1 00:00:00,200 --> 00:00:04,000 So the first thing that I put together for them was actually a 2 00:00:04,400 --> 00:00:07,300 mini data, visualization of sorts a scatter plot Matrix, 3 00:00:07,300 --> 00:00:09,800 which of course didn't exist. Even by name at that point. 4 00:00:09,800 --> 00:00:14,000 This was in 2090, personal stuff. 5 00:00:14,700 --> 00:00:19,500 You can do in Excel. Yeah, nine percent of the stuff 6 00:00:19,700 --> 00:00:23,900 you can do in these tools that we are still can't do this one 7 00:00:23,900 --> 00:00:25,100 person. That neither of these can do 8 00:00:25,100 --> 00:00:28,700 one, go to have JavaScript python, whatever you need. 9 00:00:41,700 --> 00:00:46,000 Welcome to data, Shadow the podcast, on all things data. 10 00:00:46,900 --> 00:00:51,400 This podcast is a series of conversations with experts and 11 00:00:51,400 --> 00:00:54,400 Industry leaders in data. And each week. 12 00:00:54,600 --> 00:00:58,300 We aim to unpack a different compartment of the data 13 00:00:58,300 --> 00:01:01,500 suitcase. Your host that the chassis that 14 00:01:02,000 --> 00:01:05,500 I'm a blogger newspaper, columnist, book author, and a 15 00:01:05,500 --> 00:01:07,900 former data and strategy consultant. 16 00:01:08,500 --> 00:01:12,100 I currently heads analytics and business intelligence for 17 00:01:12,100 --> 00:01:15,300 delivery. One of India's largest logistics 18 00:01:15,300 --> 00:01:18,100 companies. You can follow me on Twitter at 19 00:01:18,200 --> 00:01:25,900 act Karthik s and read my blog at no into.com that is noen the 20 00:01:25,900 --> 00:01:29,800 HUD a.com all opinions expressed. 21 00:01:29,900 --> 00:01:33,400 In this podcast belong to me, and the podcast, guess and do 22 00:01:33,400 --> 00:01:35,800 not reflect the views of any organizations. 23 00:01:35,800 --> 00:01:38,400 We may be associated with nothing. 24 00:01:38,400 --> 00:01:41,200 Disgusting, his podcast should be taken as Financial or legal 25 00:01:41,200 --> 00:01:43,700 advice. Now onto Today's Show. 26 00:01:48,400 --> 00:01:51,900 When we think of data, many of us instinctively think of 27 00:01:51,900 --> 00:01:54,700 spreadsheets. And that means, Microsoft exit, 28 00:01:54,700 --> 00:01:58,000 the world's most populous big sheets of metal and the time of 29 00:01:58,000 --> 00:02:02,100 recording at least in some ways, the use of excel in the world of 30 00:02:02,100 --> 00:02:04,900 data analytics is much underappreciated. 31 00:02:05,400 --> 00:02:08,600 Another thing that many of us instinctively, think of when we 32 00:02:08,600 --> 00:02:13,900 think about data is graphics and visualizations bar graphs line, 33 00:02:13,900 --> 00:02:18,200 graphs dashboards and the like in this Not get a lip is sort of 34 00:02:18,200 --> 00:02:21,200 data shatter. We bring together these two 35 00:02:21,400 --> 00:02:26,400 obvious Concepts in analytics. My guest today is s Anand or 36 00:02:26,400 --> 00:02:31,700 stud Anand as he's known in. I am Bangalore circles and is a 37 00:02:31,700 --> 00:02:36,500 co-founder of granular data science company, leads the team 38 00:02:36,700 --> 00:02:40,500 that automates insights from data and narrates, these as 39 00:02:40,500 --> 00:02:44,600 visual data stories is recognized as one of India's top 40 00:02:44,600 --> 00:02:47,100 10 data scientists and is a regular text speak. 41 00:02:47,200 --> 00:02:51,200 Ow, another is a gold medalist at IIM, Bangalore and an alumnus 42 00:02:51,200 --> 00:02:56,100 of IIT Madras London Business School, IBM, enforces Lehman, 43 00:02:56,100 --> 00:02:57,300 Brothers. And BCG. 44 00:02:57,900 --> 00:03:01,600 Most importantly is hand transcript, every Calvin and 45 00:03:01,600 --> 00:03:05,400 Hobbes script ever and dreams of watching every film. 46 00:03:05,500 --> 00:03:09,900 The IMDb top 250. You can follow him on Twitter at 47 00:03:09,900 --> 00:03:17,600 s and 0 that is s a nando64. Oh and his website is It's a 48 00:03:18,000 --> 00:03:23,900 document. That is s Dash DN A and D dot - 49 00:03:23,900 --> 00:03:32,000 you get into this because you would have heiko sadly 50 00:03:32,000 --> 00:03:34,600 consultant at BCG from what I from what I know. 51 00:03:34,600 --> 00:03:37,500 So, how do we go from there to kind of being a hardcore data? 52 00:03:37,500 --> 00:03:40,700 Gay know, the strategy Consulting was the mistake. 53 00:03:40,700 --> 00:03:47,100 I was actually at it again. Okay, so meaning I wanted. 54 00:03:47,200 --> 00:03:52,400 To be in first used computers. When I was, what in class 6. 55 00:03:52,400 --> 00:03:56,600 We had a BBC micro at school, and they taught us programming 56 00:03:56,600 --> 00:03:57,800 and I thought that was a really cool. 57 00:03:57,800 --> 00:04:00,000 That's, very impressive. Comes considering you are much 58 00:04:00,000 --> 00:04:05,400 older than me and I started in class 7, so yeah, 86 is when I 59 00:04:05,600 --> 00:04:10,100 started, okay, and then in 87, my dad bought me a ZX Spectrum. 60 00:04:10,100 --> 00:04:12,700 So initially it was playing games and the learning a little 61 00:04:12,700 --> 00:04:16,899 bit of basic and then slowly it went into generating Fibonacci 62 00:04:16,899 --> 00:04:18,800 series. He's and then slowly went into 63 00:04:19,000 --> 00:04:22,200 fractals creating a mandelbrot set, slightly more efficiently 64 00:04:22,200 --> 00:04:27,000 and creating a 3D rendering with Phong shading and stuff like 65 00:04:27,000 --> 00:04:28,800 that. All of it on a tile is Eric 66 00:04:28,800 --> 00:04:31,600 Spectrum with 48k. So it was fun. 67 00:04:32,000 --> 00:04:35,900 Yeah, and I was hoping to get into a computer science degree, 68 00:04:35,900 --> 00:04:38,400 but pretty much no College. Gave me a computer science 69 00:04:38,400 --> 00:04:41,000 degree the closest I could get to that was tight. 70 00:04:41,000 --> 00:04:44,100 The EM where I say, I basically ticked off every Branch other 71 00:04:44,100 --> 00:04:47,100 than chemical engineering and then the Prof. 72 00:04:47,200 --> 00:04:50,200 Who was supposed to be guiding? He looked at this and said, no, 73 00:04:50,200 --> 00:04:51,800 for your rank. You will get chemical 74 00:04:51,800 --> 00:04:53,700 engineering support to take against chemical engineering. 75 00:04:54,100 --> 00:04:55,600 It separately don't like intentionally. 76 00:04:56,100 --> 00:04:57,500 No, it's okay. Go ahead. 77 00:05:01,300 --> 00:05:04,900 That's what I was. It's so I spent those four years 78 00:05:04,900 --> 00:05:07,500 trying to do anything other than chemical engineering and 79 00:05:07,600 --> 00:05:09,900 computer science department was my retreat. 80 00:05:10,000 --> 00:05:13,700 Okay. So in 96, when I got a job offer 81 00:05:13,700 --> 00:05:16,300 with I've been said, okay great. Good, riddance. 82 00:05:16,300 --> 00:05:18,600 Goodbye, and it Three, good years of thought. 83 00:05:19,500 --> 00:05:22,400 Then then that 99 is where the mistake started. 84 00:05:22,400 --> 00:05:25,500 Actually, the Mystic started in 96 when I cared and didn't get 85 00:05:25,500 --> 00:05:27,200 through. So I was pretty pissed off. 86 00:05:27,500 --> 00:05:30,600 So I decided to write cat again just to show them that I can get 87 00:05:30,600 --> 00:05:33,500 through and I wrote it and I got through and then know this whole 88 00:05:33,500 --> 00:05:35,900 pressure builds up. No, no, you have to get there. 89 00:05:35,900 --> 00:05:38,400 So I got the winter. I am a interviews income. 90 00:05:38,400 --> 00:05:42,000 You have to go to any sense too far away as I am be, that's as 91 00:05:42,000 --> 00:05:43,700 far as I was able to resist or IBM. 92 00:05:43,700 --> 00:05:46,900 Was thinking, I'd be most impacted. 93 00:05:47,200 --> 00:05:49,000 So have you was enough? Okay, okay. 94 00:05:50,900 --> 00:05:53,900 And so it was almost a repeat of 1992. 95 00:05:53,900 --> 00:05:56,600 Again, enough air pressure. So I said, okay, so let's do it. 96 00:05:56,608 --> 00:05:59,700 Yeah. And MBA and then it just 97 00:05:59,700 --> 00:06:01,900 continued after that. Now you have to get into an 98 00:06:01,900 --> 00:06:05,000 investment banking career or Consulting career. 99 00:06:05,100 --> 00:06:07,300 Luckily. It did an internship at Lehman 100 00:06:07,300 --> 00:06:10,300 Brothers and you that your investment banking is nice good 101 00:06:10,300 --> 00:06:13,500 and all that but not for me. So as a chill, let's learn 102 00:06:13,500 --> 00:06:16,200 something that lasted for four years where I dcg. 103 00:06:16,200 --> 00:06:19,300 I basically tried to work my way into every technology. 104 00:06:19,500 --> 00:06:21,000 Project. That was in fact, I want strong 105 00:06:21,000 --> 00:06:23,800 but there was a piece of work. We were going to do for Oracle 106 00:06:24,300 --> 00:06:27,000 and others. The First Technology project 107 00:06:27,000 --> 00:06:30,200 that I come our way. This was based out of Delhi. 108 00:06:30,200 --> 00:06:33,100 I was in Bombay by default, Consultants from Delhi would get 109 00:06:33,100 --> 00:06:35,200 staffed. So I remember calling our 110 00:06:35,200 --> 00:06:39,900 partner James and that was the morning when they gauge meant 111 00:06:39,900 --> 00:06:42,700 was I think about to start our kickoff or something. 112 00:06:42,700 --> 00:06:45,200 I said James. I'm at the airport. 113 00:06:45,800 --> 00:06:48,700 If you tell me, I'll buy a ticket, get on a flight and come 114 00:06:48,700 --> 00:06:51,000 over there. If not, I'll go back. 115 00:06:51,300 --> 00:06:52,200 Yes. He said yet. 116 00:06:52,200 --> 00:06:54,000 If you're that desperate, they're okay to hop on the 117 00:06:54,008 --> 00:07:01,300 flight and come but 2005. I had clear I had Clarity that. 118 00:07:01,300 --> 00:07:04,400 Yeah, I'm going to get back into technology of course having done 119 00:07:04,400 --> 00:07:07,200 strategy Consulting. Nobody really gives me a job 120 00:07:07,200 --> 00:07:09,600 into technology. Yeah, so it was disconcerting. 121 00:07:09,600 --> 00:07:11,400 I'm good. Yeah. 122 00:07:13,400 --> 00:07:18,300 Now you have to say going from McKinsey to Google or Facebook 123 00:07:18,600 --> 00:07:20,600 wherever, right? Now, it's quite a common today. 124 00:07:20,600 --> 00:07:23,500 But yeah, most people said, look, the best I can do is give 125 00:07:23,500 --> 00:07:25,600 you a sales job or a marketing job. 126 00:07:25,900 --> 00:07:28,100 Yes. I want to program. 127 00:07:28,400 --> 00:07:32,200 So Infosys Consulting was the closest where it was a quasi 128 00:07:32,200 --> 00:07:35,700 technology quasi Consulting, kind of a role, and there was 129 00:07:35,700 --> 00:07:38,800 mostly sitting and coding doing stuff like this, IMDb to goofy. 130 00:07:39,100 --> 00:07:41,900 So it wasn't quite a shift away from strategy Consulting. 131 00:07:41,900 --> 00:07:47,000 It was a desperate claw back from 99, 2005 to technology, 132 00:07:47,000 --> 00:07:48,700 which is what I really love. Yep. 133 00:07:49,800 --> 00:07:51,300 Okay, today, this very interesting. 134 00:07:51,300 --> 00:07:54,100 So I probably we should take you here on a slightly alternate 135 00:07:54,100 --> 00:08:00,600 path because I got my first computer when I was 11 or 12 in 136 00:08:00,600 --> 00:08:03,900 a couple of years, learn to code, then started doing these 137 00:08:04,000 --> 00:08:06,900 random graphic things on our 386, 10 things like that. 138 00:08:07,200 --> 00:08:10,200 Then I decided, I wanted to study computer science actually 139 00:08:10,800 --> 00:08:14,400 managed to get computer science in IIT Madras and then like, I 140 00:08:14,400 --> 00:08:16,400 completely fell out of love with computers things. 141 00:08:17,100 --> 00:08:20,900 For the those four years, I completely sort of. 142 00:08:21,800 --> 00:08:24,800 I hated programming. I was reputed to be the best 143 00:08:24,800 --> 00:08:27,500 programmer in my school, but I completely hated programming. 144 00:08:27,500 --> 00:08:29,400 So for me, I am be was a respite. 145 00:08:29,600 --> 00:08:33,200 It was like I want to get away from take so and so I am be 146 00:08:33,200 --> 00:08:35,400 happy. Very similar to you internship 147 00:08:35,400 --> 00:08:37,100 at JPMorgan happened. And I was like, no, I don't want 148 00:08:37,100 --> 00:08:38,799 to do this. Then I joined 80 Connie. 149 00:08:38,799 --> 00:08:41,600 And then I said, I don't want to do this either and I left in 150 00:08:41,700 --> 00:08:45,500 within the within the first few months and then like and then 151 00:08:45,500 --> 00:08:49,300 yeah, like, I mean, I 3500, various places went back to 152 00:08:49,300 --> 00:08:50,700 invest in banking for a bit and so on. 153 00:08:50,700 --> 00:08:53,900 But yeah, you know, it took me about five years after 154 00:08:53,900 --> 00:08:56,900 graduating, from computer science in, IIT Madras to that 155 00:08:56,900 --> 00:09:00,700 liking to code again, ho ho, ho. Wow. 156 00:09:00,700 --> 00:09:04,700 So maybe maybe in some way by not doing computer science and 157 00:09:04,700 --> 00:09:07,700 doing chemical, you manage to sort of retain your love for 158 00:09:07,700 --> 00:09:11,300 coding through your shit. Okay, then I have to thank that 159 00:09:11,300 --> 00:09:17,100 Professor, not curse him. Hey, it's also where it's from 160 00:09:17,100 --> 00:09:20,900 Infinity to the thing you say to grammar or like you you deserve. 161 00:09:21,300 --> 00:09:25,900 Yeah, yeah, 2011, my ex-boss from IBM, ROM. 162 00:09:25,900 --> 00:09:29,000 He reached out and said, took all of us old guys. 163 00:09:29,000 --> 00:09:31,100 We are looking to do something you're interested. 164 00:09:31,100 --> 00:09:33,300 I said was, yeah. Absolutely. 165 00:09:33,400 --> 00:09:35,100 Let's try again. Okay. 166 00:09:35,300 --> 00:09:38,900 Okay, awesome. So, I did like, I mean, you sort 167 00:09:38,900 --> 00:09:42,200 of like, I mean, I also like we sort of bumped into each other 168 00:09:42,200 --> 00:09:44,500 at various places. First, know you as a sort of a 169 00:09:44,700 --> 00:09:47,200 Visualization Guru. So, how did you get into Data 170 00:09:47,200 --> 00:09:50,300 visualization, which is not exactly programming, right? 171 00:09:50,300 --> 00:09:56,000 So, I think there are two parts that let me there. 172 00:09:56,300 --> 00:10:02,100 The earlier one was just an interest in graphics programming 173 00:10:02,300 --> 00:10:03,900 its key. I like playing games. 174 00:10:04,200 --> 00:10:05,200 Yep. The question becomes. 175 00:10:05,200 --> 00:10:08,000 Now, can I create games and for that, I have to learn some of 176 00:10:08,008 --> 00:10:13,000 the basics of Graphics fractals were always an interest and 3D 177 00:10:13,000 --> 00:10:16,300 rendering was always interests. So, and my dad's an architect, 178 00:10:16,300 --> 00:10:21,400 so at his office, I was interning of sorts creating 3D 179 00:10:21,400 --> 00:10:23,300 models for him and sketches and stuff like that. 180 00:10:23,300 --> 00:10:27,300 That was one phase where I was generally interested in 181 00:10:27,300 --> 00:10:31,700 programming stuff that generates visuals, even if it's a simple 182 00:10:31,700 --> 00:10:37,100 matter of the ray tracing a seen, those is software called 183 00:10:37,100 --> 00:10:39,800 all free which I've been using for quite some time. 184 00:10:39,800 --> 00:10:43,600 Of course, autocad's autodesk's 3D Studio was another that I've 185 00:10:43,600 --> 00:10:47,600 been using quite It was on one side, but the data hadn't really 186 00:10:47,600 --> 00:10:50,300 come in at that point data independently. 187 00:10:50,800 --> 00:10:56,000 During my, I am days. I got into mostly playground 188 00:10:56,000 --> 00:10:58,200 with financial data and a bunch of other things. 189 00:10:59,100 --> 00:11:03,700 But during my BCG days. I did get an interest in visual 190 00:11:03,700 --> 00:11:06,900 design, simply in terms of slide design, for example, because I 191 00:11:06,900 --> 00:11:11,100 really sucked at it. So, for many years, I had been 192 00:11:11,100 --> 00:11:14,500 spending time trying to read learn the principles of design. 193 00:11:14,600 --> 00:11:17,600 I'm and honestly, the one book that taught me. 194 00:11:18,300 --> 00:11:23,300 What little I know of design was this book by Robin Williams, not 195 00:11:23,300 --> 00:11:28,500 the actor, called the non-designers design book. 196 00:11:28,700 --> 00:11:32,000 Okay, and so brilliant book. It tells you a few simple 197 00:11:32,000 --> 00:11:34,100 principles that you follow to make sure that you get. 198 00:11:35,200 --> 00:11:37,900 Design, that is not bad, not a sign that is good. 199 00:11:37,900 --> 00:11:41,700 But designer is not back. And while I was doing that since 200 00:11:41,700 --> 00:11:46,500 I maybe was familiar with programming and said, what does 201 00:11:46,500 --> 00:11:50,500 it take to make design programmatic and automate as 202 00:11:50,500 --> 00:11:53,900 much as I could. And since I was also looking at 203 00:11:54,000 --> 00:11:57,400 data left, right and Center, what does it take to present 204 00:11:57,400 --> 00:11:59,800 this in a cleaner nicer? Professional way. 205 00:12:00,400 --> 00:12:03,500 So some of the early data visualizations that I put 206 00:12:03,500 --> 00:12:07,200 together, I distinctly remember One when I was at a Consulting I 207 00:12:07,208 --> 00:12:09,400 was doing this piece of work for S. 208 00:12:09,400 --> 00:12:13,000 Co.com., The British Retailer's website. 209 00:12:14,400 --> 00:12:19,000 So one of the things they had was a 40-page PowerPoint 210 00:12:19,100 --> 00:12:24,100 presentation that talks about what where the weekly statistics 211 00:12:24,100 --> 00:12:26,900 from Google analytics. Okay said what would it take to 212 00:12:26,900 --> 00:12:29,700 create this in the form of an infographic? 213 00:12:29,700 --> 00:12:34,000 A brochure high-quality design your at takes the data from 214 00:12:34,000 --> 00:12:35,800 Google Analytics. It's and programmatically 215 00:12:35,800 --> 00:12:38,700 generate set. So I put something together. 216 00:12:39,000 --> 00:12:40,700 It was an interesting experiment in many ways. 217 00:12:40,700 --> 00:12:46,800 It was very information dense design a dread Edward tufte by 218 00:12:46,800 --> 00:12:49,600 then. So okay had a sense of what kind 219 00:12:49,600 --> 00:12:55,500 of information dense displays one can create put it together. 220 00:12:56,200 --> 00:12:58,600 It was a python script. I still probably have it 221 00:12:58,600 --> 00:13:01,200 somewhere that pulled the data and then I tried an experiment. 222 00:13:01,200 --> 00:13:04,800 Is it what kind of attention would this grab from? 223 00:13:04,900 --> 00:13:10,600 And who does it really attract? So it was late, one, Tuesday 224 00:13:10,600 --> 00:13:13,400 evening. I took color print outs of this 225 00:13:13,400 --> 00:13:16,600 after everybody had left for, which the UK is easy. 226 00:13:16,700 --> 00:13:19,300 So, yes, basically around him or something. 227 00:13:19,400 --> 00:13:22,500 Okay, they worked a little harder. 228 00:13:22,500 --> 00:13:29,200 They're left these printouts on pretty much, everyone's desks. 229 00:13:29,400 --> 00:13:33,300 Okay, with my hypothesis being well, not even hypothetical my 230 00:13:33,300 --> 00:13:36,500 question being Who is likely to pick this up? 231 00:13:36,900 --> 00:13:41,300 Okay, so then came in the next day and I hadn't put my name on 232 00:13:41,300 --> 00:13:42,600 it. I had put anything so it's like 233 00:13:42,700 --> 00:13:44,200 nobody knows where this comes from. 234 00:13:44,500 --> 00:13:47,300 Yeah, so this is support sitting in there and I check 235 00:13:48,100 --> 00:13:49,800 mid-morning. It is interesting. 236 00:13:50,600 --> 00:13:53,400 These are the reports had been picked up by every one of the 237 00:13:53,408 --> 00:13:56,400 exits. Pretty much everyone in the 238 00:13:56,400 --> 00:14:00,500 marketing team had picked it up. Only the head of operations had 239 00:14:00,500 --> 00:14:02,800 picked it up. No one from the vendor's teams 240 00:14:02,800 --> 00:14:04,600 had even touched the report number. 241 00:14:04,900 --> 00:14:06,200 From the front team have touched. 242 00:14:06,200 --> 00:14:08,500 The report was just laying there in from called kind of thing. 243 00:14:08,500 --> 00:14:11,700 So it gave a beautiful perspective of who actually is 244 00:14:11,700 --> 00:14:15,300 interested in this kind of data and force that landed mean, 245 00:14:15,300 --> 00:14:19,800 probably because it was a big Witch Hunt of sorts that started 246 00:14:19,800 --> 00:14:23,400 saying okay, who's this person who seems to have gotten access 247 00:14:23,400 --> 00:14:26,100 to all of our data and it's doing stuff with this scent. 248 00:14:26,100 --> 00:14:27,700 Okay, let's happening. Yeah. 249 00:14:28,100 --> 00:14:33,700 Ended up well, but that was the first time I realized what 250 00:14:33,700 --> 00:14:36,900 happened was then. The head of marketing after I 251 00:14:37,000 --> 00:14:39,300 shared that. Yeah, I could sneak the head of 252 00:14:39,300 --> 00:14:43,400 marketing came over and said, why did you do this? 253 00:14:44,200 --> 00:14:48,000 Say, why do you ask? She said, well, look, this is a 254 00:14:48,000 --> 00:14:51,300 professionally designed report. So I thought we had paid someone 255 00:14:51,300 --> 00:14:54,400 to do this, and I had no clue whom we had paid the same. 256 00:14:54,500 --> 00:14:58,600 Oh, okay. Something that's professional. 257 00:14:58,800 --> 00:15:00,700 Yes, not a bad thing to start off with. 258 00:15:00,800 --> 00:15:03,600 Yeah. That's where the idea hit that. 259 00:15:03,600 --> 00:15:06,500 This could be a thing. Safe before data visualization 260 00:15:06,500 --> 00:15:14,900 was even thing. Yep, which year was 2008 or 2009 261 00:15:14,900 --> 00:15:20,800 so Miranda, so wow. Questions like I mean, so let's 262 00:15:20,800 --> 00:15:23,600 do the difficulty to the static. You said that the marketing 263 00:15:23,600 --> 00:15:26,900 everybody had picked it up operations, only the head had 264 00:15:26,900 --> 00:15:28,900 picked it up and nobody else was interested. 265 00:15:29,000 --> 00:15:31,900 And in the vendor team. Nobody was interested to, why do 266 00:15:31,900 --> 00:15:35,300 you think that happened? And like let's talk about Let's 267 00:15:35,300 --> 00:15:38,300 use this to get into. What kind of visualizations are? 268 00:15:38,300 --> 00:15:42,500 What kind of reports work for? Which kind of people like, based 269 00:15:42,500 --> 00:15:46,000 on what they doin sir. And so one thing I've learned is 270 00:15:46,200 --> 00:15:53,000 finance teams actually do well with numbers and not, that they 271 00:15:53,000 --> 00:15:56,700 can't do poorly with visualizations, but they just so 272 00:15:56,700 --> 00:16:02,300 much better with the numbers and need the numbers so much yet. 273 00:16:02,300 --> 00:16:06,000 We are probably better off staying away from I shouldn't 274 00:16:06,000 --> 00:16:08,000 even say Finance. I should really say account. 275 00:16:08,500 --> 00:16:11,100 Yeah, That's Amore out. Yep. 276 00:16:11,700 --> 00:16:15,700 I learned that the hard way because the purpose of a 277 00:16:15,708 --> 00:16:18,200 visualization is to give an overview to save. 278 00:16:18,200 --> 00:16:21,600 This is bigger than this for the accountant. 279 00:16:21,700 --> 00:16:26,400 It, it's kind of for a manager. Yep. 280 00:16:26,400 --> 00:16:31,900 One cent versus $100 is 10,000 x different to accountant. 281 00:16:31,900 --> 00:16:37,500 A discrepancy is a discrepancy. And that is something that I 282 00:16:37,700 --> 00:16:39,800 missed for quite some time. To be honest. 283 00:16:41,400 --> 00:16:44,800 It in very be visualizations, invariably work for senior 284 00:16:44,800 --> 00:16:49,400 execs, because ultimately, it helps them get a perspective. 285 00:16:49,400 --> 00:16:51,200 What's big, who, what's important? 286 00:16:51,200 --> 00:16:54,500 What should I focus on? Because prioritization is one 287 00:16:54,500 --> 00:16:56,900 thing that we realization helps dramatically in. 288 00:16:57,100 --> 00:17:01,700 And that's one of the key challenges analysts. 289 00:17:01,700 --> 00:17:05,500 I increasingly find are using this because Helps them from an 290 00:17:05,500 --> 00:17:06,800 exploration perspective. Again. 291 00:17:06,800 --> 00:17:09,200 It's a prioritization thing. What should I focus on? 292 00:17:09,200 --> 00:17:12,700 Where should I dive in? Yeah, and that is happening. 293 00:17:12,900 --> 00:17:16,400 Typically at Junior levels when people are saying okay, if I can 294 00:17:16,400 --> 00:17:19,700 use a visualization little help me dive deeper, that works 295 00:17:19,700 --> 00:17:23,400 across Fields, whether its sales and marketing or whether its 296 00:17:23,400 --> 00:17:27,400 operations or admin or even H are now finding that the less 297 00:17:27,400 --> 00:17:30,700 numerically Savvy. The people are the more visually 298 00:17:31,300 --> 00:17:33,500 attuned. They are the think it's simply a 299 00:17:34,100 --> 00:17:36,800 yet Style. Love thinking or working. 300 00:17:37,200 --> 00:17:41,100 So net, net. I have two simple rules of thumb 301 00:17:41,300 --> 00:17:47,500 for 33 on whether a person who will dive into visualization 302 00:17:48,200 --> 00:17:49,300 senior vs. Junior. 303 00:17:49,600 --> 00:17:54,600 Yep, seniors dive in more numerically Savvy versus 304 00:17:54,700 --> 00:17:59,100 numerically non Savvy and the numeric lean on savvy are more 305 00:17:59,100 --> 00:18:06,600 likely to die even more. Yep, and the Actually, I'm just 306 00:18:06,600 --> 00:18:08,500 these two third of the subset of the second. 307 00:18:08,900 --> 00:18:12,300 Okay, so mapping with back to your original example, I guess 308 00:18:12,300 --> 00:18:16,500 the marketing people were more like they were likely to be like 309 00:18:16,900 --> 00:18:20,000 I gets less numerically Savvy and so they all picked it up and 310 00:18:20,500 --> 00:18:24,300 like the report. Is that the no, I know I think 311 00:18:24,600 --> 00:18:27,200 even if I had just the numbers there, they would have picked it 312 00:18:27,200 --> 00:18:29,800 up because this was literally about sales and marketing. 313 00:18:29,800 --> 00:18:31,900 See this case. It will serve domain bias. 314 00:18:31,900 --> 00:18:34,200 I suspect that if I had put together something similar for 315 00:18:34,200 --> 00:18:36,100 operation. It's in Terror operations, team 316 00:18:36,100 --> 00:18:38,000 would have picked it up and only the sales and marketing head 317 00:18:38,000 --> 00:18:39,800 would have picked it up fair enough. 318 00:18:40,700 --> 00:18:42,700 And what explains the windows not taking that they give us 319 00:18:42,700 --> 00:18:45,400 completely relevant to them the window managers even care. 320 00:18:45,400 --> 00:18:48,500 Yes, you did tell me to do this. Yes, sir. 321 00:18:48,700 --> 00:18:51,900 Okay. Okay, you know, we are very 322 00:18:51,900 --> 00:18:53,300 busy. I mean, like, as you might know, 323 00:18:53,308 --> 00:18:55,700 for the last few months, I've been working for delivery taken 324 00:18:55,700 --> 00:18:59,300 up a job after a very long time. So I have a friend in finance. 325 00:18:59,300 --> 00:19:02,300 She was like, can you when will you do some analytics 326 00:19:02,300 --> 00:19:05,100 visualizations for us? I was like, look, I'm Doing 327 00:19:05,100 --> 00:19:07,600 visualizations for you because your needs are too precise for 328 00:19:07,600 --> 00:19:12,200 me like actually, right? Because the finance is like it's 329 00:19:12,200 --> 00:19:14,900 all about like which is what I mean going back 15 years, which 330 00:19:14,900 --> 00:19:17,600 is what I figured out during my little internship in Investment 331 00:19:17,600 --> 00:19:19,700 Banking. As well that click the need for 332 00:19:19,700 --> 00:19:22,100 precision was like, yeah, I'm like you need thousand two 333 00:19:22,100 --> 00:19:25,600 hundred dollars have come 2001. 98 you have word just round it 334 00:19:25,600 --> 00:19:27,500 and you're dead and they're like, no, we need to. 335 00:19:27,500 --> 00:19:29,800 We need to match the last penny and I was like, okay. 336 00:19:32,200 --> 00:19:33,400 Yeah, I guess we're visualization. 337 00:19:33,400 --> 00:19:36,900 Is that, since works for when? You are when you want to produce 338 00:19:36,900 --> 00:19:41,200 the show brought trains, let's say or when you want to Lexa. 339 00:19:41,700 --> 00:19:44,800 So I guess so if you have to present it to finance people or 340 00:19:44,900 --> 00:19:47,000 other otherwise numerically Savvy people. 341 00:19:47,000 --> 00:19:50,300 I guess it's more about yes, give them tables and give them 342 00:19:50,300 --> 00:19:55,100 well formatted ajj table. Exactly in the areas that they 343 00:19:55,100 --> 00:19:57,600 are looking for very refined. Visualizations. 344 00:19:57,600 --> 00:20:02,300 Helpful is our to help them see stuff that they otherwise don't 345 00:20:02,300 --> 00:20:06,600 see to give you an example. You said the two dollar founding 346 00:20:06,600 --> 00:20:10,700 of a new gas. Now their point is that to two 347 00:20:10,700 --> 00:20:13,800 dollars, two thousand dollars. It's all the same and error is 348 00:20:13,800 --> 00:20:17,900 an error in which case the metric that we are tracking 349 00:20:17,900 --> 00:20:21,200 starts becoming different. It's not the magnitude of error. 350 00:20:21,200 --> 00:20:25,100 It's the presence of an error and then we get into something a 351 00:20:25,200 --> 00:20:26,900 lot of roughly along the Realms of data quality. 352 00:20:26,900 --> 00:20:30,700 And now, can we visualize data? Quality can be visualized fraud 353 00:20:31,000 --> 00:20:34,500 but it's data T. We did something interesting 354 00:20:34,500 --> 00:20:38,000 there. Was an issue where they were 355 00:20:38,000 --> 00:20:43,600 getting fraudulent purchases, adulterated matches, and a big 356 00:20:43,600 --> 00:20:46,600 part of the problem was trying to trace where this was coming 357 00:20:46,600 --> 00:20:47,500 from. Okay. 358 00:20:47,500 --> 00:20:50,700 So what they did was they actually took about 100,000 359 00:20:50,700 --> 00:20:54,600 batches and looked at the flow of these hundred thousand 360 00:20:54,600 --> 00:20:57,600 batches from beginning to end. And at the end that these are 361 00:20:57,600 --> 00:20:59,700 batches that they had money, re-inspected and found that a 362 00:20:59,700 --> 00:21:01,900 certain proportion of them where it will treated. 363 00:21:01,900 --> 00:21:04,600 And when they had the whole supply chain here. 364 00:21:04,800 --> 00:21:07,600 Facing the flow of these are all great, advances across the 365 00:21:07,600 --> 00:21:11,600 supply chain, and being able to visualize that now that's an 366 00:21:11,700 --> 00:21:15,100 Auditors Delight. This particular guy. 367 00:21:15,100 --> 00:21:16,900 Oh, I know this guy. He's actually colluding with 368 00:21:16,900 --> 00:21:19,300 this fellow because he's his brother-in-law at this plant. 369 00:21:19,300 --> 00:21:21,700 So I know why this is happening. Ah, yep. 370 00:21:21,700 --> 00:21:24,900 It's a very good. Yeah, which is harder to do with 371 00:21:24,900 --> 00:21:27,900 the numbers and it wasn't something that they had fought 372 00:21:27,900 --> 00:21:30,600 off. So generally we think about 373 00:21:30,600 --> 00:21:34,000 presenting this differences, give them what they know they 374 00:21:34,000 --> 00:21:36,600 want as Numbers. Yeah, them what they didn't even 375 00:21:36,600 --> 00:21:38,300 know. They wanted as visuals. 376 00:21:38,300 --> 00:21:40,600 That works. Really, really. 377 00:21:41,500 --> 00:21:44,800 And, and what do you what do you use to buy? 378 00:21:44,800 --> 00:21:47,200 I mean, now I guess you've come a long way, like, having been 379 00:21:47,500 --> 00:21:49,700 running this company for a decade and things like that. 380 00:21:49,700 --> 00:21:52,900 So, what did you start? In terms of a, you said you 381 00:21:52,900 --> 00:21:55,000 wrote a python script for your original thing when you were 382 00:21:55,000 --> 00:21:59,800 working for Tesco and then like, so what how is your tool Journey 383 00:21:59,800 --> 00:22:04,100 evolved over the course of time? So the one constant in the tool 384 00:22:04,100 --> 00:22:06,000 Journeys? And I'm sure we'd want to talk a 385 00:22:06,008 --> 00:22:07,500 lot more. Of course, of course, of course, 386 00:22:07,500 --> 00:22:08,900 we come to that in a bit. Yeah. 387 00:22:09,300 --> 00:22:14,100 Yeah, but the programmatically so well since I was in Texas 388 00:22:14,100 --> 00:22:16,700 Bay's, I'd always been playing around with Visual Basic. 389 00:22:16,700 --> 00:22:20,400 So that was part of the Toulon since usual, basic actually can 390 00:22:20,400 --> 00:22:23,500 be almost copy pasted into python, which I thought was 391 00:22:23,500 --> 00:22:24,800 remarkable. Maybe. 392 00:22:25,600 --> 00:22:29,600 Yeah, in 32, cam in Python. I've done this. 393 00:22:29,600 --> 00:22:34,200 I've actually recorded macros in Excel and copy pasted them and 394 00:22:34,200 --> 00:22:37,700 with Very few changes, it actually runs in Python. 395 00:22:38,000 --> 00:22:39,600 If you import the winter to come Library. 396 00:22:40,000 --> 00:22:43,600 Wow, so I'm not that surprised, because there was a news, a few 397 00:22:43,600 --> 00:22:48,000 years back that Microsoft was planning to replace Visual Basic 398 00:22:48,200 --> 00:22:52,600 with visual python to allow you to manipulate Excel sheets using 399 00:22:52,600 --> 00:22:54,200 python. So I guess they would have also 400 00:22:54,300 --> 00:22:57,500 seen this similarity in some sense quite possibly. 401 00:22:57,500 --> 00:23:00,900 They've gone the JavaScript route now, which okay, actually, 402 00:23:01,200 --> 00:23:04,500 I'm very glad for because for us from visualization. 403 00:23:04,700 --> 00:23:08,700 Stick to python was the de facto server-side language because it 404 00:23:08,700 --> 00:23:12,300 was really good with data and any language could do 405 00:23:12,300 --> 00:23:16,800 visualizations, but the one disadvantage of any language 406 00:23:16,800 --> 00:23:19,000 except JavaScript is. It doesn't run natively on the 407 00:23:19,000 --> 00:23:22,000 browser, and if you want interactive visualizations, it's 408 00:23:22,000 --> 00:23:23,600 got to work on JavaScript. Yeah. 409 00:23:23,600 --> 00:23:27,400 Overtime. JavaScript became so good and 410 00:23:27,700 --> 00:23:29,900 good. Enough on the server side that 411 00:23:30,100 --> 00:23:33,100 in 2015 or so. We shifted our visualization 412 00:23:33,100 --> 00:23:36,400 tuning from, bye. Ethan to JavaScript, right? 413 00:23:36,400 --> 00:23:41,200 But barring that there hasn't been a major language change on 414 00:23:41,200 --> 00:23:45,600 the library, side on the server side, the python always had the 415 00:23:45,600 --> 00:23:53,400 likes of matplotlib and now Seaborn book Etc, but I've never 416 00:23:53,400 --> 00:23:56,200 really used those. Those are for conventional 417 00:23:56,200 --> 00:23:59,700 charts. And these generally reason can 418 00:23:59,700 --> 00:24:02,200 exist is because of her unconventional visualizations. 419 00:24:02,200 --> 00:24:04,500 It's very doing what these like these can't do. 420 00:24:05,100 --> 00:24:08,600 So we ended up creating svg's using templating languages and 421 00:24:08,600 --> 00:24:12,600 that's in my mind still over the last 10 years, that one 422 00:24:12,600 --> 00:24:16,700 technique of creating svg's using templates is the one that 423 00:24:16,700 --> 00:24:18,900 has led to the proliferation of the kinds of visuals. 424 00:24:18,900 --> 00:24:22,100 We create today and is still arguably the most powerful way 425 00:24:22,100 --> 00:24:26,400 of creating unconventional visual representations, so that 426 00:24:26,400 --> 00:24:30,300 we did in Python and then shifted over to D3 initially, 427 00:24:30,700 --> 00:24:33,300 but the trouble with D3 is it requires too much programming, 428 00:24:33,300 --> 00:24:37,500 too much learning? And Vega was a good mid-level 429 00:24:37,500 --> 00:24:40,600 alternative, higher, than that. And Vega light is a good high 430 00:24:40,600 --> 00:24:42,400 level alternative. On top of that. 431 00:24:42,700 --> 00:24:44,900 I wouldn't say that. The problem is solved. 432 00:24:44,900 --> 00:24:48,700 We still need a certain amount of tooling on top of Vega light 433 00:24:48,700 --> 00:24:50,300 for more people to be able to create it. 434 00:24:50,300 --> 00:24:53,600 Basically between Vega late and Excel, there is still that Gap, 435 00:24:54,100 --> 00:24:57,100 but if that can be bridged and a little more flexibility were 436 00:24:57,100 --> 00:25:00,700 brought into these libraries. I think we could say that the 437 00:25:00,900 --> 00:25:03,600 visualization problem is solved yet. 438 00:25:03,600 --> 00:25:05,700 Probably. Every step of the way there. 439 00:25:07,100 --> 00:25:09,800 Okay, actually, I mean it's interesting that I my journey 440 00:25:09,800 --> 00:25:11,600 has been like a briefly disjoint from this. 441 00:25:11,800 --> 00:25:18,200 I moved directly from Excel to our to ggplot and I got repulsed 442 00:25:18,200 --> 00:25:21,700 by python, because Matlab matplotlib, it's so difficult to 443 00:25:21,700 --> 00:25:24,500 sort of handle back road little Seaborn. 444 00:25:24,500 --> 00:25:26,300 It's like I just couldn't get the hang of it. 445 00:25:26,300 --> 00:25:29,200 So I almost like a, this one assignment. 446 00:25:29,200 --> 00:25:32,000 I was doing it a couple of years back where like the client was 447 00:25:32,000 --> 00:25:33,400 like, can you please give us code in Python? 448 00:25:33,400 --> 00:25:36,400 Because it will be easier for us to Integrate I will I did it for 449 00:25:36,400 --> 00:25:38,600 six months and then I told him. No, I'm shifting to higher 450 00:25:38,600 --> 00:25:40,800 because I'm 10x faster than the are then by. 451 00:25:41,600 --> 00:25:45,500 So fair is like, I guess personal preferences and like 452 00:25:46,100 --> 00:25:49,700 also I'm not a corded corded anymore in some sense. 453 00:25:49,700 --> 00:25:53,800 So right here, we are really looking for the answer more than 454 00:25:53,800 --> 00:25:55,300 to the repeatability of the answer. 455 00:25:55,800 --> 00:25:58,800 And what we're doing really is more helping someone else figure 456 00:25:58,800 --> 00:26:01,700 out the answer. I am less interested in telling 457 00:26:01,700 --> 00:26:04,500 you someone the answer dancing. Here's something that will 458 00:26:04,700 --> 00:26:06,700 Constantly, keep telling you, the answer in the perspectives 459 00:26:06,700 --> 00:26:09,300 are very different. Luckily, for exploration. 460 00:26:09,300 --> 00:26:13,200 Our is forgive much better. Is a much better environment. 461 00:26:13,200 --> 00:26:16,900 And even after we factor in the likes of Jupiter lab and 462 00:26:16,900 --> 00:26:19,000 whatever other advances have come into the ecosystem. 463 00:26:20,000 --> 00:26:24,000 This whole lot easier to it's flat. 464 00:26:24,000 --> 00:26:25,700 It is a whole bunch of these libraries. 465 00:26:25,700 --> 00:26:28,800 Each of these libraries. There, something specific you 466 00:26:29,200 --> 00:26:30,400 go. Look at the documentation. 467 00:26:30,400 --> 00:26:32,100 You try it. It works finished. 468 00:26:32,100 --> 00:26:33,600 Move on. Yep. 469 00:26:33,600 --> 00:26:38,300 Yeah, that is In Python, you make sure that well, it fits 470 00:26:38,300 --> 00:26:40,700 well with all of the other pieces, the beginning and at the 471 00:26:40,700 --> 00:26:44,100 end and it's repeatable. So it's engineered in a very 472 00:26:44,100 --> 00:26:45,400 different way. Yeah. 473 00:26:45,500 --> 00:26:48,300 Well, I think I have sacrificed on engineering which is why I 474 00:26:48,300 --> 00:26:51,700 like I get a lot of sort of benefits on the other side, 475 00:26:51,700 --> 00:26:53,000 right? In terms of how quickly you can 476 00:26:53,000 --> 00:26:55,400 do things, how easily you can do that at how much programming you 477 00:26:55,400 --> 00:26:56,700 need. And all those things though. 478 00:26:57,000 --> 00:27:00,500 Like it's like like the tortoise and I am be a long time but 479 00:27:00,500 --> 00:27:03,300 everything is about it laid off anyway, so let's come to your 480 00:27:03,300 --> 00:27:07,500 favorite topic which is The so how do you feel having? 481 00:27:07,500 --> 00:27:10,000 I would ask you how you got to accept because it as a strategy 482 00:27:10,000 --> 00:27:11,200 consultant. I think that would have been 483 00:27:11,200 --> 00:27:14,700 your 99% of your time, kind of thing. 484 00:27:14,700 --> 00:27:17,500 So and then, when you do start doing sort of protocol is 485 00:27:17,500 --> 00:27:19,900 gymnastics in Excel and like creating all those cool things 486 00:27:19,900 --> 00:27:23,300 in one. You know, I actually don't 487 00:27:23,300 --> 00:27:24,700 remember. I think it must have been during 488 00:27:24,700 --> 00:27:28,900 my IBM days. It is when I learned Visual 489 00:27:28,900 --> 00:27:31,400 Basic or maybe even before that. Even during IIT was playing 490 00:27:31,400 --> 00:27:33,800 around a little bit with Excel and learning a little bit of 491 00:27:33,800 --> 00:27:37,100 Visual Basic. But the fact that you can press 492 00:27:37,100 --> 00:27:39,200 a button and get it to do something cool. 493 00:27:39,700 --> 00:27:43,100 That was always interesting. Yeah, first really interesting 494 00:27:43,100 --> 00:27:46,700 piece that I actually remember doing was at Lehman Brothers. 495 00:27:46,900 --> 00:27:49,300 So there was this guy Scott Tucker. 496 00:27:49,300 --> 00:27:58,700 He had he had the ETA of how the markets where correlated with is 497 00:27:58,700 --> 00:28:01,800 basically had historical performance of the indices. 498 00:28:02,100 --> 00:28:04,700 All the major indices. Mm 40, 50 of them. 499 00:28:05,200 --> 00:28:09,000 So the first thing that I put together for them was actually a 500 00:28:09,500 --> 00:28:12,400 mini data, visualization of sorts a scatter plot Matrix, 501 00:28:12,400 --> 00:28:14,900 which of course didn't exist. Even by name at that point. 502 00:28:14,900 --> 00:28:18,400 This was in 2000. So pretty much did all kinds of 503 00:28:19,800 --> 00:28:23,800 index match and we cup jugglery to take all of those scenes and 504 00:28:23,800 --> 00:28:27,200 create that scatter plot Matrix of the correlations between 505 00:28:27,200 --> 00:28:32,100 those, another one was looking at the US Treasury yields across 506 00:28:32,100 --> 00:28:35,600 time. So take the US Treasury yield on 507 00:28:35,600 --> 00:28:37,800 a given date and it's a line graph. 508 00:28:38,000 --> 00:28:40,600 Yep. So what I did was I created a 509 00:28:40,600 --> 00:28:44,600 slider, which, As you move the slider, it changes the date and 510 00:28:44,600 --> 00:28:46,000 plots a graph on a different day. 511 00:28:46,300 --> 00:28:50,600 So as you drag the slider it smoothly animates. 512 00:28:50,700 --> 00:28:54,800 Yep, the USD and it was possible to see that the treasury yield 513 00:28:54,800 --> 00:28:57,900 was clearly flattening because visually really powerful. 514 00:28:58,100 --> 00:29:00,800 Okay. So among other things that got 515 00:29:00,800 --> 00:29:05,000 me placement of, okay, good. I deserve, I think back when 516 00:29:05,200 --> 00:29:08,800 the, it boom was still on - Ruth, summer of 2000. 517 00:29:08,800 --> 00:29:13,800 Yeah, exactly. Because it was literally, I 518 00:29:13,800 --> 00:29:16,700 think a week before the.com bust. 519 00:29:16,900 --> 00:29:18,600 Yeah. I was in Tokyo at that time. 520 00:29:18,600 --> 00:29:23,300 And in fact, in the Night before it crashed and everyone's like 521 00:29:24,000 --> 00:29:25,500 partying. Things are going great. 522 00:29:25,500 --> 00:29:28,600 And so on wanted to keep your very worried about the bubble. 523 00:29:28,600 --> 00:29:30,900 I've actually everybody was called confident that it was a 524 00:29:30,908 --> 00:29:33,400 boo-boo. But question is, when is it 525 00:29:33,400 --> 00:29:36,500 going to burst? And then next day was oh my God, 526 00:29:36,700 --> 00:29:40,300 the dating field of a bloodbath. I can employ young exactly 527 00:29:40,300 --> 00:29:40,900 around that. Yeah. 528 00:29:41,100 --> 00:29:44,500 Yeah. Yeah, I okay. 529 00:29:44,600 --> 00:29:47,200 Yeah, and I think the thing with Excel is that like, I mean, 530 00:29:48,200 --> 00:29:51,700 especially once you have learnt Visual Basic, the number of 531 00:29:51,700 --> 00:29:55,000 things that you have, you can do with it, like multiplies, like, 532 00:29:55,000 --> 00:29:59,500 sort of with a large Factor. Absolutely, absolutely. 533 00:30:01,000 --> 00:30:02,900 Arguably. Even before that. 534 00:30:03,600 --> 00:30:08,900 I think in the developer tab, is at least one feature that I 535 00:30:08,900 --> 00:30:13,900 think is as powerful as visual. Eric is simply the ability to 536 00:30:13,900 --> 00:30:17,300 connect the scroll bar to excel As you move. 537 00:30:17,300 --> 00:30:19,500 The scroll bar the value in the cell changes in bicycle. 538 00:30:19,500 --> 00:30:22,100 Yeah. Now that's effectively linking a 539 00:30:22,100 --> 00:30:26,100 visual element with control, effectively slider to a salon. 540 00:30:26,100 --> 00:30:28,200 Once you've got something into a cell then formulas can take care 541 00:30:28,200 --> 00:30:30,000 of all kinds of things. Yeah. 542 00:30:30,700 --> 00:30:34,500 So just with that, and of course, similarly radio buttons 543 00:30:34,500 --> 00:30:37,200 were required and so on. So putting together and 544 00:30:37,400 --> 00:30:39,700 interface with these kinds of control, sometimes even just 545 00:30:39,700 --> 00:30:41,900 drop downs for which you don't even need the developer tab. 546 00:30:42,000 --> 00:30:45,300 I felt that was remarkably powerful. 547 00:30:45,300 --> 00:30:47,600 Even more powerful than the likes of pivot table than 548 00:30:47,600 --> 00:30:49,900 vlookup which in themselves are extraordinarily powerful. 549 00:30:50,200 --> 00:30:51,700 Yep. But yeah, we should base it 550 00:30:51,700 --> 00:30:56,700 takes it a whole realm beyond that but so high that I very 551 00:30:56,700 --> 00:31:01,800 rarely needed to resort to that. So things like for example using 552 00:31:01,800 --> 00:31:05,500 an external service, connect to web application and do some 553 00:31:05,500 --> 00:31:08,200 complex processing on the server or get social data from there. 554 00:31:08,300 --> 00:31:10,200 Yeah. That absolutely needs. 555 00:31:10,400 --> 00:31:12,900 The likes of visual. Basic which is unfortunate 556 00:31:12,900 --> 00:31:17,200 because on Google Sheets, we can just import a Json or XML using 557 00:31:17,200 --> 00:31:20,100 a formula. So those formulas are built in 558 00:31:20,100 --> 00:31:22,800 and it's a Pity that actually doesn't quite well and how it 559 00:31:22,800 --> 00:31:25,600 kind of does. But it did for a very long time. 560 00:31:25,800 --> 00:31:27,800 Have you informed that that could fetch data? 561 00:31:27,800 --> 00:31:32,300 But yeah, outside of that, the kind of stuff that you can do, 562 00:31:32,300 --> 00:31:38,100 especially Excel has the shapes. And the fact that you can start 563 00:31:38,100 --> 00:31:40,800 coloring shapes Based on data is really powerful. 564 00:31:41,500 --> 00:31:46,000 Yes, so for me, the bulk of the Visual Basic that I have written 565 00:31:46,000 --> 00:31:49,500 is largely about mapping shapes two numbers. 566 00:31:50,000 --> 00:31:50,700 Okay? Yep. 567 00:31:50,900 --> 00:31:51,700 Yep. Yep. 568 00:31:51,700 --> 00:31:54,500 Okay, interesting. And I think one cool thing that 569 00:31:54,500 --> 00:31:57,100 you are done in Excel. I mean like which I mean again, 570 00:31:57,100 --> 00:31:59,800 I don't work from a very long time because they think you can 571 00:32:00,400 --> 00:32:02,700 create Maps using exit colored mastering. 572 00:32:02,700 --> 00:32:04,300 This is during some election or something. 573 00:32:04,300 --> 00:32:07,400 Some work you some election was you will talk talking about a 574 00:32:07,408 --> 00:32:10,000 new have created colored Maps using Excel. 575 00:32:10,000 --> 00:32:12,700 I mean, that blew my mind. And I hadn't thought that's 576 00:32:12,700 --> 00:32:17,800 possible in Excel. So seriously, the actually, I 577 00:32:17,808 --> 00:32:21,800 don't remember how that came up. But the need was always there. 578 00:32:22,000 --> 00:32:25,300 No matter what kind of format a shape file is in. 579 00:32:25,800 --> 00:32:30,500 It's just a nightmare even today, color a map, it, I can't 580 00:32:30,500 --> 00:32:34,100 do it anymore. I have this with the same. 581 00:32:34,200 --> 00:32:37,000 It's not practical for me. It would take me an hour to 582 00:32:37,008 --> 00:32:40,400 color a map in any way other than on, except if it's there on 583 00:32:40,400 --> 00:32:42,800 Excel, it takes A minute. That's okay. 584 00:32:42,800 --> 00:32:46,600 You should quickly difference that need was always there and 585 00:32:46,600 --> 00:32:49,100 it kept nagging me. So the first experiment was if I 586 00:32:49,100 --> 00:32:53,200 change number, can I change the color on a set and serious? 587 00:32:53,300 --> 00:32:54,800 Yes, with a little bit of Visual Basic. 588 00:32:54,800 --> 00:32:59,000 So once I cracked that, then it got pretty exciting, which is 589 00:32:59,000 --> 00:33:02,200 now can I figure out? Can I take a color scale and 590 00:33:02,200 --> 00:33:04,400 interpolate based on that color scale? 591 00:33:04,400 --> 00:33:07,400 So interpolating colors was a little bit harder to get on 592 00:33:07,400 --> 00:33:10,100 Visual Basic and Visual Basic as a language just sucks. 593 00:33:11,300 --> 00:33:13,300 Coming back to practically any other language data. 594 00:33:14,500 --> 00:33:17,500 Is there an array? I don't even know by and large. 595 00:33:17,500 --> 00:33:20,100 My answer to the question is a featured as a feature exists on 596 00:33:20,100 --> 00:33:21,500 between basic is? I don't know. 597 00:33:21,500 --> 00:33:23,600 I'm going to do a Google search. I'm going to copy paste. 598 00:33:23,600 --> 00:33:26,300 Try it out if it works great. So it's something that I really 599 00:33:26,300 --> 00:33:28,600 don't know Visual Basic at all. Okay, what. 600 00:33:29,400 --> 00:33:31,200 Yeah. Interpolated colors finally 601 00:33:31,200 --> 00:33:34,400 managed to find or write something like the we that does 602 00:33:34,400 --> 00:33:36,800 it, which end up being reasonably compact. 603 00:33:37,200 --> 00:33:40,700 So now we can integrate colors. The toughest part was reading a 604 00:33:40,700 --> 00:33:42,900 shapefile. File and there are drawing it. 605 00:33:43,000 --> 00:33:46,400 So again at the time I wrote it, there wasn't any Library good 606 00:33:46,400 --> 00:33:49,100 enough that could read a shapefile and tell me what the 607 00:33:49,100 --> 00:33:51,800 points. Where, so, the closest I could 608 00:33:51,800 --> 00:33:56,600 get to was using think some JavaScript library that would 609 00:33:56,700 --> 00:34:00,300 render it as SVG, and then read the S EG. 610 00:34:00,500 --> 00:34:06,100 And, for each point in the SVG, draw a free-form in in Excel. 611 00:34:06,600 --> 00:34:10,100 So, the good thing is, this is where pythons ability to work 612 00:34:10,100 --> 00:34:14,000 with Excel comes in. I can be literally anything that 613 00:34:14,000 --> 00:34:16,300 can be done in Visual Basic Works in Python. 614 00:34:16,500 --> 00:34:19,199 So in visual basically, I basically record a macro where 615 00:34:19,199 --> 00:34:22,199 I'm drawing a shape and it gives me the code for it. 616 00:34:22,300 --> 00:34:25,100 Just clean Visual Basic code paper copy paste that into 617 00:34:25,100 --> 00:34:28,199 python just change the points, put it into a loop and that 618 00:34:28,199 --> 00:34:30,600 works. So, we took a whole bunch of 619 00:34:30,600 --> 00:34:35,500 shapefiles, converted them into CG convert them into Excel, put 620 00:34:35,500 --> 00:34:37,500 them online, which will basically at that time the 621 00:34:37,699 --> 00:34:40,000 election of the Parliamentary constituency is the assembly 622 00:34:40,000 --> 00:34:43,600 constituency is for a country for each state exactly, which 623 00:34:43,600 --> 00:34:47,000 makes it a whole lot easier for journalists to just fill in the 624 00:34:47,000 --> 00:34:50,100 numbers publish. It made a huge Larry. 625 00:34:50,100 --> 00:34:53,500 These are still being used. In fact, literally this morning. 626 00:34:53,800 --> 00:34:59,900 I was talking to strike on that collapse who was doing some 627 00:34:59,900 --> 00:35:05,500 support Initiative, for covid, terms of getting beds and to get 628 00:35:05,500 --> 00:35:10,100 the donors to see what areas have less pets more beds. 629 00:35:10,100 --> 00:35:12,400 He said over a shapefile. I could go to that an actual map 630 00:35:12,400 --> 00:35:16,700 and it's a whole lot easier for their team, label them. 631 00:35:16,700 --> 00:35:18,100 However, they want put in arrows. 632 00:35:18,100 --> 00:35:21,400 However, they want, exported it works seamlessly. 633 00:35:21,800 --> 00:35:22,600 Yep. Yep. 634 00:35:23,300 --> 00:35:25,900 So why is it that like, I mean, I have two questions. 635 00:35:26,500 --> 00:35:29,600 Why is it that in general data scientists don't like Excel? 636 00:35:29,800 --> 00:35:34,700 Think it demeans the nobility of their talent. 637 00:35:35,500 --> 00:35:38,400 I'm obviously being ridiculously sarcastic here, of course, but 638 00:35:41,200 --> 00:35:42,800 it'll probably take a few seconds. 639 00:35:42,800 --> 00:35:45,000 Maybe even a minute to work that out of my system. 640 00:35:45,200 --> 00:35:45,600 Yeah. Yeah. 641 00:35:45,700 --> 00:35:52,000 At some level if I'm, I don't know dick close enough to a 642 00:35:52,000 --> 00:35:56,400 real-life problem. But if I am a surgeon and you 643 00:35:56,400 --> 00:35:59,500 come to me for advice, my advice is going to be well, chop it 644 00:35:59,500 --> 00:36:00,500 off. Yeah. 645 00:36:00,500 --> 00:36:02,500 As opposed to a general physician who's going to say, 646 00:36:02,500 --> 00:36:05,700 well look, I don't know what surgery but medicines, might 647 00:36:05,700 --> 00:36:10,900 cure it now, what pays a data scientist more? 648 00:36:12,000 --> 00:36:15,400 Bitin or exit. Let's go even more precise. 649 00:36:15,400 --> 00:36:20,900 What weather data centers Force? I touch or pandas or exit. 650 00:36:21,200 --> 00:36:25,100 And the answer is, probably in orders of magnitude is forgive 651 00:36:25,100 --> 00:36:28,900 me multiples higher. So given that. 652 00:36:29,100 --> 00:36:34,100 Why would I want to do something filling my experience in a tool? 653 00:36:34,200 --> 00:36:39,400 That pays me less? Yeah, and therefore if I have 654 00:36:39,600 --> 00:36:43,800 problems are scarce at Level. So time is scarce certainly. 655 00:36:43,800 --> 00:36:47,100 So if I have a problem at hand and I can choose to use my time, 656 00:36:47,100 --> 00:36:50,100 building my skill in a particular tool, that is going 657 00:36:50,100 --> 00:36:52,700 to pay me more. I'd rather do that if I'm 658 00:36:52,700 --> 00:36:57,100 already really good at and US python by torch and the whole 659 00:36:57,400 --> 00:36:59,200 stack. Let's say, yeah, then I'm going 660 00:36:59,200 --> 00:37:01,200 to look for the next one. That's going to pay me more. 661 00:37:01,200 --> 00:37:03,500 Not the one that I left behind. You're not going to be 662 00:37:03,500 --> 00:37:05,500 programming in Haskell. You need a suit, right? 663 00:37:05,500 --> 00:37:09,600 Even if of course, no Bhaskar nor am I, I think it's exactly 664 00:37:09,600 --> 00:37:12,200 the same thing. The other hand. 665 00:37:14,100 --> 00:37:15,500 I think Excel is the better tool. 666 00:37:16,300 --> 00:37:20,900 Lets, you know, I read that I think one shortcoming that I've 667 00:37:20,900 --> 00:37:23,400 noticed with accelerate, especially I mean over the last 668 00:37:23,400 --> 00:37:25,800 few months that I've been extensively were getting data, 669 00:37:25,800 --> 00:37:28,200 out of databases. Is that with Excel? 670 00:37:28,200 --> 00:37:33,400 Like it's little automating tasks, into end seems a little 671 00:37:34,500 --> 00:37:37,500 more difficult compared to like, with our equation. 672 00:37:37,500 --> 00:37:44,000 So, absolutely. And this is where I just Yeah, I 673 00:37:44,000 --> 00:37:46,700 just hate that Excel Works. Only on Windows and sheets 674 00:37:49,300 --> 00:37:50,900 because Excel is quite automatable. 675 00:37:51,200 --> 00:37:57,200 If you have Python and windows, servers aren't quite as good as 676 00:37:57,200 --> 00:38:01,800 Linux servers. See almost on a Linux machine 677 00:38:02,200 --> 00:38:04,300 practically anything is automatically? 678 00:38:05,100 --> 00:38:08,300 Yeah, heck browsers are automatable to am in with the 679 00:38:09,300 --> 00:38:12,300 chromium and specifically Puppeteer coming in. 680 00:38:12,700 --> 00:38:15,700 Yeah, there's nothing that you can't do on the server side that 681 00:38:15,700 --> 00:38:18,700 you can do the proxy. Literally mimics every action 682 00:38:19,300 --> 00:38:20,700 that is still not true for Excel. 683 00:38:20,700 --> 00:38:27,400 So yeah, I was talking to the visit is be names deeper. 684 00:38:27,400 --> 00:38:31,500 She was talking about how she wanted to build simulations for 685 00:38:31,500 --> 00:38:33,800 classrooms. Okay, and could we build a tool 686 00:38:33,800 --> 00:38:35,300 that will allow students to do it? 687 00:38:35,600 --> 00:38:39,300 The use case is basically Perfect. 688 00:38:39,300 --> 00:38:42,200 For example, you have a set of formulas. 689 00:38:42,200 --> 00:38:45,200 You have linkages, you build a full-fledged model around it. 690 00:38:45,400 --> 00:38:49,400 The student weeks, a few things gets to see the result. 691 00:38:49,500 --> 00:38:54,800 Now if Excel could be exposed, like a little mini web 692 00:38:54,800 --> 00:38:58,100 application, you send an input, it does all the calculations and 693 00:38:58,100 --> 00:39:00,900 sends it back for her. It will be trivial to edit the 694 00:39:00,900 --> 00:39:03,100 model. And for the user, it will be in 695 00:39:03,100 --> 00:39:06,800 just seamless because hundreds of people can use it exploring. 696 00:39:06,800 --> 00:39:11,300 They were then, So I guess yeah, I'm actually I'm doing nothing 697 00:39:11,300 --> 00:39:13,600 more than repeat what you just said. 698 00:39:13,900 --> 00:39:16,900 In many more words. This literally are the lack of 699 00:39:16,900 --> 00:39:19,300 automate ability in Excel such a way. 700 00:39:20,500 --> 00:39:24,200 This is why I mean, in some ways I have not really used Excel for 701 00:39:24,200 --> 00:39:26,400 over a decade. Now, as when I use it. 702 00:39:26,400 --> 00:39:29,400 I always make sure even on a personal computer. 703 00:39:29,400 --> 00:39:32,600 I have a Microsoft 365 subscription just for exit 704 00:39:32,800 --> 00:39:36,900 because I got one without it, but I haven't done any real 705 00:39:36,900 --> 00:39:37,900 work. Work on exit. 706 00:39:38,100 --> 00:39:40,700 Well, I think except release course is in terms of 707 00:39:40,700 --> 00:39:42,400 interaction with business letter. 708 00:39:42,400 --> 00:39:45,300 For example, you have to the business, guys are all very 709 00:39:45,300 --> 00:39:47,200 proficient at Exit. If you have to give them 710 00:39:47,200 --> 00:39:50,000 something like okay, you tweak this then need not even be a 711 00:39:50,000 --> 00:39:52,000 slider. They can they can change the 712 00:39:52,000 --> 00:39:55,300 cells and you change the cells and this is what it pops up. 713 00:39:56,000 --> 00:39:58,500 Excel is so much Superior for them to them, than giving them a 714 00:39:58,500 --> 00:40:03,400 web app or any of the other things like that, crazy, two 715 00:40:03,400 --> 00:40:07,700 clicks, and not just for the spreadsheet. 716 00:40:08,000 --> 00:40:10,800 Except itself, but one of my colleagues to talk was telling 717 00:40:10,800 --> 00:40:15,900 me that we are giving except for one of our clients as the to in 718 00:40:15,900 --> 00:40:18,400 which they related stuff, dynamically generating fairly 719 00:40:18,400 --> 00:40:20,900 complex supports and in fact, models and formulas in Excel. 720 00:40:21,400 --> 00:40:25,300 So because the shortcut keys are so familiar with identification, 721 00:40:25,300 --> 00:40:28,000 they could quickly jump around from section to section and do 722 00:40:28,000 --> 00:40:30,100 stuff. And it's so much more productive 723 00:40:30,300 --> 00:40:32,900 to do that yet. So perhaps this isn't even a 724 00:40:32,908 --> 00:40:37,400 case of this may also be a case of a tool that they are familiar 725 00:40:37,400 --> 00:40:39,800 with. Yep, as much as it is the right 726 00:40:39,800 --> 00:40:41,800 tool for this, for the problem. Forget. 727 00:40:41,800 --> 00:40:44,600 It's a good combination of a tool that they are familiar 728 00:40:44,600 --> 00:40:48,700 with, and which has which is reasonably flexible and 729 00:40:48,700 --> 00:40:51,100 Powerful. So you bring those two together 730 00:40:51,100 --> 00:40:54,000 and like you can create a great interface method. 731 00:40:55,100 --> 00:40:58,600 It was it's just traffic talking, which Simon picking 732 00:40:58,600 --> 00:41:01,700 Jones, who has, in fact, written, few papers talking 733 00:41:01,700 --> 00:41:04,200 about how Excel is actually a turing-complete. 734 00:41:04,200 --> 00:41:07,500 Programming language. It is meets all the criteria. 735 00:41:08,000 --> 00:41:08,800 Yep. Yep. 736 00:41:08,800 --> 00:41:11,600 I have probably come across the existence of the paper, but I 737 00:41:11,607 --> 00:41:14,400 haven't really read. It will put a link here for 738 00:41:14,500 --> 00:41:16,500 people who are interested by the way, they cup. 739 00:41:16,900 --> 00:41:20,100 So see the allocation high-risk. What's your view on Google 740 00:41:20,100 --> 00:41:22,000 Sheets? Even that you are such a big fan 741 00:41:22,000 --> 00:41:24,800 of fix it. Oh, brilliant. 742 00:41:24,800 --> 00:41:29,800 So why would I not use? Google Sheets? 743 00:41:30,300 --> 00:41:33,700 A because it's not on my system. So sometimes when I'm traveling 744 00:41:33,700 --> 00:41:36,500 on a bus car in a flight, whatever I need Excel. 745 00:41:38,700 --> 00:41:43,600 Why else? Because shortcut keys are not 746 00:41:43,600 --> 00:41:48,400 the same as. Yep, but short of that, I use 747 00:41:48,400 --> 00:41:51,100 Google Sheets like crazy. The specific youth. 748 00:41:51,400 --> 00:41:54,600 Basically our website grammar.com is powered by Google 749 00:41:54,600 --> 00:41:55,700 Sheets. Okay. 750 00:41:55,700 --> 00:41:58,100 Okay. The content is actually all on 751 00:41:58,100 --> 00:42:00,300 Google Sheets. The marketing team is goes to 752 00:42:00,308 --> 00:42:04,500 some spot and edits, some text and click submit button. 753 00:42:04,600 --> 00:42:08,100 And it refreshes our website and not just that whole bunch of 754 00:42:08,500 --> 00:42:10,800 websites are powered by that. This is not even an uncommon 755 00:42:10,800 --> 00:42:12,500 thing. Hey, the New York Has micro 756 00:42:12,500 --> 00:42:16,700 sites, powered by Google Sheets collaborative editing in a 757 00:42:16,707 --> 00:42:19,000 structured way is just remarkably powerful. 758 00:42:19,200 --> 00:42:21,700 In fact, one of my side project which is on hold for almost a 759 00:42:21,707 --> 00:42:26,100 year now is to come up with so Excel is great with table 760 00:42:26,100 --> 00:42:28,900 structures. It's naturally structured as a 761 00:42:28,908 --> 00:42:31,800 table. Is there a standard by which we 762 00:42:31,800 --> 00:42:33,500 can create a hierarchical structure? 763 00:42:33,900 --> 00:42:40,200 Okay, so I want Inside which I have a table inside which I have 764 00:42:40,200 --> 00:42:42,000 a given potential. Yep. 765 00:42:43,000 --> 00:42:45,700 And the reason for that is one of the cells literally needs to 766 00:42:45,700 --> 00:42:47,800 be table, that kind of a concept. 767 00:42:47,800 --> 00:42:51,900 So, now if I am still working on some kind of a standard by which 768 00:42:51,900 --> 00:42:53,700 we can represent this in spreadsheets in a way that's 769 00:42:53,700 --> 00:42:57,900 possible by systems and humanely, highly readable. 770 00:42:58,400 --> 00:43:02,500 But if that existed, right, our website will become a hell of a 771 00:43:02,500 --> 00:43:05,500 lot managing. Our website will become a hell 772 00:43:05,500 --> 00:43:08,200 of a lot simpler. But put on With Google Sheets is 773 00:43:08,200 --> 00:43:11,000 now the database for our content management system. 774 00:43:11,700 --> 00:43:15,000 Okay, why do I like it? One? 775 00:43:15,100 --> 00:43:20,900 It has its collaborate offices, which way now does provide that, 776 00:43:20,900 --> 00:43:23,300 but super funky very clunky. Yeah. 777 00:43:23,300 --> 00:43:24,800 Exactly. That's not quite the same. 778 00:43:24,900 --> 00:43:29,000 Yeah, s. Its web native in more ways than 779 00:43:29,500 --> 00:43:31,400 Google than Excel is specifically. 780 00:43:31,400 --> 00:43:35,400 I can you know formula import from a webpage just big chunks 781 00:43:35,400 --> 00:43:38,500 and read from it. Third, I can write macros in 782 00:43:38,500 --> 00:43:42,800 JavaScript though. I'm not entirely happy about how 783 00:43:42,800 --> 00:43:45,100 it's been done. Not recall. 784 00:43:45,100 --> 00:43:46,900 I'm not at all. Happy about how it's been done. 785 00:43:47,100 --> 00:43:52,400 But that it's been done is not, I think is a great thing. 786 00:43:53,000 --> 00:43:56,200 But yeah, but it's in descending order fact that it's 787 00:43:56,200 --> 00:44:00,800 collaborative the fact that it's all that native, until I can 788 00:44:00,800 --> 00:44:03,400 write stuff in job. I cannot commit in JavaScript. 789 00:44:03,500 --> 00:44:06,800 These are wide why I choose Google chief. 790 00:44:07,000 --> 00:44:10,600 That accept who I choose Excel or Google Sheets is familiarity 791 00:44:10,600 --> 00:44:13,600 and all climaxes. Whatever term visualization on, 792 00:44:13,900 --> 00:44:15,900 I mean, on Google Sheets. Have the I have never even 793 00:44:16,000 --> 00:44:18,600 really used it for making graphs and things like that. 794 00:44:19,000 --> 00:44:21,600 Not right now. I use it for I use except for 795 00:44:21,600 --> 00:44:23,000 it. Even though if Arabic. 796 00:44:23,000 --> 00:44:26,500 But never Google G2, we are. I haven't really thought about 797 00:44:26,500 --> 00:44:28,300 why either, but you're right. I've never done that hike. 798 00:44:30,000 --> 00:44:32,100 Strings. Yeah, so maybe that's something 799 00:44:32,100 --> 00:44:36,900 that Google really I okay the maybe Google didn't really think 800 00:44:36,900 --> 00:44:38,700 about that as being a use case because I think the 801 00:44:38,700 --> 00:44:42,600 collaborative - itself to cover so much that like they didn't 802 00:44:42,900 --> 00:44:45,800 feel like they needed to offer proper powerful Graphics or 803 00:44:45,800 --> 00:44:49,500 something true. So I'm doing course right now, 804 00:44:49,500 --> 00:44:53,100 with an organization called landmark and set of leadership, 805 00:44:53,100 --> 00:44:57,700 course, they run this course for the whole series of metrics and 806 00:44:57,700 --> 00:45:01,900 these metrics are tricky. Person gets a really, really 807 00:45:01,900 --> 00:45:06,000 sophisticated Google sheet in which data is drawn not just 808 00:45:06,000 --> 00:45:10,200 from across sheets, but from across works, okay, and they've 809 00:45:10,200 --> 00:45:14,100 connected that into Master dashboard for the coaches to get 810 00:45:14,100 --> 00:45:19,300 to see the summary across all of their coaches that holds and 811 00:45:19,300 --> 00:45:22,400 higher level for the organization acts like a network 812 00:45:22,400 --> 00:45:25,500 of Google Sheets. Now, I don't think that can be 813 00:45:25,500 --> 00:45:27,600 done except not to the kind of Fitness. 814 00:45:27,900 --> 00:45:29,200 Yeah. Yeah. 815 00:45:29,200 --> 00:45:33,600 Yeah. Let's get back to visualization. 816 00:45:33,600 --> 00:45:36,000 I think we have spoken enough over to Excel and like let's get 817 00:45:36,000 --> 00:45:37,900 back to visualization. So what do you what is your 818 00:45:37,900 --> 00:45:39,900 opinion on stuff? Like they say? 819 00:45:39,900 --> 00:45:43,600 If I mean I use are we both agreed that python is not great 820 00:45:43,600 --> 00:45:46,100 for visualization. We both agree that like Excel is 821 00:45:46,100 --> 00:45:48,200 great for visualization and what we can do. 822 00:45:48,200 --> 00:45:50,900 And so on whatever it other your tools, you have all these 823 00:45:50,900 --> 00:45:54,100 dashboarding tools, like Tableau and click View and also have you 824 00:45:54,100 --> 00:45:56,900 used any of them, does your company use them and equality of 825 00:45:56,908 --> 00:45:59,200 view of the graphics element of of that? 826 00:46:00,200 --> 00:46:06,300 So a blow is very so okay. Firstly, all of them are a notch 827 00:46:06,300 --> 00:46:09,000 above Excel. I must be okay. 828 00:46:09,700 --> 00:46:14,700 And come with the associated fiction partly of learning. 829 00:46:15,000 --> 00:46:17,900 Yeah, partly of availability, which is a function of living. 830 00:46:18,200 --> 00:46:23,300 Everybody has, except everybody doesn't have more power, bi or 831 00:46:23,400 --> 00:46:25,900 click. So I can't really speak for 832 00:46:26,000 --> 00:46:29,800 click because I haven't really used not, we have that all use. 833 00:46:30,400 --> 00:46:35,000 Click, but I've been using power bi and Tableau server button for 834 00:46:35,000 --> 00:46:38,400 be a more so than Tableau just a bit more. 835 00:46:40,900 --> 00:46:46,400 All of them offer our more than accept and that's kind of like 836 00:46:46,400 --> 00:46:51,300 saying. 90% of stuff you can do in Excel. 837 00:46:51,700 --> 00:46:57,900 Yeah nine percent of the stuff you can do in these tools that 838 00:46:57,900 --> 00:47:00,600 they are still can't do this one person that neither of these can 839 00:47:00,600 --> 00:47:06,500 do and go to our JavaScript python, whatever you need, for 840 00:47:06,500 --> 00:47:10,200 the 90% of it, still use Excel. Okay, it's really only for that 841 00:47:10,200 --> 00:47:13,000 9%. What I've seen is that the 842 00:47:13,000 --> 00:47:16,600 people that use these tools, the likes of Tableau power, B, Etc. 843 00:47:16,900 --> 00:47:19,100 It's not as much fun. Self-consumption. 844 00:47:19,400 --> 00:47:22,100 It's for production ization, which is a very big thing. 845 00:47:22,500 --> 00:47:24,700 These are supposed to be self-serve tools. 846 00:47:24,900 --> 00:47:28,000 But the majority of the users are part of a reporting team, 847 00:47:28,100 --> 00:47:33,000 who create reports for others, who in theory are supposed to be 848 00:47:33,000 --> 00:47:37,000 able to click explore and so on, and a small fraction of them do 849 00:47:37,000 --> 00:47:40,000 that, but the majority of them, export it to Excel and play 850 00:47:40,000 --> 00:47:41,700 around with it there. Okay? 851 00:47:41,700 --> 00:47:44,000 For a couple of reasons, they have other data in Excel that 852 00:47:44,000 --> 00:47:47,100 they want to link with, or they have a workflow, which fits with 853 00:47:47,100 --> 00:47:50,900 Excel or Because they just more familiar with excess shortcuts 854 00:47:50,900 --> 00:47:54,500 and that's the tooling system. They prefer. 855 00:47:55,100 --> 00:47:58,800 So at some level the promise of self-service bi has been 856 00:47:58,800 --> 00:48:03,600 realized a lot more people can create reports yet the other end 857 00:48:04,000 --> 00:48:07,700 it was promising a need that probably didn't exist in that 858 00:48:07,700 --> 00:48:10,600 self-service bi has existed ever since it's still Excel existing. 859 00:48:11,100 --> 00:48:15,300 So, I believe these tools have a very important role in that nine 860 00:48:15,300 --> 00:48:18,900 percent range, but whatever the quality of the Fix-It that, they 861 00:48:18,900 --> 00:48:20,800 could example, if I see it, I don't know. 862 00:48:20,800 --> 00:48:24,400 I mean, I'm that I'm telling my opinion here, if I see a tableau 863 00:48:24,400 --> 00:48:26,200 table. I'm like, I just want to close 864 00:48:26,200 --> 00:48:28,300 my eyes somehow just like a blue Graphics. 865 00:48:28,300 --> 00:48:31,100 I mean, somehow they just it just doesn't look good. 866 00:48:31,400 --> 00:48:34,500 Like, I don't know what what it is about them. 867 00:48:34,500 --> 00:48:38,300 And like, though, recently. I read that like Tableau 868 00:48:39,000 --> 00:48:42,400 completely subscribes to the. So, you know, the ggplot in our 869 00:48:42,400 --> 00:48:46,100 stance of grammar of Graphics. It was like, Tableau is also a 870 00:48:46,107 --> 00:48:48,000 direct descendant of the grammar of gas. 871 00:48:48,200 --> 00:48:51,400 That has its own. But if I look at the graphics in 872 00:48:51,400 --> 00:48:53,800 the summer, I don't know. It just doesn't by default. 873 00:48:53,800 --> 00:48:57,500 It doesn't look good to you. So think about it, D room for 874 00:48:57,500 --> 00:49:01,000 the gracias reproduce with Excel 97 and Excel 2001. 875 00:49:01,200 --> 00:49:02,200 Yeah. Yeah. 876 00:49:02,900 --> 00:49:05,400 The defaults of pretty terrible there and I think it was Excel 877 00:49:05,400 --> 00:49:09,300 2007 or 30 year, 2007. I think we're through the 878 00:49:09,300 --> 00:49:11,600 quality of the defaults in Excel started. 879 00:49:11,600 --> 00:49:14,700 Improving dramatically 2013, to get a notch higher. 880 00:49:18,900 --> 00:49:24,700 Because of the power, I think in Tableau people create what they 881 00:49:24,700 --> 00:49:29,100 can and leave it at the default. I don't think the quality of 882 00:49:29,400 --> 00:49:32,900 people working on Excel versus startup Loan. 883 00:49:33,000 --> 00:49:36,000 In terms of their design ability is any significantly different? 884 00:49:36,100 --> 00:49:37,900 How come neither have any design ability? 885 00:49:38,200 --> 00:49:41,600 Yes, so they're just going with what's out there. 886 00:49:42,100 --> 00:49:46,600 So at one level, you're right excels managed to improve the 887 00:49:46,600 --> 00:49:51,000 quality of the The default Graphics to a level higher than 888 00:49:51,000 --> 00:49:54,600 what Apple has been able to achieve arguably, Tableau faces, 889 00:49:54,600 --> 00:50:02,700 a tougher challenge in that. There are more things that 890 00:50:02,700 --> 00:50:07,700 people can do and Excel has some minor, but important advantage 891 00:50:07,700 --> 00:50:09,900 of see, just aligning to the Grid in Excel. 892 00:50:10,100 --> 00:50:12,400 You just an ALT drag away. Yeah. 893 00:50:12,400 --> 00:50:16,500 So alignment, which is arguably, the single most important design 894 00:50:16,500 --> 00:50:18,900 aesthetic. If you ask me, it is easier 895 00:50:18,900 --> 00:50:21,500 because somebody says, oh, look, these are in the lines and is 896 00:50:21,900 --> 00:50:24,300 okay, worst case, they don't all drag the manually drag. 897 00:50:24,300 --> 00:50:26,600 But anyone who knows? It's a you just all Dragon snap 898 00:50:26,600 --> 00:50:28,300 it to another ruined. Columbo dancing. 899 00:50:28,300 --> 00:50:31,500 Things are now automatically aligned and it's a resize 900 00:50:31,500 --> 00:50:36,800 columns and other tasty aligned that W doesn't have a grid that 901 00:50:36,800 --> 00:50:39,100 people use by default. Yes. 902 00:50:39,200 --> 00:50:42,000 It supports a great but it's a very different if you don't 903 00:50:42,000 --> 00:50:44,400 think grid first. When you think of Tableau any 904 00:50:44,400 --> 00:50:48,400 more than think of great source when you think of PowerPoint, so 905 00:50:49,700 --> 00:50:52,300 some of these small little things basically mean that 906 00:50:52,700 --> 00:50:55,400 Tableau designs are very, I think Excel designs. 907 00:50:55,400 --> 00:51:02,100 We're in The five, but I think another part of the problem is 908 00:51:02,100 --> 00:51:07,100 the rapid growth now that the eye is big and people are 909 00:51:07,100 --> 00:51:11,600 getting in the volume of work that we're seeing done by people 910 00:51:11,600 --> 00:51:14,300 with less than let's say two years of experience in Tableau. 911 00:51:14,600 --> 00:51:18,400 Is it honest of break somebody who's in 2005? 912 00:51:18,400 --> 00:51:21,000 We had people who use Excel for a decade. 913 00:51:21,200 --> 00:51:23,100 Yep. They're veterans yet. 914 00:51:23,100 --> 00:51:25,300 We haven't yet. Gotten to those Tableau 915 00:51:25,300 --> 00:51:28,200 veterans, who are Aren't really concerned as much about how to 916 00:51:28,200 --> 00:51:30,400 do stuff. They know how to do stuff. 917 00:51:30,600 --> 00:51:33,200 The veteran knows it, and now he's looking at the same. 918 00:51:34,200 --> 00:51:36,500 I don't like it. So they get bored of what they 919 00:51:36,500 --> 00:51:38,200 create that. They start looking for 920 00:51:38,200 --> 00:51:40,600 Aesthetics. That stage hasn't come yet in 921 00:51:40,600 --> 00:51:42,700 bulk attic. Yeah. 922 00:51:42,700 --> 00:51:45,000 Yeah. Yeah, I'd also like the other 923 00:51:45,000 --> 00:51:47,900 thing is the experience graph. If you look at it, like it's 924 00:51:47,900 --> 00:51:51,200 very bottom heavy in tablet compared to excel at least like 925 00:51:51,200 --> 00:51:55,100 a yeah, even 15 years back. It was in that bottom heavy. 926 00:51:55,100 --> 00:51:58,200 So it was like, people Using it, for various reasons. 927 00:51:58,200 --> 00:52:00,400 And so I took that out their own stuff, I guess. 928 00:52:00,400 --> 00:52:04,000 So, okay. So let's get to the, possibly 929 00:52:04,000 --> 00:52:05,900 the most controversial part of the thing. 930 00:52:05,900 --> 00:52:09,100 I mean like this, a few quick. What do you think of pie chart? 931 00:52:09,100 --> 00:52:16,200 I've probably gone the complete circle on this and from using 932 00:52:16,200 --> 00:52:20,000 them because they look nice to eating them because of the said. 933 00:52:20,000 --> 00:52:22,800 So and I could see why, and going back to sink. 934 00:52:23,600 --> 00:52:25,100 Yeah. They have a place in life. 935 00:52:25,200 --> 00:52:32,100 They are good. Good, they have very limited 936 00:52:32,100 --> 00:52:34,100 data density. So the amount of information 937 00:52:34,100 --> 00:52:37,600 that you can communicate with those pretty small, but the 938 00:52:37,600 --> 00:52:39,400 aesthetic behind them is reversed. 939 00:52:39,400 --> 00:52:41,000 So where do they have that place? 940 00:52:41,000 --> 00:52:43,200 Where would you use a pie chart are not useful. 941 00:52:44,000 --> 00:52:47,200 Well, the classic case which I guess even tufte would agree 942 00:52:47,200 --> 00:52:51,300 with this Harvey balls. So just a matrix of Harvey 943 00:52:51,300 --> 00:52:55,300 balls, is actually a pretty efficient way of communicating. 944 00:52:55,500 --> 00:52:58,300 Okay, this one. Low medium high kind of a dick. 945 00:52:58,400 --> 00:53:02,900 So okay that works and that's effectively a pie chart with two 946 00:53:02,900 --> 00:53:06,600 variables. I would never recommend the pie 947 00:53:06,600 --> 00:53:08,500 chart with more than two variables. 948 00:53:08,600 --> 00:53:12,500 So it's really only proportion. Yeah, the Second Use case is 949 00:53:12,500 --> 00:53:14,800 where you're sharing a proportion and you want to get a 950 00:53:15,900 --> 00:53:19,700 better aesthetic, simply showing something that is different. 951 00:53:19,900 --> 00:53:24,000 It also lends itself to far more info graphic variations. 952 00:53:24,000 --> 00:53:26,900 You can put in a picture into a pie chart that looks What 953 00:53:26,900 --> 00:53:30,100 different. You can shape the pie chart into 954 00:53:30,100 --> 00:53:34,200 a variety of circular shapes or coin of, which is like sort of 955 00:53:34,200 --> 00:53:36,200 buy a pie out of which is slice of the pie. 956 00:53:36,400 --> 00:53:40,800 So many other things that also has value and these things are 957 00:53:40,800 --> 00:53:43,000 at one level. They are charged chunk at 958 00:53:43,200 --> 00:53:46,500 another level chart. Junk has value, the Normans gone 959 00:53:46,500 --> 00:53:51,600 through this cycle, as well from no functional design, in the 960 00:53:51,600 --> 00:53:55,500 Design of Everyday Things too emotional design where he has an 961 00:53:55,500 --> 00:53:58,300 ordering point of view. Aesthetics has its place. 962 00:53:58,700 --> 00:54:00,300 So I've gone to that kind of a journeyman. 963 00:54:00,300 --> 00:54:02,800 So short answer pie, charts. ER, don't use it for anything 964 00:54:02,800 --> 00:54:06,800 more than one variable, or one proportion and use a matrix of 965 00:54:06,800 --> 00:54:10,800 features if you can, but for those cases, yeah, it works. 966 00:54:11,100 --> 00:54:14,600 Oh, wait, I think we're going to be my last question to you. 967 00:54:14,600 --> 00:54:17,600 So, while talking about pie, charts you mentioned update. 968 00:54:17,700 --> 00:54:22,800 And and data density. Can you quickly in maybe five 969 00:54:22,800 --> 00:54:25,300 minutes or so? Like I think most of our 970 00:54:25,300 --> 00:54:28,500 listeners would not have Really, unless the real Vision, they are 971 00:54:28,500 --> 00:54:30,400 real visualization weeks. They would have come after 972 00:54:30,400 --> 00:54:33,200 stuffing. So, can we just talk about this? 973 00:54:33,700 --> 00:54:37,900 Perfect. So, Edward tufte is the god of 974 00:54:37,900 --> 00:54:41,400 father, of modern data visualization, his book. 975 00:54:41,400 --> 00:54:44,000 The visual display of quantitative information is 976 00:54:44,200 --> 00:54:48,200 still the seminal work in the field and was created at the 977 00:54:48,200 --> 00:54:52,300 time when even charts, we're fairly popular. 978 00:54:52,300 --> 00:54:55,300 Let alone data visualization which goes beyond that. 979 00:54:55,500 --> 00:55:00,400 Pretty much all of this. Books are a visual Delight day. 980 00:55:00,400 --> 00:55:02,700 Share some fairly deep with circles. 981 00:55:03,800 --> 00:55:06,000 These are principles that we have. 982 00:55:06,000 --> 00:55:09,000 I believe absorbed and gone beyond the sense. 983 00:55:09,000 --> 00:55:12,100 We know where they work, where they don't work, but it's kind 984 00:55:12,100 --> 00:55:14,500 of like Newton's Laws. Like you don't study quantum 985 00:55:14,500 --> 00:55:17,600 mechanics before you figured out that Newton's Laws, really 986 00:55:17,600 --> 00:55:21,300 absorb them, really applied them and of these principles lay out 987 00:55:21,300 --> 00:55:23,300 the equivalent for data visualization. 988 00:55:23,900 --> 00:55:30,500 One of which is data density. One way he puts it is Data to 989 00:55:30,500 --> 00:55:33,200 Ink ratio. Yeah, whiskey use as little ink 990 00:55:33,300 --> 00:55:40,400 as possible to show something and this is far better seen than 991 00:55:40,400 --> 00:55:43,800 explain, but I'll try and do what I can to explain it. 992 00:55:43,800 --> 00:55:53,800 Supposing you had the sales of beer, shown as a bar chart with 993 00:55:53,800 --> 00:55:58,000 beer bottles on top. It looks nice, but the beer 994 00:55:58,000 --> 00:56:00,400 bottles are not adding anything to the information. 995 00:56:00,400 --> 00:56:03,100 Content knocked him off. Yeah, keep applying that 996 00:56:03,100 --> 00:56:05,800 principle knock off. Whatever you can knock off 997 00:56:06,000 --> 00:56:11,300 without making it unclear. And you may find that in fact 998 00:56:11,300 --> 00:56:13,700 knocking stuff, off makes it clearer. 999 00:56:13,800 --> 00:56:16,900 So use just like, they say it less is more. 1000 00:56:16,900 --> 00:56:19,100 Use the fewest words to convey a point. 1001 00:56:19,400 --> 00:56:21,400 Edward tufte is data, density principles. 1002 00:56:21,400 --> 00:56:25,000 Use the fuel, the least amount of ink to convey a piece of 1003 00:56:25,000 --> 00:56:48,800 data. Thank you for listening to data 1004 00:56:48,800 --> 00:56:51,600 shatter. If you like this show, please 1005 00:56:51,600 --> 00:56:54,600 leave a comment, share and subscribe to the podcast. 1006 00:56:55,000 --> 00:56:59,200 You can find this podcast on. The podcast Spotify or wherever 1007 00:56:59,200 --> 00:57:01,200 else, you go to get your podcasts. 1008 00:57:01,900 --> 00:57:04,200 Once again, this is Karthik signing off. 1009 00:57:04,500 --> 00:57:05,000 Thank you.