I'll be using that one, you know, and I'm trying to talk myself into doing something and I'm finding it a bit hard, because sometimes that happens, yes? Yes. Yeah. And I'd like to ask, what piqued your interest to become a part of House of Steve? I know you said that you started together, but most models would rather just continue the modeling career. Not many of them would like to open a business or anything. Most of them are so focused on the modeling that they don't delve into other things. So what made you have that interest? I wanted to impact lives. Okay. Yeah, and so reaching out to somebody who doesn't feel they're good enough for the thing was my kind of stuff. So I just like impacting lives. I want to reach out to those who feel they're low, because in this country there's so many models and mainly we offer training for those who are upcoming, those who are starting from nowhere. Yeah, that's exactly what we do. Yeah. And as a managing director in previous Miss University of Nairobi, what other dreams do you have? It looks like you want to write a list Miss University, House of Steve Manjidarita. I still have big dreams in modeling, like currently I'm going to contest for Miss University Kenya. Okay. We're yet to be told down the dates. So I hope I win and then I go for Miss University Africa, you know, that kind of level. Slowly. I have a lot of dreams in this modeling industry and I want to become a supermodel. You want to become a supermodel? Yeah. Okay. That's amazing. By the way, hey, I'll root for you. I don't think we have supermodels from Kenya. We have models, but supermodel is a huge title. Yeah, it's a very huge title. My friends, yeah, do it. Have to go international. Yeah, do it. Do it. I'm rooting for you. And if I could go to Steve, what are some of the dreams you have apart from House of Steve? Is there anything else that interests you? Yeah. I'm looking forward to building House of Steve, first of all, because you have to build your brand. You have to develop it. At least you'll be identified from that place. Okay. At least somehow in future people will ask you, what have you tried? So that when you try something else, they believe in you. So I'm looking forward to expand it right now. So much focus in growing it. I want it to be something so big that is majorly right now quit modeling and other things. Focusing on House of Steve, developing people, making names. Yeah. Okay. Speaking of developing people, I've heard something about you people saying nurturing. You know, this is a youth station and we have a lot of university people who are watching and things are tough in the sense that employment is very low. And so sometimes our youth, especially our young girls and also some men, excuse me, also some young men tend to do extraordinary things to get money. Maybe that dating a richer lady, older lady who is rich or dating an older man who is rich, what we call your sponsors or what we call your sugar mummies. I love what you guys are doing because what you're doing is you're building each other. When you're still young, you're not with some old man. You're not with some old lady. You're trying to make this money together. I find that very important and if you could please speak on that. Okay. The major problem with the current generation, our peers specifically, most of us want the quick money or the shortcut way. We find that most of us are afraid of ourselves and we are so much impatient. When you vent into hustling, you quickly want the income. Yes. We are so much impatient and there is peer pressure, especially in campus. You find most ladies, they join when they are so innocent. Then they get into very bad companies of joining them into sugar, that is the sponsor thing. So for us, we decided we can grow ourselves at our age. That's why we started something. At least in the future, we can support each other and I'm proud of her where there's a problem. She helps me, she's my advisor and I'm a tour advisor, so we help each other. You're each other's backbone. And I encourage my fellows, the other youth, that it's never too late. We are still young. Please encourage them. We are still young. We are at 20s, 30s. That's still young. It's too early to give up. You're young until you're 40. Don't let anyone tell you otherwise. You're still young. So you find that most of us want to be rich at 20. Imagine. Two shortcuts. What's their rush?  And there's also pride in having your own man, your husband.   I think there's nothing much worth appreciating than that. But just someone tells you, I love how you do your things and that's what keeps me moving and keep us moving. Okay. Yeah. How about, what would you say about? I would like to tell the youth, actually I'm 20 years old. Oh, you're so young. Yeah. And so don't let your age come between you and your dreams. Go for your dreams and do your best. Actually, this issue of sponsors or being in a relationship with older men, it just destroys you because cash that comes in, either you're going to spend it on mine, you want to impress your fellows, you want to wear nice shoes and everything. But it has an end because at the end, this point is going to go back to his family. Exactly. And then you're going to be there. So get your guy, build something together and follow your dreams. And build each other together. Get that money together. Yeah. Yeah. I love what you guys are doing. It's really beautiful, by the way. I can see why you wanted me to introduce you guys as a couple because what you're doing is unique. Most people don't do that at TE, guys in university at the age of 20, opening a business together. That's actually very rare. Actually, we face a lot of challenges. Yes. But ladies in campus, getting a girlfriend may be too tough if you're hustling. And ladies just tells you, I miss the day to come to a camp, for sure. Why? Because you're not working. And you find that most ladies have been raised currently. They live all the luggage to their guys, all the problems, all their needs. You find that I go to campus, she equally goes to school. We start, we have lessons, but at the end of the day, she, you find that someone expects you to do everything. Yeah.  Yes. Yeah. So that's what I encourage our youth, that it's possible people can do it, people can grow together, it's something so, we are, we are still not very old. Yeah, that's true. That's true. We're not. We have a long way to go. We always say, you have time until you're 40, I don't like to hear, I'm old, I'm old, I'm old. There's still time to do, even getting married at 40 happens, by the way. So yeah, take a chill pill, relax. Let's go to the next question. I'd like to know, you've spoken of challenges in your relationship. Do you mind telling me some of the challenges when it came to opening up House of Steve as well as running House of Steve? What are some of the challenges you came across in terms of even competition? Challenges are very mainly, by the way, insensitive. Something like an agency is very sensitive. If you look very well at the pictures, most of the models are ladies. Yes. And these are some of the challenges people face. So for us, that's why we started it with her. You find that most guys start an agency and then they start exploiting ladies. So temptations, it happens. Then three weeks, four weeks, your name get ruined. Of course. This guy goes with ladies, so takes advantage. So ours is strictly professional. That was the first challenge we could have faced, but at least we had some shortcuts through it. You had a shortcut through it. Yeah. Second challenge, being that we are still growing, we're still young, so fans and other things. That's why we're trying hard, at least if we can get support. We're asking different personalities and at least we thank God there are people showing interest. OK. So we have some people showing interest in your work. Yeah, we have some people showing interest. OK. And we are really thanking God for that. OK. Yeah, another challenge could be dealing with models very hard. Really? Very hard. What? Many people use these three pictures and they think, wow, I want to join. Oh, that's you over there, isn't it? Or is that someone that looks like you? That's me. OK. So when you see this picture, you find many people want to join. Actually, all ladies look good. Wow, I want to be part of the team. Yeah. You know what these people do behind the scenes? The practice we put there, a lot of them, because we want quality stuff. So we have to train intensively. Yeah. Yeah, most of our trainings are on weekends. Do the models tend to cooperate or are sometimes there are arguments or there are no arguments? No arguments, but sometimes, because usually we train during the weekend, Saturday and Sunday. So somebody is like, oh, I have a family meeting, I want to make it. You know that pulls us back because she hasn't learned anything and you have to teach her. It kind of pulls it back. It kind of pulls things back and it slows the schedule down, doesn't it? Yeah, exactly. All right. Have you finished with the challenges or is there any other challenge you'd like to discuss? That's a serious challenge. What about in terms of competition? Are you trying to partner with anybody at the moment or later on? Later on. Later on. We first want to build it. OK. If someone, they can't see something you've done, yeah, we don't want to hurry things. OK. And we actually, I need two more questions. I need to ask what are the challenges that were presented to you when you were modeling and you tried to transition into a business lady? Is it something that you've always had in your mind? Have you always been business-minded? Because not everyone is business-minded. Some people would rather sit on a desk from nine until five, you know? And there are those who would say, no, I have to be my own boss, I don't want a boss. So how did that transition go for you? OK.  So the challenges that I experienced is, you know, not everybody can believe in you. So some other people will try and drag you down and they'll be like, no, you're not doing this right, oh, you're not doing this right, but I thank God I prayed and it's good. It's good. Doing business with what you love, what I passionately do, it's pretty good. OK. So far, no more challenges. That's good, no more challenges. And the last question I'd like to ask as we wrap up our interview, I'd like to know what do you envision for House of Steve as a whole, maybe in the next three years or five years, what is your three-year plan for House of Steve? In three years, we want to build a brand, we want to go international. In three years, we want to be able to have many models, branches, because currently we wanted to admit some models, but some were in Meru, Kisumu, and they cannot come to Nairobi for training. So in three years, we want to have branches situated in different places to help models from all over the country. Right, branches in Kisumu, Meru, like you've said. Yeah, exactly. So that may be in Africa and then the whole world. Yeah. That's great. I wish you well. I wish you luck. And to add on to that, we want to have a unique brand. A unique brand? We want to offer something different from what other people are offering. All right. Like I told you, we want to admit the crippled, the unique, the plus size, the people who the society think they cannot do it, those are the kind of people we are so much interested in. We want to nurture them so that we create a different image and attractive in the government for support. Of course. Because we don't want to blame the government for not supporting modeling if they cannot see if it is worth or they don't see a way they can benefit from it so that the image you want to create in the next three years something so unique that no one else has ever done. Of course. That is very unique. Like I said, it's rare to see the kind of models you're trying to bring in, but continue to bring them in and continue to do your research on all types of diverse models. There are so many people out there. Like sometimes I look at some models and I'm like, wow. Some of them are, I don't know how to explain, have you had something called vitiligo?  It's so interesting. Yes. It's so interesting. So I wish you the best. Thank you so much for coming. Before we sign out, do you mind sharing your social media handles, please, how people can reach you? No problem. Start with you. On Instagram, this is well, on Facebook, Valentine Williams. And we also have our YouTube channel, House of Steve Modelling Agency. Okay. Okay. My personal Instagram account is House of Steve. That is for company. Then that's Instagram. Then my personal is this is Steve. Then Facebook is Steve Cow. So I hope your viewers have been able to catch the social media handles. Remember, this has been Why In The Morning. Richeson tells what you think about the wonderful relationship and building a business together and building a name together as they're still in university and also being youth. And also make sure you put in the hashtag Why In The Morning, hashtag Tuesday Entrepreneurship and find me on underscore Joi Mochache. So have a good day, you guys. Have a beautiful, beautiful day. We're signing out. Muah.


I'll go box clips I Do think let me read or anybody who's actually using the system you should use it like this Let me let me tell you how this was designed to do it Yes, we're having fun with playing on continuous mode and just smashing you know hundreds of trades and whatnot over time But here's how it's designed.   That's one, two That's a winner. Unfortunately. I can't count it there. Let's scroll back and find Three losers somewhere out here. That's four o'clock So go to some point of the day where you've got you know, three losers in a row And then you come over here and select one of the two enter next or enter continuous continuous If you want to take like if um, here's a good scenario where continuous would be helpful right there, right? Because if it took the first one it's entered, but you want to take that second one Well, it would automatically disable itself after it took the next one Well continuous is helpful to be able to do dual entries system setups like this, right? So that would be a spot where you would want to enter continuous You would want to do your cadence, you know one two and let's say that almost three cool Then you want to take that trade maybe this one fails that one wins and then you take so there's winner There's winner winner winner chicken dinner. Boom. You're done make sense And then you look for a bonus trade and you execute it on your own make sense Press the like button share subscribe and comment below with your thoughts and what you'd learn watch this next video


We are so close, think about it, we're so close from maybe a day or two away for retesting this 50-day moving average. You can see how important this is last time. Welcome to Access a Trader, the number one community for those who are committed to taking control of their trading in order to achieve success, profitability, and longevity. Thank you for joining us. Here's Dan Shapiro to help you find your edge, master your process, and own your future. Hey guys, good evening everybody. Welcome to a Thursday's edition of theAxisTrader.com nightly wrap-up show. Sorry, there was no video last night. Usually I take Thursdays off to kind of reset my brain for Friday. But my daughter had a soccer game, a second round New Jersey State Cup, which they won yesterday. So sorry about I missed that. Boy, there's a lot of things that changed in the last 24 hours. We'll get to that in a second. Just a little bit of a business to discuss with you guys, for all you guys who know and maybe don't know, this is the live Twitter feed, right? This is where we put the pivots in the morning for all you guys who can't make the live webinar, whether you have jobs, whatever the case may be, life gets in the way. So here's all the pivots and all that good stuff. What we did, what we decided to do was kind of upgrade, right? Upgrade the pivot feed and now allow not only are you getting the pivots, now you are getting the audio of the live webinar. Obviously, this is kind of a big deal because you hear me literally speaking six, seven hours a day, answering questions all day, hearing the Squawk box, all the option flow and all that stuff. So it's a really, really cool upgrade for all you guys who are ever on the fence of getting into it, right? Or already on it, obviously you can wake up in the morning and have a little bit more value for your money. But the most important part is, again, we're always trying to add, continue to add more value to develop traders. So the reason why I'm bringing this up tonight, tonight only, we are keeping the old rate, I think it was like 79 bucks. We're only going to keep it for one night. Tomorrow it's going to go to 149, I believe. I have to check with the guys. But if you are interested, tonight is the last night to kind of grandfathered into the old price and tomorrow starts a new journey. So if you are interested, again, act tonight because tomorrow will be a completely different price. I think a pretty good value, especially for the developing trader. So let's talk about the market. A lot of stuff has completely gone different in the last 24 hours. Last time we talked, we were rejected off the 20-day moving average, rejected again, rejected again, lost the 5-day moving average, lost it again, confirmed lower. And yesterday the bulls did an absolutely great job of not only reclaiming the previous day's loan, but they reclaimed the 5-day moving average. And that was a very, very big deal because, again, the 5-day moving average is the absolute shortest sentiment of what's going to happen. It's not a green light for a full-blown bull stampede, but it is a very, very good sign that the bulls at least took control. And that's exactly what happened yesterday on the close. And what we saw today was this really good, aggressive gang-busting rally. Okay, if you look at the scoreboard, very, very impressive. You have the Dow Jones up 500 points. You have the Nasdaq up 250, almost 300 points. You have this really, really big stampede, but the most important part of this was technically not only did we stay above the 5-day moving average. We reclaimed back the 20-day. They got rejected back to back areas. And now we are in a very, very good spot to go back to the 50-day moving average. There's absolutely no supply between this and this right here. So we're talking about still another three and a half, four points available for the Nasdaq to run before we get to our next point of interest, which is the utmost important 50-day moving average. And I keep on reiterating this point. Nothing good, right? You ever hear that thing was one of these Chris Rock specials that says, if you're out at 3, 4 o'clock in the morning, nothing good is ever going to happen to you, right? Same thing with the bull market. The bull market can't function. Okay, where the market can't function any properly or can't have any good things happen to it unless it's reclaiming the 50-day moving average. And now we are so close. Think about it. We're so close from maybe a day or two away for retesting this 50-day moving average. You can see how important this is. Last time we hit the 50-day moving average, got rejected and rolled back over. So the bulls are not only need to confirm today's prices, but I think we need to confirm the 50-day moving average and then and only then we start filling in this whole entire gap to the upside, which is going to be very, very important. And, you know, today was a little bit odd in the morning, okay? Just because when the market gapped up today, if the Q's gapped up, they gapped initially right to this 363 level. And the reason why this was important, that was daily supply. That was the last time we got rejected on the 20-day moving average. So we kind of knew at least at the open, a lot of names were going to be completely stuffed and get sold. And that's exactly what happened within the first, you know, 15, 20 minutes. And to the market's credit, everything got really, really good, very, very strong. And it wasn't like the whole market exploded. Again, keep this in mind. A lot of names are still underneath supply just because the Q's reclaim doesn't mean everything under the sun. That's included in the NASDAQ 100 is going to reclaim at the same time. But slowly but surely, we started seeing a lot of names wake up. Again, we'll get to the pivots in a second. But the most important part was we started seeing a lot of names not only reclaim the 5 and 10-day moving average, they started, they almost kind of started jumping the gun and reclaim the 50-day. Like, look at AMD, right? Look at AMD. Yesterday, we claimed 50-day on the close, right, this aqua line. Today confirmed it. This is the highest close on AMD since going back to September the 1st, right? Took out this 1185 area and now the stock is trading 1220s after the close. Look in the video. This was definitely the move of the day today. Again, look at the importance of the 50-day moving average. It took out this whole channel here, confirmed the 50-day moving average. They started coming in very, very aggressively for the October 29 expiration 225 and 230 calls. Big move here confirmed this whole 13, 1350, 14 level. And now this thing looks like a 224 moonshot if the market continues to be strong. Very, very aggressive there as well. Look at Google, right? Another name completely reclaimed the 50-day moving average today. Took out this whole channel here. You started seeing weekly 2850 calls coming in. If you look at the upper channel, if the market continues to rally tomorrow, you could see your pot of gold going all the way up to this 2866 level as well. Tesla continues to be an absolute beast, but it's not the absolute beast we've kind of known for the last several years, right? It breaks out $15, $20. And the next day, it doesn't open another $15, $20. You know, goes up another four, goes up another five. And that's kind of what it's been doing now for the last three days. It took out this 807 level and it's almost in a weird way grinding higher. And now that it's pretty much closed at the highest formation of this whole cycle, it has one day left, right? One day left. And if Tesla could just get above the January 2021 highs, well, look at this, right guys? It stopped there perfectly today. If they could just get above the February 2021 highs, then look, man, we have a moonshot possibility to 900. We're still seeing super aggressive call buyers coming into the name for the 870s, for the 890 calls, all near-term expiration with six and seven-figure bets. So the market looks really, really good. And now our job is to wait for other names to kind of, you know, being pulled up at the same time. And I think if we can get even kind of a light open tomorrow, you know, any type of weakness, names like Google, names like AMD, names like Nvidia, names like Tesla, you have to buy them into rising 60-minute support because if everything gets pulled up, these are the strongest names coming out of 50-day channels. If they get confirmed, then everything else is going to get pulled up as well. So there's a lot of good names on tap for tomorrow. The most important thing is just don't jump the gun, right? Don't jump the gun. Don't anticipate. And the most important part is wait for these channels to confirm properly. Again, it's not a race. Remember, when indexes start to reclaim, okay, you know, not everything goes up, right? You need one by one by one. There's a lot of names still underneath supply, right? Amazon, not really participating. Apple, as strong as it appeared to be, it's still way underneath supply, right? Facebook, still way underneath supply. Names that I do like, though, that are still starting to come out of supply. Look at Ambrella, right? Look at Ambrella, beautiful-looking chart. Look at a name like, I think you guys have some names that are kind of shaping up here. Look at DoorDash, right? Look at DoorDash. Got above the channel today. Stopped at the linear regression line. If this thing can wake up and confirm, I mean, look how much room you have. You have room all the way back to the top of the channel here. Looks really, really good. Snapchat had some incredibly aggressive call-buying today. Very, very aggressively. They were coming for the 79 and the 80 calls. I like this channel here. It's been rejected here several times. If it confirms, maybe this thing can wake up as well. Square, look at Square. Maybe it doesn't confirm tomorrow. But look how close this thing is confirming this whole channel here. And if it does, you can have a really, really aggressive move here, especially Square has exposure to the whole Bitcoin thing. So there is definitely a catalyst. But I'm telling you, everything starts getting pulled up and we start confirming today's prices. You're going to have a day-to-move into possibility, the 50-day moving average on the queue. So let's talk about some pivots today. Again, some really, really aggressive moves. Again, this is where Tesla broke out. The 805, 807 levels, potential swing, stock traded today into the 820s level. Why is this 821 important? Again, we just talked about it. It's the February 2001 highs. This channel gets confirmed, guys. This thing is going to absolutely stretch. So definitely, definitely keep an eye tomorrow on Tesla. Nvidia, this is a big one, really, really big move here. 213.50 needs to build, potential swing over anything over. Literally over anything over 213 would reclaim the 50-day moving average. Nvidia went absolutely nuts. Stalled out a little bit today off that 13.5, 14 level. But when this thing finally got going, man, look at this whole channel here, right? This whole $13.50, $14 channels, it just really, really started grinding up very, very aggressively. Trading in the 18s here after hours. Again, you could see the potential where the stock can go, especially if there's more aggressive call buyers. Roku didn't do anything. It opened up basically 234 and died the rest of the day. GameStop did the same thing. Snow had a really, really nice move. A little tooth in for me, not really my thing, but 231.50. 232 needs to build. Look at this first move here on Snow. Took out this whole 231.50, 232 channel. Went all the way up to 338. Really, really strong move on Snow, right? AMD, again, really, really nice move on AMD. They came for the 12 and the 13 weeklies. It stopped perfectly at the pre-market highs. Of 1117 needs to build over that area. AMD right now is trading in the 12s after hours. Really, really nice move. Here's the 1117 area we talked about. It took that out and really, really nice move here. Pretty much trading at session highs after the close in the 1220s. Nice move there. XPEV, not a big move, only went up like 20 cents. Airbnb, I'm very surprised and never confirmed. I'm very, very surprised, especially had such a nice looking chart. Square, I still like, never got there as well. Again, 238 supply and that was exactly the highs. Nvidia, first supply to 1440s. They traded all the way up to 38 queues. Again, here's the macro channel. 365.70 is a very bullish signal for tomorrow. And this is why, right? So here's the channel right here. 365.70 closed at the highs of dollar and change higher. So again, you can see there's about $3 potentially left in this move. So nice move there on the queues. MMSI, nothing there yet. AMD pushing 12s. Here comes 1750s. They're coming for the 1750s. They started coming for the 225s and the 230s on the video. Google, beautiful, beautiful move on Google. Let's see here. Google, yeah. So 2870 needs to build. They were coming in for the 2840 and the 2850 calls. Here is Google, right? It took out the 2815 level and closed about 10, 12 bucks higher as well. Looks really, really good for tomorrow. So we're set up for tomorrow, guys. We're definitely set up for tomorrow. There's a lot of names potentially could wake up for tomorrow. Now our job is to kind of wait for them. Okay, wait for them to organically confirm and trade one by one to make sure everything is playing out the right way. So again, guys, if all you guys who want to join us on the Twitter feed, again, really, really cool feature, the audio of the webinar is now included. Today is the last day to kind of take advantage to lock in the rate and then tomorrow it starts again brand new. Guys, God bless. Have a great night and God's help. We'll see you all tomorrow.


So we'll try our best to hold the audience. We have an amazing panel at hand here with extensive background from banking to programmatic buying, with healthcare as well. I was just discussing with Ashish what they're trying to solve for at Narana Health. So today, before I kick off the session, I have a bowl you can actually drop in your questions. I don't know if you'll have time to answer all questions, but if you could, the bowl is being passed around with a notepad and a pen in it. Preruna, could you raise your hand please? So she's gonna pass the bowl. If you have any questions regarding big data, smart data, please drop in a question there and towards the end we'll try to pick them up and answer them. And the way that we've structured this panel is we have a couple of questions, about four or five of them. The panel members are just gonna express their expertise in that. Then we have a rapid round, rapid fire questions, which hopefully is fun as well. And then we will cool off with a few questions which actually help businesses that are looking to get into the journey or have already started their journey in big data or smart data and put that through as well. I just wanna put the note there that this is a very sensitive topic for many businesses because these projects are usually confidential. So we are not getting into the detail for any business but taking the expertise view, how they may have seen other businesses or their expertise in projects that they have seen will be expressed today. Without further ado, my name is Santosh. I head the dense to CXM practice in India. With my back, I am mostly a techie. I have worked through and through in technology such as VMware and Amazon and before founding. One of my companies is Socrates. We founded that business with the notion of big data when it was not even a moniker those days. And I've overseen projects across automotive, CPG segment as well. And I'll talk to, if time permits, talk to a few case in points that I have addressed. I'll pass on the baton to Mehir to introduce himself as well. I'll just talk about his twist with big data and smart data. Santosh Till then, who gets the hamper for the rapid fire? Am I audible? Sorry, yeah. Am I audible? Yeah. Yeah, okay, perfect. Mehir is suddenly a passionate towards technology and data as a marketer which is slightly different than what probably I should have done. But my academic background was technology and I think I wanted to become a software developer at some point of time in my life before I moved into marketing. So that still remains in there and hence I touch on my tech team more often than I showed. Thio. Good evening, everyone. So I'm Uthio. I had digital marketing, my tech liabilities and propositions at Yes Bank. Been a career marketer, been in BFSI for donkeys years now. So my passion is actually consumer behavior and that's what drew me into marketing. So when I started off, obviously there was very little of digital marketing. But over a period of time, the topic that we are speaking about, data, data is behind a lot of the consumer insights that you draw and how we understand consumer behavior and that's what's drawn me more towards the digital marketing side of things. So looking forward to a fantastic conversation ahead. Thank you. Siddharth, over to you. Yeah, hi, I'm Siddharth. I had a business called MIQ. So we are into programmatic and data and my journey has been with tech industry for a long time. I was earlier at IBM and Microsoft in the tech business and in the Microsoft days, I saw all the data migrating into cloud and that's where really the big data journey started. And then I worked at Google and I was heading Criteo's business in India as well. And I realized that I think there is a very important need for these platforms to have the data layer and convert that big data into smart data so that the customization layer for brands comes into play. And that's where at MIQ, we do a very exciting work in customization. We have a global data lake. We have 600 million consumers data lake in the India market and we are able to have a lot of third party data partnerships all put into a data lake through a data studio. And all this magic is of course done by our data science team. I look after the business part. But it's fascinating to see how we are able to enrich the first party data with our data lake and then figure out what is the right consumer segment for a brand. How do we take the planning and targeting to the next level? How do we look at the data analytics from a marketing perspective also to the next level? And what I also realize is that media is kind of becoming a bit of commodity and the data layer and the analytics when it is get attached with media, then a lot of value comes into it and that's where we are able to customize it for a lot of our clients. And we work with our agency partners very, very closely on that. We'll talk about a few case studies also, but yeah, this has been my big data to smart data journey. Thanks. I'm Dr. Ashish, just an asterisk. Please don't call me in emergencies. I'm just a PhD. Moving on, I'm working with Narayana Health and we are trying to shift from just being known as a hospital brand to an integrated health care brand. I think we've spent enough. Let's move on. Sure enough. So actually the first question goes to you, Ashish. So in the pharma space or health care, what do you feel is smart data? And how is it different from big data? Can you live without either of them or basically just open-ended? What does it mean to health care, essentially? What big data or to smart data is? So smart data is a definition we are still trying to understand and I think a lot of us. But from a big data point of view, I think it's one of the industry that actually works on personalization because each and every individual data is different. And there are more than 6,000 data points for your health care that needs to be managed or mapped over the course until the time you are 40 years old. So if you're talking about that kind of data points for almost, let's to start with 1.4 billion of us, that means it's huge, huge, huge volume that you are playing with. Now, from understanding or movement from big to smart, I think we were, while the big data came to the entire forefront, we were already applying a lot of academic filters by ourselves. We were saying, okay, fine, even if I'm seeing this, there can be certain other ways to look at it. And I think that is what transformed into looking the right cohorts. Not all 6,000 might be important for you given your age. Not all 6,000 might be applicable to you given your geography, given your gender, given your DNA. So from that point of view, you have to have to be smart in terms of knowing which are the ones that move in the direction with your age. So that's how we plan to kind of utilize it for the betterment and not bringing you to hospitals. Got it, got it. That's a pretty interesting case. We were just talking about how preventive is by itself a segment of its own and using smart data there is something that can trigger. Uteo, so what is your interest with big data and smart data? What is, how do you differentiate that in the FSI, the FSI space? So I'll take possibly one step back and I will try and draw an analogy in terms of what big data and smart data means to me at least. So every monsoon season, there is a fair amount of media coverage in terms of, you know, how much rain, what millimeters of rain the city has received, right? And then there is also coverage in terms of, you know, what is the percentage of the lake levels which supply water into Mumbai, right? So every day you have a news item saying Vaitarana is at 80% and, you know, Modak Sagar is at 60% et cetera, et cetera. Now, obviously what, finally we are trying to achieve or what finally we are interested in as consumers is the water which comes into our homes, right? Now, for me, the water, the data is the rainfall. The water which is getting accumulated in the lakes is big data. There is some amount of filtration of that data which is happening while that water is being transported to your house, right? Which maybe the municipality is doing or the water supplier is doing. Then once it reaches your house, you do another level of filtration through your water purifiers before that water comes to your glass and it becomes consumable for you, right? That is smart data. Something which is consumable, which helps me quench my thirst, which helps me get to what I want out of this entire, you know, universe of from rainwater to lakes to water supply to coming into your glass, right? And that's for me how I look at big data and smart data. Now, I would say smart data is possibly something which has been jargonized a little bit. It is something which inherently we have always been doing, right? The vast amount of data that we accumulate, all of us study that data, we work with that data to drive the outcomes that we are trying to drive for our respective businesses. In various ways, now maybe a lot of it was a lot of manual intervention, manual analytics that we were doing and we were doing all the analytical models. Now, you have a lot of algorithms which is helping you filter out the data and give you what is actually consumable to you. The reason this is absolutely required is also because the volume of data is so huge. There's two and a half, three quintillion bytes of data which is getting generated every single day in the world, right? Now, obviously we cannot store so much of data and storage costs are going up by the day. So how do we then define what are the data signals which we want to consume, which we want to retain, which we then want to filter and make it into actionable intelligence which will help us drive the outcomes that we want. So that's what big data and smart data is for. Nice, that's a fantastic analogy actually to put this in perspective as well. How, and said here, I would love to know how in the programmatic world and in media buying, like how is this, of course, we are crunching a lot of numbers. We are measuring our success with QPSs. How is it that the big data is more consumed and how does this make our smart data perspectives, another make our campaigns better or smarter? Can you just give your perspective? Yeah, we were discussing this, right? So we have a very interesting case study with luminous inverters. So it's a category where you don't get up in the morning and say that today I aspire to buy an inverter, right? It's a utility, but what you want to do is you want to reach out to the consumer when they are really thinking about the category and thinking about your brand also probably. So for this category, what we did was we found out a government website where there is a lot of data on scheduled power cuts across India, right? And so our engineering team created a crawler and got that data and fed it into the DSP, right? So this is an example of converting big data into smart data. So as an example, if in Jaipur, 2 to 6 p.m. on Sunday, there's a power cut scheduled. So the campaign will go live at 2 p.m. You will see the ad on your mobile during that time. And it is also a dynamic creative in terms of the languages vernacular. So it will be in Hindi. And geocontextual intelligence is also there. So it will also tell you the nearest address of Luminous Inverter Dealer where you can go and buy the Luminous Inverter. And for unscheduled power cuts, what happens is consumers will tweet, right? When I'm in my locality when power goes off and to express my frustration, probably I will tweet or so we collected all that data as well. So even for unscheduled power cuts, we were able to take the campaign in those areas in the right way. And so the campaign was a big success because consumers really got their information in the right context when they're really thinking about having a power inverter. 6x times benchmarks, huge amounts of footfalls, huge amounts of sales increase as well. And the agency with whom we did, they won MVs and a lot of other awards as well. So that's the power of converting big data into the right data, as you rightly said. And then I was able to really give the right information to the consumer at exactly the right time. Fair enough. Actually, the part that I resonate the most is that before we tried to solve, like getting into solving a big data or what AI or smart data essentially, I think the bigger ask is what is the core problem and what can data at times need not even solve the problem. But is there enough, again, see, anything with big data needs volumes of data to put this together. And then comes the ability to, if there is a volume of data, then taking that data, coming, making smarter insights off of it. But the core thing is identifying what the core problem is and then tracing it back. And here, we love to understand how EdTech is kind of taking this shape up. And what's the... You are taking me away from marketing a little bit, but then I think Siddharth will always do a better, justice to marketing than me, right? So I think the first premise that, which all of us understand is, if you go back in time, there was data, but there was not so much. And then we got ability to store a lot more data and we started storing a lot more data. So we became hungry for data. And in our hunger, we started collecting so much data that we weren't able to look at. When you collect so much of what you can't utilize is when you have a bit of a trouble, right? While, that is true, there was still, as mentioned by Mr. Day, that there was still folks who look at the data, create filters and try to make meaning out of that and then utilize it. Eventually, we've come to a time when, I think we've come to a time where all of us don't have patience, right? We don't want to watch a long-form video. We want to see a short-form video. And we don't want to spend hours running a report on SPSS and wait for something to come next morning. We want to look at some insight right away. So that's where I think all of us want to move to. I don't think all of us have moved there yet. We have a lot of data, but moving towards making meaning out of that data and using probably AI to try and look at the data and answer some of our questions is what, in my mind smart data, the term should try and look at. Now, the premise which you are talking about, right, is the most critical premise. Earlier, when you were trying to filter out, you had a question, you had a hypothesis, and you were looking at that hypothesis to find out and answer, that premise doesn't change, right? So if you want to have AI on top of your big data and it's not going to do anything unless you ask AI what the right question or what right hypothesis is. And then you will get a certain data as an output which is input for your thought process, and then you will have to still think through whether that makes sense to you or not, right? And I'll give you an example of marketing site first too. If you look at any media analysis tool and try to understand what your audience does, okay? And most often you say, hey, my audience is interested in entertainment. My audience is interested in music. But that's like given, that's like 95% of the internet population, right? That's just information for me and it's not smart, right? Now, I have to figure out what are the other things that the data is talking about and look at what's right thing to do, right? For EdTech, it's slightly away from marketing for me, but I think there's a lot of room for, you know, education providers, right? I'm not saying EdTech only, I'm saying education providers to try and look at the data that they have to identify how they can solve for some real problem, right? The real problem could be, hey, you know, my faculty has a very good rating, but, you know, the students are not sailing for example, you know, is there a drop rate in the lectures that they attend and things like that, right? Or the other way around, right? My faculty has a very poor rating, but everybody is stuck around to the lecture. Did he like not give them a very good score, right?  But this is where he'll probably make sense for somebody to look at the data and identify what the real problem is, right? Because would you look at, you know, without putting intelligence, we'll probably drive you towards things, you know, which is absolutely bizarre. There enough. But in this market, right, which is, so in India specifically, again, we are now the largest in population as well. And the country is mostly focused on branding and identifying. Mostly the problems are towards customer acquisition, right? What is, and here, what should, why would businesses invest and in big data, actually, what is it? How does that enhance the end consumer experience through big data? Should the focus be, like, because if your core problem is customer acquisition and branding solves that to a large extent, but how has big data and AI helped in enhancing consumer experience and as a result, improved customer acquisition or retention for that matter? So is that, what's your take on that? Fantastic question. So, you know, when we speak about, so let's start with data, then arrive at big data and then try and answer the question. So, you know, each of us, all our organizations, we have our internal source of data, right? So, say for example, in a bank, we know the transaction data of each of our customers.  How do I make sense of those millions of transactions? Can I afford to sit through each and every transaction and figure out what is happening? Now, that is data which I have with me. That data will give me some sort of a sense of how Santosh is as a person, for example, right? But then the way Santosh interacts with a bank or uses his credit card does not define who Santosh is, right? So how is he interacting, say for example, on social media websites, right? Within social media websites, let's go, you know, one level further. Am I the same person when I am on Instagram or Twitter versus the same person when I'm on LinkedIn, right? So, there are a multitude of data sources which help me understand what Santosh is. What is he looking for? What are his, say, passions, interests, et cetera? And can I then utilize that data to figure out that Santosh is planning a vacation and therefore should I pitch and travel insurance to Santosh at this point in time? That's where you know that the journey from data to big data to assimilation to filtration and finally targeting the person at the right moment at the right time comes through. And that's where I think smart data comes in. I'll just pitch in. So rather than pitching him the travel insurance, pitch him how to make money for that travel, I think you will win him for life. So why you're on it actually. So here is again in the space of healthcare. Does it board up on to actually invest into big data and smart data? Do actually do customer acquisition or retention? How does that play in the perspective of? See, as a brand, we don't want you to come again. Absolutely not. And that's our philosophy as well and for the past couple of decades. And I think we're gonna be on the same path. But coming back to the question, I would not see it as acquisition or retention. Let's look at it as your journey of healthcare. We are currently working towards solving the problem of what next and giving you well in advance the things that can allow you to make it what next, what next. Just like how it works in Ecom, what's the best next offer? We changed it to by saying that what is the next negative that can come? But these are the four solutions that can definitely, if you follow, can definitely make those negative go away. And in a sense, how does this improve customer experience or in this, I mean, I don't wanna say patient experience, but how does this improve the customer experience? It's absolutely a consumer experience.  And the whole experience of healthcare is under stress. You're not happy paying that money, right? Now, if you're taking out that stress out of that equation and if as a brand you're able to do that, then that means I think from our point of view, at least inside out, you've delivered the best consumer experience. And this will start getting understood by patients or consumers over the, say, couple of years or say a decade or so. But that's our intention. And that's definitely add to loyalty. I was gonna say advocacy of brand and the campaign essentially. I just want to quickly add here. So there's a different case study which we have, which is not consumer acquisition, but so there's a client of ours, cab player in US market. And so they were seeing this consumer behavior that some consumers would book a cab, but they will not turn up. So what we did was we basically understood the profile and we did a breakage analysis. And we said that, you know, okay, for marketing channels and even from an organic perspective also, let us not reach out to these consumers. Right, right. So it's the opposite of consumer acquisition. As a use case from there. True, true. And what I have also seen is in many of these cases, if you're able to harness the power of this, you can create the ideal customer journey and even devise those trigger points to actually make those who probably are in the same cohort, but have not taken that journey. And as a result, one is oppression use case. The others are identifying what the next best offer or next best action could be and take that up from there in. But may I tell you one thing? Like if a business needs to discuss or kind of get into this journey of, like should they explore a build? They build it on their own. And of course, I'm gonna get to you very soon, but because you've taken that journey through, but should they build versus buy? How should a business decide? When I say build versus buy, here is where, see, building the entire infrastructure of big data requires significant vision as opposed to, I mean, money's to do so, but here I'm asking what would be the priority index to actually build versus buy for business to take up this human best task of big data and AI? Yeah, Mike. Sorry, I wasn't hurry to try and tell you my thoughts. I forgot the mic, yeah. So, see, it's actually not build versus buy. Few things you'll have to buy, few things you'll have to build, and eventually, for example, if you want to store your data unless you are Amazon or Google or Facebook, you don't want to build, unless your data's so much, you don't want to build a solution to store your data.  And then start your journey of storing the data. But that's about storing the data, big data, collection, right? You can probably buy some collection solutions, and that's fine, right? But when you want to get into the journey of collecting data to making meaning out of that, right? Or to turn that, there will be few off-shelf solutions which you can purchase, but in a lot of cases, you will not be able to get an off-shelf solution, right? Then you'll have to definitely go for a build now. Build doesn't necessarily mean that you build it yourself. You can hire a tech partner to build it for you. Right. But yeah, it's a mix of both that should work in most cases. Right. Unless you're very small and you know that there's something for your industry which is off-shelf ready, available from somewhere. And sure, Ashish, I mean, we were discussing about this, and what would be your chain of thought in this direction versus build was in buying this? The organization took our decision six, seven years back, and it was not that we had the resources or we wanted to build, and nor that we had a vision of how to utilize it. The requirement at that point in time was our current providers were getting out of hand in terms of servicing the kind of data that we had. Right. So when the decision was made, decision was made to just keep the ship running, the engine running. And today, I think it could be learning for somebody who's planning to do, but we can definitely say today, we are able to serve data. And now we are passing on that legacy and intelligence to other hospital chains as well. Yeah, and that's a great journey, actually. And what I have seen is at times when the decision, such as this has to be made, it also depends on how soon would you like to go to market. And when you do decide to go to market with an off-the-shelf product, then you need to know that it won't support all use cases. So we'll have to make a conscious call what use cases they are and shortlist because your priority is to go to market faster. I have seven minutes left and I have a lot more questions, but in interest of time, let's get into the rapid fire. So this, by the way, this question has no right answers. I have some seven of them here, and I think we should take about a minute. But here, please, whoever wants to answer to it, please do. And again, as I said, there is no right answer here. So. No hamper also. Sorry? Hamper. There are some, there are, well, I mean, we got to take collective responsibility towards it. So do you feel businesses are embarking on this journey of big data and AI, due to competition or peer pressure? Peer pressure. Competition. Competition. I think, so after COVID, a lot of brands or a lot of companies have realized that everything is going digital. And that's why they are now more aware of data and how they should use it, et cetera. So it's a mix of competition and, you know, digitalization due to COVID as well. Garret. Garret. I thought this was going to be funny. I think a lot of, was my answer will not be correct. It'll be a little funny. I think a lot of guys are getting into this journey so that they can waste the money that they have. It's that shiny guard that I want because my neighbor has it. That's true, that's true. Which industry do you feel has embraced this big data and AI effectively? I think Attic has done a lot, right? Then BFSI is doing a lot as well. I would say, Pharma, we are seeing a lot progressively. Yeah, e-commerce, of course. Garret. Yep, I think all the major players, right? Your Google's, Amazon's of the world. Hitech. Yeah. So they obviously are using it very well. BFSI, like you said, I was reading somewhere, you know, some global data survey which was done. So apparently some 23% of the overall big data sort of revenues are driven by BFSI companies. So to that extent, there's a huge amount of investment on this side of things for BFSI. And at the end of the day, you know, just maybe, I know there's rapid fire, but I'll take 15 seconds, maybe to go back to the previous point, you know, the shiny new car. I would potentially put it slightly differently. It's about whether the consumer is buying your car or your competitor's car. And for that, I need to understand the consumer better. So if I don't invest in understanding the consumer better and you know, like we've said at the top of the conversation, you know, the time to market is very critical. So somebody else will go ahead and sell their car ahead of you. True, true, sense. Which industry do you feel is not going to reap any benefit at this point in time? A very controversial and you may choose not to answer but also that's fine. Any industry that you feel is not going to reap benefit right now. I can't think of. This, sorry, you're going to say? I would say that for very large B2B sort of industries, their use cases are limited as opposed to B2C companies. But having said which everybody has need in varying degrees and that's why, you know, every company will have their own strategy. And like to varying degrees, there needs to be investment into smart data. Even during elections also, now a lot of big data will get converted to smart data and will be used. You get, you create videos now based upon how the consumption of that data is essentially. Absolutely true. How, when would you imagine this would have happened, right, if you thought of it? So do you roll your eyes if someone starts selling you use cases of big data or smart data? I'd rather the question is more, what facial expression would you make when someone is selling big data or smart data to you? So they roll your eyes or is it like? My reaction is curiosity, right? I would want to understand what they're doing, how they're doing it. Okay. Yeah. So there are more use cases still to be solved, essentially is what that is. Yeah, I mean, we are just starting. Got it. For me, I think what I am more interested in is in how other industries are using it. Are you using it? Fair enough. So whenever someone is wanting to have a discussion, I would be more interested, not in terms of what they are doing for BFSI, but what they are doing for SEA healthcare or SEA net tech. Fair enough, fair enough. I think I'm always curious to know. No. Got it. Always. Do you feel we have enough talent pool in India to manage the big data? What do you feel about it? Is it fantastic, good or bad? I think just throw any problem at us. We're always good at it. Good at solving it. Yeah. We are good, we will be fantastic for sure. Fair enough. Any take? I think there's a lot of talent in India. What I see is that we should have more and more apprentice kind of programs where students and apprentices are able to work with the industry. And really, because whatever I've learned, whatever I've done, I've learned through my job. All right? So I think that's an urgent need for us to do it for sure. Got it, got it, got it. Another? I think we have shortage of talent. I feel that too. I think it operates in cohorts where some parts we are really good at. And if you take specific technologies and look for talent in it, there is shortage. And the shortage has driven up whatever their asks are as well. So it is becoming a tough spot to be in, essentially. I mean, while I'm saying that, I'll not say that we are not producing enough. Yeah. But we are exporting a lot more than we should. A lot more than we should. That's true. A fun one here. Between Darius III and Alexander, the great, or would he still be the great if he had access to big data, if they had access to big data or AI? Who would have won the war? So I'll rephrase this. Between Darius III and Alexander, who would have won the war if they had access to big data and AI? This is 329 BC, so. No, if that time there would have been access to data and AI, probably a peacemaker would have prevailed. That's true. What a word. We needed them to also have, you know, Instagram and TikTok. Then there was no chance of no war. If I took a very controversial stand, I think Alexander would have still won because he was actually in it. He was leading from the front, so chances are he would have been thinking about what the vision would be towards winning the war and then trying to kind of use big data the way it is. No? You're already, I already know what your next move is. Right. Which department in the company would you completely offload to GPT and make that as a primary touch point? It could be internal, external, but what department would you completely offload to GPT? Again. I'm not sure. Please feel free to pass. That's fine too. I'm not sure if completely, but I think a fair amount of the MIS work, which is done, can be done, financial analytics or got it. A fair amount of, I would say, dashboarding, for example, and a lot of the MIS is that the team spent so many hours to create, or now we are seeing some use cases in terms of PPTs getting generated at that click, how good or bad they are. That's a discussion for another day. Just add on top of it all the mundane jobs. Because you're going to see those excels day in, day out, you'll see those PPTs day in, day out, everything, like which I will not come to office for. Right. Let's finish them off. Repeatable tasks, essentially, that's true. This is the last set of questions. We are two minutes over time. I'm sorry. Do I still have time? Oh, man, she's okay. So let's take those questions then. All right, some special questions coming from the audience, I believe. We'll take, I mean, there are many here. So I'll try my best to answer these offline. The question is, how to use the data that helps us to navigate and optimize to increase the latency with the help of JNAI or follow the compliance? As follow the compliance, okay. So basically, I think to interpret this would be what would help optimize and navigate using JNAI? What kind of data helps to do that? Look, so JNAI is something which is still, I would say, a fair amount in its nasancy. The problem, so while JNAI can help solve for a lot of things, and we are, every day we come across new use cases where it is getting used, the problem that AI or JNAI will always have is that we do not know what has gone into delivering the outcome. You will only see the outcome, right? So especially where we are looking at analytics, what sort of understanding has gone into processing the data? What are the data signals which have been captured and on what basis has that outcome been given to you? Is not known to us as humans. That is not something that you get from the machine. And that for me is something which we need to carefully consider. So if we go into the market, just based on the responses that we get there, there lies a risk of it also not working at some points in time. Yeah, the hallucinations or the artifacts of JNAI, essentially. There's another question here. How will smart data slash AI and big data in collaboration impact media and advertising ecosystem? So would you want to take that? No, I think it's already impacting a lot, right? Meaning all the big tech are using AI extensively. And so the more the data comes in in terms of digital format, it can be used to understand the right consumer to be targeted, right creative to be communicated. And also in analysis that, you know, which are the segments which are really working well for you. So already so much of usage of AI for media is happening. Can I add something? So we are in a world where we see probably about 150 to 200 ads a day on an average for that 650, 700 million Indians that we are. And probably some of those who consume internet so much more probably see a lot more ads than they should. We are also getting into a situation where a lot of us don't want to see so many ads and we have blind spot for those ads. If AI with big data can help a market here reach out to the right guy, minimize the waste, it essentially means that I am not going to sell so many ads and still reach out to the right guy. I am ready to pay the money that I was going to pay for those three, four, five additional impressions instead of reaching out to that one guy. To that one impression, which essentially mean that industry at some point of time will be able to reduce the collision of ads, reduce the frequency of ad and increase the yield, and improve the user experience. This is possible. I'm not saying that this is going to happen. But this is a very, very strong use case for marketing platforms to look at. While I'm saying that it's not going to happen tomorrow. It's going to take a lot longer. But yeah, a lot of investment will have to go from tech platforms for that. And I will to say that I will reduce the number of impressions. I have many more questions here, but sorry, apologies. We are six minutes over time. Thank you so much, panel. It was an amazing round with fantastic experience from there. Thank you so much.