1 00:00:00,440 --> 00:00:03,080 I think the true way to add value to any company is to be 2 00:00:03,080 --> 00:00:04,400 proactive and is to be strategic. 3 00:00:05,080 --> 00:00:07,640 Data is only as valuable as the action that you can drive with 4 00:00:07,640 --> 00:00:11,280 it and the impact that you can get from that action. 5 00:00:19,760 --> 00:00:23,880 Welcome to Data Center and Karthik Co, Founder and CEO of 6 00:00:23,880 --> 00:00:26,800 Babbage Insight, where we are building our proactive insights 7 00:00:26,800 --> 00:00:29,240 engine. This season of data chatter is 8 00:00:29,240 --> 00:00:32,240 all about talking to industry leaders to find out how they use 9 00:00:32,240 --> 00:00:34,240 data in their day-to-day decision making. 10 00:00:34,800 --> 00:00:37,400 So to start off with, can you tell us a little bit about 11 00:00:38,160 --> 00:00:40,880 yourself and also what job that you do? 12 00:00:42,040 --> 00:00:43,760 Yeah, absolutely. I'm happy to be here and excited 13 00:00:43,760 --> 00:00:46,200 for this conversation. Karthik, my name is Aswan. 14 00:00:46,280 --> 00:00:51,400 I lead the FB and a team here at Quadrics FB and a, for those 15 00:00:51,400 --> 00:00:55,640 that might not be familiar, is a team of financial analysts and 16 00:00:55,640 --> 00:01:00,280 managers who manage the short, medium and long term financials 17 00:01:00,280 --> 00:01:02,760 of a company. My team is responsible for 18 00:01:03,000 --> 00:01:07,200 managing the expense envelopes, budgeting process on the cost 19 00:01:07,200 --> 00:01:08,600 side, but also on the top line side. 20 00:01:08,640 --> 00:01:12,640 So my team is always focused on data and is always looking for 21 00:01:12,640 --> 00:01:15,800 insights to help the company make better resource allocation 22 00:01:15,800 --> 00:01:18,960 decisions across the board. So yeah, excited to be here. 23 00:01:19,280 --> 00:01:23,040 And in terms of maybe a quick history, I've spent the last 24 00:01:23,040 --> 00:01:26,600 like 10 plus years in various finance roles across tech 25 00:01:26,600 --> 00:01:29,680 companies. And prior to that, I've spent 26 00:01:29,680 --> 00:01:31,080 time in the private wealth industry. 27 00:01:31,080 --> 00:01:34,680 So really close to data across the board through my career. 28 00:01:35,040 --> 00:01:37,200 Thank you. So I assume that being a finance 29 00:01:37,200 --> 00:01:39,960 person you, my assumption is that you'll be using a lot of 30 00:01:39,960 --> 00:01:43,800 Excel, but can you talk through how you get the data, what kind 31 00:01:43,800 --> 00:01:48,120 of data you look at, what kind of analysis you do and at what 32 00:01:48,200 --> 00:01:51,520 frequency and so on as part of your day-to-day job? 33 00:01:52,440 --> 00:01:57,520 Yeah, I work with a team of about 35 ish analyst and 34 00:01:57,520 --> 00:02:02,120 managers and at any given point that are probably 10s of 35 00:02:02,120 --> 00:02:06,160 analysis that are ongoing within my team that ultimately helps 36 00:02:06,160 --> 00:02:08,280 the company make better decisions. 37 00:02:08,600 --> 00:02:11,520 And the scope of the data that my team works with is pretty 38 00:02:11,520 --> 00:02:15,760 expensive. So it could start all the way at 39 00:02:15,760 --> 00:02:20,400 the at the very end of, if you can think of the value chain of 40 00:02:21,520 --> 00:02:26,920 revenue, it could start with cash, it could work backwards in 41 00:02:26,920 --> 00:02:29,400 terms of Billings. It could look at like revenue, 42 00:02:29,400 --> 00:02:33,040 it could be about annual recurring revenue or ARR. 43 00:02:33,400 --> 00:02:36,200 And then you could keep going further upstream in terms of 44 00:02:36,600 --> 00:02:39,840 that data. We could be looking at customer 45 00:02:39,840 --> 00:02:42,320 transactions, you could be looking at new customers like 46 00:02:42,320 --> 00:02:43,800 upsell. We could be looking at number of 47 00:02:43,800 --> 00:02:46,400 customers. And then even further upstream, 48 00:02:46,760 --> 00:02:50,920 if you think about in the tech industry, a lead to cash sort of 49 00:02:50,920 --> 00:02:54,960 value chain, like we go upstream into number of leads spend 50 00:02:54,960 --> 00:02:59,760 against marketing programs. So the data that my team works 51 00:02:59,760 --> 00:03:04,680 with is really expensive and it could be very varied depending 52 00:03:04,680 --> 00:03:06,400 on the time of the year, depending on the time of the 53 00:03:06,400 --> 00:03:08,800 month, depending on the specific member of my team. 54 00:03:09,720 --> 00:03:16,520 A few examples could be that the business is considering a 55 00:03:16,520 --> 00:03:19,600 potential short term sales incentive to accelerate a 56 00:03:19,600 --> 00:03:22,680 certain portion of the business be a product or region. 57 00:03:23,200 --> 00:03:26,840 So my team is working through what could be the impact of an 58 00:03:26,840 --> 00:03:30,200 incremental dollar in sales compensation that we spend on 59 00:03:30,200 --> 00:03:33,080 that product or region and what is the impact that we should be 60 00:03:33,080 --> 00:03:36,360 expecting and what would be the ROI of that incremental dollar 61 00:03:36,360 --> 00:03:38,000 that's spent. So this could be a very quick 62 00:03:38,000 --> 00:03:40,640 analysis that my team does. My team would also work on like 63 00:03:40,640 --> 00:03:44,200 very long term strategic analysis, like how much should 64 00:03:44,200 --> 00:03:49,240 we be committing to with some of our largest like cloud or server 65 00:03:49,240 --> 00:03:51,280 vendors in the next like three to five years? 66 00:03:51,280 --> 00:03:53,560 Because we want to look for long term contracts because we want 67 00:03:53,560 --> 00:03:56,920 better economics and discounts from our service providers. 68 00:03:57,120 --> 00:04:01,440 So this could be around sizing that and getting confidence 69 00:04:01,440 --> 00:04:05,200 around that. So My, my, yeah, a whole variety 70 00:04:05,200 --> 00:04:07,560 of different data problems and challenges on different time 71 00:04:07,560 --> 00:04:10,360 horizons working with different business partners from product 72 00:04:10,360 --> 00:04:14,400 and engineering or like go to market, sales, marketing or even 73 00:04:14,400 --> 00:04:17,680 like our GNA functions, thinking about allocation strategy, 74 00:04:17,680 --> 00:04:21,720 thinking about like benefits. So the challenges really depend 75 00:04:21,720 --> 00:04:25,040 on the the time of the year. Planning means different 76 00:04:25,040 --> 00:04:27,320 problems. Ongoing execution through the 77 00:04:27,320 --> 00:04:29,320 quarters means different kinds of problems that my team works 78 00:04:29,320 --> 00:04:31,040 on. Got it. 79 00:04:31,120 --> 00:04:33,720 And can you talk a little bit about your text stack in terms 80 00:04:33,720 --> 00:04:37,960 of like how you get the data, how you do the analysis of who 81 00:04:37,960 --> 00:04:41,400 are your stakeholders, to whom we have to present your numbers? 82 00:04:41,400 --> 00:04:43,720 How? Yeah, this takes through the 83 00:04:43,720 --> 00:04:45,120 process. Yeah. 84 00:04:45,480 --> 00:04:50,560 So our CRM system today is we use Salesforce and the data from 85 00:04:50,840 --> 00:04:55,440 Salesforce is dropped into a database solution, I believe 86 00:04:55,440 --> 00:04:59,400 it's Redshift. And from there, we have plenty 87 00:04:59,400 --> 00:05:01,760 of analysts across the team who work directly with sequel 88 00:05:01,760 --> 00:05:04,520 queries, pulling the right data from the databases. 89 00:05:05,120 --> 00:05:09,320 We also have Tableau, which is the data sort of visualization 90 00:05:09,320 --> 00:05:12,120 reporting layer that some of the analysts on the teams use. 91 00:05:12,400 --> 00:05:18,320 And yeah, it, I, I wish that there was more consistency. 92 00:05:18,320 --> 00:05:21,200 I wish there was more interconnectedness of the 93 00:05:21,200 --> 00:05:24,080 different data elements that are coming upstream from the 94 00:05:24,080 --> 00:05:25,960 different systems, which is one of the challenges that we 95 00:05:25,960 --> 00:05:30,280 struggled with today as a company on how spread out our 96 00:05:30,560 --> 00:05:34,120 data ecosystem is. And sometimes pulling together a 97 00:05:34,120 --> 00:05:37,360 simple analysis would require cobbling together different data 98 00:05:37,360 --> 00:05:38,800 sources to get the right insight. 99 00:05:39,560 --> 00:05:42,440 The other thing that I'm working through with my team within the 100 00:05:42,440 --> 00:05:45,800 company is democratizing data and having various analysts 101 00:05:45,800 --> 00:05:49,480 across the team get access to this data versus a specialized 102 00:05:49,480 --> 00:05:53,360 individualized data team being sort of the stewards of this 103 00:05:53,360 --> 00:05:56,240 data and supplying that data to various organizations. 104 00:05:56,240 --> 00:05:59,560 So democratizing data is going to be critical for at least 105 00:05:59,920 --> 00:06:02,480 where I sit today in terms of getting the most amount of 106 00:06:02,480 --> 00:06:05,000 impact. The stakeholders that we 107 00:06:05,000 --> 00:06:08,160 interact with is basically every business leader across the 108 00:06:08,160 --> 00:06:12,280 company who has goals that are financial either top line or 109 00:06:12,280 --> 00:06:14,480 bottom line driven that are either looking for how do they 110 00:06:14,480 --> 00:06:19,120 accelerate their business or how do they allocate their cost 111 00:06:19,120 --> 00:06:21,280 dollars in the most impactful areas. 112 00:06:21,280 --> 00:06:26,360 So that's any leader across the company who is wanting to push 113 00:06:26,360 --> 00:06:28,280 the envelope on top line or or bottom line. 114 00:06:28,280 --> 00:06:33,440 So this could be the chief security officer, this could be 115 00:06:33,640 --> 00:06:38,480 the chief like IT officer, it could be any like senior sales 116 00:06:38,480 --> 00:06:40,920 leader, It's definitely the chief operating officer, the 117 00:06:40,920 --> 00:06:43,320 chief people officer. So I have analysts on my team 118 00:06:43,320 --> 00:06:47,200 that will partner directly with each of these leaders in terms 119 00:06:47,200 --> 00:06:50,160 of understanding where their biggest pain points are, where 120 00:06:50,160 --> 00:06:51,760 their biggest opportunities and challenges are. 121 00:06:51,760 --> 00:06:54,440 So it's literally every single business leader across the 122 00:06:54,440 --> 00:06:57,960 company who works with an FPNA partner to make prudent 123 00:06:57,960 --> 00:06:59,360 financial decisions for the company. 124 00:07:00,560 --> 00:07:02,360 Got it. And the analysts of your team 125 00:07:02,360 --> 00:07:05,520 might imagine to all the data work themselves that do that. 126 00:07:06,200 --> 00:07:09,200 Basically you have in house the part of the data and seeing that 127 00:07:09,520 --> 00:07:12,880 you require for your job I assume. 128 00:07:14,360 --> 00:07:19,560 Yeah, they they do the, the analysts do the most of their 129 00:07:19,560 --> 00:07:23,120 data pulling the analysis, the insight generation and the 130 00:07:23,120 --> 00:07:26,480 partnership to go convert that insight into action so the 131 00:07:26,480 --> 00:07:29,200 company can see impact. All of that happens in house. 132 00:07:30,240 --> 00:07:33,800 As I was saying earlier, I wish the the tools and the 133 00:07:33,800 --> 00:07:36,560 interconnectedness of the data was a lot better. 134 00:07:36,800 --> 00:07:40,200 If it were, then the analysts would spend a lot less time 135 00:07:40,600 --> 00:07:45,000 pulling data, but be spending a lot more time on digesting the 136 00:07:45,000 --> 00:07:48,520 data into insight or even like using the insight to drive 137 00:07:48,520 --> 00:07:52,840 action for the company. Because ultimately data is only 138 00:07:53,080 --> 00:07:55,880 as valuable as the action and the impact that we can get out 139 00:07:55,880 --> 00:07:59,640 of it. So over time I hope for better 140 00:07:59,640 --> 00:08:05,920 systems and and data pipelines that helps the team move up the 141 00:08:05,920 --> 00:08:07,880 value chain into action and impact. 142 00:08:09,480 --> 00:08:12,440 Can you elaborate a little bit about this interconnectedness 143 00:08:12,440 --> 00:08:15,000 that you've been talking about? So, So what is the precise 144 00:08:15,000 --> 00:08:17,160 problem here? Is it that you have fragmented 145 00:08:17,160 --> 00:08:20,640 data sources or is it that, like you, the team struggles to sort 146 00:08:20,640 --> 00:08:22,360 of reconsign data across sources? 147 00:08:22,360 --> 00:08:26,240 Because I assume that as a finance team, you guys must be 148 00:08:26,240 --> 00:08:30,360 fairly sort of you want stuff to be accurate and things like that 149 00:08:30,360 --> 00:08:31,960 based on my experience in finance and so on. 150 00:08:31,960 --> 00:08:34,960 So how does it work? So what are the issues that you 151 00:08:34,960 --> 00:08:41,480 face right now? Yeah, Quadrics has been on a 152 00:08:41,480 --> 00:08:44,960 journey in the last like several years depending on how much you 153 00:08:44,960 --> 00:08:50,720 follow the news, which means that like going public, being 154 00:08:50,720 --> 00:08:52,640 bought over, then being taken private. 155 00:08:52,840 --> 00:08:55,360 The company's priorities from a data perspective have changed 156 00:08:55,720 --> 00:08:58,000 meaningfully over the years, which means that we have 157 00:08:58,000 --> 00:09:02,840 different systems, different implementations that does not 158 00:09:02,960 --> 00:09:07,840 set up for success. Cobbling together data from 159 00:09:07,840 --> 00:09:11,600 different sources or even the movement of data from one system 160 00:09:11,600 --> 00:09:15,120 to another, say a Salesforce, which is our transaction system, 161 00:09:15,120 --> 00:09:17,960 CRM system to next week, which is our general Ledger. 162 00:09:18,400 --> 00:09:21,040 There's a lot of information that either gets lost or is 163 00:09:21,040 --> 00:09:25,840 mistranslated that makes data less compatible across some of 164 00:09:25,840 --> 00:09:30,680 these systems. And the the unique identifiers 165 00:09:30,680 --> 00:09:34,280 that you'd want to use at a customer level or at like a 166 00:09:34,280 --> 00:09:36,000 contract level could also be different. 167 00:09:36,040 --> 00:09:40,160 That makes combining different pieces of data across the value 168 00:09:40,160 --> 00:09:43,880 chain very difficult. And that makes the insight that 169 00:09:43,880 --> 00:09:47,800 we can get from some of this data a little less intuitive. 170 00:09:47,800 --> 00:09:51,000 So it just takes a lot of effort to prepare the data from these 171 00:09:51,000 --> 00:09:56,240 different systems to then be able to gather or glean insights 172 00:09:56,240 --> 00:09:57,600 off of this data. So that's what I mean by 173 00:09:57,600 --> 00:10:00,680 interconnectedness of data. It's a consequence of different 174 00:10:00,680 --> 00:10:03,440 systems, different implementations that were either 175 00:10:03,440 --> 00:10:06,080 done perfectly right or not not at all. 176 00:10:06,240 --> 00:10:10,880 That makes data from the past less compatible and less 177 00:10:10,880 --> 00:10:14,080 immediately useful, without spending a lot of time first 178 00:10:14,080 --> 00:10:17,280 preparing that data first. Got it. 179 00:10:17,480 --> 00:10:21,320 And how, how does this compare to your previous organizations? 180 00:10:21,320 --> 00:10:24,280 Because I think you've worked with slightly sort of more tech 181 00:10:24,280 --> 00:10:28,160 companies in the past and stuff. So how does the data experience 182 00:10:28,680 --> 00:10:30,840 from a finance perspective compare? 183 00:10:31,960 --> 00:10:42,280 Yeah, I think it really depends on how Core Data is to accompany 184 00:10:42,640 --> 00:10:46,480 in how products are built and how systems are built and how 185 00:10:46,640 --> 00:10:50,400 the company chooses to scale. I I genuinely believe that if 186 00:10:50,400 --> 00:10:53,320 you look at a company's existing data stack and their 187 00:10:53,320 --> 00:10:57,560 implementations and how and the tools that analysts across the 188 00:10:57,560 --> 00:11:00,480 company used to get insights from that data, you'll be able 189 00:11:00,480 --> 00:11:05,000 to tell how much focus there was on data in that company's 190 00:11:05,000 --> 00:11:07,520 history. In my past, I have worked for 191 00:11:07,520 --> 00:11:12,320 companies that are really massive, that have invested like 192 00:11:12,320 --> 00:11:15,640 years, maybe even like over decades in cleaning up that data 193 00:11:15,640 --> 00:11:19,800 infrastructure and creating better end user tooling for 194 00:11:19,800 --> 00:11:24,240 analytics that makes using that data incredibly straightforward. 195 00:11:24,400 --> 00:11:28,000 But that's after like years and years and maybe a decade, over a 196 00:11:28,000 --> 00:11:29,760 decade of investment in that space. 197 00:11:30,000 --> 00:11:32,520 I've also worked for like similar mint to large capital 198 00:11:32,520 --> 00:11:39,760 companies that have been built with the ethos of data and using 199 00:11:39,760 --> 00:11:44,640 data to make better decisions that make a lot more data 200 00:11:44,640 --> 00:11:49,000 accessible to a lot more teams, a lot more metadata about 201 00:11:49,320 --> 00:11:53,720 product usage or the customer usage or like a go to market 202 00:11:53,920 --> 00:11:56,440 like Rep engagement with a customer. 203 00:11:56,680 --> 00:11:59,600 All of that data can be helpful to make better decisions for a 204 00:11:59,600 --> 00:12:00,920 company. So I've, I've worked at 205 00:12:00,920 --> 00:12:02,840 companies that have done that right. 206 00:12:03,800 --> 00:12:08,080 So yeah, it, it really depends on how much focus and investment 207 00:12:08,080 --> 00:12:11,040 that has been in ensuring good clean data. 208 00:12:11,440 --> 00:12:15,240 Ultimately, it also comes back to the business rigor and the 209 00:12:15,280 --> 00:12:21,120 financial, I would say acumen of a leadership team as well that 210 00:12:21,240 --> 00:12:24,840 creates the investment in data over like many years because it 211 00:12:24,840 --> 00:12:27,040 takes a lot of work. It takes a lot of work to 212 00:12:27,040 --> 00:12:34,800 retrofit broken or disconnected systems and data pipelines to 213 00:12:35,560 --> 00:12:37,840 intuitively and like immediately make sense of them. 214 00:12:37,840 --> 00:12:43,360 So yeah, in my career I've seen very different levels of that 215 00:12:43,360 --> 00:12:48,600 interconnectedness of data and the readiness of data to be made 216 00:12:48,600 --> 00:12:50,200 into insights that helps decisions. 217 00:12:51,320 --> 00:12:53,560 Awesome. And talking of insights, I 218 00:12:53,560 --> 00:12:56,720 understand that there are two frameworks that you use to kind 219 00:12:56,720 --> 00:13:00,680 of when it comes to data. So can can you sort of mention 220 00:13:00,680 --> 00:13:02,920 that first and then like elaborate about them in terms of 221 00:13:02,920 --> 00:13:05,960 like how you look at data and data this is making? 222 00:13:06,800 --> 00:13:09,280 Yeah, the do for frameworks that I share with you offline. 223 00:13:09,280 --> 00:13:12,520 The first one is I mentioned briefly and like one of the 224 00:13:12,520 --> 00:13:16,000 earlier questions, data is only as valuable as the action that 225 00:13:16,000 --> 00:13:19,520 you can drive with it and the impact that you can get from 226 00:13:19,520 --> 00:13:22,480 that action. So I, I spent a lot of my time 227 00:13:23,000 --> 00:13:28,520 thinking about with my team, how do you use data to get insights 228 00:13:28,600 --> 00:13:31,880 about the business problem or opportunity that you're looking 229 00:13:31,880 --> 00:13:35,080 to solve? And how do you use that insight 230 00:13:35,080 --> 00:13:38,880 to come up with meaningful recommendations for the business 231 00:13:39,160 --> 00:13:42,520 to action? And then how do you action those 232 00:13:42,520 --> 00:13:45,640 recommendations? It takes a lot of influence to 233 00:13:45,640 --> 00:13:50,160 do that because having the best idea and the best data and the 234 00:13:50,160 --> 00:13:56,720 best insight does not mean that will immediately be accepted and 235 00:13:56,720 --> 00:13:58,240 action on. So there's a lot of influence 236 00:13:58,240 --> 00:14:02,560 that goes in a cross functional matrix large organization to get 237 00:14:02,560 --> 00:14:05,920 that insight into action. And once you have the action, 238 00:14:05,920 --> 00:14:08,080 then you have to track the impact of that decision that you 239 00:14:08,080 --> 00:14:09,400 made. Then you learn from those 240 00:14:09,400 --> 00:14:12,160 actions. So the next set of insights that 241 00:14:12,160 --> 00:14:15,120 leads to the next set of action is going to be accreted to the 242 00:14:15,120 --> 00:14:18,320 company and that the company is getting better at this. 243 00:14:18,480 --> 00:14:21,560 So to me, it's very, very important thinking about this 244 00:14:21,560 --> 00:14:23,720 value chain of data to like impact. 245 00:14:25,160 --> 00:14:30,120 I think there's a lot more focus on the rigor of data and like 246 00:14:30,120 --> 00:14:33,320 data analytics, but I tend to focus a lot more of my time on 247 00:14:33,640 --> 00:14:37,280 insight to the impact part of the value chain as well, because 248 00:14:37,280 --> 00:14:40,960 that's how you realize the impact of, of any sort of 249 00:14:40,960 --> 00:14:42,320 analytics or insight that you put together. 250 00:14:42,840 --> 00:14:45,880 That's one. The the second one and it's, and 251 00:14:45,880 --> 00:14:50,640 this one also sort of relates to the first one, is the impact 252 00:14:50,640 --> 00:14:53,880 that you can achieve with the data that you have. 253 00:14:56,200 --> 00:15:00,920 You have to calibrate how much effort or time you spend trying 254 00:15:00,920 --> 00:15:02,440 to solve that problem or opportunity. 255 00:15:03,320 --> 00:15:06,960 You might be familiar with a Pareto principle of 8020 that 256 00:15:06,960 --> 00:15:11,280 generally holds true in more situations, 8020% of your 257 00:15:11,280 --> 00:15:15,040 customers bring 80% of your revenue. 20% of X give you 80 258 00:15:15,160 --> 00:15:20,120 gives you 80% of block. X is the import and like Y that 259 00:15:20,120 --> 00:15:23,200 is the output of the outcome. I feel very strongly about that 260 00:15:23,200 --> 00:15:25,040 in terms of data analytics as well. 261 00:15:26,000 --> 00:15:29,320 For most business problems or challenges that my team tackles 262 00:15:29,320 --> 00:15:34,320 today, I believe our 20% effort will get us 80% of the impact on 263 00:15:34,320 --> 00:15:38,560 that answer and majority of the situations that is going to be 264 00:15:38,560 --> 00:15:42,080 enough to drive the right decision. 265 00:15:43,120 --> 00:15:46,720 We in, in the work that we do in FBNA, we don't aim for 266 00:15:46,720 --> 00:15:49,040 precision. One of my finance professors 267 00:15:49,040 --> 00:15:54,040 from grad school, like 10 plus years ago told me once that in 268 00:15:54,040 --> 00:15:57,000 finance, like all data is wrong. Like you forecast something, 269 00:15:57,000 --> 00:15:59,080 it's going to be wrong. Like there's no point obsessing 270 00:15:59,080 --> 00:16:02,680 about precision and accuracy. So for the most of the work that 271 00:16:02,680 --> 00:16:05,520 my team does, it's 8020. It's like put 20% of your effort 272 00:16:05,520 --> 00:16:06,840 into this, get 80% of the outcome. 273 00:16:06,840 --> 00:16:10,840 Let's move on. But that is a scale for me and 274 00:16:10,840 --> 00:16:13,080 that that's the framework that I talked to my team about. 275 00:16:13,560 --> 00:16:17,240 If it's 20% impact effort for 80% of the impact, if you put 276 00:16:17,240 --> 00:16:19,920 10% effort in, you'll likely get 60% of the impact. 277 00:16:19,960 --> 00:16:22,520 Or if you put 2% effort in, you'll get 20% of the impact. 278 00:16:22,520 --> 00:16:27,200 So it's, it's sort of a sliding scale of how much effort should 279 00:16:27,200 --> 00:16:29,720 you put into this problem. And it is super important 280 00:16:29,800 --> 00:16:35,800 because any data problem or challenge or opportunity can be 281 00:16:35,800 --> 00:16:42,040 solved in 5 minutes or five hours or five days or five 282 00:16:42,040 --> 00:16:44,800 months, or you can even take five years, depending on how 283 00:16:44,800 --> 00:16:49,120 academically rigorous you want to be about that problem or how 284 00:16:49,200 --> 00:16:51,600 accurate or precise you want that solution to be. 285 00:16:52,520 --> 00:16:54,960 An example would be like, if you're launching rockets, yeah, 286 00:16:54,960 --> 00:16:56,120 you want, you want to spend five years. 287 00:16:56,120 --> 00:17:00,200 You want to be absolutely sure if you're, if you're saving 288 00:17:00,200 --> 00:17:02,320 lives, if you're like a neurosurgeon, like, yeah, you 289 00:17:02,320 --> 00:17:04,640 want to like put all that effort into like practice because you 290 00:17:04,640 --> 00:17:06,640 get something wrong, like it goes wrong. 291 00:17:06,640 --> 00:17:12,960 So you there you want to have a 90 9% effort for like 99.999% of 292 00:17:12,960 --> 00:17:16,160 the impact. But it actually matters in 293 00:17:16,160 --> 00:17:20,119 finance a lot because I am almost always pushing my team to 294 00:17:20,319 --> 00:17:22,119 calibrate less on the effort scale. 295 00:17:22,680 --> 00:17:25,800 I'm usually asking my team for like, hey, give me a 2 to 5% 296 00:17:25,800 --> 00:17:28,359 effort that gets me 20 to 40% of the impact. 297 00:17:28,600 --> 00:17:32,000 An example might be a business leader wants to know, hey, if I 298 00:17:32,000 --> 00:17:37,840 want to invest 10 resources in this problem, can you tell me 299 00:17:37,840 --> 00:17:40,800 what it's going to cost? You could probably solve that 300 00:17:40,800 --> 00:17:44,000 with mental math of average, like cost per head. 301 00:17:44,000 --> 00:17:45,200 And then like when do you invest? 302 00:17:45,200 --> 00:17:47,280 And like you could have a response in 10 seconds. 303 00:17:47,280 --> 00:17:49,640 And for that business leader that is going to be sufficient. 304 00:17:49,840 --> 00:17:52,880 Or you could take the entire next two days to go pull 305 00:17:53,680 --> 00:17:56,320 specific like cost at like a specific level, like what 306 00:17:56,320 --> 00:17:58,920 location, what like level. It's not going to be helpful for 307 00:17:58,920 --> 00:18:01,560 that business leader, right? So it is really, really 308 00:18:01,560 --> 00:18:05,080 important to understand and calibrate how much effort you 309 00:18:05,280 --> 00:18:11,480 put into solving data problems for the impact that is possible 310 00:18:11,480 --> 00:18:14,480 to achieve in answering that problem because that is a cap 311 00:18:14,720 --> 00:18:17,520 and any investment of effort or time beyond that is not going to 312 00:18:17,520 --> 00:18:20,080 be useful. So that's the other like 313 00:18:20,080 --> 00:18:22,680 framework. And I've tried to make that a 314 00:18:22,680 --> 00:18:26,320 part of my team's terminology on when I ask a question or when I 315 00:18:26,320 --> 00:18:29,800 ask someone to do an analysis. I'll usually say, give me a 5 to 316 00:18:29,800 --> 00:18:32,320 10% solution on this, which means that they know that they 317 00:18:32,320 --> 00:18:34,600 don't need to spend days and that I'm looking for something 318 00:18:34,600 --> 00:18:36,560 quick. Something quick means that I 319 00:18:36,560 --> 00:18:39,960 also need to give them grace if the 20% answer is slightly 320 00:18:39,960 --> 00:18:44,560 different, which is OK because the answer can be a little bit 321 00:18:44,560 --> 00:18:45,760 different and we'll tweak as we go. 322 00:18:45,800 --> 00:18:49,280 So that's the second framework that I found really helpful in 323 00:18:49,280 --> 00:18:52,520 working with my team to make sure that they're allocating 324 00:18:52,520 --> 00:18:54,960 their time and resources in the best possible places. 325 00:18:56,120 --> 00:18:58,760 That's awesome because my experience with finance has been 326 00:18:58,760 --> 00:19:02,240 that I will say the cost is 100 and they'll come back and say 327 00:19:02,240 --> 00:19:07,320 no, it's not 100, it's 99.8. So because I think some finance 328 00:19:07,320 --> 00:19:09,440 fee will come from the, I don't know if it's because they come 329 00:19:09,440 --> 00:19:12,760 to the control side or if it is like when you're preparing your 330 00:19:12,760 --> 00:19:15,120 financials, you look at numbers from a certain kind of 331 00:19:15,120 --> 00:19:17,840 precision. And for FPLA you probably don't 332 00:19:17,840 --> 00:19:22,040 need that because like you, what you need is a sort of a broad 333 00:19:22,040 --> 00:19:24,760 direction in terms of like what the company needs to be doing. 334 00:19:24,880 --> 00:19:26,360 It's more of a strategic role, right? 335 00:19:27,440 --> 00:19:29,240 Absolutely. And sometimes I'll even say, 336 00:19:29,680 --> 00:19:31,600 hey, can you give me a sizing of this cost? 337 00:19:31,600 --> 00:19:34,360 Like ±5 million is fine. Like, because beyond that, I 338 00:19:34,360 --> 00:19:36,080 don't care. I don't care for it to be more 339 00:19:36,080 --> 00:19:39,400 precise than that. Or I might say, oh, this one's 340 00:19:39,400 --> 00:19:41,720 important, so give me something that's a little more accurate. 341 00:19:41,720 --> 00:19:44,360 So you're absolutely right. Like there's no point debating 342 00:19:44,360 --> 00:19:47,080 99.8 versus 100 because there's no value. 343 00:19:47,240 --> 00:19:49,080 There's no value. We're not going to make a 344 00:19:49,080 --> 00:19:52,320 different decision as a company whether it was 99.8 or 100 or 345 00:19:52,320 --> 00:19:55,240 even 98 for that matter. So, yeah, yeah. 346 00:19:56,200 --> 00:20:00,600 Slightly changing tracks, how do you sort of like look at, let's 347 00:20:00,600 --> 00:20:04,480 just call it a data analysis, But I mean, FPNA job can be 348 00:20:04,480 --> 00:20:07,200 broadly described as data analysis in some sense being 349 00:20:07,200 --> 00:20:09,640 proactive versus reactive. Because what I've seen in the 350 00:20:09,640 --> 00:20:13,960 past and also in the present is that like analytics teams can 351 00:20:13,960 --> 00:20:18,160 come in two flavours. 1 is where in your job it could be a team 352 00:20:18,160 --> 00:20:21,040 coming to you and say how much do you think we should be able 353 00:20:21,040 --> 00:20:22,800 to we should spend on this new project? 354 00:20:23,120 --> 00:20:27,200 Was this your team proactively monitoring the numbers and then 355 00:20:27,200 --> 00:20:31,040 going back to some team and saying that OK, this project is 356 00:20:31,760 --> 00:20:35,000 tracking as per expectation thing so and so how do you see 357 00:20:35,000 --> 00:20:39,160 the balance between proactive and and reactive insights when 358 00:20:39,160 --> 00:20:41,520 it comes to data? How does it work with your team 359 00:20:41,520 --> 00:20:44,320 and other teams that you that you work with and so on? 360 00:20:45,320 --> 00:20:51,640 Yeah, I think the only way to do finance is to be proactive. 361 00:20:53,120 --> 00:20:55,360 It depends on the maturity and the capabilities of an 362 00:20:55,360 --> 00:20:57,720 organization and where they are on that journey. 363 00:20:57,840 --> 00:21:01,600 But on a long enough timeline, I think the true way to add value 364 00:21:01,600 --> 00:21:03,360 to any company is to be proactive and is to be 365 00:21:03,360 --> 00:21:07,280 strategic. And by strategic, I mean you are 366 00:21:07,280 --> 00:21:09,480 looking to influence the future. That to me is strategic. 367 00:21:10,480 --> 00:21:13,800 If you combine proactive and strategic together, any finance 368 00:21:13,800 --> 00:21:16,600 person will be able to bring so much value to the business that 369 00:21:16,600 --> 00:21:19,760 they support. To do both, you need a deep 370 00:21:19,760 --> 00:21:22,880 understanding of the business. You need curiosity, you need 371 00:21:22,880 --> 00:21:27,000 empathy for the organization, the team, the business leader, 372 00:21:27,080 --> 00:21:28,640 or the function that you support. 373 00:21:28,920 --> 00:21:33,880 Once you have that understanding and that empathy, waking up in 374 00:21:33,880 --> 00:21:37,160 the morning and thinking about what can you do today as a 375 00:21:37,160 --> 00:21:40,520 finance person to help this business be better or help this 376 00:21:40,520 --> 00:21:42,320 business leader achieve their goals? 377 00:21:42,560 --> 00:21:46,320 To me, that is being proactive. And that's the only way to do it 378 00:21:46,320 --> 00:21:52,080 because a reactive mode is like you're, you're solving for like 379 00:21:52,080 --> 00:21:54,280 what is in front of you. You're prioritizing the things 380 00:21:54,280 --> 00:21:56,760 that are on your plate. Sometimes you, when you do that, 381 00:21:56,760 --> 00:22:00,200 you miss the bigger picture. You miss the opportunities that 382 00:22:00,320 --> 00:22:02,400 are not in front of you. There's a lot of availability 383 00:22:02,400 --> 00:22:05,000 bias. When you're reactive, you're at 384 00:22:05,000 --> 00:22:06,360 that point like you're already on the back foot. 385 00:22:06,360 --> 00:22:09,280 You're, you're prioritizing or like you're saying no, or like 386 00:22:09,560 --> 00:22:11,560 you're thinking about the value of the work that you do. 387 00:22:12,160 --> 00:22:16,160 So stepping out of that mode and getting into a proactive mode 388 00:22:16,440 --> 00:22:19,880 because once you start thinking about the ways that you can add 389 00:22:19,880 --> 00:22:23,360 value from the data to insight to like action to like impact, 390 00:22:23,640 --> 00:22:27,320 the better position you will be to bring strategic value to an 391 00:22:27,320 --> 00:22:31,040 organization. So to me, as I said on a long 392 00:22:31,040 --> 00:22:34,360 enough timeline, not just like finance, I think about that like 393 00:22:34,400 --> 00:22:38,880 any function. If a business leader be ACEO, be 394 00:22:38,880 --> 00:22:43,120 it a Chief Operating officer or be it AVP of sales or Director 395 00:22:43,120 --> 00:22:48,120 of customer success, they're looking for partners with the 396 00:22:48,120 --> 00:22:52,480 capabilities of translating data to insight to tell them what 397 00:22:52,480 --> 00:22:55,400 they should do differently. And that to me is proactive and 398 00:22:55,400 --> 00:22:58,960 strategic. So yeah, I think that's the only 399 00:22:58,960 --> 00:23:00,880 way to bring true sustained value, in my opinion. 400 00:23:03,160 --> 00:23:05,560 How has AI changed your role in the last couple of years and how 401 00:23:05,560 --> 00:23:08,400 do you see it changing your role in the next few years? 402 00:23:11,200 --> 00:23:14,880 I think there's a lot more change coming in my role in 403 00:23:14,880 --> 00:23:22,200 adjacent roles, especially as I think about data to insight, 404 00:23:22,960 --> 00:23:26,080 like inside the action, I think is going to take a lot longer, 405 00:23:26,240 --> 00:23:29,480 especially because these aren't like system actions. 406 00:23:29,480 --> 00:23:32,240 These are like business decisions that need to be 407 00:23:32,240 --> 00:23:35,600 contemplated in a broader scale. Coming back to the data to 408 00:23:35,600 --> 00:23:44,640 Insight, AI hasn't changed that very much just yet because a lot 409 00:23:44,640 --> 00:23:47,120 of the AI evolution that we've seen in my opinion in the last 410 00:23:47,120 --> 00:23:50,920 like 3 years has been on language models and that has to 411 00:23:50,920 --> 00:23:56,640 do with language writing content like documents, structures. 412 00:23:57,760 --> 00:24:00,240 The data capabilities of LLM models is non existent. 413 00:24:00,240 --> 00:24:04,480 I don't think that's their purpose. 10 years ago, I worked 414 00:24:04,480 --> 00:24:09,480 on a machine learning predictive forecast for a large tech 415 00:24:09,480 --> 00:24:12,840 company. And I don't think the machine 416 00:24:12,840 --> 00:24:15,960 learning or predictive modelling technology has meaningfully 417 00:24:15,960 --> 00:24:20,200 changed in the last three years. And but I do think that there's 418 00:24:20,200 --> 00:24:25,800 a lot more that is coming in AI that is going to evolve that 419 00:24:25,800 --> 00:24:29,120 significantly. The other vector to think about 420 00:24:29,120 --> 00:24:33,880 is when the company was incorporated, what is the text 421 00:24:33,880 --> 00:24:35,800 stack look like? Again like interconnectedness of 422 00:24:35,800 --> 00:24:38,800 the data. If you want to put an agent or 423 00:24:39,280 --> 00:24:43,480 another like SAS, AI driven like SAS application on top, if you 424 00:24:43,480 --> 00:24:45,600 don't clean up the foundations, if you don't have a tight 425 00:24:45,600 --> 00:24:48,720 foundation of data, it is not going to work. 426 00:24:49,360 --> 00:24:53,520 So I think majority of the companies today have to retrofit 427 00:24:53,520 --> 00:24:57,760 AI into their existing landscape, which is going to be 428 00:24:57,760 --> 00:25:00,720 a humongous effort. And I think that's going to 429 00:25:00,720 --> 00:25:02,320 change as well in the next like 2 years. 430 00:25:02,320 --> 00:25:05,040 I think there's going to be evolution and technologies that 431 00:25:05,040 --> 00:25:08,360 makes it a lot easier, more autonomous, more agentic. 432 00:25:09,520 --> 00:25:12,800 So I talked to my team a lot about how AI is going to upend 433 00:25:13,200 --> 00:25:17,440 the FBNA job in general. I don't have a sense of how that 434 00:25:17,440 --> 00:25:22,480 is going to happen yet. I keep my eyes and like ears to 435 00:25:22,480 --> 00:25:28,920 the ground on the evolution in FBNA use cases for AI, but I 436 00:25:28,920 --> 00:25:32,080 haven't found any that are incredibly sticky that are like 437 00:25:32,240 --> 00:25:35,280 off the shelf. I can deploy with my team and 438 00:25:35,280 --> 00:25:37,840 have them use because of all the constraints that I talked about 439 00:25:37,840 --> 00:25:39,520 previously, but I think we're going to get there. 440 00:25:39,520 --> 00:25:42,800 So it's going to be a pretty big priority for me and my team in 441 00:25:42,800 --> 00:25:47,320 the next year to really think about how the FBNA job function 442 00:25:48,000 --> 00:25:51,680 is going to evolve in the next few years in the new age of AII 443 00:25:51,680 --> 00:25:55,160 know it's not a it's not a a clear answer, but I just know 444 00:25:55,160 --> 00:25:56,640 that it is. I just don't know how. 445 00:25:57,560 --> 00:26:00,080 Yep. No, the one thing you mentioned 446 00:26:00,080 --> 00:26:03,840 about how AI is all, I mean, LLMS are all fundamental 447 00:26:03,840 --> 00:26:05,280 language models. I mean, it's in the name. 448 00:26:05,360 --> 00:26:08,920 And so yeah, if you ask them to do any arithmetic, they're 449 00:26:09,000 --> 00:26:11,960 absolute shifted. But but the one thing that we 450 00:26:11,960 --> 00:26:14,480 have found that we are leveraging in Babbage and quite 451 00:26:14,480 --> 00:26:16,400 a few others are also leveraging, is that what you can 452 00:26:16,400 --> 00:26:18,440 do is to get them to write SQL queries. 453 00:26:18,960 --> 00:26:23,080 So instead of you LLMS can do math. 454 00:26:23,560 --> 00:26:26,600 But what they are very good at is writing code that can do 455 00:26:26,600 --> 00:26:28,840 math. And so based on that, for 456 00:26:28,840 --> 00:26:33,040 example, what what we are we are doing here at barrages in terms 457 00:26:33,040 --> 00:26:36,920 of like how do you use LLMS to sort of like proactively query 458 00:26:36,920 --> 00:26:40,560 data to find out insights and then proactively find out why 459 00:26:40,600 --> 00:26:44,120 those things are happening. So for example, in an FDA kind 460 00:26:44,120 --> 00:26:47,920 of a situation, you might have a situation where you have, let's 461 00:26:47,920 --> 00:26:50,960 say, I don't know, let's say you have a budget for a particular 462 00:26:50,960 --> 00:26:52,040 hit. And then like you have the 463 00:26:52,320 --> 00:26:58,080 actual spend to to track the spend versus the budget, how it 464 00:26:58,080 --> 00:27:02,240 is going, where whether it's going as to plan in all parts of 465 00:27:02,240 --> 00:27:05,200 the business or if there is something going along somewhere. 466 00:27:05,280 --> 00:27:09,160 And if, if, if something's not working, why it is not working, 467 00:27:09,160 --> 00:27:13,560 it takes so and the entire stack in some sense can be built using 468 00:27:13,560 --> 00:27:16,400 just getting LMS to write word in some sense. 469 00:27:16,840 --> 00:27:20,600 So, so from that perspective, I understand that like I assume 470 00:27:20,720 --> 00:27:25,560 that you guys have get to use any of the AI for analytics kind 471 00:27:25,560 --> 00:27:28,880 of tools of which the word 100 I would say so. 472 00:27:30,080 --> 00:27:31,880 So you just wanted to mention that? 473 00:27:32,120 --> 00:27:34,480 Yeah, absolutely. I completely agree with you. 474 00:27:34,480 --> 00:27:39,560 I think maybe maybe the content definition that I use for LLMS 475 00:27:39,560 --> 00:27:42,000 is definitely like broader, like coding is definitely a good 476 00:27:42,000 --> 00:27:46,040 example. And and to be fair, like I 477 00:27:46,080 --> 00:27:51,560 encourage and almost like insist that my team start with AI for 478 00:27:51,600 --> 00:27:55,440 any use case that they have, whether they're writing a 479 00:27:55,440 --> 00:27:59,240 document about like root cause analysis or they're pulling like 480 00:27:59,240 --> 00:28:02,160 data together and they want to write supervise to me, I think 481 00:28:02,160 --> 00:28:05,560 there's a lot of progress in those step one or like horizon 482 00:28:05,560 --> 00:28:10,320 one use cases for AI for sure. But I do think the, the next 483 00:28:10,320 --> 00:28:14,120 step is going to be an evolution more than a Step 2 in terms of 484 00:28:14,320 --> 00:28:16,880 how AI can truly change the DNA function. 485 00:28:17,000 --> 00:28:19,800 So that's what I, I, I, I spend a lot of my time thinking about. 486 00:28:20,000 --> 00:28:23,320 But there's definitely like several use cases for AI that my 487 00:28:23,320 --> 00:28:27,000 team leverages today that gets us like incremental efficiency, 488 00:28:27,000 --> 00:28:30,280 efficacy in how we do our work. OK. 489 00:28:30,720 --> 00:28:32,200 Ashley, thanks a lot for your insights today. 490 00:28:32,440 --> 00:28:34,520 Absolutely happy to enjoy our conversation today. 491 00:28:34,600 --> 00:28:34,840 Thank you.