1 00:00:00,200 --> 00:00:04,400 Retail podcast a global conversation hosted by Alex 2 00:00:04,400 --> 00:00:07,000 rezze Van Veen. Hello and welcome to the retail 3 00:00:07,000 --> 00:00:09,400 podcast. Today we're joined by Robin 4 00:00:09,400 --> 00:00:12,100 satara. She filled CTO at data bricks 5 00:00:12,400 --> 00:00:15,000 and for full disclosure, I've had the pleasure of working with 6 00:00:15,000 --> 00:00:17,000 Robin and yes, I think she's thoughts. 7 00:00:17,400 --> 00:00:20,800 Why do I think she's awesome because in my humble opinion to 8 00:00:20,800 --> 00:00:24,100 create better working environment better, technology 9 00:00:24,100 --> 00:00:28,500 outcomes, better data sets that are not subject to conscious and 10 00:00:28,500 --> 00:00:31,800 unconscious bias. You need diversity, and Robin 11 00:00:31,800 --> 00:00:34,200 has been a champion of diversity, not only for the 12 00:00:34,200 --> 00:00:38,100 customers that she served, but for the companies that she works 13 00:00:38,100 --> 00:00:42,700 on and hence her immensely successful career in becoming 14 00:00:42,700 --> 00:00:49,000 top 20, women in data and Tech in 2023, 23 data IQ top women 15 00:00:49,300 --> 00:00:52,300 100. And I'm sure a whole raft of 16 00:00:52,300 --> 00:00:56,700 other accolades that Robin has. So, without any further Ado, let 17 00:00:56,700 --> 00:00:59,600 me introduce you to Robin. So Robin. 18 00:00:59,600 --> 00:01:01,000 Good morning. How are you doing? 19 00:01:01,400 --> 00:01:04,000 Good morning, Alex, how are you? Yeah, very well. 20 00:01:04,000 --> 00:01:06,100 Thank you. So, where in the world are you 21 00:01:06,900 --> 00:01:08,900 today? I happen to be in Oslo. 22 00:01:08,900 --> 00:01:12,600 So despite the American accent, I feel like I have been all over 23 00:01:12,600 --> 00:01:14,200 the world in the last few months. 24 00:01:14,200 --> 00:01:16,800 But yeah, my lovely hotel room here in Norway. 25 00:01:17,200 --> 00:01:20,300 Well, thank you for taking the time out before you start your 26 00:01:20,500 --> 00:01:22,700 busy schedule. One of the things we're going to 27 00:01:22,700 --> 00:01:26,400 do a little bit differently, normally I dive in and start 28 00:01:26,400 --> 00:01:28,400 talking about, you know, data brick. 29 00:01:28,800 --> 00:01:31,800 What are they to fix about? What's your day job habla? 30 00:01:32,300 --> 00:01:35,000 What I love to do because this is all about the part of the 31 00:01:35,000 --> 00:01:39,900 series of Brilliant Minds, in terms of diverse thinkers, 32 00:01:39,900 --> 00:01:45,900 people who are glazing, or leaving a trail of of goodness 33 00:01:45,900 --> 00:01:49,800 behind them and empowering people as they go along. 34 00:01:50,100 --> 00:01:52,600 And so I'd like to do is just take a couple of minutes and 35 00:01:52,900 --> 00:01:56,500 take a deep dive into you as a person before we get to the 36 00:01:56,500 --> 00:01:58,300 corporate stuff is that. Okay, Dad. 37 00:01:58,300 --> 00:01:59,600 So Sounds great. Thank you. 38 00:01:59,900 --> 00:02:02,600 So one of the most amazing things when I was doing my 39 00:02:02,600 --> 00:02:05,500 little research, is the fact that you are an Apache 40 00:02:05,500 --> 00:02:08,699 helicopter, electric one at Armament repairer. 41 00:02:09,199 --> 00:02:13,200 So early in your career, you're obviously come from a mechanical 42 00:02:13,200 --> 00:02:17,500 engineering background. I'm just curious to anyone who's 43 00:02:17,500 --> 00:02:20,500 listening, who is at the start of their career? 44 00:02:21,100 --> 00:02:22,900 What advice would you offer them? 45 00:02:22,900 --> 00:02:26,300 Or what advice would you offer the Robin at that point in her 46 00:02:26,300 --> 00:02:29,900 life when she was sort of just At the beginning of the, of the 47 00:02:29,900 --> 00:02:34,100 ladder of starting yoga Korea. Yeah, I always have to admit, I 48 00:02:34,100 --> 00:02:38,600 tell people like, I started in the mud right in the Korean, DMZ 49 00:02:38,800 --> 00:02:43,700 repairing, Apache helicopters and hellfire missiles and 50 mm 50 00:02:43,700 --> 00:02:47,400 machine guns. And if I look if I look back you 51 00:02:47,400 --> 00:02:50,500 know, that that I won't tell you how many decades ago that was. 52 00:02:50,500 --> 00:02:55,000 But you know, if I look back and sort of say, do I think I would 53 00:02:55,000 --> 00:02:58,700 have been where I am today. There's no way like you Just 54 00:02:58,700 --> 00:03:02,300 don't sit and sort of those positions and sort of say, oh, 55 00:03:02,300 --> 00:03:05,200 I'm gonna end up being, you know, a chief data officer or 56 00:03:05,200 --> 00:03:07,900 chief technology officer at one of the, you know, some of the 57 00:03:07,900 --> 00:03:10,300 largest technology companies in the world. 58 00:03:10,800 --> 00:03:14,900 And so I think if I if I had to look back and sort of say, what 59 00:03:14,900 --> 00:03:18,200 were the things that I wish I had known earlier in my career? 60 00:03:18,200 --> 00:03:22,300 I think they're sort of three lessons from my perspective. 61 00:03:22,300 --> 00:03:25,600 The first one is be confident in yourself. 62 00:03:25,600 --> 00:03:28,100 I've always sort of been in these male-dominated. 63 00:03:28,600 --> 00:03:32,000 Trees are. And so typically own being the 64 00:03:32,000 --> 00:03:35,300 only female in the room are the only female on the flight line 65 00:03:35,300 --> 00:03:38,200 or the only female at the executive board table. 66 00:03:39,300 --> 00:03:42,700 I often, I even struggle with this, maybe sometimes today is, 67 00:03:42,700 --> 00:03:48,100 I often observe and listen, and I'm not as vocal, or is verbal 68 00:03:48,100 --> 00:03:52,000 about sharing my opinion or bringing sort of a different 69 00:03:52,000 --> 00:03:55,500 perspective to board. And so, I wish I had been a 70 00:03:55,500 --> 00:04:00,300 little more, you know. Act and assertive earlier in my 71 00:04:00,300 --> 00:04:05,100 career too short of the share that that perspective because it 72 00:04:05,100 --> 00:04:07,400 is very different than everybody else in the room. 73 00:04:07,400 --> 00:04:12,600 And so when you get those varying sort of points of view 74 00:04:12,600 --> 00:04:16,300 and those varying experiences that's when organizations and 75 00:04:16,300 --> 00:04:20,700 teams and companies do their most Innovative and amazing 76 00:04:20,700 --> 00:04:22,600 work. And so I wish I had had more 77 00:04:22,600 --> 00:04:24,000 confidence in myself at the beginning. 78 00:04:24,000 --> 00:04:27,800 Think the second part I wish I are the second piece of advice 79 00:04:27,800 --> 00:04:31,300 that I I wish somebody had given me was start early with your 80 00:04:31,300 --> 00:04:35,300 network and your building out sort of your ecosystem. 81 00:04:35,500 --> 00:04:37,900 And I've talked about this before, but, you know, building 82 00:04:37,900 --> 00:04:41,400 up that that Network that it consists of, you know, people 83 00:04:41,400 --> 00:04:44,600 that are your cheerleaders, that are people that give great, sort 84 00:04:44,600 --> 00:04:47,000 of feedback, like you just did in the introduction, which is 85 00:04:47,000 --> 00:04:50,700 always so humbling people that are always going to sort of 86 00:04:51,100 --> 00:04:54,100 encourage you to continue the work that you do because it is 87 00:04:54,100 --> 00:04:55,800 lonely. Sometimes I think trying to 88 00:04:55,800 --> 00:04:58,900 drive some of those messaging but make sure you Also have the 89 00:04:58,900 --> 00:05:01,200 people that are going to challenge you, that are going to 90 00:05:01,200 --> 00:05:03,700 sort of push, you outside of your comfort zone. 91 00:05:04,100 --> 00:05:07,900 And then make sure you have, you know, sponsorship because I 92 00:05:07,900 --> 00:05:12,000 think as I look at many of the people that have been very, very 93 00:05:12,000 --> 00:05:15,400 successful in their career, you can look back and see where they 94 00:05:15,400 --> 00:05:18,500 created somebody that was advocating on their behalf when 95 00:05:18,500 --> 00:05:20,900 they weren't in the room. And I didn't create those 96 00:05:20,900 --> 00:05:24,600 relationships until probably the last decade or so. 97 00:05:24,600 --> 00:05:28,400 So if you look at my career, I stayed relatively stagnant. 98 00:05:28,800 --> 00:05:32,000 First per personal reasons, but the other part of that was 99 00:05:32,000 --> 00:05:36,600 really I didn't focus enough on building that ecosystem around 100 00:05:36,600 --> 00:05:38,700 me that was going to help me be successful. 101 00:05:39,100 --> 00:05:41,500 And then I think the third piece of advice would be grand your 102 00:05:41,500 --> 00:05:45,400 self in your superpower, right? Like I remember interviewing for 103 00:05:45,400 --> 00:05:49,600 my purse sea level job and sort of telling the, the hiring 104 00:05:49,900 --> 00:05:55,100 executive I do my superpower is creating structure out of chaos. 105 00:05:55,300 --> 00:05:58,100 And once I could really articulated, I think in a simple 106 00:05:58,100 --> 00:06:01,400 way As opposed to just articulating, a bunch of things 107 00:06:01,400 --> 00:06:04,800 that I had accomplished or business outcomes or kpis or 108 00:06:04,800 --> 00:06:07,600 metric. But once I could really distill 109 00:06:07,600 --> 00:06:10,900 the unique value proposition that I could bring to a role. 110 00:06:11,300 --> 00:06:15,300 I've really found opportunities on lock from that perspective. 111 00:06:15,800 --> 00:06:20,700 It just to Echo to two parts, the other guests are the CEO 112 00:06:21,600 --> 00:06:24,400 from list. She she mentioned as well. 113 00:06:24,700 --> 00:06:28,300 The voice in the room is such an important thing for her. 114 00:06:28,600 --> 00:06:32,900 As a man, you should always have no one's perfect. 115 00:06:32,900 --> 00:06:35,300 So, everyone's got their internal dialogues, right? 116 00:06:35,400 --> 00:06:38,200 But the fact that you both have mentioned, that makes me feel 117 00:06:38,200 --> 00:06:41,900 that there is a some aspect there in terms of, I don't know, 118 00:06:41,900 --> 00:06:46,400 having seeking permission to talk or just questioning what 119 00:06:46,400 --> 00:06:49,600 you're going to say, especially in a male-dominated environment 120 00:06:49,600 --> 00:06:53,800 and her the thing that resonated me with me there is just you 121 00:06:53,800 --> 00:06:58,300 know, forget that don't don't limit Yourself by that type of 122 00:06:58,400 --> 00:07:01,800 Oh, I shouldn't say that all. And I said this to a women, 123 00:07:01,800 --> 00:07:05,000 don't say that type of thing or women don't speak whatever that 124 00:07:05,000 --> 00:07:07,900 stereotype is, is to challenge it. 125 00:07:08,300 --> 00:07:10,700 So, there's two things that I see in. 126 00:07:10,800 --> 00:07:13,700 In terms of obviously, again, having worked with you. 127 00:07:13,700 --> 00:07:17,600 I know that you're a magnet in towards people, wanting to do 128 00:07:17,600 --> 00:07:20,000 good work with you. So obviously you must that 129 00:07:20,000 --> 00:07:23,900 structure out of chaos. There is some secret Source 130 00:07:23,900 --> 00:07:26,300 there that people drift towards that. 131 00:07:26,400 --> 00:07:28,900 But the two points I just like to sort of get your A thoughts 132 00:07:28,900 --> 00:07:33,500 on how do you manage priorities because obviously, you sort of 133 00:07:34,000 --> 00:07:36,500 hinted towards you know, family, priorities. 134 00:07:36,500 --> 00:07:39,200 For example and how you prioritize that. 135 00:07:39,400 --> 00:07:44,000 And then if you do have any regrets or experiences that 136 00:07:44,000 --> 00:07:47,400 again, when looking back, you would wish someone would have 137 00:07:47,400 --> 00:07:50,800 said something to you about. You know, this prioritizing this 138 00:07:50,800 --> 00:07:53,800 way or, you know, I'm just curious on your thoughts on 139 00:07:53,800 --> 00:07:56,700 that. Yeah, I think I've shared this 140 00:07:56,700 --> 00:08:01,800 before I was actually in a domestic abuse sort of 141 00:08:01,800 --> 00:08:05,500 relationship for the first 10 years of my career and so much 142 00:08:05,500 --> 00:08:09,400 of what I pivoted on those first 10 years were my family, my 143 00:08:09,400 --> 00:08:13,100 children, my safety, there's lots of reasons. 144 00:08:13,100 --> 00:08:16,800 Why, because the question I get my ass the most often was a sure 145 00:08:16,800 --> 00:08:20,100 career seems to really have done well over the last decade where 146 00:08:20,100 --> 00:08:24,800 the first, right? We didn't even see See you or 147 00:08:24,800 --> 00:08:27,100 hear you, this was within the workplace, right? 148 00:08:27,100 --> 00:08:29,600 Because obviously we were at Microsoft at the time. 149 00:08:30,700 --> 00:08:34,200 So I always sort of tell people just be, you know, be cognizant 150 00:08:34,200 --> 00:08:37,400 of your teams and the people you're working with and people 151 00:08:37,400 --> 00:08:39,299 that you're trying to collaborate with you, just have 152 00:08:39,299 --> 00:08:42,700 no idea what's going on necessarily behind closed doors, 153 00:08:42,799 --> 00:08:45,200 being the patent Dynamic, sort of opened that up a little more 154 00:08:45,200 --> 00:08:49,900 that it was acceptable to have these other influences in these 155 00:08:49,900 --> 00:08:52,000 other things vying. For your time. 156 00:08:52,400 --> 00:08:56,300 For me it was about being really Shirred, which sometimes I think 157 00:08:56,300 --> 00:08:58,900 drives my family crazy, but I am. 158 00:08:58,900 --> 00:09:01,800 I'm a planner. I plan everything and sort of 159 00:09:01,800 --> 00:09:03,600 dress. I you know, dropped it out. 160 00:09:03,700 --> 00:09:07,500 And so for me, it's being very, very hyper critical about where 161 00:09:07,500 --> 00:09:10,900 I invest time. How do I make sure that I 162 00:09:10,900 --> 00:09:14,100 balance it and giving myself permission to say know that I, 163 00:09:14,100 --> 00:09:17,700 you know, you just don't have the bandwidth to deliver 164 00:09:17,700 --> 00:09:20,400 something the way that, you know, that it either should be 165 00:09:20,400 --> 00:09:22,900 delivered or that you want to, you know, be able to sort of 166 00:09:22,900 --> 00:09:27,000 deliver against it. And so it is okay to say, you 167 00:09:27,000 --> 00:09:30,600 know, no or no not now and and sort of giving yourself 168 00:09:30,600 --> 00:09:35,200 permission to balance and and shift those priorities depending 169 00:09:35,200 --> 00:09:38,100 on the internal or external influences. 170 00:09:38,100 --> 00:09:41,100 That may be guiding those things, you haven't mentioned 171 00:09:41,100 --> 00:09:42,400 this. But again, having worked with 172 00:09:42,400 --> 00:09:46,500 your, I see, one of the, the areas and getting the what 173 00:09:46,500 --> 00:09:49,800 you've just said, empathy and Leaders with empathy, that 174 00:09:49,800 --> 00:09:53,400 ability to understand, you know, it's obviously again, something 175 00:09:53,400 --> 00:09:55,800 that comes across. When you're talking to not only 176 00:09:55,800 --> 00:09:59,900 clients but t or teams and it's great. 177 00:10:00,300 --> 00:10:04,800 I am so torn because I can spend another half an hour, just 178 00:10:04,800 --> 00:10:06,800 unpacking all of that. Because I just think there's so 179 00:10:06,800 --> 00:10:09,300 much so many lessons that we don't talk about in the 180 00:10:09,300 --> 00:10:12,300 corporate world, that will help the future Robbins. 181 00:10:12,300 --> 00:10:15,500 But I am conscious. You're in a hotel about to start 182 00:10:15,500 --> 00:10:18,300 your day. So why don't we shift focus and 183 00:10:18,300 --> 00:10:20,900 look at data bricks and your day-to-day job and what you're 184 00:10:20,908 --> 00:10:23,100 doing. So tell us what is data bricks 185 00:10:23,100 --> 00:10:25,800 all about? Yeah, so data rix's. 186 00:10:25,800 --> 00:10:29,100 I think it just an amazing sort of innovative company they've 187 00:10:29,300 --> 00:10:33,600 been around for about 10 years created by the founders of spark 188 00:10:34,000 --> 00:10:38,000 out of UC Berkeley. So an amazingly, brilliant group 189 00:10:38,000 --> 00:10:40,300 of Founders. I think as we really thought 190 00:10:40,300 --> 00:10:45,400 about how do we unlock the power of Big Data as it comes across 191 00:10:45,400 --> 00:10:47,900 organizations? And so, data breaks were 192 00:10:47,900 --> 00:10:51,600 actually the creators of the lake house Paradigm which is now 193 00:10:51,600 --> 00:10:54,400 recognized by Gardner and essentially what that means. 194 00:10:54,408 --> 00:10:58,900 In says it's a way for us to tie together all those data silos 195 00:10:58,900 --> 00:11:00,400 that organizations struggle with. 196 00:11:00,400 --> 00:11:04,300 So if you think about traditional data warehouses that 197 00:11:04,300 --> 00:11:08,000 were typically your business insights and analytics platforms 198 00:11:08,000 --> 00:11:13,500 versus data Lakes, which were used for your more data science 199 00:11:13,500 --> 00:11:16,700 and machine learning as sort of workloads and capabilities. 200 00:11:16,700 --> 00:11:20,800 They are they implications of the lake house or that they tie 201 00:11:20,800 --> 00:11:23,400 those things together so that you can have a single platform 202 00:11:23,400 --> 00:11:28,800 in a single set of Of tools to be able to drive insights and 203 00:11:28,800 --> 00:11:31,800 data science out of the same data assets. 204 00:11:31,800 --> 00:11:34,700 Whether it's structured data or unstructured data, or 205 00:11:34,700 --> 00:11:38,000 semi-structured data, you want to be able to move your 206 00:11:38,000 --> 00:11:40,200 organization from backward-looking and sort of 207 00:11:40,200 --> 00:11:42,600 reporting and dashboards that we're all familiar with. 208 00:11:42,600 --> 00:11:46,400 When we think about data to how do we now unlock the power of 209 00:11:46,400 --> 00:11:49,600 things like generative AI, which we're all talking about over the 210 00:11:49,600 --> 00:11:53,000 last several months. And how do you do that against 211 00:11:53,000 --> 00:11:56,000 all of the data assets that Exist within your organization. 212 00:11:56,000 --> 00:11:59,700 So it's an amazing platform that helps organizations tie all of 213 00:11:59,700 --> 00:12:04,100 that together with one tool the so that your data Engineers, 214 00:12:04,100 --> 00:12:07,600 your data, scientists, your data analyst, you know, you can sort 215 00:12:07,600 --> 00:12:10,600 of spread their capabilities with the limited data talents 216 00:12:10,600 --> 00:12:12,400 that exist in the ecosystem today. 217 00:12:12,400 --> 00:12:17,400 So, super exciting times, particularly with this quick 218 00:12:17,400 --> 00:12:20,300 evolution of large language models that we all see between 219 00:12:20,300 --> 00:12:24,200 chat gbt you know Dolly has come out to show an example of how 220 00:12:24,200 --> 00:12:27,700 you can can do it without large datasets and relatively cheaply. 221 00:12:27,700 --> 00:12:31,200 So yeah, some great conversations that I'm having in 222 00:12:31,200 --> 00:12:37,000 my role which as one of the four feet field ctOS globally, I have 223 00:12:37,000 --> 00:12:39,500 a great opportunity to talk to hundreds and hundreds of 224 00:12:39,508 --> 00:12:42,700 customers on sort of, what are they doing with their data? 225 00:12:43,000 --> 00:12:46,200 How are they thinking about the evolution and the revolution 226 00:12:46,200 --> 00:12:50,700 that generative AI presents as they think about unlocking more 227 00:12:50,700 --> 00:12:53,900 of those citizen? You know, citizen experiences 228 00:12:53,900 --> 00:12:57,200 across the Going to station where you and I both know having 229 00:12:57,200 --> 00:12:59,600 had experience. That's where the real Innovation 230 00:12:59,600 --> 00:13:02,100 comes from is those that sit within the business. 231 00:13:02,400 --> 00:13:04,800 And so yeah, super exciting times. 232 00:13:04,800 --> 00:13:09,100 I think, as we think about that, how do we leverage this amazing 233 00:13:09,100 --> 00:13:13,100 technology to unlock the power of our of our talent within our 234 00:13:13,100 --> 00:13:16,900 companies? One of the things that that I 235 00:13:17,000 --> 00:13:19,800 that I saw that you talked about, and I mentioned earlier, 236 00:13:20,000 --> 00:13:24,000 it's about when companies are trying to get data sets to be 237 00:13:24,000 --> 00:13:27,800 sort of of unbiased and, you know, in their Engineers that 238 00:13:27,800 --> 00:13:31,400 are they don't end up in this sort of echo chamber of people 239 00:13:31,400 --> 00:13:34,900 thinking the same just out of interest from all the customers 240 00:13:34,900 --> 00:13:39,500 that you've seen apart from recruiting diverse engineers 241 00:13:39,800 --> 00:13:42,900 and, you know, trying to build it from the ground up, how do 242 00:13:42,900 --> 00:13:45,200 you is there any way to avoid that? 243 00:13:45,200 --> 00:13:47,400 Because that, you know, I've seen horrible examples of when 244 00:13:47,400 --> 00:13:50,700 it goes wrong, but I've not seen it in the Enterprise well, 245 00:13:50,700 --> 00:13:52,300 because I guess, no one wants that. 246 00:13:52,700 --> 00:13:56,400 Are that in that way? But we've seen, you know, we've 247 00:13:56,400 --> 00:13:59,700 seen many examples of when the bias is built in and you end up 248 00:13:59,700 --> 00:14:04,600 getting really horrible outcomes, just curious, How do 249 00:14:04,600 --> 00:14:06,800 you avoid that? Is there a way to avoid that? 250 00:14:07,100 --> 00:14:10,400 Yeah, interestingly I saw I was at Garden or the gardener London 251 00:14:10,400 --> 00:14:13,000 event earlier this week and we were having this exact 252 00:14:13,000 --> 00:14:17,100 conversation about you know, an inability to correct historical 253 00:14:17,100 --> 00:14:20,800 data sets necessarily. So I don't know if anybody's 254 00:14:20,800 --> 00:14:23,300 ever read that book. Invisible women by Caroline 255 00:14:23,300 --> 00:14:27,400 criado Perez but it's a great example of how we just aren't 256 00:14:27,400 --> 00:14:29,300 even aware. So if you think about things 257 00:14:29,300 --> 00:14:32,900 like medical testing or typically done on the, you know, 258 00:14:32,900 --> 00:14:37,300 a standard nerd 30 year old male that weighs 160 pounds. 259 00:14:37,500 --> 00:14:39,200 Yeah. And and so now how do you 260 00:14:39,200 --> 00:14:41,900 translate that into women? So it's not like you can go back 261 00:14:41,900 --> 00:14:44,300 and hurt. You know fabricate? 262 00:14:44,300 --> 00:14:47,500 50 years of medical data that are going to accommodate for 263 00:14:47,500 --> 00:14:50,200 that or like the seat belt and a car, right? 264 00:14:50,200 --> 00:14:53,300 So for a woman like me, that's only five for the seat belt, 265 00:14:53,300 --> 00:14:54,900 cuts, into my neck. And why? 266 00:14:54,900 --> 00:14:58,500 Because the seat belt and an automobile was designed for the 267 00:14:58,500 --> 00:15:03,000 average male. And so, I think for me, it 268 00:15:03,000 --> 00:15:06,300 really Not necessarily about going back and correcting, all 269 00:15:06,300 --> 00:15:08,300 of these data sets. But how do we make sure that the 270 00:15:08,300 --> 00:15:11,400 creation of net new data sides? Because if you think about the 271 00:15:11,400 --> 00:15:15,800 rate of data creation, right? The veracity and velocity, that 272 00:15:15,800 --> 00:15:19,100 data is being created. Today, we have an opportunity to 273 00:15:19,100 --> 00:15:20,800 influence. What is the data that were 274 00:15:20,800 --> 00:15:23,400 collecting? And are we, are we trying to 275 00:15:23,400 --> 00:15:26,600 minimize and mitigate the biases, you know, in the 276 00:15:26,600 --> 00:15:29,600 construct of how we're collecting data going forward, 277 00:15:29,900 --> 00:15:32,700 but the other thing absolutely does come down to. 278 00:15:32,700 --> 00:15:36,800 Do you have If a diverse set of perspectives across the table 279 00:15:36,800 --> 00:15:39,500 and if it isn't within your data team because you can't find the 280 00:15:39,500 --> 00:15:43,300 talent, which we could have a whole, I think our conversation 281 00:15:43,300 --> 00:15:46,500 on, how do you mitigate that? I think, you know, making sure 282 00:15:46,500 --> 00:15:50,900 that you're opening feet for feedback from diverse 283 00:15:50,900 --> 00:15:56,100 perspectives and creating an inclusive environment for 284 00:15:56,100 --> 00:16:00,300 everybody to have a voice at the table to express their, you 285 00:16:00,300 --> 00:16:03,300 know, their lived experience, their cultural perspective. 286 00:16:03,500 --> 00:16:07,000 Their sexual preference. The, you know, sort of 287 00:16:08,400 --> 00:16:11,500 perspectives, that gender perspective, there's all these 288 00:16:11,500 --> 00:16:15,100 diverse perspectives. And so making sure that as 289 00:16:15,100 --> 00:16:20,600 you're creating these models and ecosystems and platforms, are 290 00:16:20,600 --> 00:16:23,100 you taking into account all of those different voices? 291 00:16:23,100 --> 00:16:26,000 And if it's not within your direct team this is where that 292 00:16:26,000 --> 00:16:27,700 collaboration comes in super helpful. 293 00:16:27,700 --> 00:16:29,400 Are you soliciting out of the business? 294 00:16:29,400 --> 00:16:31,900 Are you soliciting from your customers? 295 00:16:31,900 --> 00:16:35,400 Are you soliciting from across? Ross, your supply chain, all of 296 00:16:35,400 --> 00:16:39,300 these different ways that you could bring in all these 297 00:16:40,100 --> 00:16:43,100 opposing, sort of perspectives to really make sure that you're 298 00:16:43,100 --> 00:16:46,200 aware of in the historical data. What? 299 00:16:46,200 --> 00:16:48,300 Biases exist. How do you prevent that from 300 00:16:48,300 --> 00:16:50,800 going forward? And is there a way that you can 301 00:16:50,800 --> 00:16:54,900 mitigate the biases in your in your system today to make sure 302 00:16:54,900 --> 00:16:59,000 that you don't continue to propagate that historical biases 303 00:16:59,000 --> 00:17:03,200 that exist in the system's understood just moving on. 304 00:17:03,300 --> 00:17:07,099 Little bit in terms of obviously you must meet with so many 305 00:17:07,099 --> 00:17:10,200 Executives, you know around the world from from and different 306 00:17:10,200 --> 00:17:12,400 Industries. I know obviously from retail to 307 00:17:12,400 --> 00:17:17,099 finance to manufacturing what's on people's mind right now. 308 00:17:17,099 --> 00:17:21,500 And then I don't know if this is part of it but one of the things 309 00:17:21,500 --> 00:17:27,099 the that I read or heard this morning was that, how much code? 310 00:17:27,099 --> 00:17:32,300 Low code, no code, you know, 60 or 70% of future applications 311 00:17:32,300 --> 00:17:35,800 over the next couple of years. Years will be written by citizen 312 00:17:35,800 --> 00:17:38,400 developers, right. Which I used to think was, you 313 00:17:38,400 --> 00:17:42,600 know, a little bit of maybe marketing from from people who 314 00:17:42,600 --> 00:17:46,400 would sell that stuff. But now with the the tools 315 00:17:46,600 --> 00:17:50,000 rapidly enabling people to be able to do that. 316 00:17:50,400 --> 00:17:52,400 And it so I don't know if that's one of the things but I'm just 317 00:17:52,400 --> 00:17:54,700 curious. What have you heard us as the 318 00:17:54,700 --> 00:17:57,000 themes from the execs that your meeting? 319 00:17:57,600 --> 00:17:59,300 Yeah. So right now, the big buzz is 320 00:17:59,300 --> 00:18:02,600 around generative AI, right? And sort of how are we going to 321 00:18:02,600 --> 00:18:06,100 integrate that? Into our into our ecosystems. 322 00:18:06,100 --> 00:18:09,300 So, for example, at Gardner we gave a keynote that was really 323 00:18:09,300 --> 00:18:12,500 talking about, where do we see the revolution of AI, impacting 324 00:18:12,500 --> 00:18:15,300 Industries and organizations the most? 325 00:18:15,300 --> 00:18:18,200 And there's a there's a few sort of low-hanging fruit because 326 00:18:18,200 --> 00:18:20,800 everybody everybody sort of worried we don't want to repeat 327 00:18:20,800 --> 00:18:23,900 a Samsung we don't want to put our IP out on the internet, 328 00:18:24,400 --> 00:18:25,800 right? We want to make sure that we're 329 00:18:26,500 --> 00:18:29,600 protecting our proprietary information particularly because 330 00:18:29,600 --> 00:18:32,800 I have that you know that masters of Law and intellectual 331 00:18:32,800 --> 00:18:34,700 property. I'm getting a lot of questions 332 00:18:34,700 --> 00:18:37,800 about how do we leverage generative AI in a way that 333 00:18:37,800 --> 00:18:42,900 still protecting our assets? But in a way that we don't miss 334 00:18:42,900 --> 00:18:45,900 the opportunity to really revolutionize the way that we do 335 00:18:45,900 --> 00:18:48,700 business. So organizations today are 336 00:18:48,700 --> 00:18:51,300 really talking about what are the things that they can start 337 00:18:51,300 --> 00:18:54,200 to do with generative AI, which I think will ultimately replace 338 00:18:54,200 --> 00:18:56,400 low code and no code. If you start thinking about 339 00:18:56,400 --> 00:18:59,800 prompt natural language, sort of currying and prompt engineering, 340 00:19:00,200 --> 00:19:03,000 those things are going to make low code, no code almost 341 00:19:03,000 --> 00:19:05,700 obsolete. Elite because now you really are 342 00:19:05,700 --> 00:19:09,000 unlocking the power of your citizen capabilities. 343 00:19:09,000 --> 00:19:12,700 And so there's a few thing 11 is around that techniques that 344 00:19:12,700 --> 00:19:15,800 enable moment, right? Of organizations. 345 00:19:16,100 --> 00:19:19,500 So some of the conversations I've been having about, you 346 00:19:19,500 --> 00:19:22,600 know, how are you thinking about setting up sand boxes within 347 00:19:22,600 --> 00:19:25,600 your environment to test on? But the other thing is encourage 348 00:19:25,600 --> 00:19:30,400 your talent across the entire business to start doing things 349 00:19:30,400 --> 00:19:32,900 like prompt engineering. I think, for me, I've done it. 350 00:19:33,300 --> 00:19:36,400 Build out my weekly menu or give me a recommendation for a 351 00:19:36,400 --> 00:19:38,300 workout before. The next marathon that I'm 352 00:19:38,300 --> 00:19:40,600 training for, right? There's lots of things that you 353 00:19:40,600 --> 00:19:44,000 could do in your personal life. I leveraging this technology 354 00:19:44,000 --> 00:19:47,600 just to learn how to get better at prompt engineering and 355 00:19:47,600 --> 00:19:50,600 natural language query. And so I think that's going to 356 00:19:50,600 --> 00:19:55,700 unlock a whole next generation of talent that doesn't 357 00:19:55,700 --> 00:19:58,700 necessarily have to come from the python or the sequel, sort 358 00:19:58,700 --> 00:20:01,500 of backgrounds. And so encourage that in your 359 00:20:01,500 --> 00:20:04,700 organization, encourage your team, If you don't have an 360 00:20:04,700 --> 00:20:08,700 internal ecosystem setup yet to support it, think about doing 361 00:20:08,700 --> 00:20:11,300 the sandbox environments that are protecting your data and 362 00:20:11,300 --> 00:20:14,100 setting up the boundaries and the ethical sort of 363 00:20:14,100 --> 00:20:16,800 responsibility things that you want the organization to think 364 00:20:16,800 --> 00:20:20,300 about, but also encourage people to do it in their, in their real 365 00:20:20,300 --> 00:20:23,000 lives. Because those things, you know, 366 00:20:23,000 --> 00:20:26,400 have a lot of impact in just preparing people for this next 367 00:20:26,400 --> 00:20:30,600 wave of not having to have a hard coding background. 368 00:20:31,200 --> 00:20:34,100 The other thing is, sorry. We Carry On, Anna. 369 00:20:34,300 --> 00:20:36,000 Yeah. The other thing is really also 370 00:20:36,000 --> 00:20:38,300 focusing on what are those internal things? 371 00:20:38,300 --> 00:20:40,300 So, I think one of the big things organizations struggle 372 00:20:40,300 --> 00:20:42,500 with his Knowledge Management. We've all seen it where we've 373 00:20:42,500 --> 00:20:46,900 searched a billion share points or write a million confluent 374 00:20:46,900 --> 00:20:51,100 Pages trying to find things like HR, you know, the employee 375 00:20:51,100 --> 00:20:54,500 policy or how do I, you know, how do I take time off? 376 00:20:54,500 --> 00:20:57,200 How do I find my payroll? Those types of things. 377 00:20:57,200 --> 00:21:00,100 You can easily create within your organization minimal 378 00:21:00,100 --> 00:21:05,400 impact, other than I think it's Maximum Impact on the impact. 379 00:21:05,400 --> 00:21:07,700 It has on all of your employees to start seeing. 380 00:21:07,800 --> 00:21:12,600 Wow I can now query and are just a regular question, you know and 381 00:21:12,600 --> 00:21:15,400 it searches the entire internal ecosystem. 382 00:21:15,400 --> 00:21:17,400 So things all like your back office. 383 00:21:17,400 --> 00:21:21,800 So your HR your Finance, there's lots and lots of opportunity for 384 00:21:21,800 --> 00:21:23,800 low-hanging fruit for you to tackle. 385 00:21:23,800 --> 00:21:28,700 Those use cases to use generative AI in a controlled 386 00:21:28,900 --> 00:21:31,100 sort of way. And I do think we're also seeing 387 00:21:31,100 --> 00:21:34,900 lots of Mission in the engineering team. 388 00:21:34,900 --> 00:21:39,000 So how do we how do we really sort of tackle you know using 389 00:21:39,500 --> 00:21:42,900 the copilot types of scenarios? I think you and I both probably 390 00:21:42,900 --> 00:21:45,700 attended build this week or at least listen to the recordings 391 00:21:45,700 --> 00:21:49,200 later and all of the integration of those types of Technologies. 392 00:21:49,200 --> 00:21:53,400 I think we'll continue to see a real sort of propagation and 393 00:21:53,400 --> 00:21:57,300 increase of those capabilities and lots of Technology 394 00:21:57,300 --> 00:22:00,700 platforms, which then helped you, you know, really focus on 395 00:22:00,700 --> 00:22:04,300 the people and process site of of this Mission that we're see. 396 00:22:04,800 --> 00:22:08,600 So, one of the things I that I one of the questions that 397 00:22:08,600 --> 00:22:11,200 someone asked me once is a lie. Why you focus so much on 398 00:22:11,200 --> 00:22:13,300 diversity, right? You're a guy. 399 00:22:13,300 --> 00:22:16,800 Why are you trying to, you know, what, what's your angle, and 400 00:22:16,800 --> 00:22:21,000 what am I humble beliefs that, I genuinely believe as, when we 401 00:22:21,000 --> 00:22:24,000 get to parity in the workplace, it actually. 402 00:22:24,000 --> 00:22:27,300 And you mentioned this earlier on, it actually brings the 403 00:22:27,300 --> 00:22:31,300 goodness across all diversity, diverse elements, whether that's 404 00:22:31,300 --> 00:22:35,100 religion race. Gender whatever that is but you 405 00:22:35,100 --> 00:22:40,100 need to anchor on one and within the Enterprise world I find that 406 00:22:40,100 --> 00:22:43,700 sometimes it's very scattered gun in its approach to 407 00:22:43,700 --> 00:22:45,400 diversity. So you're trying to please 408 00:22:45,600 --> 00:22:47,500 everyone and end up pleasing. No one. 409 00:22:47,800 --> 00:22:50,300 And again, I'd be curious on your thoughts Robin in terms of 410 00:22:50,300 --> 00:22:54,500 when you look at all the experiences and leaders that you 411 00:22:54,500 --> 00:23:00,200 spoken to about diversity and creating a better environment. 412 00:23:00,700 --> 00:23:03,100 How do you do that? What are those areas? 413 00:23:03,600 --> 00:23:06,900 That organizations Enterprises need to think about. 414 00:23:07,400 --> 00:23:10,300 Yeah, I think the big one is, it's not just enough to have the 415 00:23:10,300 --> 00:23:12,900 representation around the table, you have to create an 416 00:23:12,900 --> 00:23:15,800 environment that's being inclusive and giving people a 417 00:23:15,800 --> 00:23:18,000 voice, right? So as we talked about really 418 00:23:18,000 --> 00:23:23,500 early in our conversation, how do you make sure that that we as 419 00:23:23,500 --> 00:23:27,500 sort of current leaders at the table are creating the right 420 00:23:27,500 --> 00:23:30,900 environment that people have the opportunity to speak up and give 421 00:23:30,900 --> 00:23:36,100 feedback a true collaboration? Doesn't necessarily mean that 422 00:23:36,400 --> 00:23:39,900 you just have the representation but you have to think about the 423 00:23:39,900 --> 00:23:44,700 inclusivity of creating the at that environment, where if a 424 00:23:44,700 --> 00:23:47,500 person doesn't feel comfortable speaking at the table, are you 425 00:23:47,600 --> 00:23:50,900 proactively going out and seeking their opinion in a 426 00:23:50,908 --> 00:23:53,600 different environment or in a different sort of way? 427 00:23:53,900 --> 00:23:56,600 What I found throughout the course of my career, 428 00:23:56,600 --> 00:23:58,900 particularly over the last decade or so? 429 00:23:59,200 --> 00:24:03,000 Is that not everybody receives or gives information the same 430 00:24:03,000 --> 00:24:06,200 way? And so the truly create equity 431 00:24:06,200 --> 00:24:11,000 and equality across the table. You really have to think about 432 00:24:11,100 --> 00:24:14,400 meeting people where they are creating the environments that 433 00:24:14,400 --> 00:24:17,800 is collaborative that gives them the way to give and receive an 434 00:24:17,800 --> 00:24:21,200 impact and influence and commentary in a way that's 435 00:24:21,200 --> 00:24:24,800 comfortable for them. And so whether you solicit 436 00:24:24,800 --> 00:24:29,000 outside of the big open Forum that, not everybody is 437 00:24:29,000 --> 00:24:31,600 comfortable in speaking. And how do you aggregate that 438 00:24:31,600 --> 00:24:34,500 information to feed it back into the Team to make sure that you 439 00:24:34,500 --> 00:24:39,600 have this sort of agile feedback loop that's creating that next 440 00:24:39,600 --> 00:24:42,100 evolution of collaboration across the organization. 441 00:24:42,400 --> 00:24:44,100 I think it everybody can do that. 442 00:24:44,100 --> 00:24:47,400 If you see somebody at the table who's not speaking, don't call 443 00:24:47,400 --> 00:24:49,300 them out on the spot. They may not feel comfortable 444 00:24:49,300 --> 00:24:52,500 but absolutely walk up to them after the meeting and have a 445 00:24:52,500 --> 00:24:55,800 one-on-one conversation there. Would they might be more 446 00:24:55,800 --> 00:24:58,400 comfortable and opening up and sharing their opinion. 447 00:24:58,600 --> 00:25:01,800 And I think that's something that all of us can do is really, 448 00:25:02,200 --> 00:25:07,000 you know, make sure that Creating a trusted sort of safe 449 00:25:07,000 --> 00:25:10,200 environment that everybody feels that they can share their 450 00:25:10,200 --> 00:25:12,200 feedback. Their experiences, their 451 00:25:12,200 --> 00:25:17,200 concerns, their ideas in a way that that truly unlocks sort of 452 00:25:17,200 --> 00:25:19,300 innovation across an organization. 453 00:25:19,700 --> 00:25:22,000 Yeah. Well, you need good leaders and 454 00:25:22,000 --> 00:25:24,600 I think that's, that's the, that's the, the challenge that 455 00:25:24,600 --> 00:25:28,200 because I do think sometimes, when you go a layer up in some 456 00:25:28,200 --> 00:25:32,100 companies, there is group think everyone does come from the same 457 00:25:32,100 --> 00:25:35,400 background, everyone. Is from the same gender or race 458 00:25:35,400 --> 00:25:37,700 class and so, therefore, it just, you know. 459 00:25:38,600 --> 00:25:42,000 Yeah, it's sometimes the across the masses, interestingly 460 00:25:42,000 --> 00:25:45,300 enough, I was listening to and from most people do listen to 461 00:25:45,300 --> 00:25:49,500 The Diary of a CEO and they were discussing burn out what burnout 462 00:25:49,500 --> 00:25:53,300 is and this concept that actually whether or not burnout 463 00:25:53,300 --> 00:25:57,600 is a real thing or not, but it's when employees or workers feel 464 00:25:57,800 --> 00:26:01,200 that the task that they've been given they have no control or 465 00:26:01,200 --> 00:26:06,700 power over is when Then filburn out, right as opposed to 466 00:26:06,700 --> 00:26:10,700 empowering them to do that and I think understanding them you 467 00:26:10,708 --> 00:26:15,400 know, them in their whole sense is really important. 468 00:26:15,500 --> 00:26:18,800 Okay. Looking in terms of closing and 469 00:26:18,800 --> 00:26:22,800 looking to the Future, where do you see the future? 470 00:26:22,800 --> 00:26:24,500 Obviously, I'm not going to ask you about data bricks in the 471 00:26:24,500 --> 00:26:26,200 IPO. Don't worry when we're going to 472 00:26:26,200 --> 00:26:29,700 break any rules, their SEC filings or anything, not there. 473 00:26:29,700 --> 00:26:34,000 There is an IPO on Hearts. But where do You see the future? 474 00:26:34,000 --> 00:26:38,100 What's what's on the horizon you mentioned generative, AI, you 475 00:26:38,108 --> 00:26:40,300 know, looking two or three years down the line? 476 00:26:41,300 --> 00:26:43,700 What are your thoughts? Oh, my goodness, two or three 477 00:26:43,700 --> 00:26:45,000 years? I think I would love to stick 478 00:26:45,100 --> 00:26:47,200 with that all the next three to six months. 479 00:26:47,200 --> 00:26:52,300 I think as far as fast this technology as a movie, I think 480 00:26:53,400 --> 00:26:55,700 generative AI is definitely going to be the one. 481 00:26:55,700 --> 00:26:59,500 So if you are in any role, I think you can definitely take 482 00:26:59,500 --> 00:27:01,700 advantage of. How do you learn that new skill, 483 00:27:01,700 --> 00:27:04,300 set, people always ask me. Many jobs, is this going to 484 00:27:04,300 --> 00:27:07,200 replace what roles are going to go away? 485 00:27:07,200 --> 00:27:09,400 How do I think is my role at risk? 486 00:27:09,700 --> 00:27:11,700 Right? I think I even saw a study from 487 00:27:11,700 --> 00:27:15,800 ey like what's the next evolution of consultative types 488 00:27:15,800 --> 00:27:18,400 of businesses, right? And where is that going to go? 489 00:27:18,700 --> 00:27:24,300 Now that chat GPT and similar models, can create a, you know, 490 00:27:24,300 --> 00:27:28,400 a data strategy with just a few well-worded prompts. 491 00:27:28,500 --> 00:27:31,900 How do we think about the evolution of functions and so 492 00:27:31,900 --> 00:27:36,400 everyone should be Talking about how do you learn the capability 493 00:27:36,400 --> 00:27:39,100 to do this natural language? Sort of queries? 494 00:27:39,100 --> 00:27:41,100 How do you think about prompt engineering? 495 00:27:41,300 --> 00:27:45,200 How do you integrate it you know into your into your current 496 00:27:45,200 --> 00:27:47,400 roles? That's not sharing IP, I'm not 497 00:27:47,400 --> 00:27:50,500 recommending that. Anybody put IP our proprietary 498 00:27:50,500 --> 00:27:54,000 information into those systems, but if you don't have the 499 00:27:54,000 --> 00:27:57,700 capability to do it for work, do it, do it in your personal life, 500 00:27:57,700 --> 00:28:01,200 learning a skill, learn that capability because that 501 00:28:01,200 --> 00:28:04,800 absolutely is the way. Have the world that we see over 502 00:28:04,800 --> 00:28:07,900 the next three to six months, it's going to impact. 503 00:28:07,900 --> 00:28:12,400 I think everybody's roles in some capacity and so making sure 504 00:28:12,400 --> 00:28:15,300 that you're practicing those things as much as possible. 505 00:28:16,000 --> 00:28:21,100 I know that technology isn't available to everyone and so, if 506 00:28:21,100 --> 00:28:24,900 you can't get access to the technology really think about 507 00:28:24,900 --> 00:28:27,400 other ways that you can create ecosystems. 508 00:28:27,400 --> 00:28:31,800 So, if you're a teacher, can you create, you know, engagements in 509 00:28:31,800 --> 00:28:34,700 the classroom that allow us? For that type of natural 510 00:28:34,700 --> 00:28:38,100 language sort of interactions with other students. 511 00:28:38,100 --> 00:28:41,300 Can you create sort of the pseudo environments because 512 00:28:41,300 --> 00:28:45,500 while it's not strict, we know that not everybody has access to 513 00:28:45,500 --> 00:28:48,800 the technology or capability. So let's really think about the 514 00:28:48,800 --> 00:28:51,500 Next Generation coming up. What are the things that we can 515 00:28:51,500 --> 00:28:53,900 do today? That's going to help them be 516 00:28:53,900 --> 00:28:55,600 prepared for the jobs of the future. 517 00:28:55,600 --> 00:28:59,300 And I think this is part of that part of that ecosystem, that we 518 00:28:59,300 --> 00:29:02,500 see going forward, don't want a wonderful way to end the 519 00:29:02,500 --> 00:29:05,000 podcast. Astro Robin, thank you so much. 520 00:29:05,000 --> 00:29:07,700 It's been an absolute pleasure. Thank you for your honesty, your 521 00:29:07,900 --> 00:29:10,400 your humility, and thank you for all the great work that you're 522 00:29:10,408 --> 00:29:14,600 doing in promoting, you know, a true inclusive and diverse 523 00:29:14,800 --> 00:29:17,000 environment within the fields of data and AI. 524 00:29:17,600 --> 00:29:20,000 Thank you so much, Alex, I appreciate the opportunity.