Transcript
WEBVTT 1 00:00:05.919 --> 00:00:10.150 When weed more into the organizations, most of the time we are finding less 2 00:00:10.230 --> 00:00:15.990 than ten percent of the people are at the appropriate level of data liiteracy. 3 00:00:16.149 --> 00:00:23.140 Less than ten percent, which is shocking. Data strategy, data culture and 4 00:00:23.300 --> 00:00:27.940 data literacy. They're interrelated, but they are not the same thing. Today's 5 00:00:27.980 --> 00:00:33.500 conversation we kind of peel those things apart and we get specific about data literacy, 6 00:00:33.539 --> 00:00:38.609 specifically persona based data literacy. How do we make sure each person in 7 00:00:38.929 --> 00:00:44.090 each role is as knowledgeable as they need to be to perform their job effectively 8 00:00:44.490 --> 00:00:50.479 with regard to collecting and using data? Our guest is Pianca Jane. She 9 00:00:50.640 --> 00:00:55.039 is president and CEO of a ring, a data analytics company that's working with 10 00:00:55.159 --> 00:01:00.039 companies like Google, apple, adobe, paypal, box, electronic arts and 11 00:01:00.359 --> 00:01:04.829 so many more. So many solutions to helping your team get more data literate 12 00:01:06.030 --> 00:01:08.709 for the benefit of your customers. My name is Ethan Butte. I host 13 00:01:08.829 --> 00:01:14.629 the CX series here on BTB growth, I host the customer experience podcast and 14 00:01:14.709 --> 00:01:18.980 I'm chief evangelist at Bombomb, were make it easy to get facetoface through simple 15 00:01:19.060 --> 00:01:26.540 videos. Here is my conversation with Pianca Jane. Leveraging your data in order 16 00:01:26.579 --> 00:01:32.140 to lower your customer Mara acquisition costs and Improve Your customer experience. Today's guest 17 00:01:32.299 --> 00:01:36.370 is president and CEO of a ring, a company that does just that for 18 00:01:36.530 --> 00:01:41.290 clients like Google, apple, adobe, paypal, box and electronic arts, 19 00:01:41.329 --> 00:01:46.849 among many others. A few processes and techniques her team uses developing their clients 20 00:01:46.930 --> 00:01:53.000 data DNA, building data literacy throughout the organization and creating citizen analysts. She's 21 00:01:53.000 --> 00:01:59.400 a best selling author and keynote speaker on data driven decisionmaking, data analytics and 22 00:01:59.519 --> 00:02:04.790 data science. PIANCA Jane, welcome to the customer experience podcast. Thank you 23 00:02:04.870 --> 00:02:07.550 for having me anything. I'm really excited to get into this and into my 24 00:02:07.669 --> 00:02:13.110 memory. We haven't done a full and proper conversation on data here on the 25 00:02:13.150 --> 00:02:17.419 show, but it obviously underpins any effort that anyone's undertaking throughout the organization. 26 00:02:17.539 --> 00:02:22.780 But before we get going, we're recording in early to mid April. You're 27 00:02:22.819 --> 00:02:27.500 in Silicon Valley. What's the situation there with regard to coronavirus? How's it 28 00:02:27.539 --> 00:02:30.889 affecting you or your family, your team or your customers? Kind of just 29 00:02:30.169 --> 00:02:34.849 set the scene really quickly. Yeah, so we have been in shelter, 30 00:02:35.129 --> 00:02:38.889 a shelter in place for put a good four weeks now. I think I'm 31 00:02:38.009 --> 00:02:42.330 going to go with the May Forst Week of my as off. Now let's 32 00:02:42.370 --> 00:02:45.639 see how it changes. You know, the numbers up somewhat black doing or 33 00:02:45.680 --> 00:02:47.439 in nondepends on how you see it. The testing it at least this flat 34 00:02:47.479 --> 00:02:51.159 doing. Who knows? And comes out? We were, we are at 35 00:02:51.199 --> 00:02:53.639 a more team to begin it's so we are. I mean I'm based here 36 00:02:53.759 --> 00:02:58.590 Si that's some folks in East Bay La, some folks in Toronto, we 37 00:02:58.669 --> 00:03:01.030 have folks in India. So we are all remote to begin with. So 38 00:03:01.150 --> 00:03:05.150 we are set up that way. So I didn't affect us from the working 39 00:03:05.270 --> 00:03:08.229 perspective, but our clients are definitely. I can we can see there's a 40 00:03:08.229 --> 00:03:13.419 lot of panic and lot of budgets freezing and and a lot of like not 41 00:03:13.659 --> 00:03:16.300 knowing what to do. The two things we do for our couporate times as 42 00:03:16.300 --> 00:03:21.699 a consulting site that we do end to end. There's also the training part. 43 00:03:21.780 --> 00:03:23.500 We do the DETA literacy, data culture work, I think, which 44 00:03:23.500 --> 00:03:27.530 is what we're going to talk a whole lot about. And then, because 45 00:03:27.650 --> 00:03:31.370 this is a prime time, many people are seeing this as a downtime for 46 00:03:31.530 --> 00:03:37.250 their for their employees. There's a lot there's a bigger interest in data culture, 47 00:03:37.250 --> 00:03:39.969 developing Ada culture, upgrading skills and so on. So there's some shift 48 00:03:40.050 --> 00:03:44.800 happening within our our sort of our portfolio of offering as well. But, 49 00:03:45.080 --> 00:03:46.360 you know, overall, I think we all need to do the right thing 50 00:03:46.479 --> 00:03:51.840 and stay safe and this too shall pass, just like anything else. Yeah, 51 00:03:52.639 --> 00:03:54.400 but yeah, I think in general people are taking it well, for 52 00:03:54.520 --> 00:03:58.990 what I understand, very good. Yeah, I think it is. You 53 00:03:59.110 --> 00:04:01.069 know, this two shall pass. We're not sure where exactly we are in 54 00:04:01.189 --> 00:04:03.990 it right now, and I think that's why there's a lot of freezing and 55 00:04:04.150 --> 00:04:09.590 not a lot of dramatic decisionmaking. But just like, okay, O, 56 00:04:09.789 --> 00:04:12.620 caause, let's see what's growing out here and move for I'm really glad to 57 00:04:12.659 --> 00:04:15.620 hear the people are investing in training, in development. That's awesome. So 58 00:04:15.979 --> 00:04:18.579 we're going to start where we always start here, which is customer experience. 59 00:04:18.699 --> 00:04:21.819 When I say that, what does it mean to you? For me, 60 00:04:23.180 --> 00:04:27.490 customer experiences of holistic experience right like. So, for example, if I 61 00:04:27.889 --> 00:04:30.970 am buying something online, I'm a I'm a big Amazon Shopper because I'm a 62 00:04:31.009 --> 00:04:35.089 big convenience lover. You know, for me that experience of you know how 63 00:04:35.170 --> 00:04:40.279 I order, how how I can I can count on receiving when they are 64 00:04:40.319 --> 00:04:44.399 saying it's receiving. So I was six year role and I just ordered a 65 00:04:44.519 --> 00:04:47.160 butterfly project for her because we're staying at home and I need to do something 66 00:04:47.199 --> 00:04:50.439 more creative and interesting with her. And so we're going to see butterflies grow 67 00:04:50.480 --> 00:04:55.910 for next three weeks. And so I ordered something from from an kid, 68 00:04:56.029 --> 00:04:59.389 a butterfly hits from Amazon, and know sort of the data tay come and 69 00:04:59.430 --> 00:05:01.230 I know I can prep my daughter, like you know, it's coming here 70 00:05:01.310 --> 00:05:04.589 and then we will really making logs and how are we going to tract, 71 00:05:04.709 --> 00:05:08.180 like you know, and she knows all those, all these stages of the 72 00:05:08.779 --> 00:05:11.420 you know, the butterfly like a coooning, crystallism and all of that. 73 00:05:12.100 --> 00:05:15.579 So I love that, the the dependability, I love the ease things get 74 00:05:15.579 --> 00:05:18.500 dropped to me. I love the ease of you know, something is not 75 00:05:18.579 --> 00:05:21.329 working, I can call in, I can get a customer sport experience. 76 00:05:21.410 --> 00:05:25.810 Very very, very convenient, very easy. They know me and they also 77 00:05:25.850 --> 00:05:29.290 probably know me as a high value customer. So I get very easy. 78 00:05:30.050 --> 00:05:32.170 You know, easy access, easy, everything is easy and I like easy. 79 00:05:32.649 --> 00:05:36.040 So for me, be customer experience is how I get touched by a 80 00:05:36.120 --> 00:05:40.639 product company, end to end, so not only from my ordering to my 81 00:05:40.800 --> 00:05:45.839 receiving the product to my if I have a problem, you know the order 82 00:05:45.959 --> 00:05:47.720 is not arrived. What happened? You know it if you should right away. 83 00:05:47.759 --> 00:05:49.990 For you, there's something got misplace, whatever else. And US. 84 00:05:50.029 --> 00:05:54.029 So easy versus, you know, sort of having to idea the ones. 85 00:05:54.110 --> 00:05:57.829 I started shopping in Amazon on them as only we like seven years ago. 86 00:05:57.829 --> 00:06:01.189 I haven't gone back and I hardly shop. I don't I mean these days 87 00:06:01.230 --> 00:06:04.660 a gold shop anyways, but you know I hardly shop and retail stores. 88 00:06:04.660 --> 00:06:09.379 Actually, my daughter doesn't know what a Moll is because we've never probably gone 89 00:06:09.420 --> 00:06:14.019 to a man. Well this. Yeah, so they yeah, the frictionless 90 00:06:14.060 --> 00:06:16.139 experience Amazon has come up a number of times on the show, especially with 91 00:06:16.180 --> 00:06:20.569 the closing question of you know company that you really appreciate in its that frictionless 92 00:06:21.009 --> 00:06:25.050 aspect to it, and you also spoke to the transparency. You just know 93 00:06:25.290 --> 00:06:30.290 what's going on and should anything be disjointed at any point, you already have 94 00:06:30.449 --> 00:06:32.560 this trust built up there's a relationship piece to what you talked about. To 95 00:06:33.040 --> 00:06:35.959 you know, it's not just about that transaction, it's about all of the 96 00:06:36.079 --> 00:06:40.519 transactions for years to come. So you know, if you're seven or eight 97 00:06:40.560 --> 00:06:43.480 years in now, you'll obviously still be working with them seven or eight years 98 00:06:43.519 --> 00:06:46.199 from now and they'll know you better, they'll be able to serve you better, 99 00:06:46.279 --> 00:06:48.110 which is awesome, which I think leads into where I wanted to do 100 00:06:48.269 --> 00:06:53.470 next, just one step deeper into cx here, which is, from your 101 00:06:53.589 --> 00:06:59.870 point of view, what's the relationship between effective use of data and delivering effectively 102 00:07:00.350 --> 00:07:04.459 a high quality customer experience? That's a great question, Ethan, and I 103 00:07:04.579 --> 00:07:08.420 love the way you framed about trust and relationship as well as I. You 104 00:07:08.459 --> 00:07:11.339 know that's that's a big guy. That's giving some good ideas to start thinking 105 00:07:11.339 --> 00:07:14.779 about the way I think about because expedience and now putting in the context of 106 00:07:14.779 --> 00:07:18.649 data driven customer expedience. So I think that having in effect of data strategy 107 00:07:18.689 --> 00:07:24.089 or having an effective way of getting access to data, we call it data 108 00:07:24.170 --> 00:07:28.529 maturity in general, that's a foundation for to deliver a good custom experience, 109 00:07:28.610 --> 00:07:31.519 a build better products or delight your customer in any way you want. So 110 00:07:31.600 --> 00:07:36.240 that's a foundation that's a must have for you to be able to delight your 111 00:07:36.319 --> 00:07:41.480 custom for you be for for Amazon to know that I'm high value from Amazon, 112 00:07:41.600 --> 00:07:45.670 to know that you know this is this persona probably is a convenience lover. 113 00:07:45.750 --> 00:07:47.110 So don't argue with her about things. Make it easy for her, 114 00:07:47.550 --> 00:07:53.189 as she calls things like that. So you need data done well. You 115 00:07:53.350 --> 00:07:58.579 need good access to data and good and right access to data to the right 116 00:07:58.699 --> 00:08:01.500 people, because it's not, you know, all these decision is not being 117 00:08:01.540 --> 00:08:03.500 all made by mean. There's a lot of auto ml going on, there, 118 00:08:03.500 --> 00:08:07.660 a lot of machine learning, a lot of recommendation system but there's also 119 00:08:07.740 --> 00:08:11.980 people there and different people need access. A customer support needs a different access 120 00:08:11.019 --> 00:08:16.050 to my data versus the you know, the person who's doing maybe there's some 121 00:08:16.329 --> 00:08:20.529 fraud activity. Is something happened. Different job and person and you can call 122 00:08:20.569 --> 00:08:24.769 it a different job title function. Need different kind of access to my customer 123 00:08:24.850 --> 00:08:28.959 data so that the rightful access and true access, an easy access, is 124 00:08:28.319 --> 00:08:33.519 is data maturrey? That's a foundation that's injustice. What is data maturity if, 125 00:08:33.519 --> 00:08:37.440 when you have that now, you're set up to actually crack the code 126 00:08:37.440 --> 00:08:41.399 for anything this, you know, the thx be a product experience, be 127 00:08:41.509 --> 00:08:45.870 it product features, be it marketing date, whatever you want to optimize, 128 00:08:46.669 --> 00:08:50.309 as it relates to directly to customer experience. I think the way we approach 129 00:08:50.350 --> 00:08:52.269 it, at least for our clients, as we start thinking from what is 130 00:08:52.309 --> 00:08:56.100 it that you want? What is it must have experience that you want the 131 00:08:56.179 --> 00:09:00.460 customer to have, and then back built backwards from there. So, once 132 00:09:00.539 --> 00:09:03.740 you know, what do you invasion? I think, for example, you 133 00:09:03.820 --> 00:09:07.259 know, and I look up Steve Jobs, for example, the data's visionary, 134 00:09:07.700 --> 00:09:11.009 he is to talk about the experience, the experience that he wants people 135 00:09:11.090 --> 00:09:15.289 to have, like, you know, when they hold a phone and iphone 136 00:09:15.330 --> 00:09:16.850 in their hand. This is how they should feel, this is what the 137 00:09:16.970 --> 00:09:22.009 feeling if you can start from that and then work backwards to it. If 138 00:09:22.090 --> 00:09:24.960 we want to deliver that, what all do we need to enable on our 139 00:09:26.360 --> 00:09:30.840 systems and from our people's from our processes perspective, and from there, data 140 00:09:30.919 --> 00:09:33.559 can support you in, you know, in supporting that vision that you have, 141 00:09:35.000 --> 00:09:37.990 but that assuming you have good data maturity underlying it. Otherwise you will 142 00:09:39.029 --> 00:09:41.269 be like running after data and not knowing where to you know, how do 143 00:09:41.350 --> 00:09:45.070 you chase, the stays after data. It's because it's non existent, or 144 00:09:45.269 --> 00:09:48.870 is it hiding somewhere? So good. There's so many follow questions I want 145 00:09:48.909 --> 00:09:52.940 to ask your I couldn't. I should actually have been writing down some of 146 00:09:52.980 --> 00:09:54.700 my thoughts. They're so you can follow up. And a couple key things. 147 00:09:54.820 --> 00:09:58.059 You're one. Obviously, data is a means to an end, not 148 00:09:58.220 --> 00:10:01.860 an end in and of itself. It's a tool to get a job done 149 00:10:01.860 --> 00:10:07.570 and you really positioned it clearly and obviously there and that it is foundational to 150 00:10:07.889 --> 00:10:11.769 making any aspect of the business better and more effective. And and you also 151 00:10:11.850 --> 00:10:15.730 pretty well tied up where I what I wanted to ask next, which is, 152 00:10:16.090 --> 00:10:18.129 you know, for folks who aren't familiar with a ring, who are 153 00:10:18.169 --> 00:10:22.200 you? Who is your ideal customer and what are some of the problems that 154 00:10:22.320 --> 00:10:26.639 you solve for them? Sure, great, would love to share air ring. 155 00:10:26.039 --> 00:10:30.320 We are a data science consulting company. We Are Boutique, so I'm 156 00:10:30.360 --> 00:10:33.240 not large, but we are lean and mean and our flavor of data science 157 00:10:33.320 --> 00:10:37.029 we do is all about practical data signs. So we are all about you 158 00:10:37.190 --> 00:10:41.389 have some data, whatever form of data you have, you have some questions. 159 00:10:41.789 --> 00:10:45.070 How do we quickly use the data you have at hand to solve that 160 00:10:45.269 --> 00:10:48.309 problem? Now, we do do that to two ways. For the corporate 161 00:10:48.350 --> 00:10:52.179 client. One is we going and solve large, you know, high value 162 00:10:52.259 --> 00:10:56.779 problems for our clients hands on and we are known to be the fastest and 163 00:10:56.980 --> 00:11:01.700 the most efficient. So between two to three cutsertants can build a machine learning 164 00:11:01.700 --> 00:11:05.009 model with an eight to nine weeks and we are fast because we are hypothesis 165 00:11:05.009 --> 00:11:09.610 is driven. So underlying all of the work we do is a framework called 166 00:11:09.690 --> 00:11:13.529 bother, which is our recipe for baking gake. We have a recipe for 167 00:11:13.610 --> 00:11:18.759 baking data essentially, and its it marries data science with decision signs and it 168 00:11:18.879 --> 00:11:24.600 is hypothesis driven, which basically enables US fast delivery to insights and make sure 169 00:11:24.720 --> 00:11:26.759 make sure that whatever we are, whatever we were to deliver for our client, 170 00:11:26.840 --> 00:11:31.200 we get used. So that's our sort of like our footprint, so 171 00:11:31.360 --> 00:11:35.309 to say, in our blueprint of what we do. So there's one part, 172 00:11:35.389 --> 00:11:37.309 is data science from sulting, when we go into organizations and solve you 173 00:11:37.389 --> 00:11:41.629 know, we have custom acquisition issues. We want to lower TUSTMAT position to 174 00:11:41.750 --> 00:11:43.669 cost. We want a segment our customer, we want a better target. 175 00:11:43.789 --> 00:11:48.340 We want to create offering and messaging. You know that Matrix, to optimize 176 00:11:48.419 --> 00:11:52.419 Roy. You have customer retention, employee attention, you know, you name 177 00:11:52.419 --> 00:11:56.980 it, marketing to sales attribution, all of those things. We love solving 178 00:11:56.259 --> 00:12:01.529 complex problem. So that's one one end. But we are also all about 179 00:12:01.690 --> 00:12:05.730 we also a lot about evangelizing. So we are all about how can the 180 00:12:05.889 --> 00:12:09.169 empower our customers to do it better and do it themselves? And so there's 181 00:12:09.210 --> 00:12:13.250 a one big part of work we do is on data culture and detaliteracy. 182 00:12:13.330 --> 00:12:18.840 So we go into organizations who already, especially very mature organizations, organization where 183 00:12:18.840 --> 00:12:22.320 they the data. Digital transformation has happened to them, but they are not. 184 00:12:22.519 --> 00:12:26.159 The employees are not ready for data. They are scared or data. 185 00:12:26.559 --> 00:12:30.669 And we go into such organization and we establish a culture of data to a 186 00:12:30.789 --> 00:12:35.190 curated me, you know, member framework. So we first assess the data 187 00:12:35.190 --> 00:12:37.990 culture for that organization. Again, we have a methodology for that, and 188 00:12:39.110 --> 00:12:41.149 then, based on what we find the gaps in either data maturity, we 189 00:12:41.190 --> 00:12:45.580 talked about data maturity already, or data literacy. That's the second sort of 190 00:12:45.700 --> 00:12:48.139 therefore, these data culture. The second one is data literacy. The third 191 00:12:48.220 --> 00:12:52.940 one is are different leadership and the fourth one issue making process. And as 192 00:12:52.019 --> 00:12:56.500 we find gaps in any of those four, which is broken down to thirty 193 00:12:56.539 --> 00:12:58.730 dimensions, we are able to address that and fairly quickly. With then we 194 00:12:58.769 --> 00:13:03.250 can turn around to a turnaround from moving a data culture portion of five to 195 00:13:03.370 --> 00:13:07.730 above seven within nine months. If the organization is ready and a large scale 196 00:13:07.769 --> 00:13:11.690 we can stay. Look five thousand, ten thousand people. So that's our 197 00:13:11.850 --> 00:13:16.399 tow body work we do for our corporate clients. We also have an academy 198 00:13:16.440 --> 00:13:20.080 much like corset up, but it's focus on the business problem. So we 199 00:13:20.120 --> 00:13:24.039 have an academy where people can go on Academy Dot aidingcom and they can take 200 00:13:24.320 --> 00:13:28.070 the systectally for sales and marketing folks as a citizen analyst track that they can 201 00:13:28.190 --> 00:13:31.950 upscale themselves with sixteen hours of course, and then a project fall on project. 202 00:13:31.269 --> 00:13:33.909 They have a data of future data scientist track. We also have a 203 00:13:33.990 --> 00:13:39.309 current data scientist tract and an executive track. Awesome. I will link that 204 00:13:39.429 --> 00:13:41.539 up in if for folks who are listening. I we always write up some 205 00:13:41.659 --> 00:13:46.059 of these EPI we always write up these episodes whole, a handful of video 206 00:13:46.100 --> 00:13:48.580 clips we embed the audio and you can see all of that at bombombcom slash 207 00:13:48.700 --> 00:13:54.100 podcast and I'll include a link to the Academy for folks that and we're going 208 00:13:54.100 --> 00:13:56.889 to get into some of those definitions. By the way, cruis talk about 209 00:13:56.929 --> 00:14:01.370 citizen analysts, so people can self identify and maybe jump into one of those 210 00:14:01.409 --> 00:14:05.370 learning tracks as they see fit. So let's talk about some of the basics 211 00:14:05.409 --> 00:14:09.519 of data literacy. Like I expected, many organizations probably wouldn't call themselves data 212 00:14:09.600 --> 00:14:15.039 illiterate, but I'll also bet that many of them are metric rich but information 213 00:14:15.120 --> 00:14:20.559 poor. And so just talk about how do you define data literacy and, 214 00:14:20.360 --> 00:14:24.279 as you know, for the companies, maybe the eve engaged with or that 215 00:14:24.309 --> 00:14:26.029 your team is engaged with over the past couple of years, kind of what 216 00:14:26.190 --> 00:14:31.309 is a common state of data literacy and then we can maybe progress from there. 217 00:14:31.750 --> 00:14:35.230 Yeah, that's that's a great question. So you know, from the 218 00:14:35.269 --> 00:14:37.899 last base such I read coming from, I think, accenture and fuel. 219 00:14:37.940 --> 00:14:41.980 It published it together from their global, you know, sort of survey they 220 00:14:43.019 --> 00:14:46.220 did. They found sixty four person of the or more of the people were 221 00:14:46.899 --> 00:14:50.340 not identified as data literate enough. When we go into the UN, what 222 00:14:50.460 --> 00:14:54.169 is data literacy. So they are. Literacy is the ability to use data 223 00:14:54.690 --> 00:14:58.250 for making useful decisions, to use that for you to derive insights and usual 224 00:14:58.330 --> 00:15:01.889 with Mrs now, that is not one sise at all. You can't say 225 00:15:01.250 --> 00:15:05.610 I'm data literate or illiterate. It is am I at the level that I'm 226 00:15:05.649 --> 00:15:09.799 functioning? If I'm a customer support agent, do I have the right data 227 00:15:09.879 --> 00:15:13.080 literacy to be at for my level? And so how do we define that? 228 00:15:13.200 --> 00:15:15.960 We define at their personal levels. For customers support agent, maybe they 229 00:15:16.000 --> 00:15:20.360 need to be a data enthusiasts. That definition varies across business to business and 230 00:15:20.480 --> 00:15:24.429 what what the leadership wants for that role. So, for example, most 231 00:15:24.470 --> 00:15:28.389 of the time customer support agents are tend to be what what the target deliriously 232 00:15:28.429 --> 00:15:31.590 profile they should get to as is data enthusiasts. For a marketing manager, 233 00:15:31.789 --> 00:15:37.820 the target profile is often data educated for or actually are Su sometimes as an 234 00:15:37.860 --> 00:15:41.299 analyst for a product product folks. Usually it's data educated for executive. Is 235 00:15:41.340 --> 00:15:45.899 Typically data driven executive. So they are. These are definitions, but they're 236 00:15:45.899 --> 00:15:48.179 basically varying level of data literacy. Some people, you know, if you 237 00:15:48.259 --> 00:15:52.009 start from the data enthusias, they need to be enthusiastic about data and to 238 00:15:52.129 --> 00:15:58.169 be able to understand basic search charts and tables right for all the way from 239 00:15:58.169 --> 00:16:00.889 there to data scientists, they need to be able to use advanced methods, 240 00:16:02.129 --> 00:16:06.360 to be able to derive in size, make models and informed decisions and work 241 00:16:06.399 --> 00:16:08.399 with stake good is information. So there's a wide variety of those, you 242 00:16:08.480 --> 00:16:12.600 know, and everything is in between, right. So when we go into 243 00:16:12.639 --> 00:16:18.360 the organizations, most of the time we are finding less than ten percent of 244 00:16:18.590 --> 00:16:23.029 the people are at the appropriate level of data literacy. Less than ten percent, 245 00:16:23.509 --> 00:16:27.149 which is shocking you. Most People Think, oh, I can download 246 00:16:27.149 --> 00:16:30.789 data and excel and I can look at it and I can pick the number, 247 00:16:30.389 --> 00:16:33.539 so I should I am data literate, while are you data literal enough 248 00:16:33.940 --> 00:16:38.139 for your level of what you are able to do? And the surprising and 249 00:16:38.259 --> 00:16:41.379 the sad truth is most people are not. And what does that mean? 250 00:16:41.460 --> 00:16:45.259 WHY SHOULD WE CARE? They are dedly dealt it or not. We should 251 00:16:45.299 --> 00:16:49.009 share, because if they are not data little enough, they're not able to 252 00:16:49.169 --> 00:16:52.970 optimize the decision they have at hand. Your marketing manager, who's going to 253 00:16:53.009 --> 00:16:57.690 spend your hundred K next in next quarter? If they're not data little enough, 254 00:16:57.769 --> 00:17:00.919 they're not going to be optimizing that hundred case pen maybe that hundred k. 255 00:17:02.480 --> 00:17:03.880 You know, maybe they'll spend hundred K and they'll get you five hundred 256 00:17:03.880 --> 00:17:07.119 K in return from, you know, incremental sales or whatever else. But 257 00:17:07.279 --> 00:17:11.039 what if they were data literate and they could get you a million? Right, 258 00:17:11.400 --> 00:17:15.599 you're losing your leaving that five hundred k on the table. And many, 259 00:17:15.640 --> 00:17:18.990 many CEOS and CMOS and achieved a low officers. They are recognizing, 260 00:17:19.430 --> 00:17:23.549 Oh wow, we are leaving money on the table and especially in the context 261 00:17:23.630 --> 00:17:27.549 of right now, in this post pandemic situation, all of our margins have 262 00:17:27.670 --> 00:17:30.579 been eaten away. You know, we have. We have kept employees level 263 00:17:30.619 --> 00:17:34.660 to the same level. Are All of us are in sales and marketing specifically, 264 00:17:34.660 --> 00:17:38.700 are pipelines have dried up. We could have been squeezed on both ends. 265 00:17:40.500 --> 00:17:42.299 Do you think you have now you can you afford to do? Oh, 266 00:17:42.339 --> 00:17:45.690 I'm going to do these ten things and let's see which one sticks. 267 00:17:45.809 --> 00:17:49.650 Can you afford that anymore? You can't. And many, many organizing and 268 00:17:51.089 --> 00:17:56.089 in fact, I I predict that companies who invest in data atacy right now, 269 00:17:56.170 --> 00:18:00.759 who investing upskilling their their their employees. They are the ones that want 270 00:18:00.759 --> 00:18:03.599 to try post pond, I mean because post, I think the there's only 271 00:18:03.599 --> 00:18:07.160 a new word right. And if you're not, if you don't know the 272 00:18:07.240 --> 00:18:10.319 language of business, if you don't know how the language, the language of 273 00:18:10.359 --> 00:18:12.430 business is data, you don't know how to do what to do with data. 274 00:18:14.109 --> 00:18:15.309 I think what we left beyond in, you know, you're lead to 275 00:18:15.349 --> 00:18:19.470 look for some other, another profession. It's so interesting. Obviously now I 276 00:18:19.549 --> 00:18:23.789 understand why you used personas earlier in the conversation, because it's essentially what you're 277 00:18:23.789 --> 00:18:30.819 talking about is mapping the appropriate level of literacy to the appropriate role, or 278 00:18:30.900 --> 00:18:34.180 to a specific role rather. is also really interesting to think about. You 279 00:18:34.259 --> 00:18:38.299 know, coming into the conversation, as I think about my own challenges and 280 00:18:38.420 --> 00:18:44.089 using data, they're often around, you know, should I believe this source 281 00:18:44.130 --> 00:18:47.650 or that source? Am I getting the right data? Can I trust it 282 00:18:47.769 --> 00:18:51.809 it? Is it being presented the right way? Is it hygienic, etc. 283 00:18:52.289 --> 00:18:56.400 But you're talking, I mean that's a completely separate set of challenges and 284 00:18:56.519 --> 00:19:00.880 problems. You talking again it with literacy in particular, are very specifically about 285 00:19:02.119 --> 00:19:06.640 two people actually understand what's being presented. Can I would assume that asking the 286 00:19:06.720 --> 00:19:11.470 right question is probably critical to effective data literacy. So you started with that 287 00:19:11.589 --> 00:19:15.309 ten percent number. Share anything that just came to mind there and then maybe 288 00:19:15.390 --> 00:19:18.470 give a couple tips on like if you want to make that path from ten 289 00:19:18.589 --> 00:19:23.150 percent to, you know, forty percent literacy, what are some easy things? 290 00:19:25.460 --> 00:19:27.579 Shouldn't have said it that way. What are some things? What are 291 00:19:27.700 --> 00:19:32.900 some things that could be done to start moving that direction? Yeah, that's 292 00:19:33.140 --> 00:19:36.539 that's great. Their lots of less loves there and I want to address everything. 293 00:19:36.579 --> 00:19:38.609 So the part that you've talked about, which was about, you know, 294 00:19:38.849 --> 00:19:42.250 the is a data accurate and is it making sense? Where can I 295 00:19:42.329 --> 00:19:45.769 get it at that is all data maturity. So that that's why there are 296 00:19:45.809 --> 00:19:48.769 four days of data culture. The part that you talk about is the foundation 297 00:19:49.329 --> 00:19:53.119 on which everything else sets. If you don't even have easy access to single 298 00:19:53.160 --> 00:19:56.319 source of truth, everything else will be a little and because you can make 299 00:19:56.359 --> 00:20:00.480 data, you can make me, you can train a best data scientists. 300 00:20:00.480 --> 00:20:03.039 If they don't have access to the right data, what will they train their 301 00:20:03.079 --> 00:20:07.430 Bodos? Right. So data, data maturities, as the part that you've 302 00:20:07.430 --> 00:20:08.910 talked about, a lot of people, a lot of people think about data 303 00:20:08.990 --> 00:20:12.390 when they talk about day literacy. They start thinking about, Oh, you 304 00:20:12.470 --> 00:20:15.589 know, do we where do I get the data from? You know, 305 00:20:15.630 --> 00:20:18.109 all of that is data maturity. So that's set, that's a that's a 306 00:20:18.190 --> 00:20:22.380 foundation if that's given to some level. There's not a zero one for Datamaturity, 307 00:20:22.460 --> 00:20:25.420 but that's given a meaning. You are on a scale of zero to 308 00:20:25.579 --> 00:20:29.299 ten. Your seven or aw. That means you have some good cohesive sense. 309 00:20:29.420 --> 00:20:33.220 You know, maybe you have to Click Your Tableau Board and in your 310 00:20:33.259 --> 00:20:37.250 wait for five minutes instead of five seconds. okay, understand there's some there's 311 00:20:37.329 --> 00:20:41.210 some curdles and hiccups in your customer experiences, but you can get data. 312 00:20:41.369 --> 00:20:45.650 That means you have some level of data maturity that can enable the rest of 313 00:20:45.690 --> 00:20:48.609 the three Ds. The other other three Ds, as we talked about, 314 00:20:48.650 --> 00:20:52.720 is data literacy, day, diven leadership and decision making process. So talking 315 00:20:52.799 --> 00:20:56.960 specifically about data literacy. So and let me talk about the dons and then 316 00:20:57.039 --> 00:21:03.599 I can do this right. Here's what's happening in the A and the I 317 00:21:03.720 --> 00:21:06.869 can. I share this with a little bit of frustration because I am part 318 00:21:06.910 --> 00:21:12.069 of these conversations where a CEO has told maybe ahead of DNA or a head 319 00:21:12.109 --> 00:21:15.990 of bi or CIO and saying we need a culture of data. Let's up 320 00:21:17.029 --> 00:21:21.460 skill, let's get Deta Literacy, let's get detailacy. So the CIO or 321 00:21:21.539 --> 00:21:23.700 the head of DNA or some sets up a meeting with the LD leader, 322 00:21:23.819 --> 00:21:27.420 a Ch our person. Say we need data liutacy. Can you go find 323 00:21:27.460 --> 00:21:32.970 out what training we can put our employees through? And then you can imagine 324 00:21:33.009 --> 00:21:36.730 what goes after that. They start looking for training. Sometimes we surface up 325 00:21:36.730 --> 00:21:41.250 in that conversation. We I'm having a conversation with the led leader and they're 326 00:21:41.250 --> 00:21:45.250 saying we need this training. You guys have these these training. We just 327 00:21:45.410 --> 00:21:48.759 need these training. We can license it. And I'm sitting out there and 328 00:21:48.880 --> 00:21:52.400 thinking we can license these training to them, but they are not going to 329 00:21:52.480 --> 00:21:57.000 get data litric. You know why? Even the have insufficient maturity. Well, 330 00:21:57.160 --> 00:22:00.549 they can be first of all, their approaches. Let's train everybody. 331 00:22:00.710 --> 00:22:04.829 Data literacy is not one size at all. Right, the data that customer 332 00:22:04.990 --> 00:22:10.630 support agent will need to be a different level than the executive he needs. 333 00:22:10.670 --> 00:22:14.470 They need a different skill set. Yeah, the persona mapping, persona mapping. 334 00:22:14.670 --> 00:22:17.900 So you're without even persona mapping and persona map needs to be mapped to 335 00:22:18.619 --> 00:22:22.220 competencies. You haven't thought about competencies and skills. You haven't thought about personas. 336 00:22:22.539 --> 00:22:26.460 You want to put everybody through one training so in three months you can 337 00:22:26.500 --> 00:22:29.660 just say, Oh, there, letacy done. It's not gonna happen. 338 00:22:30.130 --> 00:22:32.609 And you know who you're going to blame. You're going to blame the technology 339 00:22:32.609 --> 00:22:34.450 or the training material. None of them are to be blamed. The what 340 00:22:34.650 --> 00:22:37.490 is what is to be blamed this? You haven't thought through this. How. 341 00:22:38.130 --> 00:22:41.849 What is data literacy? What is my vision for data literacy and how 342 00:22:41.890 --> 00:22:45.400 am I going to get people to these different levels of the air litacy right? 343 00:22:45.480 --> 00:22:49.480 And when you start thinking that way, you automatically start thinking about competencies. 344 00:22:49.720 --> 00:22:52.359 You're going to think about what is the out what is the output I 345 00:22:52.440 --> 00:22:56.359 want? What is the outcome I want? At which level? And that 346 00:22:56.519 --> 00:22:59.109 means you will start thinking, what are they at current levels? You're going 347 00:22:59.190 --> 00:23:02.990 to start with assessment. All those things will naturally happen when you start thinking 348 00:23:03.109 --> 00:23:06.309 that. With the other thing which people do and we all do right, 349 00:23:06.390 --> 00:23:08.589 like haven't you and I? We all gone to training. We've been so 350 00:23:08.710 --> 00:23:11.940 excited with it, online or offline or off site, you know, slipping 351 00:23:11.980 --> 00:23:17.779 a glass of wine and taking not like wow, this is so fundamental, 352 00:23:18.059 --> 00:23:22.660 this is mind blowing, and you come back to work and you've forgotten all 353 00:23:22.700 --> 00:23:23.819 about it. You mean, you know, you talk about to do your 354 00:23:23.859 --> 00:23:27.970 coworkers for two days, three days next week. We kind of don't remember. 355 00:23:29.210 --> 00:23:33.809 The week after you have forgotten what you learned there and your notes will 356 00:23:33.849 --> 00:23:37.529 sit there and you'll probably never go back to looking at them. What's happening 357 00:23:37.609 --> 00:23:44.160 there is that we have not contextualized the learning in our workflow. And Data 358 00:23:44.279 --> 00:23:51.039 Literacy is even day science is not trivial skill. So think about even a 359 00:23:51.200 --> 00:23:52.680 even are more you know, it's a more complex scale. And then you 360 00:23:53.039 --> 00:23:56.589 then you don't practice it. You didn't bring it to to Somebody's workforce. 361 00:23:56.829 --> 00:24:00.869 You just send them to training and you expect them, Oh, our team 362 00:24:00.910 --> 00:24:03.390 is our employees are data literate. They are it's not going to happen. 363 00:24:03.990 --> 00:24:08.910 You need to bring that training in the context of your workflow. So they 364 00:24:08.990 --> 00:24:12.460 need to do one thing that we found to be successful is they need to 365 00:24:12.539 --> 00:24:17.619 do a project, follow on project where they get to apply it right away. 366 00:24:17.859 --> 00:24:21.779 That's urgency. Is Very important because it's these are complex concepts. Now, 367 00:24:21.900 --> 00:24:26.089 somebody who's never seen a history brown versus by chat and you saying you 368 00:24:26.170 --> 00:24:30.970 know which information you're going to represent which way. How are they going to 369 00:24:30.089 --> 00:24:33.730 connect the dot? It's too hard. So we have to immediately, and 370 00:24:33.849 --> 00:24:37.450 that's why we use a framework, work based approach. We treat, we 371 00:24:37.599 --> 00:24:41.599 teach using bother, and the reason we do that is because it's fairly complex. 372 00:24:41.680 --> 00:24:45.519 And so if I want to today, you know, start for you 373 00:24:45.599 --> 00:24:48.519 know, for example, its and do you do? You Cook? I 374 00:24:48.680 --> 00:24:53.589 do, okay. Have you ever made fullaful? I have not. Okay, 375 00:24:53.869 --> 00:24:56.710 so the question for you is, if I ask you tomorrow, let's 376 00:24:56.829 --> 00:25:00.630 like, can we make fullawful tomorrow? How are you couldn't do it. 377 00:25:02.109 --> 00:25:04.630 I'm going to start with what are the ingredients? So I make sure I 378 00:25:04.670 --> 00:25:08.380 have them exactly. So you're going to look up some recipe, look in 379 00:25:08.380 --> 00:25:11.980 the within the recipe is going to tell you the steps and going to tell 380 00:25:11.019 --> 00:25:15.500 you the ingredients. I'm fallawful because it's for so much, so many of 381 00:25:15.619 --> 00:25:19.380 us is so out there. We need a recipe. The same way for 382 00:25:19.420 --> 00:25:23.930 data science and and getting those data science skills. We need a recipe and 383 00:25:23.970 --> 00:25:26.809 that's why we have the bothered framework. If you have the if you have 384 00:25:26.970 --> 00:25:30.730 the framework, if you have a recipe and you you're taught step by step 385 00:25:30.890 --> 00:25:33.809 how to do this, how to do this. Next step. How do 386 00:25:33.930 --> 00:25:37.799 you do next step? You have something to now take it to your work 387 00:25:37.839 --> 00:25:40.720 flow. So now you have a framework. Then we want you to apply 388 00:25:40.799 --> 00:25:44.279 it to a use case like, okay, I'M A, I'm on marketing. 389 00:25:44.640 --> 00:25:47.440 I have this hundred ki to spend. This is what I was thinking 390 00:25:47.480 --> 00:25:49.549 about spending. Now that I have this framework, let me see if I 391 00:25:49.589 --> 00:25:52.990 can do a better targeting, better segmentation, and I can do that and 392 00:25:53.069 --> 00:25:56.950 I can see whether you know what my now that I can apply it, 393 00:25:56.309 --> 00:26:00.349 and while I'm applying it, of course, this is the first time I'm 394 00:26:00.470 --> 00:26:03.630 trying, you know, frying fullaffel. I don't even know the consistency of 395 00:26:03.710 --> 00:26:06.380 the balls that need to go into the frying pan. Oh, I have 396 00:26:06.460 --> 00:26:08.140 a mentor, I have I have somebody who has done this and they say, 397 00:26:08.140 --> 00:26:11.779 Oh, this is too liquidly, we need to add more flower in 398 00:26:11.900 --> 00:26:15.380 it, whatever else. Okay, now somebody's guiding me, and that's what 399 00:26:15.579 --> 00:26:19.369 mentor data signcement, and that's what we do when we do data a little 400 00:26:19.410 --> 00:26:26.170 large, large enterprise wide data literacy. We have mentors either from our dreams 401 00:26:26.250 --> 00:26:30.210 or the train, the train of trainers we have trained who go in and 402 00:26:30.289 --> 00:26:34.440 who are mentors on this framework applied to their workflow. That but the end 403 00:26:34.640 --> 00:26:38.200 of that exercise that by the time they've done with their stargeting in segmentation, 404 00:26:38.279 --> 00:26:41.440 that one project with me, take them four weeks, maybe take them six 405 00:26:41.480 --> 00:26:47.000 weeks by the time they're done with that. Now the framework is there. 406 00:26:47.390 --> 00:26:51.309 They know how to apply this framework. Maybe they'll not get everything from the 407 00:26:51.349 --> 00:26:53.589 class, but they'll know at least they'll know how to take this framework and 408 00:26:53.630 --> 00:26:57.750 apply at least to this use case. Now you think about now. You've 409 00:26:57.789 --> 00:27:02.420 empowered many, many of these marketing folks together. They're working many of the 410 00:27:02.500 --> 00:27:06.700 sales works. You have creating a surround sound. Everybody's talking hypotheses, you 411 00:27:06.740 --> 00:27:10.940 know business question you talking the same language. You start developing a culture, 412 00:27:10.940 --> 00:27:15.259 or at least your data little sees up. But wait, it cannot if 413 00:27:15.299 --> 00:27:18.609 it starts there and your data. Your leadership is still not data driven. 414 00:27:18.650 --> 00:27:22.569 They're still making decisions I respect to your data. They're still not holding any 415 00:27:22.650 --> 00:27:26.690 their team accountable. Slowly they will be no point for that data literacy right. 416 00:27:27.049 --> 00:27:30.720 So the data little did a different leadership needs to come up as well. 417 00:27:30.319 --> 00:27:33.720 And then, lastly, they did driven decision making process. And there 418 00:27:33.839 --> 00:27:37.119 isn't a process by which decisions are being made, then the models data will 419 00:27:37.160 --> 00:27:41.640 be happening here and the decisions will be happening in a parallel path and they 420 00:27:41.680 --> 00:27:45.470 have no connection and you're not going to be optimizing your decisions based on data. 421 00:27:45.910 --> 00:27:48.190 So all data diffen decision making processes also needs to come up. You 422 00:27:48.269 --> 00:27:52.029 know the you know the review. The planning and the review process both need 423 00:27:52.109 --> 00:27:56.829 to have data in a systematic manner that you can see. Hey, we 424 00:27:56.990 --> 00:28:00.619 planned on this. We were expecting an autoway of Fenex. We actually got 425 00:28:00.700 --> 00:28:03.900 ADEX. What happened? What was us? I'm Jamad does, I'm shows 426 00:28:03.900 --> 00:28:06.819 of we all. And how can we use this information for next set of 427 00:28:06.900 --> 00:28:10.420 planning? And that's only possible when you can have this kind of process. 428 00:28:10.700 --> 00:28:12.809 So I know I I gave it a long, leveling answer here, but 429 00:28:14.089 --> 00:28:17.849 that's really good. I didn't want to stab you because you're essentially answering one 430 00:28:17.849 --> 00:28:21.849 of the questions I had, which is like this obviously needs to be cultural. 431 00:28:22.089 --> 00:28:25.049 It needs to be deeply baked in who we are. This is how 432 00:28:25.089 --> 00:28:27.559 we do it around here. You know, any cultural element is that way, 433 00:28:27.640 --> 00:28:32.240 and so one of my questions for you is going to be how do 434 00:28:32.319 --> 00:28:37.160 we make sure that this isn't just a project that those people over there are 435 00:28:37.319 --> 00:28:41.279 working on? And really you addressed, and I think that's what the For 436 00:28:41.430 --> 00:28:45.990 d's framework is really all about, is again combined with that persona mapping doing 437 00:28:47.029 --> 00:28:49.309 the project work. I know that's how I learned. Best is I'm gonna 438 00:28:49.309 --> 00:28:52.509 do all I'm going to do a little bit of studying on my own, 439 00:28:52.869 --> 00:28:55.670 but I'm going to pair up with someone who's done it once before, will 440 00:28:55.710 --> 00:28:57.819 do it together. Then I'll maybe do it on my own and know that 441 00:28:57.900 --> 00:29:02.019 that person is within arm's reach if I need to reach out for a question 442 00:29:02.180 --> 00:29:06.579 that I'm often running. And so this idea of making sure people are doing 443 00:29:07.220 --> 00:29:11.730 projects relevant to their role, relevant to the new learning right away and it's 444 00:29:11.769 --> 00:29:15.809 unique from what someone and another team is doing in a different seat, makes 445 00:29:15.849 --> 00:29:18.849 so much sense. And so, causs you're they're kind of two place I 446 00:29:18.930 --> 00:29:22.329 want ago. I guess I'll start here. Where does this process go wrong? 447 00:29:22.450 --> 00:29:26.799 I think you already alluded to some of them. is particularly around executive 448 00:29:26.839 --> 00:29:30.759 buying, but are there any other kind of common hurdles. If you're getting 449 00:29:32.279 --> 00:29:37.480 some of these pieces in place, what is prevented people from really making full 450 00:29:37.640 --> 00:29:41.509 cultural adaption? Yeah, and I can talk about some of the most common 451 00:29:41.589 --> 00:29:45.309 mistakes and seeing o they're one thing that people are doing. Like I said, 452 00:29:45.470 --> 00:29:48.430 you know, the LND leaders at our leaders, sometimes the IDA teams 453 00:29:48.470 --> 00:29:55.180 are going out there and buying courses and maybe sometimes university courses at local universities 454 00:29:55.500 --> 00:30:00.500 and local universities. I used to teach I'm a UF, I'm graduate students, 455 00:30:00.579 --> 00:30:03.779 so I was teaching previously I was teaching statistics there and then I was 456 00:30:03.859 --> 00:30:07.450 teaching calculus. So I know how statistics is taught in for a senior level 457 00:30:07.609 --> 00:30:11.690 class, for a statistical statistics and math major. I know how statistics is 458 00:30:11.769 --> 00:30:17.329 tart there also in the context of business, that statistics doesn't you know from 459 00:30:17.369 --> 00:30:21.170 you you take the class and you learn about averages and means and maybe some 460 00:30:21.329 --> 00:30:25.039 error and maybe built ship, govern normal distribution, Goesian distribution, and then 461 00:30:25.079 --> 00:30:26.480 you come to the world and saying, Oh now you apply it to you 462 00:30:26.599 --> 00:30:30.799 know, your targeting and segmentation. There such a gap between the two and 463 00:30:32.039 --> 00:30:36.549 I had to bridge that gap. So I come from two masters my both 464 00:30:36.630 --> 00:30:40.150 of my thesis and will applied statists, mathematics. I have a I'm I 465 00:30:40.269 --> 00:30:44.430 was talking about Ai, you know, robotics and Ai Auto Ml. At 466 00:30:44.470 --> 00:30:45.750 that point, twenty years. I'm beating myself, but two years ago we 467 00:30:45.829 --> 00:30:48.910 were working on that right. And then I come to the real world and 468 00:30:49.619 --> 00:30:52.779 nothing that I had learned. I mean I was doing nonlinear regression there. 469 00:30:52.819 --> 00:30:56.460 And then I come to this real world and and I'm given this I joined 470 00:30:56.539 --> 00:31:00.619 robe and has given this problem of you know, how do we better engage 471 00:31:00.619 --> 00:31:03.900 our customer? It doesn't come together, like I didn't know what to do. 472 00:31:03.099 --> 00:31:06.609 I have all the skills, like I have all the skills, but 473 00:31:06.650 --> 00:31:11.049 I don't know how do I apply it. And slowly and I failed miserably 474 00:31:11.210 --> 00:31:15.369 initially and and then over a period of time I learned this process because I'm 475 00:31:15.369 --> 00:31:18.680 so impact driven. I was dissatisfied with or just being able to do simple 476 00:31:18.799 --> 00:31:22.920 cross tabs or something. I'm like, I'm not using full utilizing the tool 477 00:31:22.960 --> 00:31:26.720 kit and as a result, I developed this the framework by the framework came 478 00:31:26.759 --> 00:31:30.400 out of this journey that I had myself of how do I take all of 479 00:31:30.519 --> 00:31:34.069 my quant background and then all of this business context and I put it in 480 00:31:34.150 --> 00:31:37.470 the context and give it to people in a recipe that they can do, 481 00:31:37.549 --> 00:31:40.509 because I'm at some point I got really good enough, my taught team got 482 00:31:40.549 --> 00:31:42.509 really good and we became a sport team for paypal and you all of that 483 00:31:42.630 --> 00:31:48.059 happened. And so I just basically contest all of that learning into this fame 484 00:31:48.140 --> 00:31:51.859 world and thereby it makes it easy. You know, you give when you 485 00:31:51.940 --> 00:31:55.420 start teaching bad in the context. Oh, here's a winery, how do 486 00:31:55.460 --> 00:31:59.819 you oper optimize their marketing spend? Oh, here's a hospital, how do 487 00:31:59.859 --> 00:32:04.049 you optimize their outcome, disease outcome and and discharge outcome and all of that. 488 00:32:04.730 --> 00:32:07.009 When you start teaching people in that context and then you teach them, 489 00:32:07.089 --> 00:32:10.009 you can teach them average is at statistical ers and all of that, but 490 00:32:10.089 --> 00:32:14.890 when you teach it in the context, that's when it connects. And I 491 00:32:15.009 --> 00:32:16.839 think too many people are going the wrong way. One is they're doing it 492 00:32:17.079 --> 00:32:22.279 for all like this training mandatory for all employees. That's waste of time. 493 00:32:22.680 --> 00:32:25.720 You're going to get what is it called fatigue? Employee fatigue, because you 494 00:32:25.799 --> 00:32:29.880 know, too many too many hours and and and they don't see their value. 495 00:32:30.000 --> 00:32:32.549 Not Everybody needs to be the right level. So that's one error. 496 00:32:32.630 --> 00:32:36.750 That the one, one mistake, and I see people doing one training for 497 00:32:36.789 --> 00:32:39.710 all. Said, will in the in the cognitive and emotional energy again to 498 00:32:39.829 --> 00:32:43.990 cover that gap that you describe, which is like, I get this uniform 499 00:32:44.069 --> 00:32:45.859 training, but how does that relate me? I know that this is important 500 00:32:45.900 --> 00:32:49.099 because we all did it, but I don't know what it means. It 501 00:32:49.180 --> 00:32:52.900 so was like all this wasted energy, yes, or the context, and 502 00:32:52.980 --> 00:32:55.339 then teaching them python. Does anybody, everybody, need to learn python? 503 00:32:55.339 --> 00:33:00.250 I mean Python is twenty percent of your problems. Should require actually you learning 504 00:33:00.329 --> 00:33:02.970 to use python and and using, you know, statistical methods, of machine 505 00:33:04.009 --> 00:33:07.890 learning methods. Why is everybody learning python? Why is everybody learning sequel? 506 00:33:07.009 --> 00:33:10.009 So you're real like you know you're investing, you're taking too much of your 507 00:33:10.490 --> 00:33:15.680 employees time and putting them through a generic training and you're going to create frustration 508 00:33:15.720 --> 00:33:19.839 and is not going to be there's going to be very little learning transfer rate 509 00:33:20.200 --> 00:33:23.440 because you're not teaching them in the context and then whatever transfers, you going 510 00:33:23.440 --> 00:33:27.119 to lose it because you're not doing use case after that. Right, so 511 00:33:27.230 --> 00:33:29.710 specifically in data and let you see, these are the things I'm going to 512 00:33:29.750 --> 00:33:32.150 talk about, which is these are do not do, because even a fail 513 00:33:32.269 --> 00:33:36.630 and and you're going to come out of six months later, you know you're 514 00:33:36.630 --> 00:33:38.190 going to say, Ay, I'm and I the other last thing is they 515 00:33:38.230 --> 00:33:44.220 don't define success metrics. You go in for a project and you ask the 516 00:33:44.220 --> 00:33:47.140 learning and transfer you know, learning and development managers and hur managers. So 517 00:33:47.180 --> 00:33:50.900 how you want to measure the success of this project? This works or not? 518 00:33:51.740 --> 00:33:53.940 Oh, that's a good question. Like if you don't define what is 519 00:33:54.019 --> 00:33:58.650 success for you, that means you don't know six months later, nine months 520 00:33:58.650 --> 00:34:01.130 later, you successful or not. You just have some surveys that people are 521 00:34:01.170 --> 00:34:05.049 filled out where their voice or it was a waste of time or whatever else. 522 00:34:05.369 --> 00:34:07.929 But how would you know what worked or not? Right? So I 523 00:34:08.010 --> 00:34:10.639 don't know, I'm there's a lot to cover here, but I'm giving you 524 00:34:10.760 --> 00:34:15.639 know some of the top things. Don't do this. Think about your strategy 525 00:34:15.800 --> 00:34:19.679 first. Think about your data lit to see goal. Think about that persona 526 00:34:19.800 --> 00:34:22.480 mapping and strategize it. Who needs to be at what they will spend that 527 00:34:22.679 --> 00:34:27.590 front and time. The other thing is it cannot be done without executive buying, 528 00:34:27.670 --> 00:34:30.469 and we again get too many of these requests. Oh we will do 529 00:34:30.550 --> 00:34:32.989 this deal see how, but we don't need to, and I reeve our 530 00:34:34.030 --> 00:34:36.710 first step is voice of executive say, Oh, what's it is a good 531 00:34:36.869 --> 00:34:38.300 we don't need to talk to executives. Why do we need to waste their 532 00:34:38.340 --> 00:34:45.780 time? You can't change a culture of the organization without executives putting in their 533 00:34:45.860 --> 00:34:50.139 skin in the game right. It's not possible. You're talking about changing the 534 00:34:50.300 --> 00:34:54.170 culture and that's shifting the mind. Who is a marketing manager to look up? 535 00:34:54.210 --> 00:34:57.650 If the if, the CEO and the leadership, the CMO, is 536 00:34:57.690 --> 00:35:01.010 still making decisions right of the Bab, the seat of the pants, why 537 00:35:01.050 --> 00:35:04.570 are they going to be later driven? Why are they going to pick up 538 00:35:04.570 --> 00:35:07.840 these skills right? So I think that's another big failure. I see people 539 00:35:07.880 --> 00:35:12.440 think, oh, it's a siloid approach. I let's just fix this team. 540 00:35:12.519 --> 00:35:15.719 The team is not functional. Let's fix this steam. You can fix 541 00:35:15.800 --> 00:35:19.840 the theme all you like, but unless you fix it systemically, you're not 542 00:35:19.960 --> 00:35:23.429 going to be able to drive culture or data. So good another drive into 543 00:35:23.590 --> 00:35:29.150 making it a cultural transition, not just a project orientation. So you've talked 544 00:35:29.150 --> 00:35:31.309 about the data drive. An executive, you talked a little bit about the 545 00:35:31.349 --> 00:35:36.579 data enthusiast. I can imagine what the data skeptic is, but I'm really 546 00:35:36.699 --> 00:35:40.179 curious to hear more about the citizen analyst. What are the characteristics of a 547 00:35:40.260 --> 00:35:45.500 cities in analyst in what are maybe a couple roles in an organization that it's 548 00:35:45.699 --> 00:35:49.659 that that maps to? Yeah, that's great question. So, going back 549 00:35:49.659 --> 00:35:52.170 to my eighty twenty eight percent of the problem, you and I, as 550 00:35:52.250 --> 00:35:59.329 individual professionals, should be able to use simple analysis and do it in Excel, 551 00:36:00.050 --> 00:36:01.929 Excel, excel, or whatever tool of choice you have, MICROSOFTC cell, 552 00:36:01.929 --> 00:36:05.440 everybody has, but know whatever tool of choice you have at hand, 553 00:36:06.039 --> 00:36:09.880 which means eighty percent of the problem in marketing, in product in customer support 554 00:36:10.079 --> 00:36:15.119 is solvable using simple man aggregate analysis, correlation analysis, sizing and Tren analysis. 555 00:36:15.800 --> 00:36:20.389 Those simple methodologies are FM, for example, for segmentation, recent AFQUENCY, 556 00:36:20.429 --> 00:36:23.789 montory. Simple stuff can be can solve eighty percent of a problem. 557 00:36:23.989 --> 00:36:29.309 So, citizen analyst, they're too like so marketing managers. Problem Managers, 558 00:36:29.349 --> 00:36:32.619 typically marketing folks, and the product folks, not product folks, need to 559 00:36:34.019 --> 00:36:37.500 to do this simple analysis and those so they need like these basic skills to 560 00:36:37.619 --> 00:36:43.179 be able to utilize excel, for exampleized set and graph and chart and the 561 00:36:43.300 --> 00:36:45.539 systematic again, bother frame. Will we call this bother framework? But hands 562 00:36:45.579 --> 00:36:50.409 on business analytics. They need hands on business atic skills for that. For 563 00:36:50.690 --> 00:36:54.050 Marketing folks they need a little bit more. They need a be testing as 564 00:36:54.130 --> 00:36:58.969 well, to be able to do control design of control experiments that if I 565 00:36:59.010 --> 00:37:01.920 did this processes, if I change the shade of this button from pink to 566 00:37:02.320 --> 00:37:06.519 read, what happens, and a lot of those things. So they're two, 567 00:37:07.360 --> 00:37:10.400 not two personas that we see, data educated and citizen analysts that we 568 00:37:10.519 --> 00:37:15.599 use typically in the organization. Sometimes they are typically in an organization. Their 569 00:37:15.599 --> 00:37:19.389 six to eight person us. Sometimes there is need for more and we introduced 570 00:37:19.550 --> 00:37:23.829 more personas. But typically for you know, marketing, product sales person they 571 00:37:23.869 --> 00:37:30.380 fall somewhere between data literate, data educated and citizen analyst, depending again on 572 00:37:30.460 --> 00:37:34.860 the outcome. Sometimes sales people, sales professionals. They need to be able 573 00:37:34.900 --> 00:37:38.340 to utilize the output from a DNA team, but not necessarily do some things 574 00:37:38.500 --> 00:37:42.739 hands on. They need to be able to understand it because they their literacy 575 00:37:42.739 --> 00:37:45.570 level need to be data needs to be Deda literate right. So there's again 576 00:37:45.610 --> 00:37:52.250 again these are not set in stone definitions. They vary by organization because the 577 00:37:52.369 --> 00:37:57.409 organization's goal is different. For some organization a product manager needs to be able 578 00:37:57.449 --> 00:38:01.679 to pull data using sequel and do real time testing and so on. They 579 00:38:01.800 --> 00:38:06.559 they need a higher skill for that level, right, higher data skill. 580 00:38:06.719 --> 00:38:09.719 So it varies, but this is kind of roughly what it fits. Really 581 00:38:09.760 --> 00:38:14.670 good. Thank you so much for that. I'm glad I ask that before 582 00:38:14.710 --> 00:38:17.829 we go to a couple kind of closing elements. Any closing thoughts for you 583 00:38:17.949 --> 00:38:22.949 here? Any hopes, your expectations for a better data future? Like you 584 00:38:22.030 --> 00:38:25.150 know, if you want to impart one, you know, big message, 585 00:38:25.469 --> 00:38:30.260 to put a button on this really interesting and wide ranging. I wish I 586 00:38:30.340 --> 00:38:34.900 wasn't so ambitious with how much we wanted to learn or how much I wanted 587 00:38:34.900 --> 00:38:37.179 to learn with my time with you, but you know, anything you want 588 00:38:37.179 --> 00:38:43.329 to add here? My last thing I want to tell your audience is this 589 00:38:43.570 --> 00:38:46.250 is a perfect time to learn. You know, one word, two one, 590 00:38:46.369 --> 00:38:50.329 two words, one one stone. Kind of situation. Right now and 591 00:38:50.369 --> 00:38:53.050 again we are according in early Aprils the pandemic and me and post pandemic. 592 00:38:53.170 --> 00:38:57.000 This kind of chaos, this news, the depressing news, and all of 593 00:38:57.079 --> 00:39:00.519 that is going to continue and it's a time of transition. Our minds are 594 00:39:00.559 --> 00:39:02.119 transitioning. When I get up every morning I don't know what's happened in the 595 00:39:02.199 --> 00:39:07.119 world like this, it's the world is transitioning. That means our mind is 596 00:39:07.760 --> 00:39:12.110 in the perfect place to learn. So if you are yourself, an individual 597 00:39:12.150 --> 00:39:15.909 wanting to invest in yourself, learn the skills. If you are a leader 598 00:39:15.389 --> 00:39:19.630 and you want your team, you know, you invest in your team to 599 00:39:19.750 --> 00:39:23.780 learn because you know post pandemic, it's a situation where you can't do without 600 00:39:23.780 --> 00:39:27.619 data and these skills are going to you know, if you invest in your 601 00:39:27.619 --> 00:39:30.179 team right now, you're going to get a hundred folds, because all each 602 00:39:30.219 --> 00:39:34.300 of an every of your decision go to be optimized for in future. And 603 00:39:34.380 --> 00:39:37.420 you want that because you know the margins are gone for two thousand and twenty 604 00:39:37.420 --> 00:39:40.409 at least right and and so for as an individual, I would also say 605 00:39:42.010 --> 00:39:45.090 invest time in learning, because you know that will. I heard from some 606 00:39:45.210 --> 00:39:50.969 podcasts. You know that fires new synaptic neuron connections and it actually makes your 607 00:39:51.090 --> 00:39:55.719 brain grow and is actually towards a better, wholesome you versus you know, 608 00:39:55.800 --> 00:40:00.239 the news that we have been bombarded with it's just it's really depressing. So 609 00:40:00.280 --> 00:40:04.119 I would say right now is a perfect time. Right now, I'm continuing 610 00:40:04.199 --> 00:40:07.550 on for attorney. Is Not an easy year. Perfect time to learn if 611 00:40:07.590 --> 00:40:09.989 you are yourself a sales and marketting individual wanting to invest in time, in 612 00:40:10.070 --> 00:40:13.909 yours, your time in it. So learning these skills is going to be 613 00:40:13.989 --> 00:40:16.389 very, very useful. And also, if you are a leader, invest 614 00:40:16.630 --> 00:40:20.710 in in data literacy, invest in data culture for your organization. It's going 615 00:40:20.750 --> 00:40:23.539 to serve you really well so that you know you're going to try as a 616 00:40:23.619 --> 00:40:29.900 company and serve your customers better. Wonderful for folks like again, I feel 617 00:40:29.940 --> 00:40:32.659 like I was overly ambitious and what I wanted to do here. If anyone 618 00:40:32.699 --> 00:40:37.050 wants to go back, we're doing full transcripts, we're doing video clips, 619 00:40:37.090 --> 00:40:42.889 we're doing summaries at bombombcom slash podcast and when you visit that page, not 620 00:40:43.010 --> 00:40:45.329 only can you see the most recent episodes, including this one, but you 621 00:40:45.369 --> 00:40:51.199 might also enjoy episode thirty six with Sarah Toms from the warten school at pen. 622 00:40:51.559 --> 00:40:54.360 We talked about the financial side of CX, which customers should you invest 623 00:40:54.599 --> 00:41:00.000 in and using LTV and other data to figure out where to invest in an 624 00:41:00.039 --> 00:41:04.670 episode sixty five. More recently with Chris Hicken, we talked about product usages 625 00:41:04.710 --> 00:41:07.510 of vanity metric and we talked about a number of metrics and how different teams 626 00:41:07.550 --> 00:41:12.550 within the organization can use them. So before I let you go, I 627 00:41:12.590 --> 00:41:15.670 would love to give you the chance, because relationships are our number one core 628 00:41:15.750 --> 00:41:19.659 value, to think or mention someone who's had a positive impact on your life 629 00:41:19.699 --> 00:41:22.699 or your career. Even we I am listening a lot these days, like 630 00:41:22.860 --> 00:41:27.059 again, so bombarded with news and not so good. I'm listening a lot 631 00:41:27.420 --> 00:41:32.809 to neuroscientists, potentials, autern reality. I'm think I'm going completely in different 632 00:41:32.849 --> 00:41:37.130 direction these days, because that helps me see bigger, bigger. I can 633 00:41:37.170 --> 00:41:39.650 zoom out of this situation and see a being, a bigger self. So 634 00:41:39.690 --> 00:41:45.769 I'm listening a lot to Dr Jode Dispenser these days and this whole aspect of 635 00:41:45.889 --> 00:41:51.000 we create on reality and Abraham Hicks, and I really love the empowerment that 636 00:41:51.199 --> 00:41:53.800 comes from that. You can change your bread. Energy makes matter. I 637 00:41:53.960 --> 00:41:59.440 love this concept and we, if we you know from from mindset and we 638 00:41:59.519 --> 00:42:01.670 know. We know this equal doing square, but I don't know how it 639 00:42:01.869 --> 00:42:07.150 translates to my world and I'm loving this idea about energy, math makes matter 640 00:42:07.349 --> 00:42:12.309 and how I can energetically change the vibration of what I am thinking, of 641 00:42:12.429 --> 00:42:16.179 what I'm doing and actually manifest matter. So I'm loving that and ass having 642 00:42:16.219 --> 00:42:20.420 a huge, insignificant impact for me. And then you know, obviously, 643 00:42:20.460 --> 00:42:23.300 all the all the good folks who came before that and open my mind. 644 00:42:23.699 --> 00:42:27.739 I'm a lifetime learners. I love learning, and so I'm learning from a 645 00:42:27.820 --> 00:42:32.250 new scientist, your your new scientists right now. Awesome. And you already 646 00:42:32.289 --> 00:42:38.769 mentioned Amazon in the context of data and customer experiences. There another company you 647 00:42:38.849 --> 00:42:43.929 maybe really appreciate or respect for the way that they deliver an experience for you 648 00:42:44.050 --> 00:42:49.880 as a customer. But I can probably name quite a few. But I 649 00:42:50.119 --> 00:42:57.400 love the new new aid, new startup companies who are all about I met 650 00:42:57.639 --> 00:43:01.070 you know, must have experiences for their for their client, and so I'm 651 00:43:01.150 --> 00:43:06.230 a customer for third love it sees one of our clients, and this this 652 00:43:06.429 --> 00:43:08.389 aspect of there, and then many, many, many others, but this 653 00:43:08.590 --> 00:43:14.420 aspect of being able to make things easy for their customers, to be able 654 00:43:14.420 --> 00:43:19.139 to give them choices, to empower them and to sell them seamlessly, not 655 00:43:19.300 --> 00:43:22.619 overtly, but you know, it sort of, I don't know, opposite 656 00:43:22.619 --> 00:43:25.179 of world, but you know, basically, make it seemless, make it 657 00:43:25.300 --> 00:43:29.050 easy, and I'm all. I'm a I'm a convenience and easy lover. 658 00:43:29.610 --> 00:43:32.650 And so all these new startups and experiences, you know, like there are 659 00:43:32.730 --> 00:43:38.010 lots of startups about like giving science kids. They're making science kids for my 660 00:43:38.090 --> 00:43:43.079 child and you know, all of those I love. I love these companies 661 00:43:43.119 --> 00:43:46.320 who make seamless experiences. That does it for me. You know, then 662 00:43:46.360 --> 00:43:50.079 it's not a question of how much money I'm going to pate, so question 663 00:43:50.159 --> 00:43:52.840 of Oh, I got the experience. And now also, I'm looking at 664 00:43:53.079 --> 00:43:58.750 these days. I'm also very I'm very attractive to positive news coming out. 665 00:43:59.110 --> 00:44:04.389 So the companies are investing in the employees and companies that are making I mean 666 00:44:04.389 --> 00:44:07.269 I'm a big fan of Bill Gates and like the kind of investment is doing 667 00:44:07.309 --> 00:44:13.460 in vaccines. And you know how facebook is using its platform to spend spread 668 00:44:13.820 --> 00:44:17.860 positive information about covid and I love these, these things that people whould so 669 00:44:17.940 --> 00:44:22.619 much such a big network and such big influence and doing such high value work 670 00:44:23.059 --> 00:44:27.369 for the humanity. So I love these. I don't know, I roundered 671 00:44:27.409 --> 00:44:30.329 on again, but no, that's really good. I'm never going to stop 672 00:44:30.409 --> 00:44:34.289 somebody who's talking about the positive. Pianka. This has been awesome. If 673 00:44:34.369 --> 00:44:37.489 someone wants to follow up with you, where they want to learn more about 674 00:44:37.530 --> 00:44:38.880 a ring like, where would you send people if they want to follow up 675 00:44:38.920 --> 00:44:44.119 on this conversation? They can go to airdingcom and from there they can reach 676 00:44:44.199 --> 00:44:46.719 us, reach out and there's a lot of lot of resources there people can 677 00:44:46.800 --> 00:44:53.110 download and and case studies and you know are we share exhibitantly. So we 678 00:44:53.190 --> 00:44:55.789 have lots of lots of cases. We write a lot, we talked a 679 00:44:55.869 --> 00:44:59.550 lot, so download stuff from there. If you do want to reach out, 680 00:44:59.989 --> 00:45:01.829 you can use the form there. You can also follow me on Linkedin 681 00:45:01.869 --> 00:45:07.110 or connect with me on Linkedin, and you can also follow me on twitter. 682 00:45:07.619 --> 00:45:10.820 My handle is analytics creen, analytics screen perfect. I love it. 683 00:45:12.300 --> 00:45:15.139 This has been so good. I appreciate your time so much. I hope 684 00:45:15.179 --> 00:45:20.820 you enjoy the butterfly hit with your daughter and I will share all of these 685 00:45:20.900 --> 00:45:23.849 links at Bombbcom podcast. Thank you so mudy. Then, there was a 686 00:45:23.889 --> 00:45:30.170 pleasure talking to you and I really enjoyed conversation. Thank you. I hope 687 00:45:30.170 --> 00:45:35.610 you enjoyed that conversation with Pianca Jane, President and CEO of a ring hey. 688 00:45:35.690 --> 00:45:40.559 If you want more episodes like this one, you can visit Bombombcom podcast. 689 00:45:40.639 --> 00:45:47.639 It's bomb Bombcomla podcast, or you can search the customer experience podcast in 690 00:45:47.840 --> 00:45:52.869 your preferred podcast player. Again, my name is Ethan Butt. I host 691 00:45:52.989 --> 00:45:57.789 this series and I welcome your feedback. Email me, Ethan etch an at 692 00:45:57.829 --> 00:46:00.150 bombombcom. What would you like to see more of? What would you like 693 00:46:00.230 --> 00:46:05.500 to see less of? which episodes or conversations really stood out to you? 694 00:46:06.059 --> 00:46:09.860 You can also share that feedback via linkedin. Hit me up Ethan Bu last 695 00:46:09.860 --> 00:46:15.340 name is spelled beute. I'm pretty sure I'm still the only one there and 696 00:46:15.460 --> 00:46:19.610 I welcome again your thoughts, your feedback, anything you have to share about 697 00:46:19.690 --> 00:46:25.929 the CX series here on BTB growth. Thanks for listening. Is Your buyer 698 00:46:25.969 --> 00:46:30.650 a betb marketer? If so, you should think about sponsoring this podcast. 699 00:46:30.010 --> 00:46:35.000 BB growth gets downloaded over a hundred and thirty thousand times each month and our 700 00:46:35.079 --> 00:46:38.320 listeners are marketing decision makers. If it sounds interesting, Sind Logan and email 701 00:46:38.519 --> 00:46:40.559 logan at sweetfish Mediacom.
