1 00:00:00,480 --> 00:00:04,040 Think about it like you know, numbers on their own are 2 00:00:04,160 --> 00:00:09,960 forgettable, but stories take. Welcome to Business Analysis 3 00:00:09,960 --> 00:00:12,000 Live. I'm your host, Susan Moore, 4 00:00:12,000 --> 00:00:15,280 community engagement manager with the International Institute 5 00:00:15,360 --> 00:00:18,480 of Business Analysis. I explore topics with industry 6 00:00:18,480 --> 00:00:21,320 guests about the work of business analysis professionals 7 00:00:21,360 --> 00:00:24,440 and how their work helps organizations achieve better 8 00:00:24,440 --> 00:00:26,520 outcomes. And Yep, we take your questions, 9 00:00:26,520 --> 00:00:27,880 too. Thanks for listening. 10 00:00:27,960 --> 00:00:35,600 Let's get started. We talk a lot about business 11 00:00:35,600 --> 00:00:39,560 analysis and we talk a lot about some of the techniques, some of 12 00:00:39,560 --> 00:00:43,640 the skills and tasks that you as business analysis professionals 13 00:00:43,640 --> 00:00:47,040 should bring to the table. So let me set up today's topic a 14 00:00:47,040 --> 00:00:51,800 little bit for you. Today you have done all of your 15 00:00:51,800 --> 00:00:56,040 data analysis. You are really excited about 16 00:00:56,400 --> 00:00:59,800 what you found and you cannot wait to share it with your 17 00:00:59,800 --> 00:01:02,280 stakeholders. Multiple choice. 18 00:01:02,280 --> 00:01:05,760 What do you do? Do you take in all of your data 19 00:01:06,040 --> 00:01:09,040 and show it to them clearly, they'll know what to do. 20 00:01:10,240 --> 00:01:13,920 Do you give it to somebody else and have them do it? 21 00:01:14,400 --> 00:01:21,080 Do you find a way to help them understand that data that really 22 00:01:21,080 --> 00:01:23,400 helps them to make a better decision? 23 00:01:23,440 --> 00:01:28,000 And if your answer is the last one, I've got good news. 24 00:01:28,000 --> 00:01:30,960 If your answer is one of the others, I've got good news. 25 00:01:31,040 --> 00:01:34,360 Today we're going to be talking about how to make your data 26 00:01:34,360 --> 00:01:37,000 impossible to ignore with a technique. 27 00:01:37,480 --> 00:01:41,880 It will really help you and your stakeholders understand the 28 00:01:41,880 --> 00:01:44,160 impacts and the stories around your data. 29 00:01:44,160 --> 00:01:49,080 So I want to introduce to the stage our guest today, Ankit 30 00:01:49,120 --> 00:01:50,960 Agrawal. How are you, Ankit? 31 00:01:52,400 --> 00:01:55,760 Hey, Susan, I'm doing really good, and thanks for inviting me 32 00:01:55,760 --> 00:01:57,400 to the show. It's always a pleasure to talk 33 00:01:57,400 --> 00:01:59,920 to you. Yes, happy to have you. 34 00:01:59,920 --> 00:02:03,560 We don't always get a chance to talk about data and I know it's 35 00:02:03,560 --> 00:02:06,680 a lot of what business analysis professionals do. 36 00:02:07,000 --> 00:02:09,400 So it's really great that you are going to bring your 37 00:02:09,400 --> 00:02:11,960 experience because you've got a lot of it and you're pretty 38 00:02:11,960 --> 00:02:15,280 passionate about storytelling. And so we're going to talk about 39 00:02:15,280 --> 00:02:17,640 that in a minute. But first, please introduce 40 00:02:17,680 --> 00:02:19,640 yourself. Tell us a little bit about you 41 00:02:19,640 --> 00:02:22,320 and what you do. Absolutely. 42 00:02:22,640 --> 00:02:25,760 Before you get into my about myself, I just wanted to thank 43 00:02:25,760 --> 00:02:27,480 everybody. And I know it's the middle of 44 00:02:27,480 --> 00:02:30,560 the week Wednesday morning. So thanks a lot for taking the 45 00:02:30,560 --> 00:02:34,480 time in the middle of the week and attending the session. 46 00:02:35,160 --> 00:02:37,360 And also, you know, it's really great. 47 00:02:37,360 --> 00:02:40,720 You know, winter is finally over and spring is here. 48 00:02:40,720 --> 00:02:43,360 So it's really excited about what's ahead of us. 49 00:02:44,000 --> 00:02:48,120 About myself, I work with Verizon, I work as ACRM lead, 50 00:02:48,120 --> 00:02:51,200 customer relationship management lead for a consumer business. 51 00:02:51,280 --> 00:02:54,520 And I help the company with the retention efforts, you know, 52 00:02:54,520 --> 00:02:58,000 helping build loyalty with the customers so that they stay 53 00:02:58,000 --> 00:03:00,480 longer. And obviously, you know, they we 54 00:03:00,480 --> 00:03:03,920 get, we get lifetime value from them. 55 00:03:04,840 --> 00:03:10,160 I did my MBA from Wake Forest University and my concentration 56 00:03:10,160 --> 00:03:12,160 was business analytics. So, you know, throughout my 57 00:03:12,160 --> 00:03:15,360 career, you know, whether it was marketing, supply chain, 58 00:03:15,680 --> 00:03:19,040 manufacturing, data analysis has been the backbone of whatever I 59 00:03:19,040 --> 00:03:21,760 have done. And I'm really passionate about 60 00:03:21,760 --> 00:03:23,720 this subject. And you know, through this 61 00:03:23,760 --> 00:03:26,640 session, I've been hope to bring some of the practitioners 62 00:03:26,640 --> 00:03:29,480 insight on what storytelling with data means. 63 00:03:29,480 --> 00:03:31,040 And I'm really looking forward to it. 64 00:03:34,360 --> 00:03:39,520 Somehow, when you and I were catching up, I failed to ask you 65 00:03:39,800 --> 00:03:42,680 where you had gone to school. I didn't know you went to Wake 66 00:03:42,680 --> 00:03:44,680 Forest. You know, Wake Forest is only 67 00:03:44,680 --> 00:03:46,320 like an hour and a half from me. Yes. 68 00:03:46,520 --> 00:03:47,880 Yes. I've been to Charlotte a few 69 00:03:47,880 --> 00:03:48,600 times. Yeah. 70 00:03:48,640 --> 00:03:50,960 I don't go there as often as I would like to. 71 00:03:51,200 --> 00:03:52,960 I'm in Northeast now, New Jersey. 72 00:03:53,520 --> 00:03:55,800 Every time I get an opportunity, I do go there. 73 00:03:56,440 --> 00:04:00,400 Well, stay away. Right now it is spring and the 74 00:04:00,640 --> 00:04:02,680 we just get dust clouds of pollen. 75 00:04:02,720 --> 00:04:07,600 So that's my struggle for today. So, OK, so storytelling Now as 76 00:04:07,600 --> 00:04:10,760 you and I were getting ready today, one of the things I said 77 00:04:10,760 --> 00:04:13,400 to you is that, you know, when we talk about storytelling, I 78 00:04:13,400 --> 00:04:18,120 feel like business analysis professionals think it's not 79 00:04:18,120 --> 00:04:21,200 like a real technique because it, you know, when we think 80 00:04:21,200 --> 00:04:23,680 storytelling, we think of fantasy, right? 81 00:04:24,000 --> 00:04:29,520 But it actually is a really important technique for how we 82 00:04:29,520 --> 00:04:33,600 can get our stakeholders to immerse themselves in what that 83 00:04:33,600 --> 00:04:36,960 data is telling us. So why don't you help us 84 00:04:36,960 --> 00:04:39,880 understand what is data storytelling? 85 00:04:41,760 --> 00:04:44,320 Well, absolutely. And and you know Susan, before I 86 00:04:44,320 --> 00:04:48,040 even I get to storytelling with data, I want to just level set 87 00:04:48,040 --> 00:04:51,040 on why data is so important, especially in business 88 00:04:51,040 --> 00:04:54,880 environment and what does the company gain by making a 89 00:04:54,880 --> 00:04:56,600 data-driven decision? Right. 90 00:04:56,600 --> 00:05:00,280 So I'll just use an example. Imagine you're flying a plane in 91 00:05:00,280 --> 00:05:03,560 dense fog, OK, and you don't have an instrument panel. 92 00:05:04,240 --> 00:05:07,680 So you might think that you are maintaining the altitude, but 93 00:05:07,680 --> 00:05:10,720 you know, you might be losing the altitude or you might be 94 00:05:10,720 --> 00:05:14,800 heading towards mountain, you know, or you may be low on fuel, 95 00:05:15,200 --> 00:05:19,160 right? So, so without realizing you 96 00:05:19,160 --> 00:05:23,000 know, what is ahead of you, you're not able to really guide 97 00:05:23,000 --> 00:05:26,120 the plane. Data analysis is that instrument 98 00:05:26,120 --> 00:05:30,520 panel for the businesses. It tells you exactly where you 99 00:05:30,520 --> 00:05:33,480 are, where you're headed, and what's the trouble that lies 100 00:05:33,480 --> 00:05:37,760 ahead of you, right. So using that analogy in today's 101 00:05:37,760 --> 00:05:41,040 business environment, you know, think about from a, if you put 102 00:05:41,040 --> 00:05:45,600 customer at the center of everything, we know you know a 103 00:05:45,600 --> 00:05:48,480 lot of data about the customer. We know in what behaviors they 104 00:05:48,480 --> 00:05:52,520 exhibit, what the demographics are, what the purchase history 105 00:05:52,520 --> 00:05:56,400 is, how what, what's the digital presence, what's the 106 00:05:56,400 --> 00:05:58,720 transactions they have done in the past, right. 107 00:05:59,160 --> 00:06:04,160 So using all these data points information can really help the 108 00:06:04,160 --> 00:06:07,360 companies form a good strategy, right? 109 00:06:07,720 --> 00:06:10,520 There are three or four things that you know that just out of 110 00:06:10,520 --> 00:06:13,760 the gate, you know the companies can get out of analyzing the 111 00:06:13,760 --> 00:06:16,480 data. One, it stops the kissing game. 112 00:06:17,240 --> 00:06:20,000 Typically what happens is, you know, it's the loudest voice in 113 00:06:20,000 --> 00:06:23,000 the room or you know somebody who has the highest title, you 114 00:06:23,000 --> 00:06:26,560 know they have a big say, but when you bring data into 115 00:06:26,560 --> 00:06:29,760 picture, you know the whole everything just becomes 116 00:06:29,880 --> 00:06:33,080 objective and fact based. You're not doing any guess work 117 00:06:33,080 --> 00:06:35,720 and you know that's what is helping you drive this. 118 00:06:36,160 --> 00:06:39,000 Secondly, what's important is that you know, it really 119 00:06:39,000 --> 00:06:43,240 understand, helps you understand the root cause for performance. 120 00:06:43,240 --> 00:06:45,440 OK. So if certain things are 121 00:06:45,440 --> 00:06:48,520 performing the way they are, you know what are the drivers, if 122 00:06:48,520 --> 00:06:51,880 the sales are flying through the roof, you know what drove so 123 00:06:51,880 --> 00:06:54,640 that you can double down on it. Or if you know the customers are 124 00:06:54,640 --> 00:06:57,800 leaving left and right, you can stop the bleeding looking at and 125 00:06:57,800 --> 00:07:00,160 what the root causes are. So that that's another 126 00:07:00,160 --> 00:07:02,680 advantage. Thirdly, I'll point out is that 127 00:07:03,240 --> 00:07:05,960 it helps you create the future. You know, looking at the past 128 00:07:05,960 --> 00:07:09,920 data is indicative of what might happen in the future and that 129 00:07:09,920 --> 00:07:12,520 really helps companies to do business planning. 130 00:07:12,760 --> 00:07:16,520 I know capital allocation, revenue management, all of that. 131 00:07:17,000 --> 00:07:20,080 And last but not the least, you know it really helps improve the 132 00:07:20,080 --> 00:07:23,360 customer's experience. If you're look having a 133 00:07:23,360 --> 00:07:26,600 data-driven approach and if you look at the customer's life 134 00:07:26,600 --> 00:07:30,440 cycle journey, there are several points of friction that happens 135 00:07:30,600 --> 00:07:34,160 along the way. And if you have inserted those 136 00:07:34,160 --> 00:07:37,720 triggers that identifies those signals that cause friction in 137 00:07:37,720 --> 00:07:41,840 the customer's journey, you can intercept those moments and 138 00:07:41,840 --> 00:07:45,360 actually put a treatment in place, whether it's onboarding 139 00:07:45,360 --> 00:07:50,200 or competitive response or, you know, any transactional issues 140 00:07:50,200 --> 00:07:54,560 that the customers are facing and really help to improve that 141 00:07:54,720 --> 00:07:58,080 customer experience and move towards personalization that 142 00:07:58,080 --> 00:08:02,600 that's that's really important. Every customer has a name, 143 00:08:02,680 --> 00:08:04,240 right? So those are some of the 144 00:08:04,240 --> 00:08:09,280 advantages of looking at the data and analyzing the data. 145 00:08:10,000 --> 00:08:12,920 Now that we have level set on, you know, why data is important 146 00:08:12,920 --> 00:08:15,360 and why, you know, every company should have really have a 147 00:08:15,360 --> 00:08:18,160 data-driven approach. I want to come back to the 148 00:08:18,160 --> 00:08:21,400 original question of, you know, why is storytelling important? 149 00:08:22,000 --> 00:08:27,240 And think about it like, you know, numbers on their own are 150 00:08:27,360 --> 00:08:30,200 forgettable and what stories take. 151 00:08:30,880 --> 00:08:35,679 If I were to say that Earth's existence has been around 4.5 152 00:08:35,679 --> 00:08:42,360 billion years and life first appeared around 3 1/2 billion 153 00:08:42,360 --> 00:08:48,000 years, Dinosaurs reigned between 250 and 66,000,000 years and 154 00:08:48,000 --> 00:08:51,320 much of the recorded human history is 500,000 years old. 155 00:08:51,840 --> 00:08:54,200 I'm sure you will. Your heads head will start 156 00:08:54,200 --> 00:08:58,720 spinning and I bet you're going to have more like need more. 157 00:08:59,600 --> 00:09:02,440 It's kind of dry. It's kind of dry, right? 158 00:09:02,440 --> 00:09:05,720 You know, And I'm sure that, you know, tomorrow, you know, you're 159 00:09:05,720 --> 00:09:07,840 going to lose everything and it's not going to stick. 160 00:09:08,400 --> 00:09:12,000 But what if I told this story in a different way? 161 00:09:12,680 --> 00:09:16,680 And I said that, hey, if you squeeze the existence of Earth 162 00:09:17,000 --> 00:09:21,640 in 24 hour day life appeared at around 5:00 AM in the morning. 163 00:09:22,240 --> 00:09:25,800 Dinosaurs rained between somewhere between 11:00 and 164 00:09:25,800 --> 00:09:29,480 11:30 PM. And much of the human history is 165 00:09:29,480 --> 00:09:33,840 only, you know, 3 seconds old. You know, I'm sure, you know, 166 00:09:33,840 --> 00:09:36,600 that that's going to stick. So that's what storytelling 167 00:09:36,600 --> 00:09:38,280 does. That that's, that's the 168 00:09:38,280 --> 00:09:41,040 importance of storytelling. You know, it provides context. 169 00:09:41,040 --> 00:09:46,480 It provides A-frame of reference that really is important to get 170 00:09:46,480 --> 00:09:47,840 the buy in. Yeah. 171 00:09:48,080 --> 00:09:50,320 And you capture people's imagination. 172 00:09:50,520 --> 00:09:54,720 So if we lean into that part of storytelling, I mean, the 173 00:09:54,720 --> 00:09:58,440 importance of storytelling is that we are creating a picture 174 00:09:58,440 --> 00:10:02,280 for someone and we want them to envision themselves in that 175 00:10:02,280 --> 00:10:04,680 moment. And so I can think about in your 176 00:10:04,680 --> 00:10:09,120 story, oh, if I'm a watch, I understand what a watch is or I 177 00:10:09,120 --> 00:10:12,680 understand 24 hours in a day and oh, well, that's really 178 00:10:12,680 --> 00:10:16,160 interesting. Boy, human written human history 179 00:10:16,160 --> 00:10:19,960 has only been available or tracked for the past 500,000 180 00:10:19,960 --> 00:10:21,520 years. That that's three seconds. 181 00:10:21,560 --> 00:10:25,440 That's so little. So yeah, and it's, you know, you 182 00:10:25,440 --> 00:10:29,560 get people interested in a different way than just a slide 183 00:10:29,560 --> 00:10:31,120 deck with a bunch of graphs on it. 184 00:10:31,200 --> 00:10:34,200 So yeah, absolutely. A lot of times, you know, the 185 00:10:34,200 --> 00:10:37,480 numbers gets thrown, but you know, frame of reference is 186 00:10:37,480 --> 00:10:40,040 extremely important. Comparison point is extremely 187 00:10:40,040 --> 00:10:43,040 important. And if you don't have then you 188 00:10:43,040 --> 00:10:45,120 know, you all your stating is just facts. 189 00:10:45,600 --> 00:10:49,120 So yes, it does not drive a lot of things. 190 00:10:49,640 --> 00:10:51,240 Yeah. And ultimately what we're 191 00:10:51,240 --> 00:10:55,120 wanting people to do with that data is understand the 192 00:10:55,120 --> 00:10:57,800 situation. But then we we generally want 193 00:10:57,800 --> 00:11:01,520 them to take an action. And I guess we just have to face 194 00:11:01,520 --> 00:11:04,880 facts here. Those actions are going to be 195 00:11:05,560 --> 00:11:10,080 more impactful if they are driven by a sense of urgency, a 196 00:11:10,080 --> 00:11:14,360 sense of, oh, I understand why we need to make the decision and 197 00:11:14,360 --> 00:11:16,640 it's now and it's this particular thing. 198 00:11:16,880 --> 00:11:20,880 So it really is important in a way that just facts on paper 199 00:11:21,120 --> 00:11:23,160 can't help our stakeholders to understand. 200 00:11:23,480 --> 00:11:28,800 So, so you've done quite a bit of data analysis and helping 201 00:11:28,800 --> 00:11:33,040 your organization to understand the impacts of that data. 202 00:11:33,040 --> 00:11:36,320 So tell us, you mentioned that you've got a framework, yeah, 203 00:11:36,480 --> 00:11:39,720 that you've used. So tell us about a framework for 204 00:11:39,720 --> 00:11:43,160 good storytelling that our audience might use in their 205 00:11:43,160 --> 00:11:44,360 work. Yeah, definitely. 206 00:11:44,520 --> 00:11:48,080 So when I think about, you know, storytelling, you know, it has 207 00:11:48,080 --> 00:11:54,760 two main components. 1 is the decision that you're trying to 208 00:11:54,880 --> 00:11:58,200 drive, you know, from the data, obviously whatever data you have 209 00:11:58,200 --> 00:12:00,320 analyzed and what's the decision that you're trying to drive. 210 00:12:00,880 --> 00:12:04,320 And second one is the decision maker that you're trying to 211 00:12:04,320 --> 00:12:07,000 influence. So those are the two important 212 00:12:07,000 --> 00:12:10,520 things that anybody needs to understand that, you know, 213 00:12:10,520 --> 00:12:12,960 what's the decision you're trying to drive and what's the 214 00:12:13,480 --> 00:12:15,240 and, and who's the stakeholder in this? 215 00:12:15,280 --> 00:12:19,800 Who's the decision maker when it comes to driving the decision? 216 00:12:19,920 --> 00:12:22,200 Obviously, you know, there are three more three important 217 00:12:22,200 --> 00:12:23,880 things that somebody needs to look at. 218 00:12:24,400 --> 00:12:28,120 First, obviously you need to analyze the historical data. 219 00:12:28,120 --> 00:12:31,440 What has happened in the past and what did we learn from it, 220 00:12:32,200 --> 00:12:35,640 right in the current context, the problem that we are trying 221 00:12:35,640 --> 00:12:38,360 to solve and why does it matter? Or what did we learn from the 222 00:12:38,360 --> 00:12:43,160 past behaviors and what should we do in the future that will 223 00:12:43,160 --> 00:12:46,760 help achieve the goal? So you know what decision it it 224 00:12:46,760 --> 00:12:49,200 drives. Second one is you know the 225 00:12:49,200 --> 00:12:52,200 stakeholders, which is, you know, what are the decision 226 00:12:52,200 --> 00:12:53,800 makers that you are trying to influence? 227 00:12:54,040 --> 00:12:58,320 And it's absolutely important to know the audience the way I 228 00:12:58,320 --> 00:13:01,120 think about data and storytelling and audiences like 229 00:13:01,120 --> 00:13:03,040 in a think about your Google map, right? 230 00:13:03,480 --> 00:13:08,360 I would say data is Google map or in Apple maps or any map, the 231 00:13:08,360 --> 00:13:11,880 story is the journey. You know, you're traversing from 232 00:13:11,880 --> 00:13:14,600 one point to another and destination is really the 233 00:13:14,600 --> 00:13:17,120 stakeholder, right? So that's the framework that you 234 00:13:17,120 --> 00:13:21,400 know, anybody needs to to think about and really use that in a 235 00:13:21,400 --> 00:13:24,800 data is really just a means to an end story storytelling. 236 00:13:24,800 --> 00:13:28,560 And it's the journey which is as important as the destination. 237 00:13:28,560 --> 00:13:31,680 And obviously, you know, the end goal is the destination, right. 238 00:13:32,080 --> 00:13:33,000 Obviously. Yeah. 239 00:13:33,560 --> 00:13:36,280 Well, I was going to say, what do you say to those folks who 240 00:13:36,280 --> 00:13:39,600 are thinking, but I've spent all this time on this data analysis. 241 00:13:40,680 --> 00:13:44,160 Isn't that important in and of itself that I've done this. 242 00:13:45,000 --> 00:13:48,280 How does it so help us build that bridge? 243 00:13:48,720 --> 00:13:53,240 How does the analyst bridge all of that work that they've done 244 00:13:53,400 --> 00:13:57,480 into that plan for telling the story? 245 00:13:58,240 --> 00:14:02,520 Yeah, absolutely. So the way I think about it is a 246 00:14:02,600 --> 00:14:06,320 you need to be aware of what's happening in and around you, 247 00:14:06,600 --> 00:14:08,760 right? Obviously, in the business 248 00:14:08,760 --> 00:14:11,560 context, awareness is extremely important. 249 00:14:11,560 --> 00:14:15,520 And what does awareness mean? Suppose I'll take an example, 250 00:14:15,520 --> 00:14:18,200 You know, you're working in marketing. 251 00:14:18,600 --> 00:14:22,160 All right, awareness means, you know, you need to be aware of 252 00:14:22,160 --> 00:14:25,480 what the products and services the company is offering, OK, 253 00:14:25,840 --> 00:14:29,000 because the value proposition is extremely important and how does 254 00:14:29,000 --> 00:14:31,400 your value proposition differentiate from others, 255 00:14:31,880 --> 00:14:33,680 right. You need to understand what are 256 00:14:33,680 --> 00:14:37,360 the operations and you know, supply chain, dealer, retailers, 257 00:14:37,360 --> 00:14:41,160 commissions, all those things. You know, you need to understand 258 00:14:41,320 --> 00:14:47,080 macro economic environment that is immigration, you know other 259 00:14:47,080 --> 00:14:50,760 things that tax and all those things, you know that impact a 260 00:14:50,760 --> 00:14:52,920 lot of things. You need to understand what some 261 00:14:52,920 --> 00:14:55,160 of the natural things that are going on in the North East 262 00:14:55,160 --> 00:14:58,720 recently, you know we got hit by a massive snowstorms, right. 263 00:14:58,800 --> 00:15:01,360 You need to know about competitive pricing, promos, all 264 00:15:01,360 --> 00:15:03,120 those things. So when you have an awareness, 265 00:15:03,440 --> 00:15:06,080 you've already done the groundwork about OK, you know, 266 00:15:06,080 --> 00:15:08,360 this is in general, this is what is happening. 267 00:15:08,360 --> 00:15:11,000 So more often than not, the analysis that you'll produce, 268 00:15:11,440 --> 00:15:15,640 we'll tie back to one of these 5 or 6 things that I told you, 269 00:15:15,640 --> 00:15:18,440 right? Product, operational, 270 00:15:18,440 --> 00:15:22,640 macroeconomic environment, you know, calamities are, you know, 271 00:15:23,280 --> 00:15:28,560 competitive of those things. Now, once you know all of these 272 00:15:28,560 --> 00:15:32,760 things, then you start the actual work of, I'll take the 273 00:15:32,760 --> 00:15:34,640 example of building a slide deck, OK? 274 00:15:34,640 --> 00:15:39,200 Because I first want to cover, you know, hey, let's go through 275 00:15:39,200 --> 00:15:41,720 like, you know, how, how will you put that in the paper in the 276 00:15:41,760 --> 00:15:43,600 on the slide deck? And then you will go and do it. 277 00:15:43,680 --> 00:15:45,760 OK, You know, how would you communicate that verbally? 278 00:15:46,320 --> 00:15:50,560 All right, so good story. Let's say has 6 important 279 00:15:50,560 --> 00:15:53,840 things. First is the problem that you're 280 00:15:53,840 --> 00:15:58,040 trying to solve that that's you want to have a problem. 281 00:15:58,560 --> 00:15:59,880 Otherwise, you know, you have a solution. 282 00:15:59,880 --> 00:16:02,400 But if you're trying to find the problem, then you know nothing's 283 00:16:02,400 --> 00:16:04,400 going to get accomplished. That's right. 284 00:16:04,400 --> 00:16:06,600 So you need to start with the problem that you're trying to 285 00:16:06,600 --> 00:16:10,360 solve, all right, every problem, once you identify what the 286 00:16:10,360 --> 00:16:14,720 problem is, obviously this, you do all the data analysis, right? 287 00:16:14,720 --> 00:16:17,480 You dig into like, you know, OK, now sales are down, sales are 288 00:16:17,480 --> 00:16:19,520 up. You know, now what's driving 289 00:16:19,520 --> 00:16:23,600 that on one of the channels or or there's something happened 290 00:16:23,600 --> 00:16:26,520 with the competitor promo, all those things, you know, so you 291 00:16:26,520 --> 00:16:29,720 identify this is the root cause that I have. 292 00:16:30,480 --> 00:16:35,840 Then you know that OK, these are the actions that I might you 293 00:16:35,840 --> 00:16:39,560 need to take in order to solve that problem. 294 00:16:39,560 --> 00:16:42,280 So that would be the third thing you know that you need to put is 295 00:16:42,400 --> 00:16:44,320 you know, hey, these are the actions. 296 00:16:44,320 --> 00:16:47,200 Not a lot of times you can have multiple solutions to solve a 297 00:16:47,200 --> 00:16:51,440 problem, all right, If you're trying to get more revenue, 298 00:16:51,480 --> 00:16:54,240 either you can sell more of the product or you can increase 299 00:16:54,240 --> 00:16:56,520 prices, right? So there are two solutions for 300 00:16:56,520 --> 00:17:00,880 this problem. So always. 301 00:17:00,880 --> 00:17:04,079 So most of the storytelling work that I have seen in A stops at 302 00:17:04,079 --> 00:17:06,839 this point, which is, you know, hey, this is the problem, this 303 00:17:06,839 --> 00:17:09,839 is the root cause and this is what the solutions may be. 304 00:17:10,560 --> 00:17:12,760 The real work actually starts after this. 305 00:17:13,000 --> 00:17:16,280 And the buying, the process of buying actually starts after 306 00:17:16,280 --> 00:17:19,040 this. Because what the executives or 307 00:17:19,040 --> 00:17:23,000 what anybody would want to know is, hey, if I do A versus, if I 308 00:17:23,000 --> 00:17:26,119 implement solution A versus solution B that you told me, 309 00:17:26,800 --> 00:17:29,680 what's the impact going to be for each of them? 310 00:17:29,680 --> 00:17:32,920 What's the return on investment I'm going to get for each one of 311 00:17:32,920 --> 00:17:37,320 those solutions? The good story needs to have the 312 00:17:37,320 --> 00:17:42,520 next component of that would be what's it has to be time bound. 313 00:17:42,920 --> 00:17:45,240 What does time bound mean? That is basically, you know, 314 00:17:45,240 --> 00:17:47,960 when can I implement the solution and by when I'll be 315 00:17:47,960 --> 00:17:49,920 able to see the return on investment because you know, it 316 00:17:49,920 --> 00:17:53,320 can't be indefinite, right? I need to most of the companies 317 00:17:53,320 --> 00:17:56,280 obviously you know, we show results on a quarterly basis 318 00:17:56,320 --> 00:17:58,680 going to impact this quarter. Is it going to impact the next 319 00:17:58,680 --> 00:18:02,400 quarter, You know, six months, 12 months down the line, that's 320 00:18:02,400 --> 00:18:04,480 going to be extremely important because that helps with the 321 00:18:04,480 --> 00:18:06,240 prioritization. So those are the some of the 322 00:18:06,240 --> 00:18:10,000 things that executives look at. Last but not the least, I would 323 00:18:10,000 --> 00:18:14,520 also call out that you know, every execution has or every 324 00:18:14,520 --> 00:18:16,720 solution that you have as an execution risk. 325 00:18:17,320 --> 00:18:20,080 Sometimes, you know, you might need legal approval, sometimes 326 00:18:20,080 --> 00:18:23,840 you need new contract, you have contractual obligations, 327 00:18:23,840 --> 00:18:27,320 sometimes you need to enter into new contracts, all those things. 328 00:18:28,000 --> 00:18:30,360 So really the first three are like table stakes. 329 00:18:30,360 --> 00:18:32,800 You know, everybody typically do that, which is, you know, 330 00:18:32,880 --> 00:18:37,040 identify the problem, identify the root cause and have the 331 00:18:37,040 --> 00:18:40,080 recommended solutions. But what executives really are 332 00:18:40,080 --> 00:18:44,040 looking for is beyond that, which is, hey, what's the ROI 333 00:18:44,040 --> 00:18:46,440 for each of the solutions that you have put for me? 334 00:18:46,920 --> 00:18:48,960 What's how soon can I implement it? 335 00:18:48,960 --> 00:18:51,760 Or, you know, when can I start seeing the benefits out of any 336 00:18:51,760 --> 00:18:57,360 solution and what's the execution risk once we have 337 00:18:57,360 --> 00:18:59,400 once. So that's that I feel is the 338 00:18:59,400 --> 00:19:02,600 foundation for any good story, which is like in these 5 or 6 339 00:19:02,600 --> 00:19:05,040 things. So if I think about like from a 340 00:19:05,040 --> 00:19:07,480 slides perspective, and obviously you build all these 341 00:19:07,480 --> 00:19:12,120 things in the slide deck for for anybody, a good story will 342 00:19:13,080 --> 00:19:17,400 always make sure that you know the audience or whosoever is 343 00:19:17,400 --> 00:19:24,200 viewing the content they go away with, you know with what what 344 00:19:24,200 --> 00:19:27,360 what's the. They go away with. 345 00:19:27,480 --> 00:19:29,600 Hey, what's the insight that is driving? 346 00:19:29,600 --> 00:19:33,040 So you know, what's if you say everything in a sentence, you 347 00:19:33,040 --> 00:19:35,400 know what that is? A lot of times what I see is 348 00:19:35,400 --> 00:19:37,480 that, you know, I'll receive this, you know, 5 or 6 slide 349 00:19:37,480 --> 00:19:40,200 deck and hey, you know, please find this slide deck attached 350 00:19:40,200 --> 00:19:42,640 with this e-mail and I'll go through it and I'll go through 8 351 00:19:42,640 --> 00:19:45,600 or 10 slides. The challenge becomes, you know, 352 00:19:47,120 --> 00:19:50,560 AI need we need to find time from a busy schedules in order 353 00:19:50,560 --> 00:19:52,800 to go through all those things. And, you know, a lot of times, 354 00:19:52,800 --> 00:19:54,320 you know, you have to interpret on your own. 355 00:19:55,120 --> 00:19:57,840 A good storyteller will always put an executive summary. 356 00:19:59,480 --> 00:20:02,080 All right. So once you have done the hard 357 00:20:02,080 --> 00:20:05,560 work, you know, that's when the smart work comes into picture, 358 00:20:05,560 --> 00:20:11,360 which is executive summary. Always go if you're sending an 359 00:20:11,360 --> 00:20:13,520 e-mail, you know, with a slide deck or something, you know, 360 00:20:13,520 --> 00:20:17,480 always make put an executive summary in the body of e-mail. 361 00:20:17,480 --> 00:20:21,520 A lot of times, you know, people may not even have time to go 362 00:20:21,520 --> 00:20:24,080 over everything. But if you can just, you know, 363 00:20:24,080 --> 00:20:26,200 put. Your points in two or three 364 00:20:26,200 --> 00:20:28,400 simple sentences. Hey, here's the problem, you 365 00:20:28,400 --> 00:20:30,440 know, sales are down in Southeast. 366 00:20:31,000 --> 00:20:33,600 The root 'cause that we found was in a competitor launched a 367 00:20:33,600 --> 00:20:36,880 promotion that drove their sales and kind of took some volumes 368 00:20:36,880 --> 00:20:41,120 away from us. And we need to, you know, launch 369 00:20:41,160 --> 00:20:43,720 a similar promo or at least you know better launch a better 370 00:20:43,720 --> 00:20:48,840 promo to kind of get retain that market share and this is when we 371 00:20:48,880 --> 00:20:51,960 can launch it. So if you put an executive 372 00:20:51,960 --> 00:20:55,920 summary, you know, that does take a lot of burden from 373 00:20:55,920 --> 00:20:58,840 anybody who's, you know, going through all that slide deck and 374 00:20:58,840 --> 00:21:01,640 really helps you get the vine and think about that executive. 375 00:21:01,640 --> 00:21:04,440 Somebody has your elevator pitch as well, right? 376 00:21:04,720 --> 00:21:09,160 If you meet met that executive in the elevator and and they ask 377 00:21:09,160 --> 00:21:11,600 what's going on, you know, you already have your talk track 378 00:21:11,600 --> 00:21:14,000 ready, but two or three simple sentences. 379 00:21:14,000 --> 00:21:17,200 So that's the beauty of a storytelling and that's getting 380 00:21:17,200 --> 00:21:20,400 the vine. And I think it's deceptively 381 00:21:20,400 --> 00:21:24,600 simple because you know, from I'm kind of synthesizing what 382 00:21:24,600 --> 00:21:27,280 you're saying. So when you're, you know, the 383 00:21:27,280 --> 00:21:31,680 data analysis is really not just the particular problem that 384 00:21:31,680 --> 00:21:36,080 you're trying to solve, but also part of our work is to 385 00:21:36,080 --> 00:21:39,440 understand that in the context of the broader organization. 386 00:21:40,080 --> 00:21:43,440 And then on top of that, we need to know who it is we're talking 387 00:21:43,440 --> 00:21:48,040 to because that is going to help us probably to craft our 388 00:21:48,280 --> 00:21:53,760 recommendation, our stories, and what kinds of decisions either 389 00:21:53,760 --> 00:21:57,800 they can make given their level or that we need for them to make 390 00:21:58,160 --> 00:22:01,080 or that we need their influence to help get made. 391 00:22:01,480 --> 00:22:04,040 That's a lot of work in and of itself. 392 00:22:05,040 --> 00:22:09,840 And then on top of that, I think what I'm hearing you say is that 393 00:22:10,280 --> 00:22:14,000 we need to be able to tell stories in a way that is 394 00:22:14,000 --> 00:22:18,200 succinct, that gets to the point, that has a perspective. 395 00:22:18,200 --> 00:22:21,240 And probably you didn't say this, but I have to believe this 396 00:22:21,240 --> 00:22:23,960 is true. The story has to have a point. 397 00:22:24,000 --> 00:22:29,120 And likely the point of that story is they need to feel some 398 00:22:29,120 --> 00:22:33,240 sense of urgency in order to make a decision, and likely we 399 00:22:33,240 --> 00:22:36,800 are providing a recommendation on that as part of it. 400 00:22:36,800 --> 00:22:40,920 Does that kind of sound like all of the elements of storytelling? 401 00:22:41,240 --> 00:22:43,440 Yeah, absolutely. And if you stick to, you know, 402 00:22:43,520 --> 00:22:46,520 that framework, 5 or 6 things that I mentioned, you know that 403 00:22:46,520 --> 00:22:49,520 that's going to get by. And obviously, like, you know, 404 00:22:50,080 --> 00:22:54,080 the content has to be there, but some of the which obviously 405 00:22:54,080 --> 00:22:56,760 depends on, you know, which function you're working, what 406 00:22:56,760 --> 00:22:58,760 most type of problem that you're trying to solve. 407 00:22:59,520 --> 00:23:04,720 But the two or three things that you know, folks miss a lot of 408 00:23:04,720 --> 00:23:07,400 times is, you know, what's the urgency in terms of, you know, 409 00:23:07,400 --> 00:23:11,800 when it can be launched or delivered, What's the ROI and 410 00:23:12,280 --> 00:23:14,920 the risks are flagging. The risks are extremely 411 00:23:14,920 --> 00:23:18,240 important. You can build a great strategy 412 00:23:18,240 --> 00:23:21,520 and you can build, you know, great promotion or anything, but 413 00:23:21,520 --> 00:23:24,200 if you're not able to execute it, everything goes in vane. 414 00:23:24,720 --> 00:23:30,040 So de risking de risking the project or de risk in the work 415 00:23:30,040 --> 00:23:32,240 is extremely important. And that is something that you 416 00:23:32,240 --> 00:23:35,120 know, any leader would want to know because you know, you can 417 00:23:35,120 --> 00:23:37,800 create all this hype, but you know, if you're, if you, if the 418 00:23:37,800 --> 00:23:40,840 project falls through the cracks, you know, later on. 419 00:23:42,520 --> 00:23:44,120 All the efforts. Gets wasted. 420 00:23:45,600 --> 00:23:49,000 So it's. So there's a lot of analysis 421 00:23:49,000 --> 00:23:52,120 that happens. But also I am wondering if you 422 00:23:52,120 --> 00:23:55,640 find yourself when you are getting ready to go in front of 423 00:23:55,640 --> 00:23:59,600 your stakeholders, do you find yourself personally like doing a 424 00:23:59,600 --> 00:24:02,760 lot of preparation for how you're going to tell the story 425 00:24:02,760 --> 00:24:05,680 and what that's going to sound like and how long it's going to 426 00:24:05,680 --> 00:24:07,640 take? Is that also an element that 427 00:24:07,640 --> 00:24:09,200 you. Yeah. 428 00:24:09,560 --> 00:24:13,320 See, at the end of the day, yeah, at the end of the day, 429 00:24:13,480 --> 00:24:17,000 whatever work, everyone wants to make sure that the work that 430 00:24:17,000 --> 00:24:21,360 they're doing is right. And ground work is extremely 431 00:24:21,360 --> 00:24:23,400 important. You have done all the hard work, 432 00:24:24,000 --> 00:24:25,760 which is, you know, you have analyzed the data. 433 00:24:25,760 --> 00:24:28,280 You have, like, you know, pulled data from multiple different 434 00:24:28,280 --> 00:24:33,880 sources, ran models, everything. But a lot of times, you know, if 435 00:24:33,880 --> 00:24:36,640 you do, don't do this prep work and everything falls through. 436 00:24:37,080 --> 00:24:40,160 So I'll give you an example. Like there are certain pitfalls 437 00:24:40,160 --> 00:24:43,720 I would call that, you know, people probably miss, but it's 438 00:24:43,720 --> 00:24:46,640 extremely important to make sure that, you know, before even 439 00:24:46,640 --> 00:24:49,920 presenting you take care of that, right? 440 00:24:50,280 --> 00:24:55,000 So a lot of times what I see is. Analysts by. 441 00:24:55,000 --> 00:24:58,160 Nature, you know, we have done a lot of hard work in order to run 442 00:24:58,160 --> 00:25:01,160 all these models and everything. So we have a tendency to put 443 00:25:01,160 --> 00:25:04,720 everything on the slide. Basically we are showing, you 444 00:25:04,720 --> 00:25:08,280 know, how we have done the work and how we have arrived at 445 00:25:08,680 --> 00:25:12,440 particular recommendation. Well, a lot of times what 446 00:25:12,440 --> 00:25:15,760 happens is that you don't need to tell how the sausage is made. 447 00:25:16,560 --> 00:25:18,320 For the most part. You know, everybody is 448 00:25:18,320 --> 00:25:21,640 interested in knowing, you know, how the sausage is and how 449 00:25:21,720 --> 00:25:24,480 what's the aroma, but you know, you don't want to show how the 450 00:25:24,480 --> 00:25:26,160 sausage is made. So that's one of the common 451 00:25:26,160 --> 00:25:29,000 pitfalls that I see is that, you know, you tend to show a lot of 452 00:25:29,000 --> 00:25:35,440 things on the slide or anywhere, which is good, but it kind of 453 00:25:35,440 --> 00:25:37,880 deviates the audience on the main thing. 454 00:25:38,840 --> 00:25:41,440 A lot of times what I also see is that, you know, your data 455 00:25:41,440 --> 00:25:43,760 does not match the golden source of truth. 456 00:25:44,400 --> 00:25:47,000 So let's make sure that you know, whatever the golden source 457 00:25:47,000 --> 00:25:50,480 of truth is for the company, just make sure that you know, 458 00:25:50,480 --> 00:25:52,240 everything is matching at the high level. 459 00:25:52,800 --> 00:25:56,400 Because if you are saying something and it's different 460 00:25:56,400 --> 00:25:59,720 from what people know at the top of their head what the metrics 461 00:25:59,720 --> 00:26:01,920 are, then you know, and then the question becomes say, is the 462 00:26:01,920 --> 00:26:04,560 data even correct? So just make sure that you know, 463 00:26:04,720 --> 00:26:07,960 you do the due diligence and make sure that it's matching. 464 00:26:08,720 --> 00:26:10,720 Third thing that I'll say is very important is 465 00:26:10,720 --> 00:26:15,720 stakeholdering. As analysts, you're bound to get 466 00:26:15,720 --> 00:26:20,760 insights that are that are kind of different from the hypothesis 467 00:26:20,760 --> 00:26:25,040 that prevails, right? And in that a scenario, if 468 00:26:25,040 --> 00:26:29,680 you're making a grand reveal of that insight in front of the 469 00:26:29,680 --> 00:26:32,640 executives, then you better stakeholder with the program 470 00:26:32,640 --> 00:26:36,120 leads and make sure that like, you know, everybody In Sync, you 471 00:26:36,120 --> 00:26:38,760 do the ground work So that you know, once you make the grand 472 00:26:38,760 --> 00:26:41,600 review, you actually talk about what actions you're going to 473 00:26:41,600 --> 00:26:43,720 take if the program is not performing. 474 00:26:43,880 --> 00:26:45,720 If you're done campaign analytics and you know the 475 00:26:45,720 --> 00:26:48,920 program is not performing, you better stakeholder with the 476 00:26:48,920 --> 00:26:51,720 program lead first before making the grand reveal in front of the 477 00:26:51,880 --> 00:26:53,720 executives. Otherwise, another question 478 00:26:53,720 --> 00:26:55,760 would be, hey, you know, what are we OK? 479 00:26:55,760 --> 00:26:58,120 You know, you have told me this, what can I do about it? 480 00:26:58,480 --> 00:27:00,920 And then it comes on you that hey, you know, I don't know, not 481 00:27:01,600 --> 00:27:03,320 a good answer. The program lead. 482 00:27:03,320 --> 00:27:05,440 And then what you have done is you have thrown the program lead 483 00:27:05,440 --> 00:27:07,560 under the bus. That should not happen. 484 00:27:07,600 --> 00:27:09,760 You need to do the stakeholdering before that so 485 00:27:09,760 --> 00:27:14,880 that you put a combined front that hey, look, obviously in all 486 00:27:14,880 --> 00:27:19,000 transparency, we absolutely need to tell the executives that hey, 487 00:27:19,000 --> 00:27:21,000 you know, something is not performing. 488 00:27:21,000 --> 00:27:23,840 But, you know, this is we brainstormed, you know, before 489 00:27:23,840 --> 00:27:26,360 you when you asked and this is the action or this is the course 490 00:27:26,360 --> 00:27:27,720 correction that we are going to do. 491 00:27:28,080 --> 00:27:30,400 It actually fosters collaboration and, you know, 492 00:27:30,400 --> 00:27:32,480 builds a relationship with the program leads. 493 00:27:32,680 --> 00:27:34,720 So that is something that's extremely important. 494 00:27:37,120 --> 00:27:39,840 And well, I was going to say, you're right. 495 00:27:39,840 --> 00:27:43,080 Sometimes the stories that we're going to have to tell are not 496 00:27:43,080 --> 00:27:47,360 happy ones. And so, you know, if you are 497 00:27:47,360 --> 00:27:51,360 going to be the deliverer of the story, that's not a happy one, 498 00:27:51,720 --> 00:27:56,120 it probably is a smart thing to do to talk with your allies, to 499 00:27:56,120 --> 00:27:59,280 give some folks some heads up, to make sure that there's a bit 500 00:27:59,280 --> 00:28:02,160 of a united front. Because if you're out there 501 00:28:02,160 --> 00:28:05,160 telling a story and it's going to be a surprise to people, that 502 00:28:05,760 --> 00:28:07,200 is probably not a good place to be. 503 00:28:07,800 --> 00:28:09,360 Yeah, agreed. Agreed. 504 00:28:09,600 --> 00:28:12,360 And sometimes I also see that, you know, people get hung up on 505 00:28:12,360 --> 00:28:15,080 the tools. Obviously, we have so many data 506 00:28:15,080 --> 00:28:16,720 visualization tools out there. Yeah. 507 00:28:17,480 --> 00:28:20,320 And hey, this tool is cool. This tool is really good. 508 00:28:20,800 --> 00:28:24,080 At the end of the day, tool is just a means when and and we 509 00:28:24,080 --> 00:28:28,680 need to even Excel or Google sheet works as long as it's 510 00:28:29,600 --> 00:28:30,960 driving. Some action. 511 00:28:31,080 --> 00:28:33,640 So don't get hung up a lot on the tools but. 512 00:28:35,280 --> 00:28:38,600 Understand. What insights you're able to get 513 00:28:38,600 --> 00:28:40,520 and what actions in it will drive. 514 00:28:42,120 --> 00:28:44,840 That's such a good. Reminder, because I know that 515 00:28:44,840 --> 00:28:49,320 people really do feel a particular way about a tool that 516 00:28:49,320 --> 00:28:53,600 they use as if that will, you know, all I have to do is just 517 00:28:53,600 --> 00:28:56,160 give my stakeholders access to this tool and they'll just see 518 00:28:56,160 --> 00:29:00,280 it all. There is a use for our work as 519 00:29:00,280 --> 00:29:04,120 being the reader of the data, the teller of the data. 520 00:29:04,560 --> 00:29:07,680 Don't give that up to a tool because the tool can't know the 521 00:29:07,680 --> 00:29:10,640 nuances of all of the things that you've talked about today. 522 00:29:11,000 --> 00:29:13,880 That's really important and that is important work that business 523 00:29:13,880 --> 00:29:16,880 analysis professionals do. But I've got a question for you 524 00:29:17,400 --> 00:29:21,120 on the kind of the tail end of sometimes our data can tell a 525 00:29:21,120 --> 00:29:26,000 not happy story, but we can also tell good stories with bad data. 526 00:29:26,800 --> 00:29:29,440 Does that sometimes happen? And what what do you do? 527 00:29:29,600 --> 00:29:31,320 Well, how do you get out of that mess? 528 00:29:32,120 --> 00:29:34,840 Well, that's called making up some shit on the fly. 529 00:29:35,440 --> 00:29:37,880 That's what I'll say. And you can get away with that, 530 00:29:37,880 --> 00:29:41,040 you know, once, maybe twice. But at the end of the day, like, 531 00:29:41,040 --> 00:29:45,800 you know, folks are who are ahead of us in the chain will 532 00:29:45,880 --> 00:29:48,280 realize and figure out that, you know, you're making up. 533 00:29:48,560 --> 00:29:53,040 So that kind of. That affects your. 534 00:29:53,040 --> 00:29:57,240 Credibility that that's what I'll say and and. 535 00:29:57,240 --> 00:29:59,000 It goes so. Credibility, building 536 00:29:59,000 --> 00:30:01,320 credibility is very important because you know, once you build 537 00:30:01,320 --> 00:30:04,920 that credibility, you don't have to show a lot of work on the 538 00:30:04,920 --> 00:30:09,200 slide, right. If because you know, a lot of us 539 00:30:09,720 --> 00:30:12,600 when we are trying to show a lot of our work on the slide or 540 00:30:12,600 --> 00:30:15,800 anywhere, you know, we are trying to build that credibility 541 00:30:15,800 --> 00:30:18,000 that look, you know, I've done my due diligence. 542 00:30:18,280 --> 00:30:20,720 But once you build that, then you don't need to show that 543 00:30:20,720 --> 00:30:23,440 because you know the expectation is that you have already done 544 00:30:23,560 --> 00:30:28,960 all the the legwork and and. And we? 545 00:30:28,960 --> 00:30:31,080 Are going to take it at face value, whatever you're saying. 546 00:30:31,080 --> 00:30:33,520 So. So so, you know, one or once or 547 00:30:33,520 --> 00:30:36,480 twice and you can get away with that, but not not often. 548 00:30:37,200 --> 00:30:39,800 OK. So that's really important, I 549 00:30:39,800 --> 00:30:45,240 think, for people to take away is that part of storytelling is 550 00:30:45,240 --> 00:30:48,800 the storyteller. That's an important element to 551 00:30:48,800 --> 00:30:52,080 this. And if you have a reputation for 552 00:30:52,360 --> 00:30:54,760 credibility, you know what you're doing. 553 00:30:54,760 --> 00:30:56,600 You've been in front of them before. 554 00:30:56,640 --> 00:30:59,440 They've seen good results in some ways. 555 00:30:59,440 --> 00:31:01,600 That saves you some work is what I'm hearing. 556 00:31:01,600 --> 00:31:04,360 It saves you some work in the future, but you still have to do 557 00:31:04,360 --> 00:31:06,960 the work today in order to build that trust. 558 00:31:07,080 --> 00:31:09,680 So trust is an important part of storytelling. 559 00:31:10,680 --> 00:31:12,800 Absolutely. Yeah, yeah. 560 00:31:13,160 --> 00:31:13,880 Wow. OK. 561 00:31:13,880 --> 00:31:17,680 So that's a big deal. Now, we are Live Today. 562 00:31:17,680 --> 00:31:20,400 So for the folks that are listening to us, we're live. 563 00:31:20,400 --> 00:31:24,040 What questions do you have about data storytelling? 564 00:31:24,160 --> 00:31:27,160 I'm going to pop a comment in. I think this is more of a 565 00:31:27,160 --> 00:31:31,120 comment from a listener and that'll give folks maybe a 566 00:31:31,120 --> 00:31:33,520 minute to think about their questions. 567 00:31:33,520 --> 00:31:37,240 So Doctor Divya Jase Wall, thanks for listening today. 568 00:31:37,240 --> 00:31:40,120 So stories help stakeholders understand the why behind the 569 00:31:40,120 --> 00:31:43,520 data, use data to build a logical case, but craft a 570 00:31:43,520 --> 00:31:46,440 narrative that connects emotionally to encourage by and 571 00:31:46,440 --> 00:31:49,240 see there's that. You got to you got to get their 572 00:31:49,240 --> 00:31:51,640 emotions. The 5C's of storytelling 573 00:31:51,800 --> 00:31:55,560 incorporate circumstance, curiosity, characters, 574 00:31:55,720 --> 00:32:02,000 conversations, and conflict. What do you think about that? 575 00:32:02,920 --> 00:32:05,560 Had you heard about these 5C's of storytelling? 576 00:32:05,560 --> 00:32:08,200 I have not heard that before to be honest. 577 00:32:08,320 --> 00:32:13,320 So in some form of action I have but exact same code. 578 00:32:13,320 --> 00:32:16,560 Probably not, but I completely agree with the comment. 579 00:32:16,800 --> 00:32:19,800 You know, situation, you know, ties back to the problem that 580 00:32:19,800 --> 00:32:23,520 you're trying to solve for and. Then audience will. 581 00:32:23,520 --> 00:32:26,080 Be engaged once they know that you know here this is the root 582 00:32:26,080 --> 00:32:29,680 cause that is driving that problem and you know what can do 583 00:32:29,680 --> 00:32:33,000 about it. Stakeholders are the characters, 584 00:32:33,120 --> 00:32:35,040 right? You need to know the audience 585 00:32:35,040 --> 00:32:39,440 and you need to make sure that like you know, you information 586 00:32:39,440 --> 00:32:44,680 or build your narrative in a way that resonates with them, right? 587 00:32:44,680 --> 00:32:47,280 And that's where all this conversation comes into picture. 588 00:32:47,280 --> 00:32:50,280 And obviously, you know, conflict, something that we 589 00:32:50,280 --> 00:32:52,880 talked about, which is like stakeholding and a lot of that 590 00:32:52,880 --> 00:32:54,400 conflict. And actually, because all 591 00:32:54,400 --> 00:32:58,560 through stakeholder, a lot of times, you know, you'll get an 592 00:32:58,560 --> 00:33:01,760 insight which is obviously no different from what everybody 593 00:33:01,760 --> 00:33:03,880 thought. So that's where, you know, it 594 00:33:03,880 --> 00:33:06,840 becomes extremely important to do the stakeholdering and make 595 00:33:06,840 --> 00:33:09,520 sure that everybody's aligned. You know, we may agree to 596 00:33:09,520 --> 00:33:14,960 disagree, but we should always be aligned and and make sure 597 00:33:14,960 --> 00:33:16,360 that like, you know, we know what's going on. 598 00:33:17,120 --> 00:33:20,400 Yeah, I can imagine that sometimes you're giving, you 599 00:33:20,400 --> 00:33:23,760 know, you're telling the story of the data is you've analyzed 600 00:33:23,760 --> 00:33:26,320 it, but that there could be a stakeholder in the room that 601 00:33:26,320 --> 00:33:29,840 takes something completely different away from the data or 602 00:33:29,840 --> 00:33:32,000 has a completely different understanding of it. 603 00:33:32,000 --> 00:33:36,280 And so being able to navigate that conflict, and I know 604 00:33:36,280 --> 00:33:39,520 sometimes we talk about conflict on here, it is something that 605 00:33:39,520 --> 00:33:43,720 can be a little bit scary, but also it's a one. 606 00:33:43,720 --> 00:33:46,280 It's a great opportunity to build alignment, but it's a 607 00:33:46,280 --> 00:33:49,280 great opportunity for business analysis professionals to build 608 00:33:49,280 --> 00:33:51,920 trust if you can help to work through that. 609 00:33:51,920 --> 00:33:56,520 So it is a really powerful. Thing is to. 610 00:33:56,560 --> 00:33:59,840 Navigate conflict. OK, so I don't see any more 611 00:33:59,840 --> 00:34:03,400 questions yet, so maybe let's keep talking. 612 00:34:03,600 --> 00:34:08,360 We've got a few more minutes. I, I feel like it cannot end 613 00:34:08,360 --> 00:34:13,080 today's conversation with you if I do not ask what the role of AI 614 00:34:13,080 --> 00:34:18,040 is in storytelling. Do you use AI as part of helping 615 00:34:18,040 --> 00:34:23,679 you to prepare or think or brainstorm stories? 616 00:34:23,679 --> 00:34:27,199 Like what do you see the future of AI for storytelling? 617 00:34:28,239 --> 00:34:30,239 Absolutely. You know, the feature is in more 618 00:34:30,239 --> 00:34:31,679 data. I would say, you know, we have a 619 00:34:31,679 --> 00:34:34,360 ton of data already, but the feature is going to be narrower 620 00:34:34,360 --> 00:34:38,000 focus. As we get. 621 00:34:38,000 --> 00:34:42,320 All these models the and AI does the heavy lifting of 622 00:34:42,320 --> 00:34:46,320 calculations. The importance of human 623 00:34:46,320 --> 00:34:51,480 storyteller becomes 10 times more important because that 624 00:34:51,480 --> 00:34:55,280 that's what is going to ultimately Dr. the decisions and 625 00:34:55,280 --> 00:34:57,800 and convert the insights into actions. 626 00:34:58,200 --> 00:35:00,360 So that's going to be extremely important. 627 00:35:00,840 --> 00:35:06,160 Now from our perspective, what I have, what we are observing for 628 00:35:06,160 --> 00:35:09,800 the most part is. Data readiness for. 629 00:35:09,800 --> 00:35:12,720 AI is extremely important. It's like the iceberg, you know, 630 00:35:12,720 --> 00:35:17,560 you see only 1/4 of the iceberg about the surface, but three 4th 631 00:35:17,560 --> 00:35:22,160 is, you know, under the surface. So data readiness is that three 632 00:35:22,160 --> 00:35:25,960 4th portion of that iceberg and what does? 633 00:35:25,960 --> 00:35:28,360 Data readiness. Mean, you know, having the right 634 00:35:28,360 --> 00:35:32,640 definitions, having. Having all the data. 635 00:35:32,640 --> 00:35:37,520 Sources, you know, at one place, making sure that like your you 636 00:35:37,520 --> 00:35:41,800 have the right it is it's like in a garbage and garbage garbage 637 00:35:41,840 --> 00:35:43,320 out. So you need to make sure that 638 00:35:43,320 --> 00:35:45,840 like in whatever data you have like and it's complete, it's 639 00:35:45,840 --> 00:35:48,760 accurate in order to drive the insights from that. 640 00:35:48,880 --> 00:35:51,560 And that that's where I think you know a lot of. 641 00:35:54,080 --> 00:35:59,040 Lot of. I would say failures. 642 00:35:59,040 --> 00:36:01,360 Come into. Picture, you know, in terms of 643 00:36:01,360 --> 00:36:05,120 implementation of the projects is, you know, you just pipelines 644 00:36:05,120 --> 00:36:07,560 are not built properly, your data is not ready and you're 645 00:36:07,560 --> 00:36:10,280 trying to learn something or you know, analyse something from 646 00:36:10,280 --> 00:36:11,600 there. So that's the first and that's 647 00:36:11,600 --> 00:36:13,280 one of the major things that we have done. 648 00:36:14,000 --> 00:36:16,480 We are doing this to make sure that, you know, we have the 649 00:36:16,480 --> 00:36:20,600 right data set up. All the sources of data that we 650 00:36:20,600 --> 00:36:24,280 have, you know, converge at one place and the definitions are 651 00:36:24,280 --> 00:36:27,760 proper and the linkage between all those data sources is proper 652 00:36:27,760 --> 00:36:30,200 so that, you know, you have the transactional data, you have the 653 00:36:30,200 --> 00:36:32,120 behavioral data, you have the demographic state. 654 00:36:33,400 --> 00:36:38,840 Everything kind of ties. In and you are able to track any 655 00:36:38,840 --> 00:36:42,000 particular customer so once you have all that that's like kind 656 00:36:42,000 --> 00:36:44,880 of the ground work and I feel that you know that's extremely 657 00:36:44,880 --> 00:36:48,400 important for any AI project once you do that then obviously, 658 00:36:48,400 --> 00:36:50,840 like you know you got to leverage the tools that are out 659 00:36:50,840 --> 00:36:54,960 there in a copilot general all those to get the insights from 660 00:36:54,960 --> 00:36:58,760 there and really once you set up that data frames correctly, you 661 00:36:58,760 --> 00:37:03,240 know the. The prompt part or or. 662 00:37:04,120 --> 00:37:07,000 That becomes much easier once you have the data set up 663 00:37:07,000 --> 00:37:09,920 correctly, yeah. So what I. 664 00:37:09,920 --> 00:37:14,760 Hear you say, is that in fact, with the introduction of AI into 665 00:37:14,760 --> 00:37:17,760 the data analysis that we would do to prepare ourselves for 666 00:37:17,760 --> 00:37:21,160 these stories, the human work becomes even more essential. 667 00:37:21,400 --> 00:37:24,560 So there's data accuracy. There's also context. 668 00:37:24,560 --> 00:37:26,560 I mean, you know, you've talked about understanding your 669 00:37:26,560 --> 00:37:28,760 environment in your audience. AI can't do that. 670 00:37:29,240 --> 00:37:31,200 You have to do that as the analyst. 671 00:37:31,200 --> 00:37:35,280 And then finally crafting that story and being able to tell it 672 00:37:35,760 --> 00:37:40,400 is absolutely a human thing. In fact, in some ways, as we're 673 00:37:40,400 --> 00:37:44,960 talking about this today, I'm, I am thinking that storytelling 674 00:37:44,960 --> 00:37:49,360 becomes even more essential to maybe more than just the data 675 00:37:49,360 --> 00:37:52,920 work that we're doing, even just a lot of other work, because it 676 00:37:52,920 --> 00:37:56,800 kind of brings us to back together to that human element, 677 00:37:57,720 --> 00:38:00,720 which AI just can't do. So it sounds pretty powerful. 678 00:38:01,280 --> 00:38:04,480 All right, we've got a couple of questions in that time. 679 00:38:04,480 --> 00:38:08,480 All right, maybe we'll try to take two just because we are 680 00:38:08,480 --> 00:38:11,160 kind of running short on time. So here's a good one. 681 00:38:11,160 --> 00:38:14,320 This was from Emmanuel. How do you adapt a data story 682 00:38:14,320 --> 00:38:17,920 when presenting to non-technical stakeholders versus technical 683 00:38:17,920 --> 00:38:20,160 teams? I'm sure that's something that 684 00:38:20,160 --> 00:38:23,200 you have to do all the time. And let me, I'll add this piece 685 00:38:23,200 --> 00:38:25,320 to it because I'm actually curious about this. 686 00:38:26,120 --> 00:38:29,640 What if you've got data analysis that you have to present to 687 00:38:29,640 --> 00:38:33,560 those two audiences? What are the things that you 688 00:38:33,560 --> 00:38:37,880 have to think about those when telling essentially the same 689 00:38:37,880 --> 00:38:39,920 story to two different kinds of audiences? 690 00:38:40,720 --> 00:38:44,600 Yeah, absolutely. The first thing that I try to 691 00:38:44,600 --> 00:38:47,880 understand is what is the metric that the stakeholder is trying 692 00:38:47,880 --> 00:38:52,320 to drive? That is essential if I am 693 00:38:52,320 --> 00:38:55,720 telling that story, to stick to a business person who is a 694 00:38:55,720 --> 00:38:59,640 business executive, right? Obviously, learning the metrics 695 00:38:59,640 --> 00:39:01,880 that they're driving is extremely important. 696 00:39:01,880 --> 00:39:06,360 And take example, you know I work with a lot of executives 697 00:39:06,360 --> 00:39:09,800 who are. One of the primary focus. 698 00:39:09,800 --> 00:39:13,240 Is subscriber growth, you know, either you gain more subscriber 699 00:39:13,240 --> 00:39:16,840 or lose or how do you prevent the subscribers from going to 700 00:39:16,840 --> 00:39:21,520 the competitors? So then the work becomes OK and 701 00:39:21,520 --> 00:39:25,520 how do I tie back my work to these metrics that executive is 702 00:39:25,520 --> 00:39:29,200 trying to drive or he is the DRI or he or she is the DRI and 703 00:39:29,200 --> 00:39:31,640 directly responsible individual, right. 704 00:39:31,840 --> 00:39:34,840 But you know, if I'm talking to the technical teams, take 705 00:39:34,840 --> 00:39:37,760 example, you know the models that they have built, right? 706 00:39:38,160 --> 00:39:43,320 So for them the metric would be we have built a model for 707 00:39:43,720 --> 00:39:47,480 subscriber editions. So for them probably the metric 708 00:39:47,480 --> 00:39:51,120 would be what sort of lift a marketing lift that model is 709 00:39:51,120 --> 00:39:52,640 trying to drive. OK. 710 00:39:52,840 --> 00:39:54,880 So then the conversation becomes, OK, what are the 711 00:39:54,880 --> 00:39:56,640 drivers of that particular model? 712 00:39:56,640 --> 00:39:58,920 What are the things that are impacting that particular model? 713 00:39:58,920 --> 00:40:01,800 So it's really depends on, you know, understanding what metrics 714 00:40:02,160 --> 00:40:05,120 that the technical stakeholder versus the non-technical 715 00:40:05,120 --> 00:40:08,120 stakeholder is driving and then in a curating the content based 716 00:40:08,120 --> 00:40:12,360 on that metric. Yeah, that makes a lot of sense. 717 00:40:12,360 --> 00:40:16,080 So again, understanding the context in which those 718 00:40:16,080 --> 00:40:20,200 stakeholders are working and when you talk about knowing 719 00:40:20,200 --> 00:40:23,000 their metrics, that's the thing that they're most interested in. 720 00:40:23,000 --> 00:40:27,760 So that's like they got you. It's absolutely, you know, if I 721 00:40:28,840 --> 00:40:32,160 if somebody's trying to drive, say obviously everything kind of 722 00:40:32,160 --> 00:40:34,160 converges and ties back together. 723 00:40:34,160 --> 00:40:37,360 But if the same thing, you know, if I'm telling it, hey, you 724 00:40:37,360 --> 00:40:41,520 know, I can increase the lift of this particular model by 2X or 725 00:40:41,520 --> 00:40:45,560 3X and they'll say it's good. But at the end of the day, you 726 00:40:45,560 --> 00:40:48,080 know, the question that they'll ask is OK, then what does it do 727 00:40:48,080 --> 00:40:50,560 to my bottom line or my top line growth? 728 00:40:51,000 --> 00:40:54,040 So it kind of goes back to their metrics. 729 00:40:54,040 --> 00:40:57,520 So understanding those two metrics, those the metrics that 730 00:40:57,720 --> 00:41:00,120 anybody is responsible for this extremely model. 731 00:41:01,680 --> 00:41:02,880 Yeah, I'm. I am. 732 00:41:02,880 --> 00:41:06,360 Thinking so you know, how do we convert that? 733 00:41:06,880 --> 00:41:09,800 This is the metric that I care most about, right? 734 00:41:09,800 --> 00:41:15,520 Every story that you've ever read always has some sort of 735 00:41:15,520 --> 00:41:18,160 element that is the challenge, right? 736 00:41:18,160 --> 00:41:21,120 And so you've got to be able to not speak to. 737 00:41:23,480 --> 00:41:26,480 Gosh, what am I trying to say? Here, so we're not wanting to 738 00:41:26,480 --> 00:41:29,560 focus on specifically the metric, but the sense of urgency 739 00:41:29,560 --> 00:41:33,520 around the growth of that thing. Sir, you're identifying that 740 00:41:33,520 --> 00:41:36,840 challenge as part of the story. Yeah, that's what's really 741 00:41:36,840 --> 00:41:39,200 important. OK, So we've got one more 742 00:41:39,200 --> 00:41:42,520 question here. This question is from Matali and 743 00:41:42,520 --> 00:41:45,520 Dated Storytelling. At what point does a complex 744 00:41:45,520 --> 00:41:49,040 visualization stop being helpful and start becoming a 745 00:41:49,040 --> 00:41:51,480 distraction? That's a good question. 746 00:41:51,680 --> 00:41:55,080 What are the key elements of a truly great slide? 747 00:41:56,040 --> 00:41:58,200 I can do another podcast on this. 748 00:42:00,880 --> 00:42:03,880 What like what would the? SEO even be for that because 749 00:42:03,880 --> 00:42:07,080 that's awesome when your visualization stops the helping 750 00:42:07,080 --> 00:42:10,520 and starts hurting. First of all, like, you know, 751 00:42:10,520 --> 00:42:12,040 visuals. Are extremely important, you 752 00:42:12,040 --> 00:42:15,080 know, because of brain processes visuals faster than the 753 00:42:15,080 --> 00:42:18,240 sentences. And the key to any great visual 754 00:42:18,240 --> 00:42:21,440 is, you know, the three second rule is what I call if the 755 00:42:21,440 --> 00:42:24,440 visual, if somebody looks at the visual for three seconds and if 756 00:42:24,440 --> 00:42:26,600 they are not able to grasp what it's trying to say, you know, 757 00:42:26,600 --> 00:42:30,000 scrap that visual. So obviously simplicity is the 758 00:42:30,000 --> 00:42:34,080 key over here and aesthetics, I won't say matter, but you know, 759 00:42:35,080 --> 00:42:36,920 aesthetics do not matter as much. 760 00:42:36,920 --> 00:42:40,560 The hero of the chart should be the insight that you are trying 761 00:42:41,040 --> 00:42:44,680 to convey. OK, that's extremely important. 762 00:42:44,680 --> 00:42:46,360 Make sure the chart is decluttered. 763 00:42:46,360 --> 00:42:48,360 Like you know, chart is not cluttered. 764 00:42:48,920 --> 00:42:51,680 Simplicity is key over here. Make sure that you know the 765 00:42:51,680 --> 00:42:56,720 fonts and everything are are consistent colors you know 766 00:42:57,840 --> 00:42:59,240 certain. Colors have a negative. 767 00:42:59,240 --> 00:43:02,320 Connotation. So make sure that you know, you 768 00:43:02,320 --> 00:43:06,560 use the colors which are a consistent with the brand with 769 00:43:06,560 --> 00:43:09,840 your branding colors, right? You know, red has a negative 770 00:43:09,840 --> 00:43:11,960 connotation to it. So, you know, if you're doing 771 00:43:11,960 --> 00:43:14,120 financial reporting and all, you know, make sure that you know, 772 00:43:14,120 --> 00:43:17,840 red denotes something which is worse, performing worse and 773 00:43:17,840 --> 00:43:20,320 green which is, you know, performing better and then the 774 00:43:20,320 --> 00:43:22,360 expectations. So that that's important. 775 00:43:22,880 --> 00:43:26,120 What type of chart you use. That is extremely important. 776 00:43:26,280 --> 00:43:29,120 You know a lot of. Times you know you. 777 00:43:29,520 --> 00:43:35,160 Choose a chart which is not representative of what the data 778 00:43:35,160 --> 00:43:38,480 is trying to tell. Like, you know, use like if I'm, 779 00:43:38,560 --> 00:43:42,760 I want to compare the category sales, I have 10 product 780 00:43:42,760 --> 00:43:44,800 categories. I want to compare the sales and 781 00:43:44,800 --> 00:43:47,320 I would rather use a bar chart or a column chart. 782 00:43:47,960 --> 00:43:51,480 If you use in a pie chart for that, for showing, you know, 10 783 00:43:51,480 --> 00:43:54,520 different categories, you're the size of the pie would be like, 784 00:43:54,520 --> 00:43:56,840 you know, less than the pieces like. 785 00:43:58,240 --> 00:44:00,800 So use the charts judiciously is what I'll say. 786 00:44:00,800 --> 00:44:04,000 You know, if you're using a pie chart, no more than two or three 787 00:44:05,120 --> 00:44:08,360 pies is what. I'll say trend. 788 00:44:08,360 --> 00:44:12,760 Lines you should use, you know, if you are just a line chart, if 789 00:44:12,760 --> 00:44:15,640 you are trying to analyze the trend over a period of time that 790 00:44:15,640 --> 00:44:19,320 that's extremely important. Some correlations, if you are 791 00:44:19,320 --> 00:44:21,560 trying to demonstrate correlations, then you know, 792 00:44:21,560 --> 00:44:23,360 obviously you scatter plot and all. 793 00:44:23,920 --> 00:44:27,240 So it's kind of, you know, that's storytelling one O 1 794 00:44:27,440 --> 00:44:30,400 actually, I would say and extremely important, you know, 795 00:44:30,400 --> 00:44:33,520 how do you visualize and a couple of other things that 796 00:44:33,520 --> 00:44:36,280 comes to my mind is, you know, legends and all, you know, make 797 00:44:36,280 --> 00:44:40,840 sure that legends and are clearly visible so that you 798 00:44:40,840 --> 00:44:43,760 know, people understand. One thing I often see is that, 799 00:44:43,760 --> 00:44:47,120 you know, on the data labels, suppose you are showing sales, 800 00:44:47,160 --> 00:44:48,760 right? And if the sales are like, you 801 00:44:48,760 --> 00:44:51,800 know, $5,000,000 for a particular product category a 802 00:44:51,800 --> 00:44:56,040 month, a lot of times you know, you just write 5 and followed by 803 00:44:56,040 --> 00:44:59,760 6 zeros. Well, that's a big the dust 804 00:44:59,760 --> 00:45:01,840 platter. So in an abbreviated data 805 00:45:01,840 --> 00:45:03,560 labels, you know, that's right, five M. 806 00:45:04,240 --> 00:45:07,240 Those are some of the basics and some of the important things 807 00:45:07,240 --> 00:45:11,760 that you should care about for making the visual simple. 808 00:45:12,360 --> 00:45:14,400 You're going to come back in June, you're going to do a 809 00:45:14,400 --> 00:45:16,280 webinar with us. And I feel like maybe 810 00:45:16,280 --> 00:45:18,960 visualizations is what is maybe the topic. 811 00:45:19,360 --> 00:45:22,360 I'm just putting that out there. Maybe visualizations are the 812 00:45:22,360 --> 00:45:25,280 topic because I think you've got some good advice and I think a 813 00:45:25,280 --> 00:45:30,520 lot of people are interested in not just, you know, how do I 814 00:45:30,520 --> 00:45:34,120 create one, but those elements that you've given, I think could 815 00:45:34,120 --> 00:45:37,880 be really helpful. Now, I don't you either you said 816 00:45:37,880 --> 00:45:42,400 this or I, I interpreted this, but I think you said something 817 00:45:42,400 --> 00:45:46,440 like you're the hero in this story, not the slide, but it 818 00:45:46,440 --> 00:45:48,440 helps. And what that made me think of 819 00:45:48,440 --> 00:45:52,840 is like Spider Man. So I mean, he's got that suit. 820 00:45:52,840 --> 00:45:56,400 The suit's really important, but actually it's Spider Man himself 821 00:45:56,400 --> 00:45:59,920 that does the things. It's just the suit makes him 822 00:46:00,160 --> 00:46:03,160 noticeable. It really backs up who he is and 823 00:46:03,160 --> 00:46:05,520 what he does. And I don't know if that's a 824 00:46:05,520 --> 00:46:08,200 great analogy for what we're talking about with data 825 00:46:08,200 --> 00:46:12,960 storytelling, but maybe that's a different way of kind of, you 826 00:46:12,960 --> 00:46:16,160 know, the difference between the storyteller and the stuff that 827 00:46:16,160 --> 00:46:18,760 backs up the storyteller is maybe that. 828 00:46:19,960 --> 00:46:21,600 Yeah. Storytelling is marketing your 829 00:46:21,600 --> 00:46:23,960 data analysis. That that's what I'll say, you 830 00:46:23,960 --> 00:46:26,120 know. Yeah, it's I'm a marketer. 831 00:46:26,160 --> 00:46:29,720 And in other storytelling is marketing your analysis work 832 00:46:29,720 --> 00:46:32,480 that you have done. And without marketing, you're 833 00:46:32,480 --> 00:46:35,400 not going to get far. Yeah, that's right. 834 00:46:35,520 --> 00:46:37,200 Yeah. Exactly. 835 00:46:37,240 --> 00:46:39,560 All right. Well, this has been a fantastic 836 00:46:39,560 --> 00:46:43,280 conversation today. I really thank you so much for 837 00:46:43,280 --> 00:46:45,600 your time. And like I said, you're going to 838 00:46:45,600 --> 00:46:49,840 come back in a couple of months and do a member webinar with us. 839 00:46:49,840 --> 00:46:53,080 And I know today we were kind of kicking around whether we might 840 00:46:53,440 --> 00:46:57,160 identify some topics for that. And so maybe we've got a couple. 841 00:46:57,160 --> 00:46:59,960 So we'll have to. Yeah, we'll be back in touch so 842 00:46:59,960 --> 00:47:02,000 we can learn more about that. Thank you again. 843 00:47:02,520 --> 00:47:04,760 Thanks a lot, Suzanne. It was a pleasure. 844 00:47:04,760 --> 00:47:06,560 And look, looking forward to the next one. 845 00:47:07,160 --> 00:47:08,080 Yeah. Take care. 846 00:47:08,320 --> 00:47:10,680 We'll see you. Thank you. 847 00:47:10,680 --> 00:47:14,880 All right. So I, you know, maybe you are 848 00:47:14,880 --> 00:47:18,200 thinking about data storytelling a little bit differently now. 849 00:47:18,640 --> 00:47:21,440 You've got some frameworks, you've got some things to do and 850 00:47:21,440 --> 00:47:23,960 things not to do. I hope that helps make 851 00:47:23,960 --> 00:47:26,200 storytelling a little more accessible to you. 852 00:47:26,840 --> 00:47:30,360 We're going to see you again in two weeks and I'll have two 853 00:47:30,360 --> 00:47:31,680 guests for that. One. 854 00:47:31,960 --> 00:47:34,720 We're going to talk about the process improvement mistakes 855 00:47:34,720 --> 00:47:39,000 that analysts make and this team of business analysis 856 00:47:39,000 --> 00:47:42,280 professionals, they are going to tell us not only some of the 857 00:47:42,280 --> 00:47:46,320 mistakes they made, but how they learned to do process 858 00:47:46,320 --> 00:47:49,760 improvement a little differently and how they have built that 859 00:47:50,080 --> 00:47:54,560 into an upcoming BBC tutorial that they're going to give. 860 00:47:54,760 --> 00:47:57,920 So I want to thank you guys again for joining us today. 861 00:47:57,920 --> 00:48:00,840 We'll see you in two weeks. Thanks for listening. 862 00:48:00,840 --> 00:48:03,240 Do you have any questions, comments, or thoughts about 863 00:48:03,240 --> 00:48:05,120 today's topic? We'd love to hear them. 864 00:48:05,120 --> 00:48:07,560 Drop us a review or leave a note in the comments. 865 00:48:07,560 --> 00:48:10,760 Then like, subscribe or share this podcast if you like what 866 00:48:10,760 --> 00:48:12,600 you heard. And hey, you can help us shape 867 00:48:12,600 --> 00:48:15,000 future episodes. What do you want to know more 868 00:48:15,000 --> 00:48:17,120 about? Send us an e-mail at Live at 869 00:48:17,120 --> 00:48:21,000 iiba.org with your ideas. See you again on our next 870 00:48:21,000 --> 00:48:21,520 episode.