1 00:00:00,100 --> 00:00:02,600 Some of the best projects that I have worked on. 2 00:00:03,100 --> 00:00:07,500 Have not involved any modeling. Have not involved any 3 00:00:07,500 --> 00:00:13,000 sophisticated stuff, but they have been simple cuts of data, 4 00:00:13,200 --> 00:00:16,900 but with very high impact, as long as you show it, right? 5 00:00:17,700 --> 00:00:20,000 You don't need to build the rocket ship out in the first 6 00:00:20,000 --> 00:00:24,700 goal, but you can at least show that, hey, there is this part of 7 00:00:24,700 --> 00:00:26,000 the rocket ship that is built in. 8 00:00:26,000 --> 00:00:28,000 That could be useful for you in this way. 9 00:00:42,300 --> 00:00:45,800 Hello and welcome to data. Shatter the podcast on all 10 00:00:45,800 --> 00:00:49,200 things data. This podcast is a series of 11 00:00:49,200 --> 00:00:52,100 conversations with experts and Industry leaders in data. 12 00:00:52,300 --> 00:00:55,100 And each week. We aim to unpack a different 13 00:00:55,100 --> 00:00:56,900 compartment of the data suitcase. 14 00:00:57,700 --> 00:01:01,500 I am your host Catholic Church that Blogger newspaper, 15 00:01:01,500 --> 00:01:04,500 columnist book author, and a former data and strategy 16 00:01:04,500 --> 00:01:07,600 consultant at currently head analytics and business 17 00:01:07,600 --> 00:01:10,400 intelligence for delivery. One of India's largest 18 00:01:10,400 --> 00:01:11,500 logistics. Companies. 19 00:01:12,100 --> 00:01:17,100 You can follow me on Twitter at Karthik s that is Kar Phi. 20 00:01:17,100 --> 00:01:21,200 K s and read my blog at no Intruder.com. 21 00:01:21,400 --> 00:01:28,300 That is n Over. N th you be a.com all opinions 22 00:01:28,300 --> 00:01:29,700 expressed in his podcast belong to me. 23 00:01:29,900 --> 00:01:32,600 My podcast this and I do not reflect the views of any 24 00:01:32,600 --> 00:01:34,600 organizations. We might be Associated. 25 00:01:34,900 --> 00:01:37,300 Nothing discussing this podcast should be taken as Financial or 26 00:01:37,300 --> 00:01:46,000 legal advice. We are now in 2021. 27 00:01:46,600 --> 00:01:50,400 Everyone wants to do data sites. When companies raise money. 28 00:01:50,700 --> 00:01:53,300 It's common for them to say that they have used the funds to 29 00:01:53,300 --> 00:01:55,400 invest more in data, science and analytics. 30 00:01:56,100 --> 00:01:59,800 Some other well-funded companies are accused of holding. 31 00:02:00,000 --> 00:02:04,900 Doesn't this back in 2006 during a bull run and crude oil prices 32 00:02:05,200 --> 00:02:09,400 mathematician, had said that data is the new oil and that's 33 00:02:09,400 --> 00:02:11,900 now become a sort of self-fulfilling prophecy. 34 00:02:12,800 --> 00:02:16,400 You're just investing in capturing data and hiring data 35 00:02:16,400 --> 00:02:20,300 scientists doesn't work. A lot of effort goes in to make 36 00:02:20,300 --> 00:02:23,500 sure the data science teams. In actually be effective. 37 00:02:24,400 --> 00:02:26,600 Today's guest is unknown, Krishna. 38 00:02:27,100 --> 00:02:29,100 Anuj was an early Employee Of Music. 39 00:02:29,100 --> 00:02:32,400 Mama one of India's largest analytics, Outsourcing companies 40 00:02:32,800 --> 00:02:36,000 data, he went on to be a co-founder of the math company 41 00:02:36,500 --> 00:02:40,000 over the course of his career, and it has left and delivered 42 00:02:40,100 --> 00:02:42,800 and it's of analytics and data science projects, and by 43 00:02:42,800 --> 00:02:46,600 analyzing data from those, he has figured out a thing or two 44 00:02:46,700 --> 00:02:49,600 on what works. And what doesn't in terms of 45 00:02:49,600 --> 00:02:51,800 making data signs work for a company. 46 00:02:57,600 --> 00:03:01,700 Let's start with the how business and data science sort 47 00:03:01,700 --> 00:03:05,500 of interactive mean. This is a sort of slightly old 48 00:03:05,500 --> 00:03:09,000 view that I have. Which is that like the business 49 00:03:09,000 --> 00:03:12,400 guys, Kenneth treat the data that is in one of two ways. 50 00:03:12,700 --> 00:03:17,300 One is that like they think the data people are gods and they 51 00:03:17,300 --> 00:03:20,100 whatever they say is the gospel in, you need to listen to them 52 00:03:20,600 --> 00:03:24,700 and the at The Other Extreme you have like you the the business 53 00:03:24,700 --> 00:03:26,900 people think. Like these datasets are doing 54 00:03:26,900 --> 00:03:29,400 some random math, which is not relevant to the business. 55 00:03:29,400 --> 00:03:30,800 So why do we need to listen to them? 56 00:03:30,800 --> 00:03:34,300 And if either cases, if you think about it, the business is 57 00:03:34,300 --> 00:03:36,400 not able to get the full value from the data. 58 00:03:37,100 --> 00:03:41,300 So start with like, is this is, this is my view, correct? 59 00:03:41,300 --> 00:03:43,500 Is it still happening? Is it Universal? 60 00:03:43,500 --> 00:03:47,500 Or is it sort of certain types of companies, where it happens 61 00:03:47,500 --> 00:03:49,400 in? Yeah, and what do you need to do 62 00:03:49,400 --> 00:03:53,400 to their sort of integrate data and business better? 63 00:03:54,500 --> 00:03:58,600 Yeah, I mean, so I think that, you know, I think a lot of it 64 00:03:58,600 --> 00:04:05,900 has to do more with how we'd how data scientists are, you know, 65 00:04:05,900 --> 00:04:09,100 that the data product itself gets delivered to business, 66 00:04:09,100 --> 00:04:14,100 right? I think because of that, if you, 67 00:04:14,100 --> 00:04:17,700 if you think about how that that telling really happens and if 68 00:04:17,700 --> 00:04:21,200 you make it more math, more Tech, and you don't do playing 69 00:04:21,200 --> 00:04:24,200 speak business, speak businesses, tend to business. 70 00:04:24,400 --> 00:04:28,300 People tend to either twist to the side on which to that side. 71 00:04:28,800 --> 00:04:32,700 In either case, as you said, it doesn't become useful in terms 72 00:04:32,700 --> 00:04:37,300 of organizations themselves who, you know, kind of go through 73 00:04:37,300 --> 00:04:39,400 that. I haven't seen this as much in 74 00:04:39,400 --> 00:04:43,500 organizations which have sort of grown with the data at its 75 00:04:43,500 --> 00:04:47,300 Center or text at its Center Sovereign more newer Edge 76 00:04:47,300 --> 00:04:49,300 organizations. I think like if you take the 77 00:04:49,308 --> 00:04:52,100 Amazons of the world, I don't think that they think that 78 00:04:52,100 --> 00:04:54,500 differently between data and business because for Them. 79 00:04:54,500 --> 00:04:58,800 The data gives them, the is the core of their value proposition 80 00:04:58,800 --> 00:05:01,200 in some ways. Right, but if you take some of 81 00:05:01,200 --> 00:05:03,800 the older organizations, it becomes a big challenge because 82 00:05:03,800 --> 00:05:07,900 they're not used to thinking of data, in a particular way. 83 00:05:07,900 --> 00:05:10,600 They're not used to using data in a particular way. 84 00:05:10,600 --> 00:05:15,800 So, so there it becomes more of a challenge where where, where 85 00:05:15,800 --> 00:05:20,400 businesses sort of tend to tune out in a lot of ways. 86 00:05:20,400 --> 00:05:23,600 When, when, when, when the data is come to come to talk 87 00:05:23,600 --> 00:05:25,000 anything. Not. 88 00:05:25,000 --> 00:05:26,600 If it is just articulation. I feel. 89 00:05:27,700 --> 00:05:31,800 I mean, I think the, the if the data product is presented in a 90 00:05:31,800 --> 00:05:38,100 way that is simple, that is usable that caters to, you know, 91 00:05:38,300 --> 00:05:42,200 a layer of translation because you do need to translate what 92 00:05:42,200 --> 00:05:47,400 has happened in your data science, sort of cooking to. 93 00:05:47,500 --> 00:05:49,100 What does it really mean to the business? 94 00:05:49,100 --> 00:05:52,200 If the translation layer is not very clean. 95 00:05:52,300 --> 00:05:55,300 I think in a lot of cases that That sort of you things to 96 00:05:55,300 --> 00:05:59,600 happen. So computer translation there. 97 00:05:59,600 --> 00:06:02,700 It says they are they whose responsibility it is a tiny or 98 00:06:02,700 --> 00:06:05,700 to phrase it another way, right? I mean one way the some 99 00:06:05,700 --> 00:06:08,400 companies took it is like get a bunch of math, the agent didn't 100 00:06:08,400 --> 00:06:12,600 do the data and then they whose responsibility is it to sort of 101 00:06:12,900 --> 00:06:15,900 take the Data Insights? And because I have seen like 102 00:06:16,400 --> 00:06:19,100 client presentations, which have the this head, this the value, 103 00:06:19,100 --> 00:06:22,200 the set that we value. And everybody was like black. 104 00:06:22,200 --> 00:06:25,600 I mean, so obviously the door, so somebody needs to take the 105 00:06:25,600 --> 00:06:27,900 responsibility who can understand both sides. 106 00:06:28,000 --> 00:06:30,700 It's okay, take the P value. And then, then the business, 107 00:06:30,700 --> 00:06:32,700 like, okay, this is what the data is telling us. 108 00:06:32,700 --> 00:06:35,800 It's also how, how does it sort of layout? 109 00:06:35,800 --> 00:06:38,900 I mean, original distant, different organizations have 110 00:06:38,900 --> 00:06:40,400 dealt with that very differently, right? 111 00:06:40,400 --> 00:06:44,500 So there are some who have who have a specific person who, 112 00:06:44,500 --> 00:06:47,300 which is a role called the analytics translator. 113 00:06:47,300 --> 00:06:52,100 If you will, who is going to do that for the business and from 114 00:06:52,100 --> 00:06:57,700 the business to the, to the, to the analytics folks, right in 115 00:06:57,700 --> 00:07:00,900 our The case, for example, I think that we have been, we also 116 00:07:00,900 --> 00:07:04,800 believe that unless the data folks themselves, learn how to 117 00:07:04,800 --> 00:07:09,000 do that, at least a little bit. It becomes a pointless exercise 118 00:07:09,000 --> 00:07:11,600 when you build a model and it doesn't get anywhere kind of 119 00:07:11,608 --> 00:07:16,400 thing, right? Different folks have done it 120 00:07:16,400 --> 00:07:20,400 differently, but I feel that the the important thing is to 121 00:07:20,400 --> 00:07:26,000 understand that, you know, you could bring in folks for playing 122 00:07:26,000 --> 00:07:29,300 different roles whether it is the same person doing that or 123 00:07:29,300 --> 00:07:33,200 not is a different question, but there are different roles that 124 00:07:33,200 --> 00:07:35,400 need to be played. And that identification 125 00:07:35,600 --> 00:07:38,400 identification of those roles is extremely important. 126 00:07:38,400 --> 00:07:42,900 You need somebody who's going to think about the cust customer or 127 00:07:42,900 --> 00:07:46,200 the business user. And I'll figure out what it is 128 00:07:46,200 --> 00:07:48,100 that they are really looking for. 129 00:07:48,100 --> 00:07:52,200 What is it is that they would like to hear and what they don't 130 00:07:52,200 --> 00:07:56,900 like to hear and make sure that someone is constantly playing 131 00:07:56,900 --> 00:08:01,900 that, I think the owner of that data science team and that that 132 00:08:01,900 --> 00:08:04,800 function, if you will and whoever that might be it could 133 00:08:04,800 --> 00:08:08,100 be the, you know, it could be the CTO. 134 00:08:08,100 --> 00:08:11,700 It could be the CIO. It could be, it could be the CD 135 00:08:11,700 --> 00:08:16,000 of whoever that is has to be very Cognizant of the fact that 136 00:08:16,000 --> 00:08:18,900 that needs to kind of happen. And if it doesn't happen, then 137 00:08:19,200 --> 00:08:20,700 everything sort of goes for a toss. 138 00:08:21,000 --> 00:08:23,900 So identifying those roles and then based on those roles, 139 00:08:23,900 --> 00:08:27,000 figuring out who's playing them and making sure that those roads 140 00:08:27,000 --> 00:08:31,600 are actually being played, but the owners to be honest, is 141 00:08:31,600 --> 00:08:34,600 always going to lie on the people who are creating the 142 00:08:34,700 --> 00:08:36,200 product, right? Whatever? 143 00:08:36,200 --> 00:08:39,600 That product. So the owners can never lie on 144 00:08:39,600 --> 00:08:42,400 the business. I keep going back to this 145 00:08:42,400 --> 00:08:46,500 example of think of your think. In consumer right at the end of 146 00:08:46,500 --> 00:08:51,600 the day, if the in consumer like a like if you don't use an app, 147 00:08:51,600 --> 00:08:54,700 you are not using an app. It can't be your fault. 148 00:08:54,700 --> 00:08:58,300 You're not using an app ever. So I think the same applies for 149 00:08:58,300 --> 00:09:03,400 business users also. So what do you say is that the 150 00:09:03,500 --> 00:09:06,300 data guys need to sort of, like, I mean, it's important for 151 00:09:06,300 --> 00:09:09,700 databases sort of know some business. 152 00:09:09,800 --> 00:09:13,300 Learn the business context solve business problems rather than 153 00:09:13,300 --> 00:09:16,700 solid mats problems because I think that's one wrap that it's 154 00:09:16,800 --> 00:09:21,500 easy for data scientists to get into right - you just go off 155 00:09:21,500 --> 00:09:26,400 into the exam, different levels of geekery, depending on what 156 00:09:26,400 --> 00:09:29,500 what problem you're solving and like that can sometimes 157 00:09:29,600 --> 00:09:31,600 temperature. Most elegant solution need not. 158 00:09:31,800 --> 00:09:36,500 He produced a good business impact in some a gesture simple 159 00:09:36,500 --> 00:09:40,200 average somewhere might do the trick rather than building some 160 00:09:40,600 --> 00:09:43,900 involved models way. Yeah, absolutely. 161 00:09:43,900 --> 00:09:49,100 I think that this is something that I've seen a lot over over 162 00:09:49,100 --> 00:09:51,500 over time. And, you know, I think a lot of 163 00:09:51,500 --> 00:09:55,600 times we like Tech Community, right? 164 00:09:55,700 --> 00:09:57,800 Whether it is a data scientist data engineer. 165 00:09:58,100 --> 00:10:01,600 Anyone we tend to get lost in our own. 166 00:10:01,800 --> 00:10:05,700 Complexity of our own sophisticated problem. 167 00:10:05,900 --> 00:10:09,500 Sorry, sophisticated solution. What kind of model? 168 00:10:09,800 --> 00:10:12,600 What kind of new technique are? I am I going to use things like 169 00:10:12,600 --> 00:10:17,700 that, without completely from beginning to end focusing on? 170 00:10:17,700 --> 00:10:22,300 What is really the problem that we are looking to solve and 171 00:10:23,600 --> 00:10:27,300 work, at least from my experience, you know, just 172 00:10:27,300 --> 00:10:31,500 thinking about it with a sort of a design lens if you will. 173 00:10:31,800 --> 00:10:36,500 Also looking at, you know, understanding where the source 174 00:10:36,500 --> 00:10:39,600 of the problem really is understanding the real solution 175 00:10:39,600 --> 00:10:41,400 that somebody's really looking for. 176 00:10:41,800 --> 00:10:48,200 And then tailoring, what? You're sort of data science 177 00:10:48,200 --> 00:10:51,700 solution. Should be needs to happen which 178 00:10:51,700 --> 00:10:53,200 lot of times just doesn't happen. 179 00:10:54,800 --> 00:10:56,800 And yes, you need to understand the business for it. 180 00:10:56,800 --> 00:11:00,100 You could get help and somebody else provides that business 181 00:11:00,100 --> 00:11:04,300 context, but somebody really He needs to always do that on a 182 00:11:04,300 --> 00:11:06,200 consistent basis. Otherwise, it just completely 183 00:11:06,200 --> 00:11:10,100 gets lost in, in the, in the, in the math and the tech of it. 184 00:11:10,400 --> 00:11:12,500 I think that the that also applies on the other. 185 00:11:12,500 --> 00:11:17,000 And if you think about it, as I have a lot of results, right? 186 00:11:17,000 --> 00:11:22,700 And we also tend to enjoy the results that are coming out and 187 00:11:23,000 --> 00:11:27,500 try to, you know, marvel at at our own sort of magnificence in 188 00:11:27,500 --> 00:11:30,500 figuring. Not that result not thinking 189 00:11:30,500 --> 00:11:31,400 about. Okay, fine. 190 00:11:32,100 --> 00:11:34,300 Tied back to the problem that we have solved. 191 00:11:34,300 --> 00:11:37,000 How do you make sure that, you know, going back to that point 192 00:11:37,000 --> 00:11:39,500 and communication? How do you make it simple for 193 00:11:39,500 --> 00:11:42,200 them? How do you make it a easy for 194 00:11:42,200 --> 00:11:46,500 them to understand and read it? You know, how do you make it 195 00:11:46,500 --> 00:11:49,800 visually appealing? How do you make it, you know, 196 00:11:50,600 --> 00:11:55,900 even, you know, something that they can quickly, touch use 197 00:11:55,900 --> 00:11:58,800 things like that, right? Thinking about it from that kind 198 00:11:58,800 --> 00:12:02,500 of lens is hard for, for of us to do. 199 00:12:03,200 --> 00:12:05,500 You could really, you know, one of the things that you could 200 00:12:05,500 --> 00:12:08,100 also do is, you know, for example, some of the things that 201 00:12:08,100 --> 00:12:11,600 we are doing is we are bringing in actual designers who are 202 00:12:11,600 --> 00:12:15,300 going to think about it, purely from the perspective of what is 203 00:12:15,300 --> 00:12:18,500 a customer who want to look at right? 204 00:12:18,500 --> 00:12:20,000 And what would they want to look at? 205 00:12:20,000 --> 00:12:24,900 What would they want to kind of see and feel touch. 206 00:12:24,900 --> 00:12:26,400 And, you know, think you thinking or things? 207 00:12:26,400 --> 00:12:29,600 Like ux, for example, which I'm sure a data scientist, wouldn't 208 00:12:29,600 --> 00:12:32,400 think about, right? If you are putting out a Patient 209 00:12:32,600 --> 00:12:34,800 tortured, the presentation kind of flow mix. 210 00:12:34,900 --> 00:12:38,200 Make sure that makes sense. Things like that. 211 00:12:38,200 --> 00:12:43,400 I think it's a very critical part which is often gets lost in 212 00:12:43,400 --> 00:12:45,300 the in the in the complexity of the solution. 213 00:12:46,200 --> 00:12:47,800 So, how do you think of the team for this? 214 00:12:47,800 --> 00:12:50,900 I mean, like that's should everyone sort of the have some 215 00:12:50,900 --> 00:12:54,900 mini meant of communication in the more, you try to bring in 216 00:12:54,900 --> 00:12:56,800 Specialists for the communications within be the 217 00:12:56,800 --> 00:12:58,800 bridge. You're like, how do you solve 218 00:12:58,800 --> 00:13:03,200 this problem? I feel that there is a the the 219 00:13:03,500 --> 00:13:05,900 there are only very few unicorns will be able to do everything, 220 00:13:06,200 --> 00:13:08,900 right? So I think that's that's one but 221 00:13:10,200 --> 00:13:13,400 there are there is there needs to be an appreciation for of 222 00:13:13,400 --> 00:13:17,500 everybody for everything whether that appreciation did not be 223 00:13:17,500 --> 00:13:21,700 deep but there needs to be a basic level of appreciation for 224 00:13:21,700 --> 00:13:25,700 the other aspects of you know, what what needs to happen. 225 00:13:25,700 --> 00:13:28,400 Even on the engineering side. For example, a lot of times I 226 00:13:28,400 --> 00:13:31,300 find that data scientists don't have Appreciation for what it 227 00:13:31,300 --> 00:13:35,800 takes to get the data to where it needs to get to, but you need 228 00:13:35,800 --> 00:13:37,200 to have that kind of appreciation. 229 00:13:37,200 --> 00:13:42,100 Otherwise, you'll kind of, you won't understand the challenges 230 00:13:42,100 --> 00:13:43,900 in the complexities that they have to deal with in your 231 00:13:43,900 --> 00:13:45,900 solution. Hence won't be designed to deal 232 00:13:45,900 --> 00:13:49,900 with those complexities, right? And on this side in terms of 233 00:13:49,900 --> 00:13:54,200 design and whatever not it is the route that we feel and what 234 00:13:54,500 --> 00:13:57,800 at least we have taken is, is more on the build basic 235 00:13:57,800 --> 00:13:59,800 appreciation. Get smes. 236 00:14:00,700 --> 00:14:02,400 That's, that's the route that we have taken. 237 00:14:02,400 --> 00:14:06,200 So we have people who are doing particular things, as you know, 238 00:14:06,200 --> 00:14:09,100 that's their core, but they have an appreciation for the other 239 00:14:09,100 --> 00:14:12,300 stuff. How do you do it in a mean? 240 00:14:12,300 --> 00:14:15,700 Your sister sort of thought they'd see your largest laddish 241 00:14:15,700 --> 00:14:19,200 organization serving several clients into on so you can 242 00:14:19,400 --> 00:14:22,800 connect with their grief so that I think it will fit but to the 243 00:14:22,900 --> 00:14:24,700 with a small team, I guess it had a rate. 244 00:14:24,700 --> 00:14:27,400 I mean you'll need to have some unique or somewhere else. 245 00:14:27,700 --> 00:14:30,600 I don't know like I'm struggling with the dog. 246 00:14:31,400 --> 00:14:34,100 Yeah, I have absolutely I think that there need to be some 247 00:14:34,100 --> 00:14:38,300 unicorns. I would also say that the needs 248 00:14:38,300 --> 00:14:42,600 to be Trini that Is really that that needs to be enabled for 249 00:14:42,600 --> 00:14:47,100 some of these people I've seen in, at least in my past, that, 250 00:14:48,300 --> 00:14:51,000 you know, doing something like a design, thinking training. 251 00:14:51,000 --> 00:14:55,700 So that people understand how to really think about it, is 252 00:14:55,700 --> 00:14:58,100 extremely critical because a lot of times we just don't get 253 00:14:58,100 --> 00:15:01,500 exposed to things like that. I think that the traitor the 254 00:15:01,500 --> 00:15:04,300 exposure is the greater, the training is even for small 255 00:15:04,300 --> 00:15:05,500 teams. I think that's extremely 256 00:15:05,500 --> 00:15:08,500 critical because it's just at least it up levels. 257 00:15:08,500 --> 00:15:13,200 The work product. Wore a into a into the into just 258 00:15:13,200 --> 00:15:16,200 that next category. Now this coming back to this 259 00:15:16,200 --> 00:15:18,600 communication regular. Having register start to the 260 00:15:18,600 --> 00:15:21,600 board broader slate in terms of like how do you mean? 261 00:15:21,600 --> 00:15:23,900 Like, let's assume you have it. Some cool model, whatever. 262 00:15:23,900 --> 00:15:25,700 The model is. It could be a regression. 263 00:15:25,700 --> 00:15:28,700 It could be some, some neural networks are all, you know, but 264 00:15:28,700 --> 00:15:31,200 like how do you sort of? Like, I think one of the 265 00:15:31,400 --> 00:15:35,200 challenges that data science cases, I mean again, let me know 266 00:15:35,200 --> 00:15:38,200 from getting this wrong, but on my personal experience is that 267 00:15:38,200 --> 00:15:39,600 trick is communicating your results. 268 00:15:39,800 --> 00:15:42,800 That's equal to, you can communicate whatever your the 269 00:15:42,800 --> 00:15:44,300 inside your phone. Like. 270 00:15:44,500 --> 00:15:47,300 How do you like maybe the data itself as a story? 271 00:15:47,300 --> 00:15:52,400 How do you sort of communicated to the to the business? 272 00:15:52,400 --> 00:15:54,800 Like it should you abstract steps to do? 273 00:15:55,100 --> 00:15:57,500 So would stop to them the way that they carry? 274 00:15:57,500 --> 00:16:00,900 Are you go words from? It goes back to the translation, 275 00:16:01,400 --> 00:16:06,700 but I think that I heard I read this somewhere that basically 276 00:16:07,100 --> 00:16:09,700 humans, basically can only understand stories. 277 00:16:09,800 --> 00:16:14,500 Efforts. And so it's I guess important 278 00:16:14,500 --> 00:16:19,100 for you to tell a story for you to contextualizing in in their 279 00:16:19,100 --> 00:16:24,000 kind of perspective to I think they need to edit is also very 280 00:16:24,000 --> 00:16:28,500 high if you're not able to edit what you want to present 281 00:16:28,500 --> 00:16:32,000 properly, and make sure that you cut out stuff that really they 282 00:16:32,000 --> 00:16:36,800 don't care about it becomes a big challenge, right thing using 283 00:16:36,900 --> 00:16:40,500 examples of you. So say you've built a 284 00:16:40,500 --> 00:16:44,200 personalization model of some sort identifying, a cohort of 285 00:16:44,200 --> 00:16:48,700 customers and showing the kind of personalization you really 286 00:16:48,700 --> 00:16:54,200 talking about as an example, bringing Comfort to them to say 287 00:16:54,200 --> 00:16:54,900 that. Hey, you know what? 288 00:16:54,900 --> 00:16:59,600 I have the model and all is doing what it's doing, but this 289 00:16:59,600 --> 00:17:02,300 is the result of it. And as you can see this meshes, 290 00:17:02,300 --> 00:17:05,000 well, with your business intelligence that you have built 291 00:17:05,000 --> 00:17:07,900 up or you are thinking about your intuitive sense. 292 00:17:08,200 --> 00:17:10,400 So you can trust that. There is data. 293 00:17:10,400 --> 00:17:13,000 So that is a flow that is kind of Happening Here. 294 00:17:13,000 --> 00:17:15,599 Bringing out some of those examples bringing out metaphors. 295 00:17:15,599 --> 00:17:18,800 Bringing out stories. I think is a better way of 296 00:17:19,500 --> 00:17:25,900 communicating the results rather than thinking of it as I will 297 00:17:25,900 --> 00:17:28,400 talk through the process of how I built my model. 298 00:17:30,000 --> 00:17:32,200 The wait, okay, so it's basically need to be covered in 299 00:17:32,200 --> 00:17:36,400 some ways just to replace. What is it about going backwards 300 00:17:36,400 --> 00:17:40,300 said, it's like taco dissolution and then like, only they had 301 00:17:40,300 --> 00:17:42,200 like really interested in. So when you picked up over the 302 00:17:42,200 --> 00:17:45,400 brush because it crosses doesn't necessarily add value to the 303 00:17:45,400 --> 00:17:47,800 business. Yeah, I think, what they caught 304 00:17:47,800 --> 00:17:51,700 what business are support more, is that is your process robust. 305 00:17:51,700 --> 00:17:55,700 Have you dealt with all the, all the possible, you know, said 306 00:17:55,700 --> 00:17:58,100 data, cleaning challenges that you might have had. 307 00:17:58,700 --> 00:18:01,700 Can I trust The data that you have used. 308 00:18:01,700 --> 00:18:04,600 These are the questions they would have and you don't need to 309 00:18:04,600 --> 00:18:08,300 go into depth to show them unless they ask for it, as you 310 00:18:08,300 --> 00:18:13,000 said, but it's not a necessity for you to get into depth to 311 00:18:13,000 --> 00:18:16,100 show it to them right there because I did sometimes. 312 00:18:16,100 --> 00:18:19,000 I mean, I guess especially something like data science like 313 00:18:19,200 --> 00:18:22,300 over selects for people who are like sort of interested in maths 314 00:18:22,300 --> 00:18:26,000 interested in processing interested in doing cool things 315 00:18:26,000 --> 00:18:31,500 like like they Instinctively forcibly over index on, on that 316 00:18:31,500 --> 00:18:36,200 rather than rather, than on the, on the story story, eats in 317 00:18:36,200 --> 00:18:38,100 Vegas. Yeah. 318 00:18:38,100 --> 00:18:40,600 Absolutely. I think that is why I think he 319 00:18:40,600 --> 00:18:44,700 designed us need help to be honest and bringing in that hit 320 00:18:44,700 --> 00:18:48,300 from as much as many corners as possible becomes critical. 321 00:18:49,500 --> 00:18:54,500 So in a small team, making sure that one person is constantly 322 00:18:54,500 --> 00:18:59,800 say thinking about it or making Show that at a bare minimum 323 00:18:59,800 --> 00:19:02,700 level, you put some standards process see saying that, this is 324 00:19:02,700 --> 00:19:05,500 how you need to be at the minimum showing your 325 00:19:05,500 --> 00:19:07,200 presentation. These are the things you should 326 00:19:07,200 --> 00:19:11,500 not be showing. I think some of that helps, you 327 00:19:11,500 --> 00:19:14,000 might say that like data scientists need help. 328 00:19:14,100 --> 00:19:17,200 So who are the kind of people who can help the data center is, 329 00:19:17,200 --> 00:19:22,100 are these like buying by the 10, but everybody wants to be a data 330 00:19:22,100 --> 00:19:24,000 scientist are like, sometimes you might have a higher data 331 00:19:24,300 --> 00:19:26,400 visualization engineer. They'll want to be a data 332 00:19:26,400 --> 00:19:30,000 scientist, you you, Hire somebody to hide over here for 333 00:19:30,000 --> 00:19:33,900 whatever reason like this also the some sort of like something 334 00:19:33,900 --> 00:19:37,100 simpler than others and so on. So so how do you get help to 335 00:19:37,108 --> 00:19:39,700 data scientists and make sure that you can get people who can 336 00:19:39,900 --> 00:19:45,000 actually help the data sent. I mean, I don't know for a 337 00:19:45,000 --> 00:19:49,300 smallish team, but at least like I can tell you from my ideas. 338 00:19:49,300 --> 00:19:52,700 My own inexperience is basically get designers clean. 339 00:19:53,900 --> 00:19:57,000 They come with a so it's like you talking about orthogonal 340 00:19:57,000 --> 00:19:59,200 perspectives, right? It is completely orthogonal 341 00:19:59,200 --> 00:20:03,300 perspective to a data science perspective and making those two 342 00:20:03,300 --> 00:20:08,600 mesh is probably the help that data scientists can have need 343 00:20:09,200 --> 00:20:11,900 this probably but that's only one example, and that's very 344 00:20:11,900 --> 00:20:16,100 specific to our case. Maybe, but if you think about 345 00:20:16,100 --> 00:20:21,500 it, I feel that, you know. We were talking about it earlier 346 00:20:21,500 --> 00:20:24,400 but bringing in more appreciation for it might help 347 00:20:25,600 --> 00:20:30,200 bringing in a proper training program for, it might help 348 00:20:31,600 --> 00:20:37,600 bringing in some some base level practices can help. 349 00:20:40,300 --> 00:20:46,700 But other than an external entity doing it in an orthogonal 350 00:20:46,700 --> 00:20:50,500 manner, it's only the up, leveling of the data scientists 351 00:20:50,500 --> 00:20:55,900 themselves, which can help what. And speaking of data scientists 352 00:20:55,900 --> 00:20:58,300 Zips is set. I mean, like I have a lease 353 00:20:58,300 --> 00:21:01,100 space on my anecdotal information, like some kind of 354 00:21:01,100 --> 00:21:04,200 a, some kind of a curse distribution to for the lack of 355 00:21:04,200 --> 00:21:06,800 a better phrase site in terms of like everybody wants to be a 356 00:21:06,800 --> 00:21:10,200 data scientist. I didn't make data save because 357 00:21:10,200 --> 00:21:14,100 of that data scientists think that they are the coolest 10 358 00:21:14,100 --> 00:21:18,000 like they are like as I read in some log musically. 359 00:21:18,000 --> 00:21:20,600 I mean that's a Blog have quoted in some five different places 360 00:21:20,600 --> 00:21:23,700 already is actually data scientists believe that business 361 00:21:23,700 --> 00:21:29,500 intelligence is for Lexington's increase and and then like, so I 362 00:21:29,508 --> 00:21:32,500 did this is impression that the people who are doing it's a 363 00:21:32,500 --> 00:21:34,100 business intelligence or analytics. 364 00:21:34,100 --> 00:21:36,300 It's not as cool as data science. 365 00:21:36,500 --> 00:21:39,800 The data scientist want to work on the latest cutting-edge 366 00:21:39,800 --> 00:21:41,800 technology. And so, how do you, how do you 367 00:21:41,800 --> 00:21:46,100 sort of like when somebody comes to that kind of a mindset? 368 00:21:46,100 --> 00:21:50,200 Like, how do you up level them in terms of better 369 00:21:50,200 --> 00:21:53,300 communication, better, appreciation of other domains 370 00:21:53,300 --> 00:21:56,800 and things like that. I think you like really like I 371 00:21:56,800 --> 00:22:00,100 think we've been dealing with this question or on business 372 00:22:00,100 --> 00:22:04,700 intelligence versus cool data science for so long now, right? 373 00:22:04,700 --> 00:22:09,800 I mean, I'm sure you've seen it for so long also, it's it's a 374 00:22:09,800 --> 00:22:13,900 fundamental problem of not understanding the objective, I 375 00:22:13,900 --> 00:22:17,900 feel and you know the focus at the end of the day. 376 00:22:17,900 --> 00:22:21,600 It's like it's a whatever you're doing is a tool to solve a 377 00:22:21,600 --> 00:22:24,000 business problem. That's what it really is. 378 00:22:24,000 --> 00:22:26,200 Some of the The best projects that I have work. 379 00:22:26,200 --> 00:22:28,800 Done. Have not involved any modeling, 380 00:22:29,100 --> 00:22:34,300 have not involved any sophisticated stuff, but they 381 00:22:34,300 --> 00:22:39,100 have been simple cuts of data, but with very high impact, as 382 00:22:39,100 --> 00:22:43,700 long as you show it, right. And tell that story right? 383 00:22:44,400 --> 00:22:48,500 Which made made customers like millions of dollars or whatever 384 00:22:48,500 --> 00:22:52,100 it is. Right, but that perspective that 385 00:22:52,100 --> 00:22:53,900 look at all all of what you're doing. 386 00:22:53,900 --> 00:22:56,600 Whatever. You're Doing, let's back to the 387 00:22:56,600 --> 00:23:00,100 business problem itself. And it does like a tool with 388 00:23:00,100 --> 00:23:02,400 which you are using, doesn't matter as much as tether. 389 00:23:02,400 --> 00:23:05,900 You actually solved it or not. I guess. 390 00:23:05,900 --> 00:23:08,300 I think that's that's the that's that perspective. 391 00:23:08,300 --> 00:23:11,500 Sometimes goes missing and you need to drive that perspective 392 00:23:11,500 --> 00:23:13,900 as much as possible into into people. 393 00:23:13,900 --> 00:23:16,700 So that then they realize the value. 394 00:23:16,700 --> 00:23:21,100 But some, some are to be honest. Extremely just fascinated by the 395 00:23:21,100 --> 00:23:24,300 by the complicated stuff and you can't stop. 396 00:23:24,400 --> 00:23:26,900 Up that. But as much as you can drive the 397 00:23:26,900 --> 00:23:28,300 perspective of here, you know, what? 398 00:23:28,300 --> 00:23:31,900 If you solve the business problem, you will go to hire 399 00:23:32,300 --> 00:23:35,100 like lengths and you know, you'll become so much a much 400 00:23:35,100 --> 00:23:39,800 better sort of, you know, you'll have much better growth as long 401 00:23:39,800 --> 00:23:41,300 as you can drive that perspective. 402 00:23:41,300 --> 00:23:45,600 I think people in a see it and understand it and and as they 403 00:23:45,600 --> 00:23:47,700 experience it themselves then they like it. 404 00:23:47,700 --> 00:23:50,600 Everybody likes things when when take many gets used. 405 00:23:50,700 --> 00:23:52,600 That's just the truth of all of us, right. 406 00:23:52,900 --> 00:23:56,100 So yeah. So when they see that okay usage 407 00:23:56,100 --> 00:23:58,700 as happened and because of usage happening, something has 408 00:23:58,700 --> 00:24:01,300 happened. You have a sense of sort of 409 00:24:01,300 --> 00:24:05,700 pride in your work and that sort of leads to leads to things, but 410 00:24:06,900 --> 00:24:08,700 going back, going back, pushing down. 411 00:24:08,700 --> 00:24:13,200 The message of the business problem itself is, is critical. 412 00:24:13,400 --> 00:24:17,600 I don't, I, I have always had an issue with this with this 413 00:24:18,600 --> 00:24:20,900 complicated stuff. Versus business intelligence 414 00:24:20,900 --> 00:24:23,200 stuff and it's just never gone away. 415 00:24:23,500 --> 00:24:24,300 Yeah. No, I guess. 416 00:24:24,400 --> 00:24:26,800 Hi, Abby, I completed a human in my experience. 417 00:24:26,800 --> 00:24:29,000 I mean, I've only been doing this for 10 12 years now. 418 00:24:29,000 --> 00:24:33,600 Like I've never really I don't robots that are single case 419 00:24:33,600 --> 00:24:37,400 where I had a complicated model that delivered like massive 420 00:24:37,400 --> 00:24:40,300 impact. All the, the best part is always 421 00:24:40,300 --> 00:24:42,700 really taken over eating somewhere again. 422 00:24:42,700 --> 00:24:46,900 Something I quote often, like the best machine learning method 423 00:24:46,900 --> 00:24:51,200 is division. Lik take 90% of your work gets 424 00:24:51,200 --> 00:24:55,600 done with average estate. I don't like which is also 425 00:24:55,600 --> 00:24:57,500 linked to having back. When I was, ER, there. 426 00:24:58,000 --> 00:25:01,000 I need this country take people would ask me. 427 00:25:01,300 --> 00:25:03,900 What kind of methods do you use in your analysis? 428 00:25:04,500 --> 00:25:06,800 And I would be the in 90% of the cases. 429 00:25:06,800 --> 00:25:11,100 I use averages in the 90% of the remaining. 430 00:25:11,100 --> 00:25:13,900 I am use regression and maybe beyond that. 431 00:25:13,900 --> 00:25:17,900 Like, it's the thumb. You almost never get there a 432 00:25:17,900 --> 00:25:21,200 quite But people have always focused on that last one for 433 00:25:21,200 --> 00:25:22,700 today. What is the cool stuff that 434 00:25:22,700 --> 00:25:24,100 you're doing? What are the cool stuff? 435 00:25:24,100 --> 00:25:27,200 I am doing? And why did I sign up for this 436 00:25:27,200 --> 00:25:28,500 job? Where you're just asking me to 437 00:25:28,500 --> 00:25:34,000 find cuts of data rather than sort of doing some models and 438 00:25:34,000 --> 00:25:37,500 stuff. So yeah, I mean, yeah, I think 439 00:25:37,500 --> 00:25:43,100 there is a there is a, you know, that is a I know mindset issue 440 00:25:43,100 --> 00:25:47,500 in some ways, I guess and but again, this goes back to what is 441 00:25:47,500 --> 00:25:50,000 the business? How are you gonna kind of solve 442 00:25:50,000 --> 00:25:51,400 it? I completely relate to what 443 00:25:51,400 --> 00:25:53,800 you're saying. The, I've always had an issue 444 00:25:53,800 --> 00:25:59,600 with the same complicated modeling where people ask me 445 00:25:59,600 --> 00:26:01,300 over. What are you going to build an 446 00:26:01,300 --> 00:26:05,600 RNN and you know of that and it's like why do you really need 447 00:26:05,600 --> 00:26:08,700 an RN M to do this trick? Maybe not. 448 00:26:08,700 --> 00:26:10,500 Right? Think about that, if you can get 449 00:26:10,500 --> 00:26:14,400 the answer quickly and you can get the answer right then why 450 00:26:14,400 --> 00:26:18,200 would you need to make it make it any more complicated? 451 00:26:18,300 --> 00:26:20,800 Get those revised. We have sworn somebody recently 452 00:26:20,800 --> 00:26:24,400 told me they were like, okay, you might be able to solve this 453 00:26:24,400 --> 00:26:26,400 problem like this, and this might be more impactful for the 454 00:26:26,400 --> 00:26:29,400 business. But for my CV, I think that is 455 00:26:29,400 --> 00:26:32,300 the, it's important that I do some more machine learning and 456 00:26:32,300 --> 00:26:34,900 things like that. Yeah, that's true. 457 00:26:35,400 --> 00:26:36,500 That's true. Yeah. 458 00:26:36,500 --> 00:26:38,500 At the same time. I don't want to say that there 459 00:26:38,500 --> 00:26:41,100 is not a time and a place for doing those things. 460 00:26:41,700 --> 00:26:46,600 Obviously there is but it cannot be the it cannot be the base of 461 00:26:46,600 --> 00:26:50,400 everything. Needs to be contextual to the 462 00:26:50,400 --> 00:26:55,000 problem, the the the data that you have so many different 463 00:26:55,000 --> 00:26:56,900 things, right? What will customer even 464 00:26:56,900 --> 00:27:01,300 understand also matters. If you build a very complicated 465 00:27:01,300 --> 00:27:05,200 solution and you can't explain it well enough then really 466 00:27:06,100 --> 00:27:10,100 doesn't make sense. Coming to the customers rate. 467 00:27:10,100 --> 00:27:13,900 So so we'll do, do you have cases of customers asking for 468 00:27:13,900 --> 00:27:16,800 like complicated solution or do they just want the problem 469 00:27:16,800 --> 00:27:18,000 solder? And you like? 470 00:27:19,700 --> 00:27:23,900 It depends on really obviously you have a spectrum of of 471 00:27:23,900 --> 00:27:25,800 different folks who ask for different things. 472 00:27:25,800 --> 00:27:29,000 But lot of times, I think business users only care about 473 00:27:29,700 --> 00:27:34,000 a, I need to, I need to make this decision and tell me give 474 00:27:34,000 --> 00:27:37,300 me information so that I can make that decision. 475 00:27:38,000 --> 00:27:42,500 Ideally, give it to me very fast and also make it easy for me to 476 00:27:42,500 --> 00:27:46,500 understand. I think, if I look at it like 60 477 00:27:46,500 --> 00:27:52,600 to 70% of, of At least the people I have worked with our 478 00:27:52,800 --> 00:27:55,100 would Fallin Fallin that kind of category. 479 00:27:55,100 --> 00:27:58,300 There is going to be some people who are either dealing with 480 00:27:58,300 --> 00:28:03,600 extremely complex problems or are trying to push the boundary 481 00:28:03,600 --> 00:28:07,400 of, you know, what is the next thing that they can do or are 482 00:28:07,400 --> 00:28:11,500 just enamored by a complicated, you know solution? 483 00:28:11,800 --> 00:28:14,400 We will ask for that. But I would say majority still 484 00:28:15,100 --> 00:28:19,000 people who are looking for help in making their decisions and I 485 00:28:19,200 --> 00:28:21,800 want to make sure that it gets done fast and wants to make sure 486 00:28:21,800 --> 00:28:23,400 that they understand what is going on. 487 00:28:24,900 --> 00:28:26,800 I'm generalizing the salad, right? 488 00:28:26,800 --> 00:28:29,800 So the next thing, you're a business person who is engaging 489 00:28:29,800 --> 00:28:31,600 a data team. I mean, either within your 490 00:28:31,600 --> 00:28:34,600 company or as a, as an outsourced vendor or whatever, 491 00:28:34,600 --> 00:28:37,700 like you're going to business purpose, in engaging, the data, 492 00:28:37,700 --> 00:28:42,200 viewer, your job is to kind of take the insights out from them 493 00:28:42,200 --> 00:28:44,600 or maybe get a product from them or something like that. 494 00:28:45,100 --> 00:28:48,600 Very question for you is like, what do you, how do you, how do 495 00:28:48,600 --> 00:28:53,700 you paint a wall more? Do you as a business person need 496 00:28:53,700 --> 00:28:56,000 to do? Take the best value out of the 497 00:28:56,200 --> 00:29:01,600 out of the data. No, so that's a tough question. 498 00:29:02,700 --> 00:29:06,900 I think providing a lot of context matters as much context 499 00:29:06,900 --> 00:29:12,200 as can be provided matters. Demanding understanding of 500 00:29:12,200 --> 00:29:15,600 problem, is critical. Tell me what you understood of 501 00:29:15,600 --> 00:29:20,500 what I want and asking for that is, is very critical. 502 00:29:22,500 --> 00:29:25,600 Maybe even asking for water sample solution, would look, 503 00:29:25,600 --> 00:29:30,400 like is very critical, get that. Get all of that out of the way 504 00:29:30,400 --> 00:29:34,800 in the beginning. So that, you know, further down 505 00:29:34,800 --> 00:29:36,100 the line. You don't have surprises as a 506 00:29:36,108 --> 00:29:37,400 business user. Right? 507 00:29:39,300 --> 00:29:43,300 I think it becomes imperative for a business user to know that 508 00:29:44,400 --> 00:29:50,100 there can be a tendency to get lost in the solutioning and to 509 00:29:50,300 --> 00:29:54,700 to keep that bounded and keep that tight is probably very, 510 00:29:54,700 --> 00:29:57,200 very critical in for a business user. 511 00:29:57,500 --> 00:30:00,400 The the beginning in, The beginning, it becomes extremely 512 00:30:00,400 --> 00:30:02,500 important. I've seen a lot of cases, where 513 00:30:02,900 --> 00:30:06,300 things fail because not enough information was given by the 514 00:30:06,300 --> 00:30:12,300 business user or not. Not, I mean, nobody took the 515 00:30:12,300 --> 00:30:18,500 effort to make sure that an end output was presented as a mock 516 00:30:18,500 --> 00:30:21,700 in the beginning. So expectations were very 517 00:30:21,700 --> 00:30:24,400 different in terms of what I'm what the business is going to 518 00:30:24,400 --> 00:30:26,800 get worse, is what the data science team is delivering 519 00:30:28,200 --> 00:30:30,600 ending. Small things like that, help a 520 00:30:30,600 --> 00:30:32,300 lot. Right? 521 00:30:32,300 --> 00:30:35,800 And does the business person need to know any little bit of 522 00:30:35,808 --> 00:30:37,500 data science at our statistics or whatever. 523 00:30:37,500 --> 00:30:40,200 They just to make sure that the data team is not bullshitting 524 00:30:40,200 --> 00:30:45,600 them more than that, more than knowing statistics or data 525 00:30:45,600 --> 00:30:48,000 science. I think, you know, applying 526 00:30:48,200 --> 00:30:52,400 their their own business knowledge and asking for for 527 00:30:53,000 --> 00:30:58,700 examples, helps, you know, saying, okay fine, so, can you 528 00:30:58,800 --> 00:31:02,100 Tell me, I mean, for example, when we work with say B2B 529 00:31:02,100 --> 00:31:07,400 customers who are selling to knowledge organizations in the 530 00:31:07,400 --> 00:31:13,100 u.s., You know, they know their accounts say in and out right 531 00:31:13,100 --> 00:31:18,100 say they know that this kind of a large large say conglomerate 532 00:31:18,100 --> 00:31:23,800 buys these things from us and you know, to ask the question of 533 00:31:23,800 --> 00:31:27,000 how much our, how much is your model saying that they are going 534 00:31:27,000 --> 00:31:30,300 to buy the next month and Obviously have a sense of 535 00:31:30,300 --> 00:31:32,000 whether that is even ballpark right? 536 00:31:32,000 --> 00:31:36,100 Not ballpark re-think checks like that become more important 537 00:31:36,100 --> 00:31:39,100 rather than knowing any progression or data science or 538 00:31:39,100 --> 00:31:45,300 anything like that. But wait now, I mean like 539 00:31:45,900 --> 00:31:48,000 learning things a little bit. I mean we were talking about 540 00:31:48,000 --> 00:31:50,000 make you spoke about a couple of cases. 541 00:31:50,000 --> 00:31:53,300 In terms of flick business. Failures data are not specifying 542 00:31:53,300 --> 00:31:55,800 the problem properly or like they're being and expectations 543 00:31:55,800 --> 00:31:57,800 mismatch. This about what all the other 544 00:31:57,800 --> 00:32:02,000 cases where they sort of data science, teams of projects field 545 00:32:02,000 --> 00:32:06,800 at why L die, I'm sorry, like through your journey. 546 00:32:06,800 --> 00:32:09,500 You would have you would have had your share of some projects 547 00:32:09,500 --> 00:32:11,100 is would have worked out. One day. 548 00:32:11,400 --> 00:32:12,900 What happens? What happens in the cases when 549 00:32:12,900 --> 00:32:17,800 you signed it with the way? Why do product project scheme? 550 00:32:19,900 --> 00:32:29,900 Yeah, so in some cases it is alignment where, you know that 551 00:32:29,900 --> 00:32:33,300 there is there isn't enough alignment between either the 552 00:32:33,300 --> 00:32:35,700 stakeholders in the business themselves. 553 00:32:36,000 --> 00:32:37,600 Right? What someone else is expecting 554 00:32:37,600 --> 00:32:40,500 versus, what some other person is expecting, is not the same 555 00:32:40,500 --> 00:32:43,000 thing. In some cases. 556 00:32:43,000 --> 00:32:46,600 It is alignment between the teams, you know, say there's a 557 00:32:46,600 --> 00:32:51,400 data science team and there is a business team. and if there is 558 00:32:51,600 --> 00:32:55,300 inherent mistrust from the business team through the data 559 00:32:55,300 --> 00:33:03,400 science team can kind of fail, sometimes it's, you know, people 560 00:33:03,400 --> 00:33:10,800 not, you know, not providing like the data's data, guys, not 561 00:33:10,800 --> 00:33:15,700 providing enough Comfort to the business teams saying that, you 562 00:33:15,700 --> 00:33:20,100 know, this is something that you can really use And this is 563 00:33:20,100 --> 00:33:22,300 something trustworthy. I think that, that, that 564 00:33:22,300 --> 00:33:25,000 translation becomes, if that doesn't happen properly. 565 00:33:25,000 --> 00:33:31,400 It kind of fails, not thinking about end output and usage of in 566 00:33:31,400 --> 00:33:35,400 doubt. Put enough, you can, I think we 567 00:33:35,400 --> 00:33:37,800 talked about it before. You can create a sophisticated 568 00:33:38,200 --> 00:33:40,700 model. But if, if it doesn't deliver 569 00:33:40,700 --> 00:33:42,500 the in doubt, put in a usable format. 570 00:33:42,500 --> 00:33:47,400 It doesn't make sense. Sometimes not being scalable in 571 00:33:47,400 --> 00:33:50,300 your solution, if it doesn't integrate with You know, 572 00:33:50,300 --> 00:33:53,100 Downstream processes, then anything that you build is 573 00:33:53,100 --> 00:33:57,900 pointless, so that that could be a reason. 574 00:33:58,900 --> 00:34:03,500 Sometimes it is not making things, simple enough, and not 575 00:34:03,500 --> 00:34:05,600 making it an enjoyable process. Right? 576 00:34:05,600 --> 00:34:09,199 I think that's critical when you are working with people. 577 00:34:09,199 --> 00:34:12,000 You have to make it enjoyable that for them also. 578 00:34:12,000 --> 00:34:16,900 And if that that is a very, you know, friction filled process, 579 00:34:16,900 --> 00:34:19,800 then it it will fail. L. 580 00:34:21,500 --> 00:34:24,900 I'm reminded of one of our assignments about some 78 years 581 00:34:24,900 --> 00:34:26,900 back. We just reattach it. 582 00:34:27,300 --> 00:34:30,199 So, what I did was I actually, like, I remember sitting down 583 00:34:30,199 --> 00:34:34,800 with the ab is one VPN 1gm, I think. 584 00:34:34,800 --> 00:34:37,900 And like, actually, taking them through the data, taking them 585 00:34:37,900 --> 00:34:40,300 through the model saying the, this is what the clustering put 586 00:34:40,300 --> 00:34:42,300 out. And, let's take a few sample 587 00:34:42,300 --> 00:34:44,800 customers, and this is what they look like and things like that 588 00:34:44,800 --> 00:34:47,100 again. And at the same time, I think 589 00:34:47,100 --> 00:34:51,000 they were working with another larger established and McNair 590 00:34:51,000 --> 00:34:54,600 and they were like, okay the they quickly figured out that 591 00:34:54,800 --> 00:34:58,500 they could use my model because I had given them the comfort of 592 00:34:58,900 --> 00:35:03,200 like the actual them who are. I mean, we did speak about it a 593 00:35:03,200 --> 00:35:05,900 little bit career would not going through the process and 594 00:35:05,900 --> 00:35:09,900 stuff but I did show them some some facts of the data. 595 00:35:10,100 --> 00:35:13,100 I showed them some sample, customers its own, and I think 596 00:35:13,100 --> 00:35:17,000 that just generated this tens of comfort for them. 597 00:35:17,800 --> 00:35:20,400 Yeah, I think you know about one thing. 598 00:35:20,400 --> 00:35:23,400 I'll also add to that is I think just the going off of the 599 00:35:23,400 --> 00:35:26,900 examples are closed set when you're working on it. 600 00:35:26,900 --> 00:35:30,800 It's also important to keep going and showing something, you 601 00:35:30,800 --> 00:35:34,700 know, there's also this tendency that in these kind of team 602 00:35:34,700 --> 00:35:37,600 sometimes, what happens is that you tell me what I have to do. 603 00:35:37,600 --> 00:35:40,000 I'll go build what I have to build and then I'll come and 604 00:35:40,000 --> 00:35:43,600 pass it back later, but that doesn't work. 605 00:35:43,600 --> 00:35:48,500 They, you need to involve them in that process and Keep going 606 00:35:48,500 --> 00:35:53,000 and giving them pieces and show how the, how the sort of the 607 00:35:53,000 --> 00:35:57,500 house is getting built and not just show them like, hey, here's 608 00:35:57,500 --> 00:35:58,400 the house. Right? 609 00:35:58,400 --> 00:36:02,300 So I think it's, it's that process has to be very, you 610 00:36:02,300 --> 00:36:04,900 know, Comfort field and enjoyable. 611 00:36:04,900 --> 00:36:07,500 Like like you said, yeah. So I think that it comes over 612 00:36:07,500 --> 00:36:10,700 again to the Exotic proper alignment and ownership and 613 00:36:10,700 --> 00:36:11,700 stuff. They didn't. 614 00:36:13,400 --> 00:36:17,900 Yeah, alignment ownership and and also trying to yeah. 615 00:36:18,600 --> 00:36:22,500 Yeah, I mean, see if there is a base level of alignment issues. 616 00:36:22,500 --> 00:36:25,400 Then you really, you know, that it is set up for failure from 617 00:36:25,400 --> 00:36:30,800 the beginning. But but if, if there is a little 618 00:36:30,800 --> 00:36:32,800 bit of an open mind to say that, hey, you know what? 619 00:36:32,800 --> 00:36:35,300 I'm skeptical, but you show me stuff. 620 00:36:35,300 --> 00:36:39,600 And I might, I might get convinced then you have scope 621 00:36:39,600 --> 00:36:42,100 for doing things which, which gives you can take to the next 622 00:36:42,100 --> 00:36:45,600 level. By having their white but I 623 00:36:45,600 --> 00:36:48,400 think you mentioned about how like we need to serve the pad 624 00:36:48,400 --> 00:36:50,900 mock-ups in the beginning you need to show that I could the 625 00:36:50,900 --> 00:36:53,000 insinuation and then sort of go backwards with it. 626 00:36:53,000 --> 00:36:58,800 So I was reminded of a in some people in writing we call ideas 627 00:36:58,800 --> 00:37:01,300 are formal term, would be quality reverse, pollute format. 628 00:37:02,000 --> 00:37:05,300 When you start with the end, you start with the headline on top 629 00:37:05,300 --> 00:37:08,900 with the main conclusion. And then die going to how you 630 00:37:08,900 --> 00:37:12,500 got, too intense one. Don't we don't keep the suspense 631 00:37:12,500 --> 00:37:16,600 to the Indeed absorb this again, really doing the data project is 632 00:37:16,600 --> 00:37:20,200 again sort of Click one thing where the worst Bollywood for my 633 00:37:20,200 --> 00:37:23,800 pulse rate you start with. This is what I want to give you 634 00:37:23,808 --> 00:37:27,400 and then make you fight these like some vaporware in there for 635 00:37:27,400 --> 00:37:29,900 now and then you will feel it in the in Lake will the modern and 636 00:37:29,900 --> 00:37:31,200 also CCS. Yeah. 637 00:37:31,200 --> 00:37:36,100 And take them through those right relations, you know, look 638 00:37:36,100 --> 00:37:39,600 at the end of the day the the the this field and this is the 639 00:37:39,600 --> 00:37:41,800 kind of projects in the work that we do. 640 00:37:42,100 --> 00:37:43,900 It is always Who is going to be titrated? 641 00:37:43,900 --> 00:37:48,100 You can't avoid it. The iterations are, are, you 642 00:37:48,100 --> 00:37:49,700 know, they're just going to exist. 643 00:37:50,000 --> 00:37:52,700 So if I durations are going to exist, it is better for you to 644 00:37:52,700 --> 00:37:57,000 kind of prepare them for it. Make sure that they understand 645 00:37:57,000 --> 00:38:00,200 what you're aiming for. Make sure that they understand, 646 00:38:00,300 --> 00:38:05,300 you know, what what what challenges you might face and 647 00:38:05,300 --> 00:38:07,400 prepare them for the right reasons and take them through 648 00:38:07,400 --> 00:38:11,100 the Journey rather than just, you know, dumping one thing in 649 00:38:11,100 --> 00:38:12,600 the integrative. Yeah, I think so. 650 00:38:13,100 --> 00:38:16,900 You have seen data Journey since I think the term data science 651 00:38:16,900 --> 00:38:19,500 got coined in 2008, which is a around the time. 652 00:38:19,500 --> 00:38:21,400 I think you enter this into the sea and so on. 653 00:38:21,400 --> 00:38:25,200 So so how was the investing world over the last decade or so 654 00:38:25,200 --> 00:38:29,700 and like, oh wow, and what, how has what excites people like, 655 00:38:29,800 --> 00:38:33,200 could be their data centers, could be the business days, 656 00:38:33,200 --> 00:38:35,200 could be clients. Like I'll have the expectations 657 00:38:35,200 --> 00:38:38,400 Change Daily. Wow, word and then in the middle 658 00:38:38,400 --> 00:38:41,400 you had this whole big data think it's one how well things 659 00:38:41,400 --> 00:38:43,800 changed in the last. Dozen of years. 660 00:38:44,800 --> 00:38:47,400 So when I started the actually, the term data scientist didn't 661 00:38:47,400 --> 00:38:49,300 exist. I remember that. 662 00:38:49,300 --> 00:38:53,500 My first designation was business analyst, so that was 663 00:38:53,500 --> 00:38:58,400 quite some time back, but I think a lot of lot has changed. 664 00:38:58,800 --> 00:39:02,500 People are a lot more, like, let's talk about it in groups of 665 00:39:02,500 --> 00:39:06,300 people, right? So to if you talk about, if you 666 00:39:06,308 --> 00:39:10,400 talk about folks, for joining this field now then they have a 667 00:39:10,408 --> 00:39:13,200 certain expectation of what they need to be doing. 668 00:39:13,200 --> 00:39:15,800 And we talk about Of that what they think school. 669 00:39:15,800 --> 00:39:19,800 And what they think is things that need to need to work on and 670 00:39:19,800 --> 00:39:22,800 things like that. I think there is a lot more 671 00:39:22,800 --> 00:39:23,800 knowledge for them. Also. 672 00:39:23,800 --> 00:39:28,300 They know it's lucrative right? Fundamentally for them to be a 673 00:39:28,300 --> 00:39:31,300 part of that this industry. I think that is that that 674 00:39:31,300 --> 00:39:33,600 knowledge is very well ingrained. 675 00:39:33,600 --> 00:39:35,800 I think in a lot of people trying to get into this field. 676 00:39:36,300 --> 00:39:41,800 So if I mean, ideally I would like more of them to be excited 677 00:39:41,800 --> 00:39:46,200 about the business problem itself rather than And rather 678 00:39:46,200 --> 00:39:50,800 than the, the, you know, better than anything else, but the 679 00:39:50,800 --> 00:39:56,900 techniques and the tools sort of take over a lot of times that's 680 00:39:56,900 --> 00:39:59,500 on the, that's on the, you know, folks joining the field 681 00:39:59,500 --> 00:40:01,900 perspective. But if you look at businesses, 682 00:40:01,900 --> 00:40:05,700 businesses also have gotten a lot more knowledgeable before 683 00:40:05,700 --> 00:40:09,400 you could go and say stuff and you know, they would they would 684 00:40:09,400 --> 00:40:12,900 listen, they would listen to you because they didn't know much 685 00:40:12,900 --> 00:40:16,500 better. Then but I think businesses 686 00:40:16,500 --> 00:40:19,500 business users. They have heard they have read, 687 00:40:20,100 --> 00:40:23,500 they know a lot more. They have tried. 688 00:40:23,500 --> 00:40:26,300 Maybe have failed, maybe has worked. 689 00:40:27,400 --> 00:40:31,100 It's just, it's a, it's just that maturity curve that they 690 00:40:31,100 --> 00:40:35,600 have also kind of gone through. And I think the expectations now 691 00:40:35,600 --> 00:40:37,800 is that he, you know, what, keep it simple for me. 692 00:40:39,100 --> 00:40:42,500 Do it fast for me. I don't have like, you need to, 693 00:40:42,500 --> 00:40:46,300 you need to come to the table. It's something that you might 694 00:40:46,300 --> 00:40:52,100 have already kind of done before you have, you know, bring your 695 00:40:52,100 --> 00:40:54,100 knowledge to the table in some form or fashion. 696 00:40:54,100 --> 00:40:59,000 Now, you know, so that you don't start from scratch, do it fast. 697 00:40:59,300 --> 00:41:00,800 Do it, do it. Do it simple. 698 00:41:00,800 --> 00:41:05,100 And make sure that whatever you do you leave me with something 699 00:41:05,100 --> 00:41:08,600 which is scalable and usable. It can't be that you know, you 700 00:41:08,700 --> 00:41:13,000 you solve this II, get you to solve this problem once and if I 701 00:41:13,000 --> 00:41:16,000 get this problem again, When I can't use your solution again, 702 00:41:17,000 --> 00:41:21,000 it has to, it has to scale. Either to other places. 703 00:41:21,000 --> 00:41:24,700 I can use it to or two other points in time. 704 00:41:24,700 --> 00:41:27,800 I need to do it in, so it needs to be scalable. 705 00:41:27,800 --> 00:41:32,000 It needs to be usable and I need you to do it really fast and but 706 00:41:32,000 --> 00:41:33,600 make sure and keep you keep it simple. 707 00:41:34,000 --> 00:41:38,000 So I think the expectations have gotten so much more higher and 708 00:41:38,000 --> 00:41:41,000 that's a that's a journey that I guess every industry kind of 709 00:41:41,000 --> 00:41:43,700 goes through and and this industry is going through that 710 00:41:43,700 --> 00:41:46,900 same. With expectations getting higher 711 00:41:46,900 --> 00:41:50,800 is the output also like, eating up, or is there a sorbonne this 712 00:41:50,800 --> 00:41:52,500 milestone in terms of what people expect? 713 00:41:52,500 --> 00:41:57,300 Anyway, what's delivered? I don't know II. 714 00:41:57,300 --> 00:42:01,300 Think it it will keep up. I think the fundamental 715 00:42:01,300 --> 00:42:04,200 economics of demand and Supply will play there and somehow it 716 00:42:04,200 --> 00:42:09,800 will keep upright. But yeah, I mean, it's not like 717 00:42:09,800 --> 00:42:12,400 it's, we could test and it's everywhere and everybody's doing 718 00:42:12,400 --> 00:42:15,400 it. But, you know, there is enough 719 00:42:15,400 --> 00:42:18,700 and more effort in, in a lot of places to, to make sure that it 720 00:42:18,700 --> 00:42:21,900 we try to keep up with that expectation and demand. 721 00:42:24,300 --> 00:42:26,400 So, you mentioned one thing about how, like, nowadays the 722 00:42:26,400 --> 00:42:29,300 businesses want the answers quickly and you need to take 723 00:42:29,300 --> 00:42:33,300 something from your brother from your previous experience and 724 00:42:33,300 --> 00:42:34,400 things like that, and so on, right? 725 00:42:34,800 --> 00:42:39,100 So I think this is one of these classic care if dichotomies, 726 00:42:39,100 --> 00:42:43,500 that businesses need some output quickly, but typically the 727 00:42:43,500 --> 00:42:47,000 especially if you are trying to get into the modeling research 728 00:42:47,000 --> 00:42:50,200 each, I never think the solution always takes time to be. 729 00:42:50,200 --> 00:42:53,000 So, how do you how do you got a kind of bridge the gap? 730 00:42:53,000 --> 00:42:55,800 Apart from? See coming up with some decent 731 00:42:55,800 --> 00:42:57,600 margins on using this. Some Leverage. 732 00:42:57,600 --> 00:43:01,500 Is it be through there? Yeah, I think one is obviously 733 00:43:01,500 --> 00:43:05,200 it's important for you to however you can you uh need to 734 00:43:05,200 --> 00:43:08,900 bring that to the table, right? That experience in terms of say 735 00:43:08,900 --> 00:43:12,800 preset models, reusable codes, whatever it is, right, how can 736 00:43:12,800 --> 00:43:16,300 you bring efficiency into? It has to come, but I think the 737 00:43:16,300 --> 00:43:21,700 other part of it is, is is, is, is basically, I trations, right? 738 00:43:21,700 --> 00:43:24,800 So you don't need to be. Build a rocket ship out in the 739 00:43:24,800 --> 00:43:28,400 first go. But you can at least show that, 740 00:43:28,400 --> 00:43:31,000 hey, there is this part of the rocket ship that is built in. 741 00:43:31,000 --> 00:43:37,400 That could be useful for you in this way and giving those bits 742 00:43:37,400 --> 00:43:40,500 and pieces over time and eventually showing the rocket 743 00:43:40,500 --> 00:43:44,900 ship becomes kind of critical. I feel because otherwise, you 744 00:43:44,900 --> 00:43:49,800 know, otherwise, it is just update that that just happens 745 00:43:49,800 --> 00:43:52,500 creating MVPs. I think is critical and goes 746 00:43:52,500 --> 00:43:54,300 back to design thinking in I miss that. 747 00:43:54,300 --> 00:44:00,400 You know, how do you how do you do prototypes quickly and show 748 00:44:00,400 --> 00:44:04,100 it and and show how it could be useful? 749 00:44:04,100 --> 00:44:06,500 And then that kind of shows the direction of where you need to 750 00:44:06,500 --> 00:44:10,200 connect it to one thing, which I think I wanted to ask you have 751 00:44:10,400 --> 00:44:12,700 which we haven't covered. Like I think we've spoken a lot 752 00:44:12,700 --> 00:44:17,200 about the relationship between business and data sets like the 753 00:44:17,200 --> 00:44:21,400 give, but the other thing which sphere it's probably gets a 754 00:44:21,408 --> 00:44:25,100 literally less footage, I think. Is the relationship between 755 00:44:25,100 --> 00:44:27,500 taking data that data sites or analytics? 756 00:44:27,900 --> 00:44:31,800 So how does use a horror? How do you see the attraction 757 00:44:31,800 --> 00:44:34,700 between taken data science? How virus has evolved over time 758 00:44:34,700 --> 00:44:38,300 and like what are they sort of put the impact practices on? 759 00:44:38,300 --> 00:44:42,100 Yeah, I think that right now at least taken data signs are 760 00:44:42,100 --> 00:44:46,200 extremely integrated. I think that it's very important 761 00:44:46,200 --> 00:44:48,100 for them to be integrated as well. 762 00:44:48,100 --> 00:44:50,300 I think going back to the solution building that I was 763 00:44:50,300 --> 00:44:55,000 talking about, especially. You have large amounts of data, 764 00:44:55,000 --> 00:44:59,200 then Tech becomes integral anyway, but even in other cases, 765 00:44:59,200 --> 00:45:01,700 say you're, you're bringing in data from so many different data 766 00:45:01,700 --> 00:45:07,400 sources for to manage that and to set it up in a way where it 767 00:45:07,400 --> 00:45:12,200 can be used in data science. Also becomes kind of critical so 768 00:45:12,200 --> 00:45:15,700 Tech and data science are actually getting closer and 769 00:45:15,700 --> 00:45:18,100 closer every every single day. Right? 770 00:45:19,100 --> 00:45:24,700 And it becomes very important for for, for For dear scientist 771 00:45:24,700 --> 00:45:29,300 to have an appreciation for what, what, what the tech side 772 00:45:29,500 --> 00:45:31,600 brings to the table? And for text, I to understand 773 00:45:31,600 --> 00:45:38,100 what data scientists need. And I think if you look at most 774 00:45:38,100 --> 00:45:40,800 organizations today, the there is a there is a close 775 00:45:40,800 --> 00:45:42,200 relationship between both of them. 776 00:45:42,200 --> 00:45:45,400 And I only see that relationship becoming stronger to be honest 777 00:45:45,400 --> 00:45:47,300 with you. I don't know about whether you 778 00:45:47,300 --> 00:45:50,800 know, whether this will sit with that or that will sit with this 779 00:45:50,800 --> 00:45:52,800 and all of that. But, but at the end of the day, 780 00:45:53,500 --> 00:45:57,300 They need to work very closely. Like we had to build out data 781 00:45:57,300 --> 00:46:02,100 engineering team and a tech team ground up to you know, to make 782 00:46:02,100 --> 00:46:04,400 that happen. Otherwise, we knew that we would 783 00:46:04,400 --> 00:46:08,400 kind of fail in whatever we're trying to do. 784 00:46:08,400 --> 00:46:11,700 The other part of it is on the other side of tech which is more 785 00:46:11,700 --> 00:46:15,000 like product depth, you know, that also becomes very important 786 00:46:15,000 --> 00:46:19,500 because if, if you are not able to do, you know, people are 787 00:46:19,500 --> 00:46:23,400 expecting products, right? This is the world we live in. 788 00:46:23,500 --> 00:46:27,000 In is hyper personalized but also like apps and all of this 789 00:46:27,000 --> 00:46:32,800 and people expect those things from from from, from whatever 790 00:46:32,800 --> 00:46:35,100 your building. So if you're not, if you don't 791 00:46:35,100 --> 00:46:39,100 have a clean productive kind of setup, then that also kind of 792 00:46:39,107 --> 00:46:42,700 becomes a challenge because then you are not going back to our 793 00:46:42,700 --> 00:46:44,600 earlier points. You're not presenting, whatever 794 00:46:44,600 --> 00:46:47,100 you're doing in a manner that they will want to use. 795 00:46:48,900 --> 00:46:51,800 So, so yeah, writing the tech tech and did data science. 796 00:46:51,800 --> 00:46:53,400 I feel are getting closer and closer. 797 00:46:53,600 --> 00:46:56,100 And more and more integrated. And we need to get more and more 798 00:46:56,100 --> 00:46:58,100 integrated. If you are if you're going to 799 00:46:58,100 --> 00:47:01,100 deliver to those expectations about ability to that adding 800 00:47:01,100 --> 00:47:04,100 technology over the last 10-15 years is completely Embrace. 801 00:47:04,100 --> 00:47:07,700 This role of the product manager to sort of like a physical big. 802 00:47:07,700 --> 00:47:11,400 You go between between business. Take the design, Etc. 803 00:47:11,400 --> 00:47:16,500 So do you use either dies or other similar grown in data? 804 00:47:16,500 --> 00:47:19,500 And if so, like what, what, what does that kind of role look 805 00:47:19,500 --> 00:47:19,900 like? Yeah. 806 00:47:19,900 --> 00:47:23,200 I think, you know, there have been attempts to kind of Define 807 00:47:23,200 --> 00:47:25,100 that Troll. But in a very narrow manner, I 808 00:47:25,100 --> 00:47:29,100 feel like I remember that. There was a whole article around 809 00:47:29,700 --> 00:47:34,000 the analytics translator and why that person is important to you. 810 00:47:35,300 --> 00:47:38,500 Things like that who's kind of it's basically the linchpin, 811 00:47:38,500 --> 00:47:40,600 who's tying everything together, right? 812 00:47:40,600 --> 00:47:44,700 I think that's kind of what, what what, what you're referring 813 00:47:44,700 --> 00:47:47,200 to and I think that that role is, is there. 814 00:47:48,200 --> 00:47:51,700 Usually what ends up happening. Is that the person whose front 815 00:47:51,700 --> 00:47:53,400 facing with the business, please? 816 00:47:53,500 --> 00:47:58,600 That role but it could technically be anyone playing 817 00:47:58,600 --> 00:47:59,900 that role. It could be the data science 818 00:47:59,900 --> 00:48:02,400 person playing that role. It could be the tech person. 819 00:48:02,400 --> 00:48:04,100 Playing that Ron, it would be a design person. 820 00:48:04,100 --> 00:48:06,500 Playing a tank. Could be anyone usually where it 821 00:48:06,500 --> 00:48:10,400 lands is is the is the person front facing to the business 822 00:48:10,500 --> 00:48:12,600 ends up playing that. I'm sure you are playing a role 823 00:48:12,600 --> 00:48:16,900 in the world in the world that you are in with the business. 824 00:48:16,900 --> 00:48:20,300 And that that's probably the easiest because of the fact that 825 00:48:20,700 --> 00:48:23,100 at the end of the day, as long as the business is happy, then 826 00:48:23,100 --> 00:48:23,700 you're good. Good. 827 00:48:44,800 --> 00:48:46,800 Thank you for listening to data shatter. 828 00:48:47,400 --> 00:48:50,900 If you like this show, please leave a comment, share and 829 00:48:50,900 --> 00:48:54,200 subscribe to the podcast. You can find this podcast on 830 00:48:54,200 --> 00:48:57,100 apple vodka. Has Spotify or wherever else? 831 00:48:57,100 --> 00:49:01,100 You go to get your podcasts? Once again, this is Karthik 832 00:49:01,100 --> 00:49:02,600 signing off. Thank you.