1 00:00:00,000 --> 00:00:01,800 So we need to step to World War One. 2 00:00:02,900 --> 00:00:07,800 Okay, there was World War One and there was it was difficult 3 00:00:07,800 --> 00:00:11,000 for folks to travel by ship between India and England. 4 00:00:11,000 --> 00:00:12,900 Okay, let's wear our story starts. 5 00:00:13,300 --> 00:00:18,100 So so far we've talked about these researchy and Academia 6 00:00:18,100 --> 00:00:21,900 stuff like that right here. So computers are in use in the 7 00:00:21,900 --> 00:00:26,000 1960s for doing grunt work. And was the grunt work was 8 00:00:26,000 --> 00:00:28,400 payroll processing through tons, okay. 9 00:00:43,000 --> 00:00:47,700 The podcast on all things data. This podcast is a series of 10 00:00:47,700 --> 00:00:50,600 conversations with experts and Industry leaders in data. 11 00:00:50,800 --> 00:00:53,600 And each week. We aim to unpack a different 12 00:00:53,600 --> 00:00:55,500 compartment of the data suitcase. 13 00:00:56,100 --> 00:00:58,600 I am your host. Got the chassis that I'm a 14 00:00:58,600 --> 00:01:01,800 blogger, use me. Columnist book author, and 15 00:01:01,800 --> 00:01:05,800 former data Etc consultant, I currently head analytics, and 16 00:01:05,800 --> 00:01:07,500 business intelligence for delivery. 17 00:01:07,900 --> 00:01:09,400 One of India's largest logistics. 18 00:01:09,400 --> 00:01:12,300 Companies. You can follow me on Twitter at 19 00:01:12,300 --> 00:01:14,700 garthok s. That is Kar. 20 00:01:14,800 --> 00:01:22,300 Thi KS and read my blog at no Intruder.com., That is n. 0e, n 21 00:01:22,500 --> 00:01:24,800 t. Hu d, a.com. 22 00:01:26,200 --> 00:01:28,500 All the is expressing his podcast belong to me, and my 23 00:01:28,500 --> 00:01:31,800 podcast Pious and it Not reflect the views of any organizations. 24 00:01:31,800 --> 00:01:33,600 We might be associated with nothing. 25 00:01:33,600 --> 00:01:43,600 Discussing this podcast should be taken as for a cheap for a 26 00:01:43,607 --> 00:01:46,700 few days that I was watching Alma Macos the Netflix 27 00:01:46,700 --> 00:01:50,500 documentary on life in IIT character in that show. 28 00:01:50,600 --> 00:01:53,600 The graduating students talk about data analytics as a 29 00:01:53,600 --> 00:01:56,400 possible career path in case they fail to die and the core 30 00:01:56,400 --> 00:02:00,000 engineering job. So how did we get here in India? 31 00:02:00,400 --> 00:02:03,500 How did data and analytics become so mainstream that 32 00:02:03,500 --> 00:02:07,200 graduating students talk about it as a possible career path or 33 00:02:07,200 --> 00:02:10,699 rather as a backup possible career path to understand this? 34 00:02:10,699 --> 00:02:13,100 We need to understand the history of what we now called 35 00:02:13,100 --> 00:02:18,200 analytics and this history goes back over. 100 years, Our Guest 36 00:02:18,200 --> 00:02:23,100 today is envious into co-founder and CEO of easa research and 37 00:02:23,100 --> 00:02:25,600 digital for the last two decades. 38 00:02:25,600 --> 00:02:27,900 He has been working on R&D and Innovation, man. 39 00:02:28,100 --> 00:02:32,100 Judgment, especially focused on ID is working on the evolution 40 00:02:32,100 --> 00:02:35,200 of Business and Technology focused on ID and related 41 00:02:35,200 --> 00:02:38,300 domains in the Indian context in earlier about that. 42 00:02:38,600 --> 00:02:41,400 He was a consultant advising msc's setting up high 43 00:02:41,400 --> 00:02:45,000 performance, our Indie and it organizations in India who was 44 00:02:45,000 --> 00:02:48,600 also a researcher in the RND arm at Infosys and quote a couple of 45 00:02:48,600 --> 00:02:49,400 u.s. Paintings. 46 00:03:05,100 --> 00:03:07,400 Let's just go very far back. Right? 47 00:03:07,400 --> 00:03:14,100 So, so I guess the analytics in some sense is sort of the tunnel 48 00:03:14,100 --> 00:03:16,400 in India. It's connected to sort of 49 00:03:16,800 --> 00:03:20,100 economics and the and industries and so on. 50 00:03:20,100 --> 00:03:23,200 So can you take us back maybe all the way back to Independence 51 00:03:23,200 --> 00:03:25,400 or something? So classic, we need Step really 52 00:03:25,400 --> 00:03:26,900 back. Okay, so we need to step to 53 00:03:26,900 --> 00:03:29,000 World War One. Okay. 54 00:03:29,000 --> 00:03:34,200 There was World War One and there was, it was difficult for 55 00:03:34,300 --> 00:03:37,000 folks to travel by ship between India and England. 56 00:03:37,000 --> 00:03:38,900 Okay. Let's wear our story starts. 57 00:03:39,500 --> 00:03:41,800 There was a young Gentleman by name PC. 58 00:03:41,800 --> 00:03:43,300 Mahalanobis. Okay. 59 00:03:43,300 --> 00:03:47,900 Who didst? I pause, try pause in Cambridge 60 00:03:47,900 --> 00:03:52,300 University of Cambridge in physics, and was set to return 61 00:03:52,300 --> 00:03:57,000 to India and death. And World War One started and 62 00:03:57,000 --> 00:04:02,100 his stay in India got extended. So just before he got to India, 63 00:04:03,200 --> 00:04:07,300 he was with a tutor and the tutor in. 64 00:04:07,500 --> 00:04:09,000 So, the studio system is pretty interesting. 65 00:04:09,000 --> 00:04:10,200 Right? So, you are bought a bunch of 66 00:04:10,200 --> 00:04:12,300 students that you sit with a tutor and you talk about 67 00:04:12,300 --> 00:04:15,800 everything under the sun and life and meaning of life and 68 00:04:15,800 --> 00:04:19,600 things like that. So, one of his tutors mentioned 69 00:04:19,600 --> 00:04:23,900 to him about relatively new field and interesting Journal 70 00:04:23,900 --> 00:04:26,200 that published. Field and this new field was 71 00:04:26,200 --> 00:04:30,800 statistics and the journal was biometrika. 72 00:04:31,300 --> 00:04:33,900 Okay, so so p.c. Mylow Nobis. 73 00:04:34,200 --> 00:04:34,700 Say that. Okay. 74 00:04:34,700 --> 00:04:38,600 Let me just say you don't read a few of these journals and he was 75 00:04:38,600 --> 00:04:42,000 so intrigued by what was published there and statistics 76 00:04:42,000 --> 00:04:46,700 in general that he bought a, you know, whole bunch of, you know, 77 00:04:46,700 --> 00:04:49,900 old issues of biometrika on his way back to India and we're 78 00:04:49,900 --> 00:04:54,200 studying them with the ship. So, the interesting fact for 79 00:04:54,400 --> 00:05:00,200 folks in non latex, is that biometrika was founded started 80 00:05:00,400 --> 00:05:02,500 by this gentleman called Karl Pearson. 81 00:05:03,000 --> 00:05:05,900 Okay, Pearson off. Yeah. 82 00:05:06,200 --> 00:05:07,900 Yeah. The correlation correlation is 83 00:05:07,900 --> 00:05:10,600 p. I'll use your Chi Square method 84 00:05:10,600 --> 00:05:12,700 of moments and stuff like that, right? 85 00:05:12,800 --> 00:05:17,900 Yep. So so, so mahalanobis was, you 86 00:05:17,900 --> 00:05:21,100 know, it just caught on to it and fell in love with 87 00:05:21,100 --> 00:05:24,300 statistics. So, he gets back to India and 88 00:05:24,300 --> 00:05:27,700 then his, obviously his return back to England is delayed. 89 00:05:28,000 --> 00:05:32,300 So he gets a faculty position at the presidency college. 90 00:05:33,100 --> 00:05:39,200 And in the due course, he starts lab all the statistics lab, and 91 00:05:39,200 --> 00:05:41,100 this lab is essentially his office. 92 00:05:41,400 --> 00:05:42,200 Okay? Okay. 93 00:05:42,200 --> 00:05:48,900 So, so the war ends, he's comfortable in Calcutta teaching 94 00:05:48,900 --> 00:05:53,000 a bit of physics and, and studying, a lot of Statistics 95 00:05:53,000 --> 00:05:55,500 and then whoever is interested in statistics. 96 00:05:55,500 --> 00:06:02,100 He offers courses in statistics to them and soon he is the, you 97 00:06:02,100 --> 00:06:04,100 know, the students of Statistics are India. 98 00:06:04,100 --> 00:06:08,500 And, you know, we have the, we have the Bengal famine coming in 99 00:06:09,000 --> 00:06:13,300 and mahalanobis was saying Gates by the government of Bengal to 100 00:06:13,300 --> 00:06:18,900 estimate the the, you know, the amount of party that will be 101 00:06:18,900 --> 00:06:21,900 harvested in Bengal and surrounding regions. 102 00:06:22,200 --> 00:06:25,800 So he does a lot of cool work there and for this uses 103 00:06:26,300 --> 00:06:29,000 mechanical calculators, right? So at that point in time, they 104 00:06:29,000 --> 00:06:32,200 know electronic calculator, so he gets that and he gets a, you 105 00:06:32,200 --> 00:06:36,300 know, a bunch of people who are actually the computers humans 106 00:06:36,300 --> 00:06:42,300 Computing to, to Crunch all the data that is collected and, you 107 00:06:42,300 --> 00:06:44,200 know, run the regulations and stuff like that. 108 00:06:45,400 --> 00:06:51,800 So in this context, he he realizes it statistics East. 109 00:06:52,100 --> 00:06:57,600 To be established as a separate subject Zone and decides to 110 00:06:57,800 --> 00:07:01,300 start a DOT for profit, which is is I in the Statistical 111 00:07:01,300 --> 00:07:08,800 Institute and I get 32. Okay, so is I starts and then he 112 00:07:08,800 --> 00:07:15,000 goes, you know, full thrust into a statistics and he's the first 113 00:07:15,000 --> 00:07:18,000 guy to import electronic computer into India. 114 00:07:18,400 --> 00:07:23,500 Okay, in 1956, motivated by the The fact that he was, you know, 115 00:07:23,500 --> 00:07:27,200 he started what's now called the national sample survey is 116 00:07:27,200 --> 00:07:31,000 essentially a sample of Indian households, you know, and data 117 00:07:31,000 --> 00:07:34,400 collect collected on what the households consume, and where 118 00:07:34,400 --> 00:07:37,600 they spend their money. And and you know, how many 119 00:07:37,600 --> 00:07:40,700 people are there and how healthy are they and things like that? 120 00:07:40,900 --> 00:07:44,100 Okay, so he starts started the early 50s and the mid 50's. 121 00:07:44,100 --> 00:07:46,500 He is the, you know, he's the author of the second Five-Year 122 00:07:46,500 --> 00:07:47,400 Plan. Yes. 123 00:07:47,400 --> 00:07:51,200 It's a lot of, you know, analytics economics going on so 124 00:07:51,200 --> 00:07:58,000 the computer can Which is a key c2m from the United Kingdom and 125 00:07:58,000 --> 00:08:01,100 that's the first electronic digital computer than India. 126 00:08:01,700 --> 00:08:05,500 So that's where the entire virtual. 127 00:08:05,500 --> 00:08:08,400 I say the discipline of Statistics analytics and even 128 00:08:08,400 --> 00:08:10,800 computer science to some extent started in. 129 00:08:10,800 --> 00:08:14,500 India comes in statistics and analytics in India is like 100 130 00:08:14,500 --> 00:08:16,700 years old. If, if it is during the first 131 00:08:16,700 --> 00:08:19,200 world war that he made his journey back to India with the 132 00:08:19,200 --> 00:08:21,800 essence papers. Yes. 133 00:08:22,200 --> 00:08:24,500 So how about? So when you were telling me a 134 00:08:24,500 --> 00:08:26,700 story with are two questions popped into my head. 135 00:08:27,100 --> 00:08:30,600 First one was like when he established this is, is the not 136 00:08:30,600 --> 00:08:33,000 for profit which became is a 1932. 137 00:08:33,299 --> 00:08:35,600 Did he manage to get other faculty members? 138 00:08:35,600 --> 00:08:39,500 And if so, like, how how do we recruit those faculty members? 139 00:08:39,500 --> 00:08:42,200 Because I assume not too many people who are studying 140 00:08:42,200 --> 00:08:44,500 statistics in India packet back at that point in time. 141 00:08:44,900 --> 00:08:49,000 And the second question is later on when he got the computer 142 00:08:49,200 --> 00:08:51,900 positive and there's a famous story about how like Infosys. 143 00:08:52,000 --> 00:08:55,600 Auntie to can import a computer into India and they had to kind 144 00:08:55,600 --> 00:08:58,400 of flick go through some lots of Hoops for that. 145 00:08:58,400 --> 00:09:01,100 Like, how easy was it for Maja numbers, to get his computer 146 00:09:01,100 --> 00:09:04,900 into I say on the UK. Okay. 147 00:09:04,900 --> 00:09:08,400 So the the first set of folks, as you write, he said the Rabia 148 00:09:08,400 --> 00:09:11,500 still not statistics folks, but they were folks were interested 149 00:09:11,500 --> 00:09:15,500 in statistics, but but higher their training and other 150 00:09:15,500 --> 00:09:19,700 subjects like physics and math. So but they just, you know, 151 00:09:19,900 --> 00:09:25,000 where students of Hello numbers, and we're intrigued interested 152 00:09:25,000 --> 00:09:27,500 and passionate about doing statistics. 153 00:09:27,500 --> 00:09:31,500 So they were the first folks who were teaching there and doing 154 00:09:31,500 --> 00:09:35,300 research. And is I, so the, the compute, 155 00:09:35,300 --> 00:09:39,300 the import of the computer was was, you know, it's pretty 156 00:09:39,300 --> 00:09:42,700 straightforward because mahalanobis and Pandit nehru 157 00:09:42,700 --> 00:09:46,900 will good friends. Yep, so he could ensure that, 158 00:09:47,200 --> 00:09:50,500 you know, that there was you know, there was never know. 159 00:09:51,300 --> 00:09:54,600 What should I say? No obstacles in terms of foreign 160 00:09:54,600 --> 00:09:58,300 exchange for travel and things of that required to get a 161 00:09:58,300 --> 00:10:02,300 computer. So, incidentally, the second 162 00:10:02,300 --> 00:10:06,200 computer in India was also too. Is I this time it was a computer 163 00:10:06,200 --> 00:10:09,500 from the USSR called the Ural. So this is a time when 164 00:10:09,500 --> 00:10:14,500 mahalanobis was starting to being towards the left and and 165 00:10:14,600 --> 00:10:17,400 decided that, you know, he could, he could get, you know, 166 00:10:17,400 --> 00:10:20,800 more bang for the buck by going in for a computer from the 167 00:10:20,800 --> 00:10:23,900 Eastern block. So So this is a 1959 or so. 168 00:10:24,000 --> 00:10:26,100 We have 59, when the computer lat. 169 00:10:26,500 --> 00:10:30,400 So, again, used for statistics largely for algebraic and 170 00:10:30,400 --> 00:10:33,700 statistical calculations correctly, and I guess back 171 00:10:33,700 --> 00:10:37,100 then, since we were sort of a pseudo socialist, sort of mixed 172 00:10:37,100 --> 00:10:38,900 economy with with five year plans. 173 00:10:38,900 --> 00:10:43,200 And so what I'm guessing that statistics would have formed it 174 00:10:43,200 --> 00:10:45,400 would be the you do been an important part for the 175 00:10:45,400 --> 00:10:47,300 government, right? Because like if you need to know 176 00:10:47,300 --> 00:10:49,900 how to allocate, if you want to know how to allocate resources, 177 00:10:49,900 --> 00:10:53,700 you need to be able to kind of This stuff will like get the 178 00:10:53,800 --> 00:10:58,200 information in the in the right Manner and and so on, so on, so 179 00:10:58,200 --> 00:11:01,600 I can imagine say we're mahalanobis, 40 patients in that 180 00:11:01,600 --> 00:11:04,400 time. B is I was so pivotal in terms 181 00:11:04,400 --> 00:11:07,300 of the statistics that you need and oh, I absolutely. 182 00:11:07,300 --> 00:11:10,700 I mean, he was, so in some sense, the father of the five 183 00:11:10,700 --> 00:11:14,900 year plans and in terms of doing large-scale Nation scale, serve 184 00:11:15,200 --> 00:11:20,200 base, so it was spot on and that's the and we are still 185 00:11:20,200 --> 00:11:23,200 following that his legacy. In terms of continuing. 186 00:11:23,200 --> 00:11:25,800 I mean not to The Five-Year plans, but definitely with the 187 00:11:25,800 --> 00:11:28,400 national sample survey and similar large surveys that we 188 00:11:28,500 --> 00:11:30,200 conduct. Yep. 189 00:11:30,600 --> 00:11:31,700 Yep. Okay. 190 00:11:32,100 --> 00:11:35,400 So, and then, like, while I is high in Calcutta was like, kind 191 00:11:35,400 --> 00:11:37,600 of importing its computers, it was doing its cats. 192 00:11:37,600 --> 00:11:41,000 Like, I guess the other big scientific Institute in India 193 00:11:41,000 --> 00:11:42,900 that time was our Indian Institute of Science in 194 00:11:42,900 --> 00:11:44,300 Bangalore. So what was happening, there 195 00:11:44,300 --> 00:11:47,600 was, was there some statistics work done there or like, was 196 00:11:47,600 --> 00:11:51,000 there some introduction to what? We now know, as analytics, that 197 00:11:51,000 --> 00:11:53,500 was kind of Happening there and so on. 198 00:11:54,800 --> 00:11:59,300 You know, so this is pretty interesting because the Indian 199 00:11:59,300 --> 00:12:04,800 Institute of science was at that time, obviously the leader in 200 00:12:05,400 --> 00:12:09,600 player in Science, Education in the country, but they were also 201 00:12:10,000 --> 00:12:13,700 getting pretty big on Engineering in the fifties. 202 00:12:14,100 --> 00:12:16,000 Okay. So India, newly independent and 203 00:12:16,000 --> 00:12:21,600 then required these Engineers to kind of, you know, to to to to 204 00:12:21,600 --> 00:12:23,600 ensure that some of the big projects are India was 205 00:12:23,600 --> 00:12:26,000 undertaking at that. In time, in terms of building, 206 00:12:26,000 --> 00:12:28,200 physical infrastructure, the electrical infrastructure and 207 00:12:28,200 --> 00:12:32,800 things like that, where required Manpower required research and 208 00:12:32,800 --> 00:12:36,200 Indian Institute of science is playing a big role there and 209 00:12:36,200 --> 00:12:41,900 India sort of things obviously from its its founding days 210 00:12:41,900 --> 00:12:46,000 always attracted a lot of foreign faculty who come and 211 00:12:46,000 --> 00:12:49,900 spend a year or two, teaching it at The Institute. 212 00:12:50,400 --> 00:12:56,800 So in 1954 55 it had a You know, electrical engineering Professor 213 00:12:56,800 --> 00:12:59,100 from Wisconsin, Professor ride out. 214 00:12:59,500 --> 00:13:05,700 So right out apparently came to Bangalore with with the reams of 215 00:13:05,700 --> 00:13:09,900 paper with with a whole set of pencils and pens and erasers 216 00:13:09,900 --> 00:13:12,800 because he was not sure about what was available in Bangalore 217 00:13:12,900 --> 00:13:16,700 and if it was available how long, or how many he could buy? 218 00:13:17,000 --> 00:13:22,200 Okay, so that's an interesting story about about Professor ride 219 00:13:22,200 --> 00:13:24,300 out, but he got something very interesting. 220 00:13:24,600 --> 00:13:27,900 Probably for the first time in India was that he got a 221 00:13:27,900 --> 00:13:30,300 transistor and offline? Okay. 222 00:13:30,400 --> 00:13:35,200 So the 74 ones that we were all used to, in our College lab 223 00:13:35,200 --> 00:13:37,800 site. So 741, but not in integrated 224 00:13:37,800 --> 00:13:42,700 circuit, but at the transistor, yep, so he got that along and 225 00:13:43,100 --> 00:13:48,000 one of the Masters students. There Raja Ramen work with him 226 00:13:48,300 --> 00:13:53,300 and they built an analog circuit using the APA, which could solve 227 00:13:53,500 --> 00:13:56,800 leader. Friendly questions. 228 00:13:57,000 --> 00:14:01,000 Okay. So an analog circuit where you 229 00:14:01,000 --> 00:14:05,600 could look at the readings of the meter and then, and then, 230 00:14:05,900 --> 00:14:10,000 you know, figure out that what the solution to the linear. 231 00:14:10,600 --> 00:14:14,600 Differential equation was pretty complicated, but that's what it 232 00:14:14,600 --> 00:14:16,600 will mean. It's like, you know, it's a it's 233 00:14:16,600 --> 00:14:23,100 a it's a 7 8 foot high board and about for sorry eight feet high 234 00:14:23,100 --> 00:14:26,600 and about the four feet. Okay, that was how it was. 235 00:14:27,100 --> 00:14:33,400 And after Professor wrote left. He left the transistor back and 236 00:14:33,400 --> 00:14:39,200 Raja Ramen added the the nonlinear differential circuits 237 00:14:39,200 --> 00:14:42,000 also, so it was actually a pretty neat fishy. 238 00:14:43,000 --> 00:14:46,600 They were a couple of for PhD students who use this analog 239 00:14:46,600 --> 00:14:51,400 computer to complete their calculations on things like, you 240 00:14:51,400 --> 00:14:54,300 know, vibrations of columns in civil engineering. 241 00:14:54,400 --> 00:14:55,200 Cheering. Okay. 242 00:14:55,200 --> 00:14:58,700 All right, in terms of calculating transients, which 243 00:14:58,700 --> 00:15:03,000 are nothing but sudden spikes in AC current. 244 00:15:03,300 --> 00:15:06,800 So these guys actually use the computer to do these things. 245 00:15:06,800 --> 00:15:10,600 So here you have a context of an engineering analytics. 246 00:15:10,800 --> 00:15:13,500 Yep, because previously people, you know, use the electrical 247 00:15:13,500 --> 00:15:16,100 calculators or mechanical calculators to do it. 248 00:15:16,100 --> 00:15:19,600 And now you have a machine, which could be, you know, which 249 00:15:19,600 --> 00:15:21,700 could be kind of plugged in the circuit. 250 00:15:21,700 --> 00:15:25,600 Would, you know, would be your problem with Duh be sorted. 251 00:15:26,000 --> 00:15:31,200 So this probably was the first time when, when India was using 252 00:15:31,500 --> 00:15:34,800 analytic X to solve engineering problems. 253 00:15:35,200 --> 00:15:37,900 That 50 years old, more than 50 years old using analytics 254 00:15:37,900 --> 00:15:39,500 resolved. Yeah, but yeah. 255 00:15:39,500 --> 00:15:42,500 Mid 50's. Yes, and how far behind we 256 00:15:42,500 --> 00:15:44,900 compared to? Let's say the Western countries 257 00:15:44,900 --> 00:15:49,000 in terms of the use of analytics for engineering problems, are 258 00:15:49,000 --> 00:15:52,000 things like that back room. This was a time. 259 00:15:52,000 --> 00:15:56,200 Okay. So the use of of digital 260 00:15:56,200 --> 00:16:01,800 computers to solve to solve engineering problems, was just 261 00:16:01,800 --> 00:16:04,100 fist towards the end of the second world war. 262 00:16:04,400 --> 00:16:09,100 And, and the first problem which was solved was probably the 263 00:16:09,600 --> 00:16:11,100 projectile problem. Yeah. 264 00:16:11,100 --> 00:16:17,400 So so if I'm having a poet sir, kind of a gun, so, where would 265 00:16:17,400 --> 00:16:22,300 my buy ammunition land? Given different kinds of wind 266 00:16:22,300 --> 00:16:24,300 velocities and different directions? 267 00:16:24,500 --> 00:16:29,200 Okay, so previously these, you know, the soldiers used to carry 268 00:16:29,500 --> 00:16:33,500 a booklet. We'll choose to do which is to 269 00:16:33,500 --> 00:16:35,200 have all the calculations with them. 270 00:16:35,500 --> 00:16:39,800 Okay, and it was done by hand and not very accurate. 271 00:16:39,800 --> 00:16:44,200 So this computer could do it for different models of these hope 272 00:16:44,200 --> 00:16:47,600 to sell guns and it could be, it could be made into a booklet are 273 00:16:47,608 --> 00:16:51,500 given to these soldiers. So that is the probably the 274 00:16:51,500 --> 00:16:58,800 first real life from Pure salt. So this is the e meac at the 275 00:16:58,800 --> 00:17:01,000 University of Pennsylvania. So, yep. 276 00:17:02,000 --> 00:17:04,099 So, okay. So it happened in the mid 40s. 277 00:17:04,200 --> 00:17:06,599 Early mid 40. So in India, we were using this 278 00:17:06,599 --> 00:17:08,599 in the 50s. So we weren't that far behind. 279 00:17:08,599 --> 00:17:11,800 You think about it? Take a technology because of his 280 00:17:12,000 --> 00:17:16,099 know, how so we were you know, we weren't lacking back by that 281 00:17:16,099 --> 00:17:18,000 many years at all. Okay. 282 00:17:18,300 --> 00:17:19,500 Okay. Okay. 283 00:17:19,500 --> 00:17:23,500 So I think like the way I look at it like we are looking at all 284 00:17:23,500 --> 00:17:26,099 the sort of the eminent scientific institutes in India. 285 00:17:26,099 --> 00:17:29,900 Like he started off with is a then we move to a see now TFR 286 00:17:29,900 --> 00:17:34,000 and we see that all of them have Sort of laid their own big Parts 287 00:17:34,000 --> 00:17:37,100 in in sort of furthering analytics, as we now call it a 288 00:17:37,108 --> 00:17:40,600 me like back then, all nobody bothered calling it analytics. 289 00:17:40,600 --> 00:17:43,900 So with, and then what happened? Where does the story go? 290 00:17:43,900 --> 00:17:45,700 Right? So a couple of interesting 291 00:17:45,700 --> 00:17:50,000 stories before I kind of want to the next, you know, the next 292 00:17:50,000 --> 00:17:56,600 phase. So the person who who, you know, 293 00:17:56,600 --> 00:17:59,600 be mentioned and I see Raja Ramen. 294 00:17:59,800 --> 00:18:02,500 Yeah, he went on to do a Steve u.s. 295 00:18:02,500 --> 00:18:06,700 Came back as a young faculty in IIT kanpur yet and he was a 296 00:18:06,700 --> 00:18:10,000 faculty in charge of the center in IIT. 297 00:18:10,000 --> 00:18:13,000 Kanpur. Yep, and he was the person who 298 00:18:13,000 --> 00:18:16,700 started the first M Tech electrical engineering with a 299 00:18:16,708 --> 00:18:18,900 specialization in computer science. 300 00:18:19,000 --> 00:18:23,100 Okay, and he was the person who started the first PTEC program 301 00:18:23,100 --> 00:18:25,800 in computer science of India. It can't put a 1978. 302 00:18:26,200 --> 00:18:28,200 Okay? Okay, so there is a connect 303 00:18:28,200 --> 00:18:31,000 between Radha, Raman, and a lot of things that it doesn't end 304 00:18:31,000 --> 00:18:33,300 there. So one of the students in the 305 00:18:33,300 --> 00:18:36,700 1978 batch of, for the first batch of computer science at IIT 306 00:18:36,700 --> 00:18:40,500 kanpur was a young kid called Rajeev motwani. 307 00:18:40,800 --> 00:18:43,900 Okay, so forget it is a PhD in Berkeley. 308 00:18:43,900 --> 00:18:48,700 Yes, Stanford and co-wrote a paper on pagerank algorithm, the 309 00:18:48,700 --> 00:18:53,900 third of students and stand for and you know who they are and 310 00:18:54,700 --> 00:18:58,100 grillin page. And then he was a board member 311 00:18:58,100 --> 00:19:00,500 at Google. And when we talk about analytics 312 00:19:00,500 --> 00:19:05,100 in the current Instead of the companies that are leaders and, 313 00:19:05,200 --> 00:19:09,000 you know, the big politics, that's like, you know, what 314 00:19:09,500 --> 00:19:10,800 planet scale and stuff like that. 315 00:19:10,800 --> 00:19:13,600 It's obviously Google. So there is a bit of a India 316 00:19:13,600 --> 00:19:15,800 connection there. So, and I think the Moto an 317 00:19:15,808 --> 00:19:17,900 eclair tragically passed away a few years back. 318 00:19:17,900 --> 00:19:23,500 I think he had Yes actually reading. 319 00:19:23,600 --> 00:19:27,100 So, both more 20 and rather. I mean, I think I had prescribed 320 00:19:27,100 --> 00:19:29,600 text by them during my btech in computer science. 321 00:19:29,600 --> 00:19:31,600 I think there was a elements of oh, yeah. 322 00:19:31,700 --> 00:19:37,600 Computers that regimen had written, which was our yes, 64, 323 00:19:37,600 --> 00:19:39,100 our introduction to commute Computing. 324 00:19:39,100 --> 00:19:42,800 The course for all the students at IIT Madras like the that was 325 00:19:42,800 --> 00:19:45,300 at the nobody bothered to buy the book but there was a 326 00:19:46,000 --> 00:19:48,700 Prosthetics and then, like, we had some, I'll go books by more 327 00:19:48,700 --> 00:19:52,200 20 and I forget to who the co-authors were later on the 328 00:19:52,208 --> 00:19:53,400 right. So right. 329 00:19:53,400 --> 00:19:57,100 Yeah, so they're Doyle's of computer or education. 330 00:19:57,100 --> 00:20:02,100 I mean, Roger Amin is literally the person who also who were So 331 00:20:02,300 --> 00:20:06,000 gave a give the government a report which essentially said 332 00:20:06,000 --> 00:20:08,200 that we need to start this program called an MCA. 333 00:20:08,600 --> 00:20:12,500 So if you're looking at these lacks of MCAS your oh, yeah, 334 00:20:12,500 --> 00:20:14,300 coming out of India universities. 335 00:20:14,300 --> 00:20:16,300 They all have to thank Professor other up. 336 00:20:16,600 --> 00:20:19,500 Well, okay. Okay, it's some days. 337 00:20:19,500 --> 00:20:21,500 It's interesting. When you go back, how the origin 338 00:20:21,500 --> 00:20:24,000 stories are somehow. There's constricted right? 339 00:20:24,000 --> 00:20:26,100 There are a few key people here in there. 340 00:20:26,100 --> 00:20:30,200 Whose names sort of pop up all over the place or people they 341 00:20:30,200 --> 00:20:32,700 have been associated with David. Have created an impact. 342 00:20:32,700 --> 00:20:35,600 Like you mentioned, you mentioned more 20 and people 343 00:20:35,600 --> 00:20:39,800 like that and so on sure. Yeah, and it's fascinating 344 00:20:39,800 --> 00:20:43,100 because the, the first batch of btech in kanpur. 345 00:20:43,300 --> 00:20:45,100 Yep. They said that the overall 346 00:20:45,100 --> 00:20:47,300 strength of B Tech is a constant, right? 347 00:20:47,400 --> 00:20:49,100 Okay. So Raja Ram a literally had to 348 00:20:49,100 --> 00:20:52,200 go and beg his colleagues. Hey, give me one seed from your 349 00:20:52,200 --> 00:20:54,600 department. Give me two seats from your 350 00:20:54,600 --> 00:20:57,700 department and that's why it started off with about 20 seats 351 00:20:57,700 --> 00:20:59,500 slightly less than that. Okay? 352 00:20:59,800 --> 00:21:04,600 And very soon. Into the to the bewilderment of 353 00:21:04,600 --> 00:21:07,700 the other faculty. Computer science is attracting 354 00:21:07,700 --> 00:21:10,100 the best of the best. So, yep. 355 00:21:10,400 --> 00:21:13,500 So any other sort of famous names from that batch is apart 356 00:21:13,500 --> 00:21:16,000 from more 28. Is there anyone like who? 357 00:21:16,600 --> 00:21:22,200 Okay, so mode 1 e is definitely one person there, but then more 358 00:21:22,200 --> 00:21:24,800 to Honey, wasn't the PGM from that patch. 359 00:21:24,800 --> 00:21:31,500 Okay, so that the PGM from that batch is a bangle, Orion Cinema. 360 00:21:31,700 --> 00:21:34,000 As prachanda, who's a faculty at replied Phoebe. 361 00:21:34,200 --> 00:21:41,500 So he went to what wani went to Berkeley and Persona went to MIT 362 00:21:41,900 --> 00:21:46,700 and Bell labs and his Peyton's probably powered. 363 00:21:46,700 --> 00:21:48,800 I don't know a few hundred million a billion dollars of 364 00:21:48,800 --> 00:21:53,600 revenue for Bell labs, and he's a bag Orion and works at apply T 365 00:21:53,600 --> 00:21:55,100 be so. Okay. 366 00:21:55,100 --> 00:21:59,600 So so my glorious can be proud. So very interesting, which is a 367 00:21:59,600 --> 00:22:01,300 very, very interesting. Okay. 368 00:22:01,300 --> 00:22:03,700 Now, Now that we now let's come back to the story. 369 00:22:03,700 --> 00:22:07,400 We were talking about, I think we spoke about Raja Ram in, in 370 00:22:07,400 --> 00:22:10,100 the IAC context, when he was a master student. 371 00:22:10,200 --> 00:22:13,300 Then we spoke over here. But and then where did it go? 372 00:22:13,300 --> 00:22:15,700 Where it first analytics and related stuff. 373 00:22:15,700 --> 00:22:16,200 Go. Okay. 374 00:22:16,200 --> 00:22:21,000 So so far we've talked about these researchy and Academia 375 00:22:21,100 --> 00:22:24,500 stuff like that, right? Yeah, so computers and they are 376 00:22:24,500 --> 00:22:27,000 used in the 1960s for doing grunt work. 377 00:22:27,300 --> 00:22:30,100 And what's the grunt work was payroll processing through the 378 00:22:30,100 --> 00:22:31,500 jute Mills? Okay. 379 00:22:31,600 --> 00:22:35,700 So it's interesting because people would ask why jute Mills 380 00:22:35,700 --> 00:22:39,800 because in Most states route Mill workers had to be paid on a 381 00:22:39,800 --> 00:22:44,300 weekly basis, and if not, they would be stripes and the strike 382 00:22:44,300 --> 00:22:47,000 would go on for nothing less than a couple of weeks or a 383 00:22:47,000 --> 00:22:50,300 month. So the owners of the journals 384 00:22:50,300 --> 00:22:55,200 wanted to ensure that payrolls processed correctly and on time 385 00:22:55,200 --> 00:22:59,500 and the IBM, guys would process it in a centralized Center at 386 00:22:59,500 --> 00:23:01,800 the end of the week. They would go and give it The 387 00:23:01,800 --> 00:23:04,900 Jew till a judgment and they would use pay the workers. 388 00:23:04,900 --> 00:23:08,500 Right? So it was just doing basic 389 00:23:08,700 --> 00:23:11,100 arithmetic stuff. You're doing it correctly. 390 00:23:11,500 --> 00:23:16,000 Yes, the first instance of some analytics, kind of work, or an 391 00:23:16,000 --> 00:23:20,500 interesting decision. Work was done by the PSU 392 00:23:20,500 --> 00:23:26,000 steelman's. Okay, and there was an IBM for, 393 00:23:26,100 --> 00:23:32,100 you know, 1501, which was used to schedule production of Of the 394 00:23:32,200 --> 00:23:36,400 offer type of skin based on the demand and Analysis of the 395 00:23:36,400 --> 00:23:39,400 demand was done. And then a production schedule 396 00:23:39,400 --> 00:23:43,400 was, was formulated using the 1401 machine. 397 00:23:43,500 --> 00:23:46,000 I've, you know, one machine. So this was probably the first 398 00:23:46,000 --> 00:23:50,100 time when there was a decision that was made help offer 399 00:23:50,100 --> 00:23:55,200 computer or or you know, modern analytics, if you can call it at 400 00:23:55,200 --> 00:23:58,700 that point in time, so it was born analytics and 1960s will be 401 00:23:58,700 --> 00:24:01,500 different from but this is the first instance. 402 00:24:01,700 --> 00:24:06,100 And our model was hit to determine what kind of 403 00:24:06,900 --> 00:24:10,400 production mix would be there. The steel factories climbing. 404 00:24:10,400 --> 00:24:14,200 So this is the mid-sixties. Okay? 405 00:24:14,400 --> 00:24:19,700 Yeah. So so or was this coming to to, 406 00:24:19,700 --> 00:24:22,800 you know, coming into mainstream in the US as well at this point 407 00:24:22,800 --> 00:24:26,000 and time. Yeah, and they were obviously 408 00:24:26,000 --> 00:24:32,000 folks, you know, from India who would travel to us for Are doing 409 00:24:32,000 --> 00:24:36,500 their higher studies and phds especially in management schools 410 00:24:36,700 --> 00:24:39,200 and an industrial engineering schools that point in time. 411 00:24:39,600 --> 00:24:46,100 So couple of them where where one is kanodia and Justin Patel, 412 00:24:46,500 --> 00:24:52,200 they join a division of Tata Sons called tecs Tata 413 00:24:52,200 --> 00:24:55,100 consultancy services. So it was an internal unit 414 00:24:55,700 --> 00:24:59,500 Roofing work for for their own group companies for Tata Sons, 415 00:25:00,600 --> 00:25:04,500 you know, companies. So, these guys said that. 416 00:25:04,500 --> 00:25:09,000 Hey now, I think we can also expand our our Target to 417 00:25:09,000 --> 00:25:13,800 companies outside of the outside of the Tata group and we have, 418 00:25:13,900 --> 00:25:15,200 you know, we can do multiple stuff. 419 00:25:15,200 --> 00:25:20,200 We can do management consulting, we can do it Consulting. 420 00:25:20,200 --> 00:25:23,600 We can also do some TurnKey work by using computers, and then 421 00:25:24,400 --> 00:25:27,700 giving just see the results to the customers. 422 00:25:28,200 --> 00:25:31,600 And, you know, I looked at one of the brochures at that point. 423 00:25:31,800 --> 00:25:34,200 Bye. So 19 early 1968. 424 00:25:34,200 --> 00:25:38,200 This is a time and mr. Coley hadn't joined TCS. 425 00:25:38,200 --> 00:25:40,500 Okay. So this was just a very nascent 426 00:25:41,000 --> 00:25:44,700 young Bunch there. So I'm just reading of one 427 00:25:45,000 --> 00:25:48,900 particular sentence from that brochure early 1968. 428 00:25:49,200 --> 00:25:53,000 So it says that the scientific approach. 429 00:25:53,000 --> 00:25:57,300 The direction of data, there are analysis and a deep 430 00:25:57,300 --> 00:26:00,700 understanding of the mechanics of business that result from a 431 00:26:00,700 --> 00:26:03,200 study. All contribute to making 432 00:26:03,200 --> 00:26:07,800 business decisions, less uncertain or at least to remove 433 00:26:07,800 --> 00:26:10,400 some of the uncertainty in the mind of the decision-maker. 434 00:26:10,400 --> 00:26:14,500 And then it continues to say since the number of alternative 435 00:26:14,500 --> 00:26:17,800 choices in a decision process can be very large, a computer 436 00:26:17,800 --> 00:26:20,900 comes in very handy in providing fast and accurate answers. 437 00:26:20,900 --> 00:26:24,400 So, this is a 1968 and if you could just you know, change the 438 00:26:24,400 --> 00:26:26,800 language a little bit, it can be used in any brochure today. 439 00:26:26,800 --> 00:26:29,500 I can use it today. I can totally use it to me right 440 00:26:29,500 --> 00:26:31,100 now. I don't need to, I don't need to 441 00:26:31,100 --> 00:26:32,700 sell the you. Computers to anyone. 442 00:26:33,400 --> 00:26:36,700 So I, but if I had to, I mean, I would, I would definitely use 443 00:26:36,700 --> 00:26:40,100 this is now extremely forward-thinking, right? 444 00:26:40,100 --> 00:26:44,100 Yeah, so it's fascinating. It's fascinating to see how, you 445 00:26:44,100 --> 00:26:47,000 know, these guys were really pioneers and, you know, trying 446 00:26:47,000 --> 00:26:51,800 to convince Indian companies to use or use analytics. 447 00:26:51,800 --> 00:26:54,300 And, you know, and also in some sense pushing the use of 448 00:26:54,300 --> 00:26:57,900 computers. So one interesting, you know, 449 00:26:58,200 --> 00:27:01,600 connection here, is that young statistical power. 450 00:27:01,700 --> 00:27:03,700 Out of this group. The CC is group. 451 00:27:03,700 --> 00:27:06,800 Okay. So his name was Kara smoothie. 452 00:27:07,300 --> 00:27:10,900 Okay, so that was part of the group and rings, the bell, 453 00:27:10,900 --> 00:27:13,500 right? So care is moved. 454 00:27:13,500 --> 00:27:16,100 He does some interesting stuff. He goes and gets his master's 455 00:27:16,100 --> 00:27:19,600 from MIT a PhD from Harvard Business, School comes back 456 00:27:19,600 --> 00:27:24,300 joins as faculty of IMM, but but the most interesting thing was 457 00:27:24,300 --> 00:27:27,300 that he comes as a director of IIM Bangalore in 1992. 458 00:27:27,700 --> 00:27:31,600 Okay, and he chips and Tatian of IIM Bangalore from a second. 459 00:27:31,800 --> 00:27:35,600 All public sector. Focus to a typical be school in 460 00:27:35,608 --> 00:27:39,000 terms of management functional Focus, right? 461 00:27:39,000 --> 00:27:44,000 And I am banknote becomes the first be school last year to 462 00:27:44,000 --> 00:27:46,800 launch an MBA and analytics. Yep. 463 00:27:46,800 --> 00:27:50,900 Yes. So so it's it's a small 464 00:27:50,900 --> 00:27:55,600 connection between the TC is of the late 60s and then the modern 465 00:27:55,600 --> 00:27:58,800 Olympics, education in India. Yeah. 466 00:27:59,400 --> 00:28:04,000 Okay, so I am be the First, first one to launch this in the 467 00:28:04,000 --> 00:28:05,300 formula billion analytics, right? 468 00:28:05,300 --> 00:28:08,100 It's not like one of these sort of a short-term courses. 469 00:28:08,100 --> 00:28:10,000 It is. No, it's not a certificate 470 00:28:10,000 --> 00:28:12,100 program. Know it's a two-year MBA 471 00:28:12,100 --> 00:28:15,600 full-time and it's a specialization analytics. 472 00:28:15,600 --> 00:28:18,600 Oh, okay. It is very, very interesting. 473 00:28:19,000 --> 00:28:20,400 Okay. Now, let's assume. 474 00:28:20,600 --> 00:28:23,800 So you mentioned that, TC yourself marketed is use of 475 00:28:23,800 --> 00:28:25,700 analytics, use of our Industries and so on. 476 00:28:25,700 --> 00:28:28,800 But back then we were a sort of a highly controlled economy. 477 00:28:28,800 --> 00:28:31,800 And so, and so was there, but demand for this from other You 478 00:28:31,808 --> 00:28:35,200 thought, because the returns to efficiency was not very high 479 00:28:35,200 --> 00:28:39,800 rate. So yeah, so see they were doing 480 00:28:39,800 --> 00:28:42,500 pretty interesting work. But obviously, you know, if you 481 00:28:42,500 --> 00:28:46,100 have a supply constraint in terms licence Raj. 482 00:28:46,400 --> 00:28:50,300 Yeah, and there's only so much that unleaded methods can do 483 00:28:51,800 --> 00:28:54,900 advising, you know, textile mills or now. 484 00:28:55,100 --> 00:28:59,600 What is the most profitable blend of Orton that you can use 485 00:28:59,600 --> 00:29:03,100 for your product line and they were Advising, you know, 486 00:29:03,100 --> 00:29:06,200 transmission companies in terms of where they eat to locate 487 00:29:06,300 --> 00:29:09,200 Lawtons Mission Towers. Yep, so and they were doing 488 00:29:09,200 --> 00:29:11,800 obviously, some sales forecasting for Consumer 489 00:29:11,800 --> 00:29:15,100 durables, and quite a few of these companies where, like, you 490 00:29:15,100 --> 00:29:17,600 know, Tomko was they do their own good company? 491 00:29:17,600 --> 00:29:22,300 Yeah, Tata electric was also, there's so the Mills were 492 00:29:22,300 --> 00:29:25,600 obviously Calico but a few other means which pair, which better 493 00:29:25,600 --> 00:29:29,400 third-party companies as well. So they could, you know, do some 494 00:29:29,500 --> 00:29:32,900 cool work at that point in time. Was not only TCS. 495 00:29:32,900 --> 00:29:35,200 There was also another interesting company at this 496 00:29:35,200 --> 00:29:37,900 time, which was doing these are latex work which was called 497 00:29:37,900 --> 00:29:42,200 operations research group. It was a companies founded in 61 498 00:29:42,200 --> 00:29:46,000 based in Baroda founded by none, other than becomes Arabic. 499 00:29:46,300 --> 00:29:49,300 Okay. So becomes our Rabbi is, you 500 00:29:49,300 --> 00:29:53,800 know, kind of a polymath Amelia. It's got his hands and in right 501 00:29:53,800 --> 00:29:57,100 from the Indian space programme to possibly create a search to 502 00:29:57,100 --> 00:30:02,300 PRL physical Laboratories. Yep, and you know, so he he 503 00:30:02,300 --> 00:30:06,800 started this to ensure that the the industries in Gujarat, 504 00:30:06,800 --> 00:30:11,200 especially the the exciting Industries and the chemical 505 00:30:11,200 --> 00:30:15,800 Industries chemical and Pharma Industries had a good grounding 506 00:30:15,800 --> 00:30:20,200 in scientific management to make their decisions. 507 00:30:20,300 --> 00:30:23,700 Okay. So so this was started in Baroda 508 00:30:23,700 --> 00:30:29,400 of all places and and obviously the connection between o RG 509 00:30:29,400 --> 00:30:32,300 become tarah by is I am I'm double. 510 00:30:32,300 --> 00:30:36,200 Yep. So yes, so, you know, at some 511 00:30:36,200 --> 00:30:40,400 time in the early Genesis time of the first few years of IMM. 512 00:30:40,400 --> 00:30:44,000 The body was a lot of interaction between IMM the bath 513 00:30:44,000 --> 00:30:48,500 and Hua ji. Okay, and I am debarred, the 514 00:30:48,500 --> 00:30:53,800 incidentally is the first be school in India to get your. 515 00:30:54,000 --> 00:30:56,000 So they want to computer on the late 60s. 516 00:30:56,400 --> 00:31:01,100 It was the HP to 1160 which was a tabletop computer. 517 00:31:01,100 --> 00:31:02,700 It wasn't me. A computer at that time. 518 00:31:02,900 --> 00:31:05,300 Okay, very nice machine and motivation. 519 00:31:05,300 --> 00:31:08,100 Was that half the school? Had this computer. 520 00:31:08,100 --> 00:31:10,600 So horrible, so I am a set that up. 521 00:31:11,100 --> 00:31:16,900 Let's get this computer for brilliant, for, for I met was 522 00:31:16,900 --> 00:31:21,000 the was the person who got it. And the computer center manager 523 00:31:21,200 --> 00:31:25,600 for this HD 2116 be was none other than a gentleman called. 524 00:31:25,600 --> 00:31:28,600 Narayana Murthy. And are not okay that explains 525 00:31:28,600 --> 00:31:31,500 today and he said, he was the need to view of his on TV on. 526 00:31:31,700 --> 00:31:35,500 Canada used to hide their, he spoke about going to another, 527 00:31:35,500 --> 00:31:39,600 but after his graduation from, from IIT kanpur to, but he 528 00:31:39,600 --> 00:31:40,900 didn't explain. What work. 529 00:31:40,900 --> 00:31:44,900 He did it. I am a so we'll see will setting 530 00:31:44,900 --> 00:31:48,000 the computer center there. Okay, very for me. 531 00:31:48,000 --> 00:31:52,000 It is also when sort of like in some sense, big kick started 532 00:31:52,000 --> 00:31:55,000 coming into India rate. Like I've been t.i., I think 533 00:31:55,000 --> 00:31:58,400 opened their office in India in 81 if I'm not wrong. 534 00:31:58,400 --> 00:32:01,500 And so so I guess like Analytics. 535 00:32:01,600 --> 00:32:07,000 It's also in terms of like offshoring and like, the working 536 00:32:07,000 --> 00:32:10,300 for Western companies that also would have started around the 537 00:32:10,308 --> 00:32:14,200 same time. Yeah, no, you just spot-on. 538 00:32:14,200 --> 00:32:18,000 So this is still continuing with the with the Rajiv Gandhi. 539 00:32:19,400 --> 00:32:22,000 Peace. When he became the Prime 540 00:32:22,000 --> 00:32:24,800 Minister. He was, you know, he was a 541 00:32:24,808 --> 00:32:28,500 person who made a couple of visits quick visits to the in 542 00:32:28,500 --> 00:32:32,100 quick succession to the US. So, he went to Paris late 543 00:32:32,100 --> 00:32:34,900 privately 87, if I'm not mistaken, so, we had the great 544 00:32:34,900 --> 00:32:40,200 rapper with President Reagan and the relationship between India 545 00:32:40,200 --> 00:32:44,300 and the, you can't talk. So this was also the time and 546 00:32:44,300 --> 00:32:48,100 from, you know, from the early 80s, I think there was a lot 547 00:32:48,100 --> 00:32:53,200 more action, you know, of the American MNC is in India, and 548 00:32:53,200 --> 00:32:56,900 then they knew that India was a, you know, was a friendly place 549 00:32:56,900 --> 00:32:58,700 to do business and things like that. 550 00:32:59,000 --> 00:33:05,000 So so one company, which is present in India, but was had a 551 00:33:05,000 --> 00:33:07,800 very small operation was the AmEx, the retail bank. 552 00:33:08,000 --> 00:33:10,800 Yeah, right. So in 1983, they decided to 553 00:33:11,300 --> 00:33:15,400 start. Are there are cards division in 554 00:33:15,400 --> 00:33:19,800 India also, like all good American Banks, they recruited 555 00:33:19,900 --> 00:33:22,700 folks for the, you know, for marketing, the cards and also 556 00:33:22,700 --> 00:33:29,000 for the back and processing. And these, you know, and they 557 00:33:29,000 --> 00:33:33,700 recruited some pretty smart folks, including one person by 558 00:33:33,700 --> 00:33:37,200 name Robin drawing for their processing of credit cards. 559 00:33:38,000 --> 00:33:43,800 So he was a chartered accountant and And you know, so so the 560 00:33:43,800 --> 00:33:47,700 number of Chartered Accountants at the back end card processing 561 00:33:47,700 --> 00:33:53,100 in in a mix grew and very soon. They were just not doing the 562 00:33:53,300 --> 00:33:56,200 routine processing that you would do for a car but also 563 00:33:56,200 --> 00:34:00,700 doing some interesting stuff like Risk and credit analytics 564 00:34:00,700 --> 00:34:05,600 and stuff like that. So this impress the the the the 565 00:34:05,600 --> 00:34:10,400 folks in HQ, okay, because they were getting some, some super 566 00:34:10,400 --> 00:34:13,600 quality work done. 50% of the cost. 567 00:34:14,500 --> 00:34:23,400 So, sometime in the mid 80s, late 80s makes back office in 568 00:34:23,400 --> 00:34:27,199 India in Delhi was doing all the back office. 569 00:34:27,300 --> 00:34:30,199 Look for a mix cuss in the APAC region. 570 00:34:30,600 --> 00:34:33,000 Okay. So that was probably the first 571 00:34:33,000 --> 00:34:37,500 time that this, you know, the whole concept of, of a back 572 00:34:37,500 --> 00:34:43,199 office from India started off and And one of the things that 573 00:34:43,199 --> 00:34:46,600 that happened in the prime minister's visit to the US was 574 00:34:46,600 --> 00:34:50,500 that was that he supposed to have met up with Jack Welch of 575 00:34:50,500 --> 00:34:55,199 GE and obviously Jack Wills wanted to sell more into India 576 00:34:55,699 --> 00:35:00,000 and some of the advisers of the Prime Minister, including Sam's 577 00:35:00,000 --> 00:35:02,800 it protons are. And I'm a supposed to have told 578 00:35:02,800 --> 00:35:04,900 them that hey, you know, that's nice. 579 00:35:05,200 --> 00:35:08,900 But why don't you also pick up some software services from us 580 00:35:09,000 --> 00:35:12,200 from India? Yep, so that was a time in He 581 00:35:12,200 --> 00:35:18,100 decided to go offshore to India and and the beneficiary, is 582 00:35:18,100 --> 00:35:21,400 there all the Indian it companies at that time, 583 00:35:21,700 --> 00:35:28,700 including TCS imposes that Pro. So, so G was pretty impressed 584 00:35:28,700 --> 00:35:32,500 with the kind of work that was getting done out of India and 585 00:35:32,500 --> 00:35:36,200 the kind of quality levels and the kind of folks working on 586 00:35:36,200 --> 00:35:40,200 their projects and decided that by 1997. 587 00:35:40,200 --> 00:35:41,800 They decided that they should come. 588 00:35:41,900 --> 00:35:44,800 Amin and start their own subsidiary in India. 589 00:35:45,500 --> 00:35:47,700 So this is pretty interesting. Right? 590 00:35:47,700 --> 00:35:54,100 So IIT is considered not as core as business processes. 591 00:35:54,600 --> 00:35:58,100 Yeah, so they were happy. Outsourcing it2 Indian service 592 00:35:58,100 --> 00:36:01,100 providers, but they decided to set up the captive to primarily 593 00:36:01,100 --> 00:36:02,800 do their business processes work. 594 00:36:02,900 --> 00:36:06,000 So we're their own, you know, company's data would land here 595 00:36:06,000 --> 00:36:08,900 and people will be working on on that. 596 00:36:09,200 --> 00:36:11,800 Yep. So they started off. 597 00:36:12,000 --> 00:36:14,600 BPO routine kind of stuff like, you know, transaction 598 00:36:14,600 --> 00:36:19,100 processing, and Ledger's and accounting, and things like 599 00:36:19,100 --> 00:36:22,900 that. And and then they decided that 600 00:36:22,900 --> 00:36:26,000 what else can we do to Europe? If you're happy doing all the 601 00:36:26,000 --> 00:36:30,300 stuff and and they wanted to figure out what else they could 602 00:36:30,300 --> 00:36:34,300 do. And in 1998, they decided to 603 00:36:34,800 --> 00:36:39,500 start their analytics division, which was essentially - print 604 00:36:39,500 --> 00:36:43,500 decision-making. Yep, and They figured out that 605 00:36:43,600 --> 00:36:46,600 maybe we could try you're doing that and India in a small way. 606 00:36:46,600 --> 00:36:50,900 So so this required a lot of quantitative skills and stuff 607 00:36:50,900 --> 00:36:54,900 like that. So like, you know, we saw 608 00:36:54,900 --> 00:36:56,100 earlier. So where do you go for 609 00:36:56,100 --> 00:37:00,200 statistics? It is is I and they apparently 610 00:37:00,200 --> 00:37:04,800 event first wire side LED. That is I Calcutta and I've got 611 00:37:04,800 --> 00:37:07,100 a few folks from there. They went to Delhi School of 612 00:37:07,107 --> 00:37:09,000 Economics. Got a few folks from there. 613 00:37:09,200 --> 00:37:13,800 Yes, and you know then, I think they did something pretty 614 00:37:13,800 --> 00:37:17,700 interesting. So, they asked the the sales 615 00:37:17,700 --> 00:37:24,300 office of couple of products that we're used to do these, you 616 00:37:24,300 --> 00:37:28,600 know, statistical calculations SAS is one of them and SPS was 617 00:37:28,600 --> 00:37:30,300 so SPSS. Is another penis. 618 00:37:30,300 --> 00:37:31,600 Yep, the awesome. So, where do you? 619 00:37:31,600 --> 00:37:34,200 Yeah, where do you sell sell? Sell these where you sell your 620 00:37:34,200 --> 00:37:36,700 software, which colleges universities by it? 621 00:37:37,000 --> 00:37:42,500 Okay, so Steve is supposed to, you know, got a reply that I am 622 00:37:42,500 --> 00:37:45,700 so my, you know, some of our packages. 623 00:37:45,700 --> 00:37:50,000 So they get to the IMF. They figure out the the fpm star 624 00:37:51,000 --> 00:37:55,100 king and on these packages. And and so the Requiem from the 625 00:37:55,100 --> 00:37:59,800 MS into their analytics centers. So that was the Genesis of 626 00:38:00,100 --> 00:38:03,400 analytics in Jake is so it stands for GE Capital 627 00:38:03,400 --> 00:38:06,600 International services in 1998. So they were the first captive 628 00:38:06,600 --> 00:38:09,000 to do analytics work in the in India. 629 00:38:09,000 --> 00:38:10,600 So they were doing some cool work for all. 630 00:38:10,600 --> 00:38:13,400 They do companies GE. See what Finance you body, g 631 00:38:13,400 --> 00:38:17,000 capital D commercial Finance, predictive risk modeling, 632 00:38:17,000 --> 00:38:19,700 supply-demand intelligence and whatnot. 633 00:38:19,700 --> 00:38:23,400 So yeah, some cool stuff. It would have been probably 634 00:38:23,400 --> 00:38:26,500 during your time at IMB that like they started like these 635 00:38:26,500 --> 00:38:28,200 analytics. Firms started hiring from their 636 00:38:28,200 --> 00:38:29,500 MJ's. Like Jake is yes. 637 00:38:30,100 --> 00:38:32,600 First for the am sending for pcbs and guessing. 638 00:38:33,700 --> 00:38:39,400 Right, so, you know, so it was the there was so much of a 639 00:38:39,400 --> 00:38:43,800 demand for people who knew SPSS and SAS is that, you know, they 640 00:38:43,800 --> 00:38:46,600 were in, you know, people were, you know, you know, okay, even 641 00:38:46,600 --> 00:38:49,700 if you put work, part-time. Okay, does it really not getting 642 00:38:49,700 --> 00:38:52,800 folks who knew some of the methods, as well as the tools 643 00:38:52,800 --> 00:38:57,500 that point and time. So yeah, that was the time when 644 00:38:57,500 --> 00:39:02,600 it all started with the Jake is so this against another 645 00:39:02,600 --> 00:39:06,100 interesting thing is that this was a time when when the word 646 00:39:06,100 --> 00:39:07,900 kpo came into play. Yes. 647 00:39:08,400 --> 00:39:12,200 This is actually a report by eval you serve so which kind of 648 00:39:12,200 --> 00:39:17,300 combined the analytics and and some of the secondary market 649 00:39:17,300 --> 00:39:21,700 research and in general, you know secondary research that you 650 00:39:21,700 --> 00:39:24,000 do to put up slides and reports and things. 651 00:39:24,100 --> 00:39:26,600 Like that. Or that God bundled into one 652 00:39:26,600 --> 00:39:30,000 larger segment called kpo? I mean, people understood BPO. 653 00:39:30,600 --> 00:39:34,800 Okay, po became a an easy thing with specially for investors and 654 00:39:34,800 --> 00:39:36,700 and finds to understand. Yeah. 655 00:39:36,700 --> 00:39:40,000 So the largest segment was obviously the secondary 656 00:39:40,000 --> 00:39:42,700 research, secondary research, that was getting done. 657 00:39:43,000 --> 00:39:46,100 And the second largest segment was analytics, but it was the 658 00:39:46,100 --> 00:39:49,600 fastest growing and it was also probably the most profitable of 659 00:39:49,600 --> 00:39:50,900 the segments. Okay. 660 00:39:50,900 --> 00:39:56,100 So after this is, so the so who came in After Jake is so after 661 00:39:56,100 --> 00:39:59,500 Jake is severe. You know, they were three 662 00:39:59,500 --> 00:40:01,600 typical threads that were happening. 663 00:40:01,900 --> 00:40:05,400 Okay. So one was that with Jake is in 664 00:40:05,400 --> 00:40:09,700 the mid-2000s was divested Shoji divested. 665 00:40:09,700 --> 00:40:12,400 Jake is in the think it was about 2005. 666 00:40:12,400 --> 00:40:15,000 Yeah, I became genpact and private Equity bought bought 667 00:40:15,000 --> 00:40:16,200 them over. Yep. 668 00:40:16,200 --> 00:40:23,200 So at this point in time the IT service providers felt that 669 00:40:23,400 --> 00:40:26,500 okay. We have We moved into BPO and 670 00:40:26,500 --> 00:40:29,900 now let's move into something, which is, you know, which is 671 00:40:30,300 --> 00:40:33,400 higher margin than vp0, which is unlit exert versus. 672 00:40:33,700 --> 00:40:38,700 Yeah, so, because they saw that, you know, that Jake is which was 673 00:40:38,700 --> 00:40:42,600 a captive and and so that probably, it's not a score as 674 00:40:42,600 --> 00:40:45,800 what it was and some of the analytics stuff, probably could 675 00:40:45,800 --> 00:40:47,400 be outsourced. Yes. 676 00:40:47,700 --> 00:40:52,900 So the, the, the IT services guys gotten and the jealous and 677 00:40:52,900 --> 00:40:56,000 the Genesis of the team. If you look at it in all the IT 678 00:40:56,000 --> 00:41:00,500 service providers, they're all folks who are moving out of Jake 679 00:41:00,500 --> 00:41:03,500 is Jetpack. Yeah, so they were the guys were 680 00:41:03,500 --> 00:41:07,900 Staffing the, the these these IT services companies. 681 00:41:07,900 --> 00:41:12,500 They were also, you know, a few startups in the early 2000s. 682 00:41:13,000 --> 00:41:16,700 One was a company based out of Delhi called Market RX yet. 683 00:41:16,700 --> 00:41:21,500 So, which was essentially working into marketing 684 00:41:21,500 --> 00:41:25,600 analytics, and little bit of I think healthcare Life Sciences 685 00:41:25,600 --> 00:41:28,300 are not mistaken. They got picked up by cognizant. 686 00:41:28,400 --> 00:41:31,400 Yeah, and the other one was a was a bankroll based company 687 00:41:31,400 --> 00:41:35,000 called Market X which was smaller and they were again into 688 00:41:35,000 --> 00:41:37,000 marketing analytics. So they were based out of been 689 00:41:37,000 --> 00:41:39,200 drawn ogre. And they got picked up by. 690 00:41:39,200 --> 00:41:43,200 Yeah, nwsl in the W. WN s? 691 00:41:43,200 --> 00:41:47,400 Yeah, which yeah, excellent. WLS was, you know, merged. 692 00:41:48,100 --> 00:41:51,800 So it's pretty interesting because I was living in in 693 00:41:51,800 --> 00:41:54,600 Grenada at that time. And so Evenings. 694 00:41:54,600 --> 00:41:58,500 I used to take a jog. I mean, it's traffic wasn't bad 695 00:41:58,500 --> 00:42:01,500 at all. So I would take a jog, the 696 00:42:01,500 --> 00:42:06,800 evenings and then drop by at, you know, at, you know, a beer 697 00:42:06,800 --> 00:42:10,200 place which had a, which had Iyengar Bakery also, so the guy 698 00:42:10,200 --> 00:42:15,400 used to give me the cuffs and so used to be, you know, having my 699 00:42:15,400 --> 00:42:19,700 dear and my puff and used to be some of these guys from Market, 700 00:42:19,700 --> 00:42:22,500 excuse to land up there at these to be discussing and things like 701 00:42:22,500 --> 00:42:23,700 that. So I don't get one. 702 00:42:24,000 --> 00:42:27,400 Meaning, when, you know, I just used to listen and that's it. 703 00:42:27,400 --> 00:42:31,900 So I just used to, you know, log off, but I think they were 704 00:42:31,900 --> 00:42:35,400 making some some, you know, pretty ridiculous statements 705 00:42:35,400 --> 00:42:39,600 about using cluster analysis to solve something. 706 00:42:39,600 --> 00:42:41,400 I just couldn't, you know, poured myself. 707 00:42:41,400 --> 00:42:45,300 So I sweated told them that both don't use cluster for this. 708 00:42:45,300 --> 00:42:47,700 Use discriminant analysis. So this is going to, you know, 709 00:42:47,700 --> 00:42:50,800 so they were like, I mean, who's this bugger with a beer bottle 710 00:42:50,800 --> 00:42:53,300 coming and telling us what kind of should do? 711 00:42:54,600 --> 00:42:59,100 Mendel video job. So the dim did they were very 712 00:42:59,400 --> 00:43:02,200 curious to know. You know, how do I do all these 713 00:43:02,200 --> 00:43:05,000 things? And you know was I working for 714 00:43:05,400 --> 00:43:08,600 tickets or something and they were first a little bit paranoid 715 00:43:08,600 --> 00:43:09,800 about this. I told them that don't know who 716 00:43:09,808 --> 00:43:13,100 I am when, you know, it guy working for enforces and stuff 717 00:43:13,100 --> 00:43:15,000 like that. And then they said, okay, I add 718 00:43:15,000 --> 00:43:17,200 this guy's a harmless guy and all that stuff, but they were 719 00:43:17,200 --> 00:43:19,800 very intrigued about how I knew about all these things. 720 00:43:20,000 --> 00:43:24,500 Okay, so I had to tell them then and then you know how things go 721 00:43:24,500 --> 00:43:27,000 and India, right? So there's like one guy said, 722 00:43:27,600 --> 00:43:29,800 Sir, I will come every evening here. 723 00:43:30,100 --> 00:43:31,600 Please. Let me know by that I'm doing 724 00:43:31,600 --> 00:43:36,000 the right thing. So it was pretty hilarious. 725 00:43:36,000 --> 00:43:38,900 So anyways, I think those were heady times. 726 00:43:39,100 --> 00:43:43,100 Yep. So the I think I'd say that word 727 00:43:43,400 --> 00:43:44,800 time. I think I had applied for a job 728 00:43:44,800 --> 00:43:46,400 to marketing's at some point in time. 729 00:43:46,900 --> 00:43:50,500 And then like I did the st. Near test which was full of 730 00:43:50,600 --> 00:43:51,600 marketing. Jargon. 731 00:43:52,100 --> 00:43:55,600 And so I get I called up the HR inside like a naughty things 732 00:43:55,600 --> 00:43:59,300 forward because this reminds me. A of the, of the things that 733 00:43:59,400 --> 00:44:01,700 that, you know, the physics guys do, right. 734 00:44:01,700 --> 00:44:03,300 So, they talk physics to the math, guys. 735 00:44:03,300 --> 00:44:05,200 I think it's something that, that, that happens analytics. 736 00:44:05,200 --> 00:44:07,400 You talk domain to the guys who, you know, that's not understand 737 00:44:07,400 --> 00:44:10,000 the domain. And then you talk stats to the 738 00:44:10,000 --> 00:44:13,100 guys who understand the debate. So actually what I found is that 739 00:44:13,100 --> 00:44:15,000 it at least in my career over the last 10 years. 740 00:44:15,000 --> 00:44:17,800 What I found is that like it's easier to talk. 741 00:44:18,000 --> 00:44:21,700 I mean while if you want to sound impressive or to do some 742 00:44:21,700 --> 00:44:24,800 kind of suit sales then like it might make sense to talk the 743 00:44:24,800 --> 00:44:28,800 other thing, but I think I'm This is what I found in the last 744 00:44:28,800 --> 00:44:30,200 10 years. Is that like you just talk to 745 00:44:30,200 --> 00:44:33,000 this language to talk Tech to The Tech Guy in business to the 746 00:44:33,000 --> 00:44:35,600 business day and so on. But but yeah, I guess different 747 00:44:35,600 --> 00:44:39,600 people have their own strategies so its own right, right. 748 00:44:40,300 --> 00:44:41,600 Oh, but by the way, so this is one. 749 00:44:41,600 --> 00:44:44,300 More startup has been. It still continues fractal 750 00:44:44,300 --> 00:44:47,000 analytics. So I think they started off as 751 00:44:47,000 --> 00:44:51,800 analytics partner for HDFC Bank. Okay, and they still are a dozen 752 00:44:52,000 --> 00:44:55,800 analytics company. So they kind of, you know, stood 753 00:44:56,400 --> 00:44:59,800 you know with the Since they were also a couple of similar 754 00:44:59,800 --> 00:45:06,100 companies, one was people research, which I think got 755 00:45:06,600 --> 00:45:09,100 picked up by ICICI once or something. 756 00:45:09,100 --> 00:45:13,200 And I think there's another one which will Casilla, which was 757 00:45:14,000 --> 00:45:16,800 down yellow. They were a few of the rules are 758 00:45:16,800 --> 00:45:18,400 being. So just to say that they were 759 00:45:19,000 --> 00:45:21,400 you know, annalistic services companies focused on the 760 00:45:21,400 --> 00:45:25,800 domestic Market as well. So, yep and like fractal, I 761 00:45:25,800 --> 00:45:30,400 guess the other big one in that segment is music man, which yes, 762 00:45:30,400 --> 00:45:34,000 which again continues are too. Right, right? 763 00:45:34,000 --> 00:45:38,200 2004-5. They started and mu Sigma was 764 00:45:38,200 --> 00:45:43,100 the worst was the, you know, it's an interesting, you know, 765 00:45:43,100 --> 00:45:45,600 case study for me, because they were the guys who actually 766 00:45:45,600 --> 00:45:47,600 scaled up big time yet. Right. 767 00:45:47,600 --> 00:45:50,700 So you don't want keeps hearing about how the Indian IT services 768 00:45:50,700 --> 00:45:53,800 provider, scaled up in the late 90s and early 2000s. 769 00:45:54,100 --> 00:45:56,600 So I think you Sigma did that in the analytic space. 770 00:45:56,800 --> 00:45:59,700 So they are a very formalized training in there. 771 00:45:59,700 --> 00:46:02,200 You know, they called it. I think the new Sigma University 772 00:46:02,200 --> 00:46:03,600 or music back. To me. 773 00:46:03,600 --> 00:46:08,000 And then they would be, don't get these fritters and of both 774 00:46:08,000 --> 00:46:11,300 from their distant schools and then convert them into this, 775 00:46:11,600 --> 00:46:16,800 unlit explore festivals. So in in you know, some of the 776 00:46:16,900 --> 00:46:21,600 notables and in the financial services domain, referral T. 777 00:46:21,700 --> 00:46:25,700 So sometime in the late 2000s, doing marketing analytics, 778 00:46:26,300 --> 00:46:29,800 customer acquisition lifecycle management cross-sell or upsell 779 00:46:29,800 --> 00:46:32,900 and things like that, then you had gold. 780 00:46:33,000 --> 00:46:36,700 When Goldman was known for its fraud analytics and those kinds 781 00:46:36,700 --> 00:46:41,000 of, you know, a lot takes of domains. 782 00:46:41,200 --> 00:46:45,100 Yep, then HSBC, again a lot of marketing analytics, our 783 00:46:45,100 --> 00:46:50,400 computer, you know, Hardware makers, HP and Dell for the 784 00:46:50,400 --> 00:46:52,600 sales and I'll take so casting. Yep. 785 00:46:52,600 --> 00:46:56,500 Then you had retailers like Target that we're doing things 786 00:46:56,600 --> 00:47:00,900 from merchandising forecasting, inventory management, system 787 00:47:00,900 --> 00:47:05,200 management, and of course at the I think companies like L Brands, 788 00:47:05,200 --> 00:47:08,600 I think would be pretty popular on the campus, doing marketing 789 00:47:08,600 --> 00:47:13,700 analytics and among their brands is, you know, the famous 790 00:47:13,700 --> 00:47:17,000 Victoria Secret. So, so all of them doing some 791 00:47:17,000 --> 00:47:20,600 interesting analytics here, so that's the captors. 792 00:47:21,400 --> 00:47:25,100 And then we have Indian companies also getting big on 793 00:47:25,100 --> 00:47:30,900 analytics. But but hul, I mean, is, is an 794 00:47:30,900 --> 00:47:34,200 outlier here, because even in the laity, 80s and 90s. 795 00:47:34,400 --> 00:47:37,800 Yeah, I doing. What was what they called as 796 00:47:37,800 --> 00:47:39,500 Market insights. Okay. 797 00:47:39,500 --> 00:47:44,300 It was nothing but pretty Advanced, you know, quantitative 798 00:47:45,600 --> 00:47:50,500 marketing research. So they had a team from the from 799 00:47:50,500 --> 00:47:56,700 the 90s but through a lot faster and bigger in the 2000s and PNG 800 00:47:56,700 --> 00:48:01,800 created in in the analytics. Eoe Singapore. 801 00:48:01,800 --> 00:48:05,200 So quite a few folks. From India, moved to PNG 802 00:48:05,200 --> 00:48:11,600 Singapore to be about analytics. Co e 3F C among the you know 803 00:48:11,600 --> 00:48:14,700 prior Bank started off with analytics division or to incur 804 00:48:14,700 --> 00:48:16,600 add analytics and marketing analytics. 805 00:48:17,700 --> 00:48:21,600 They were pretty, you know, pretty big gone undertakes in 806 00:48:21,600 --> 00:48:24,400 the 2000's. So 2001. 807 00:48:24,400 --> 00:48:30,600 I remember that in the Indian campuses, the be food purposes. 808 00:48:30,600 --> 00:48:37,100 There was, I am be Capital One for the US operations recruited. 809 00:48:38,300 --> 00:48:41,100 Use analytics operations, recruited a few folks. 810 00:48:42,000 --> 00:48:45,500 And it was a big thing because, you know, at that, at that time, 811 00:48:45,500 --> 00:48:50,200 one of the popular case studies was that the Capital One could 812 00:48:50,200 --> 00:48:55,300 decide within a minute. What whether to give, you know, 813 00:48:55,300 --> 00:48:57,400 the prospect a credit card or not. 814 00:48:57,600 --> 00:49:02,100 Yep, and if yes, what was the credit limit for that for that 815 00:49:02,100 --> 00:49:06,000 Prospect? Yep, so it was good to see, you 816 00:49:06,000 --> 00:49:10,900 know, some folks from from my MBE travel to do is to work for 817 00:49:10,900 --> 00:49:15,200 the Capital One. And this interesting, you know, 818 00:49:15,200 --> 00:49:16,600 the interesting connection there. 819 00:49:17,000 --> 00:49:20,500 So one of the guys who got picked up to work in Capital One 820 00:49:20,500 --> 00:49:26,100 came back, worked in Jen, back in the symphony genpact and the 821 00:49:26,107 --> 00:49:31,500 and then in 2010, God of God, of War has started a training 822 00:49:31,500 --> 00:49:35,600 company called zigzoo. Okay, so this was probably the 823 00:49:35,600 --> 00:49:43,100 first company in India to focus on training folks on Analytics. 824 00:49:44,400 --> 00:49:49,800 Okay, so I think their first course, was was something on on 825 00:49:50,100 --> 00:49:52,900 doing analytics with SAS. So they realize that, you know, 826 00:49:53,000 --> 00:49:57,500 we have to teach folks the concepts of the methods of 827 00:49:57,500 --> 00:50:00,500 analytics and also focus on the tools and tools as I say as 828 00:50:00,500 --> 00:50:03,500 because that was probably the market leader, at that time. 829 00:50:03,800 --> 00:50:04,900 Yep. Yep. 830 00:50:05,700 --> 00:50:10,600 So, this was a 2010 and 2008-9 was not a great year for Indian 831 00:50:10,600 --> 00:50:11,600 it right based. No? 832 00:50:11,600 --> 00:50:18,300 See. Action, so this is the, you 833 00:50:18,300 --> 00:50:21,900 know, the software Engineers with maybe up to 45 years 834 00:50:21,900 --> 00:50:25,100 experience thinking that really continue the ID path or whether 835 00:50:25,100 --> 00:50:28,600 the a to do something else. So these kinds of programs gave 836 00:50:28,600 --> 00:50:35,400 them the to move from ID into analytics and that is against 837 00:50:35,400 --> 00:50:37,300 the propelled. A lot of people into the lake 838 00:50:37,500 --> 00:50:39,800 mid-career. It people started doing these 839 00:50:39,800 --> 00:50:42,600 courses to the hope of shifting into and the technique is 840 00:50:42,600 --> 00:50:47,300 obviously and Sort of at some level to your paid better than 841 00:50:47,800 --> 00:50:51,000 software, engineering X10. Yes. 842 00:50:51,800 --> 00:50:53,700 I think. I mean, I remember, you know, an 843 00:50:53,700 --> 00:50:58,100 analysis where for the company at least. 844 00:50:58,100 --> 00:51:03,100 So this would have been say mid 2000s or something that time 845 00:51:03,100 --> 00:51:05,300 frame. I think it was a pretty high, 846 00:51:05,300 --> 00:51:07,500 remember the numbers because it's easy to remember or not 847 00:51:07,500 --> 00:51:10,200 because you have anything else. So BP over something like you is 848 00:51:10,200 --> 00:51:12,600 the 20K per annum. Okay? 849 00:51:12,600 --> 00:51:18,000 Was the Of Revenue per employee. And IIT was about 40K, per annum 850 00:51:18,300 --> 00:51:24,300 and analytic to 60k per annum. Okay, so the a bit of for, for 851 00:51:24,300 --> 00:51:27,500 analytics, which is, you know, upwards of 30% or something. 852 00:51:27,500 --> 00:51:31,800 So it was a, you know, it was definitely, you know, it works 853 00:51:31,800 --> 00:51:35,500 both ways. So since the companies were 854 00:51:35,500 --> 00:51:38,800 getting a, you know, a higher Revenue per capita, so they 855 00:51:38,800 --> 00:51:42,900 could also offer to pay you slightly more than I guess. 856 00:51:42,900 --> 00:51:45,100 The Other two streams. Yep. 857 00:51:45,500 --> 00:51:48,900 And even now I find Direct like I mean like cover as well as in 858 00:51:49,100 --> 00:51:52,600 when I recruit for analytics. I find that like you find 859 00:51:52,600 --> 00:51:55,000 mid-career software Engineers would have done a course in 860 00:51:55,000 --> 00:51:58,600 analytics looking to jump into one because it's the same 40K 861 00:51:58,600 --> 00:52:03,400 260k kind of jump, whatever you translate that to it did sell 862 00:52:03,400 --> 00:52:06,300 these now and things like that. I guess. 863 00:52:06,700 --> 00:52:08,500 Let's change that's a little bit. 864 00:52:08,500 --> 00:52:10,100 Now. I just want to understand from 865 00:52:10,100 --> 00:52:13,300 you like what analytics for business professionals are like 866 00:52:13,400 --> 00:52:15,900 In India, like a in terms of like, dude. 867 00:52:16,400 --> 00:52:18,200 I don't know how much work you have done in terms of like, 868 00:52:18,200 --> 00:52:22,600 comparing analytics people in India to elsewhere into on or if 869 00:52:22,600 --> 00:52:27,200 you could like, how were like, how do you characterize the 870 00:52:27,200 --> 00:52:29,200 analytics professionals? Like, how do they work? 871 00:52:29,200 --> 00:52:32,000 And like, is that how is that different from? 872 00:52:32,000 --> 00:52:34,400 That's a, like, software engineering or their 873 00:52:34,400 --> 00:52:42,500 counterparts abroad and so on. See, so this is based on my 874 00:52:43,000 --> 00:52:47,000 interactions with folks who've been working in analytics for 875 00:52:47,000 --> 00:52:49,000 the past 20 years. It's at least. 876 00:52:49,100 --> 00:52:50,000 Yep. Okay. 877 00:52:50,000 --> 00:52:55,400 So the first, you know, thing that, that that strikes me with 878 00:52:55,400 --> 00:52:58,400 these guys is that they are comfortable with the domain. 879 00:52:58,800 --> 00:53:02,400 I mean, it could be that, you know, they are in cpg or FM 880 00:53:02,400 --> 00:53:05,000 CTS-V call it. If they would be comfortable in 881 00:53:05,000 --> 00:53:08,800 that industry and they would be comfortable with The methods, 882 00:53:09,300 --> 00:53:12,200 the, you know, the market research methods or the 883 00:53:12,900 --> 00:53:17,900 operations management methods, you are statistical methods and 884 00:53:17,900 --> 00:53:19,800 they would be comfortable using tools. 885 00:53:20,100 --> 00:53:23,800 Yep, you know, essay as well. They want to our and stuff like 886 00:53:23,800 --> 00:53:26,100 that, right? So they are at ease. 887 00:53:26,500 --> 00:53:29,400 Yep. The the other one that I've 888 00:53:29,400 --> 00:53:34,600 observed with them is that tell this probably might be a 889 00:53:34,600 --> 00:53:37,300 difference between the folks who are just coming. 890 00:53:37,400 --> 00:53:41,400 Into the profession. And folks of in around, is that 891 00:53:41,400 --> 00:53:45,500 these guys are very parsimonious in terms of the independent 892 00:53:45,500 --> 00:53:46,800 variables. Okay. 893 00:53:46,900 --> 00:53:52,500 So they do some kind of an, you know, some heuristics intuition 894 00:53:52,500 --> 00:53:56,300 and then they figure out that okay, you know, I know that I'm 895 00:53:56,300 --> 00:54:01,300 collecting, you know, these these 70 or variables. 896 00:54:01,500 --> 00:54:04,200 Well, I think these 15 are the ones that need to go into my 897 00:54:04,200 --> 00:54:05,300 model. Okay? 898 00:54:05,900 --> 00:54:09,600 Okay, because I know that the You know how these 15 can at? 899 00:54:09,600 --> 00:54:12,000 He's directionally impact the dependent variable. 900 00:54:12,000 --> 00:54:13,700 And so, okay. This is what it's going to be. 901 00:54:13,900 --> 00:54:19,600 I don't want to crowd my model with the rest of the the 70 902 00:54:19,600 --> 00:54:22,800 plus. Okay, so maybe this is changing 903 00:54:22,800 --> 00:54:26,000 when you use machine learning and stuff like that, but I'm 904 00:54:26,100 --> 00:54:28,800 talking about a traditional statistical methods. 905 00:54:28,800 --> 00:54:36,800 Okay, so these folks use analytics even when they need 906 00:54:36,800 --> 00:54:40,400 not use, Latex or pathway where there is no expectation for 907 00:54:40,400 --> 00:54:41,700 them. Use analytics. 908 00:54:42,300 --> 00:54:46,000 So I know folks in management consulting who do a lot of 909 00:54:46,000 --> 00:54:51,300 number crunching, but this smart enough not to not to put these 910 00:54:51,500 --> 00:54:55,300 stuff in a deliverable slide, but they present this in nice 911 00:54:55,300 --> 00:54:58,100 pictorial form or in two by twos. 912 00:54:58,300 --> 00:55:04,000 Okay, so I think there are so, so I think that for some folks, 913 00:55:04,600 --> 00:55:07,000 you know, they were not be analytics folks, but they are 914 00:55:07,000 --> 00:55:10,900 doing Analytics and it's probably a state of mind a frame 915 00:55:10,900 --> 00:55:13,800 of mind so which I find pretty interesting. 916 00:55:14,800 --> 00:55:20,300 And then finally, I think there are a lot of folks who are who 917 00:55:20,300 --> 00:55:21,800 are related to the previous point. 918 00:55:21,800 --> 00:55:25,500 Is that closet and latex professionals? 919 00:55:25,800 --> 00:55:29,300 Okay, so they are not working analytics, departments. 920 00:55:29,700 --> 00:55:31,700 So they are working in sales marketing. 921 00:55:31,700 --> 00:55:34,500 They working is, as leadership guys. 922 00:55:34,500 --> 00:55:37,200 They're working as a supply chain. 923 00:55:38,500 --> 00:55:40,700 But I caught there on the attic space. 924 00:56:01,800 --> 00:56:03,800 Thank you for listening to data shatter. 925 00:56:04,300 --> 00:56:07,900 If you like this show, please leave a comment, share and 926 00:56:07,900 --> 00:56:11,100 subscribe to the podcast. You can find this podcast on 927 00:56:11,100 --> 00:56:14,000 Apple podcasts Spotify or wherever else. 928 00:56:14,100 --> 00:56:18,700 Go to get your podcasts once again, the skirt, exciting of, 929 00:56:19,000 --> 00:56:19,500 thank you.