WEBVTT 1 00:00:00.040 --> 00:00:04.679 Gloma Linda, sponsored by Teamsters Local nineteen thirty two, Protecting the Future of 2 00:00:04.759 --> 00:00:14.960 Working Families Teamsters nineteen thirty two. Dot org. The information economy has a 3 00:00:15.080 --> 00:00:20.519 rod. The world is teeming with innovation as new business models reinvent every industry 4 00:00:20.519 --> 00:00:25.839 industry. Inside Analysis is your source of information and insight about how to make 5 00:00:25.879 --> 00:00:30.679 the most of this exciting new era. Learn more at inside analysis dot com, 6 00:00:30.719 --> 00:00:40.000 Insideanalysis dot com. And now here's your host, Eric Kavanaugh. All 7 00:00:40.119 --> 00:00:43.119 right, ladies and gentlemen, Hello and welcome back once again, so the 8 00:00:43.119 --> 00:00:47.960 only coast to coast show all about the information economy. It's called Inside Analysis 9 00:00:48.039 --> 00:00:53.320 and folks, I'm so excited now to be talking to someone Ala Zaken of 10 00:00:53.399 --> 00:00:57.119 Athena Solutions. I'm actually going to be doing some work with these folks. 11 00:00:57.159 --> 00:01:00.600 Athena was founded by a guy named rich Sherman was one of the best friends 12 00:01:00.640 --> 00:01:04.879 of the business and Rick unfortunately passed away last year, which was real kind 13 00:01:04.879 --> 00:01:08.319 of a shocker to me. But the light, the cycle of life goes 14 00:01:08.359 --> 00:01:11.840 on, and I decided it's time to talk to these people, maybe do 15 00:01:11.959 --> 00:01:15.200 some work with them. So here I am. I'm going to talk to 16 00:01:15.280 --> 00:01:19.920 Ala zaikin she's a data modeler. She's all into the Kimball approach of guse 17 00:01:19.959 --> 00:01:23.439 you studied Kimball back in college, right, Alas, so tell me what 18 00:01:23.480 --> 00:01:26.319 got you in the data and why do you like making order from the chaos 19 00:01:26.319 --> 00:01:34.680 of data. Well, at the time, it was just it seems like 20 00:01:34.719 --> 00:01:38.319 a logical approach to thinking about data. And I was very excited that I 21 00:01:38.400 --> 00:01:44.200 found something that resonated with me so much, and since then, I literally 22 00:01:44.280 --> 00:01:48.359 can't let it go, both professionally and personally. I think I am making 23 00:01:48.439 --> 00:01:53.359 lists and making everything into a table. So I am that type of person. 24 00:01:53.400 --> 00:01:57.400 For better or worse, that's pretty fine. I think that it's a 25 00:01:57.439 --> 00:02:00.959 wonderful experience when you find what you've in joy and that's what I do too. 26 00:02:01.040 --> 00:02:05.239 Like for me, I used to watch Money Python way back from the 27 00:02:05.319 --> 00:02:07.680 day, and Eric Idol used to do all these talk show skits where he 28 00:02:07.759 --> 00:02:10.400 was being all being doing his talk show, and I was like, I 29 00:02:10.439 --> 00:02:13.759 want to do that. I want to do what he does. I want 30 00:02:13.800 --> 00:02:16.039 me just sit around and ask questions to learn things, because you get to 31 00:02:16.120 --> 00:02:20.719 learn for a living, but you also get to learn from the data. 32 00:02:21.280 --> 00:02:23.039 Right, And you said something I thought that was really interesting. Before the 33 00:02:23.080 --> 00:02:25.360 call, you're like, Oh, I'm a data modeler, so I have 34 00:02:25.400 --> 00:02:29.639 two screens. Why do you need two screens to be a data modeler. 35 00:02:30.719 --> 00:02:36.919 There's a lot to absorb, you know. There's when you create a data 36 00:02:37.000 --> 00:02:42.479 model, there are many conversations that are happening. The conversations with business users, 37 00:02:42.520 --> 00:02:47.080 conversations with technical people, conversations with managers. Everybody has their own goals 38 00:02:47.719 --> 00:02:52.599 and data model usually stands kind of in the middle of all of it, 39 00:02:53.240 --> 00:03:00.680 and you basically combine all of these aspirations into what you you have created. 40 00:03:00.719 --> 00:03:06.080 That's what I that's what I found, and I found that a lot of 41 00:03:06.639 --> 00:03:12.240 meaning can be created in the process, which is very attractive aspect of this 42 00:03:12.360 --> 00:03:19.800 whole work for me personally, because you know, by looking by combining perspectives, 43 00:03:20.240 --> 00:03:25.439 you have a different view of the data and different you identify different relationships 44 00:03:25.599 --> 00:03:31.080 and the meaning comes out in this work. Yeah, well, you bring 45 00:03:31.159 --> 00:03:36.199 up some really good points here, and I like your joke about ordering things 46 00:03:36.199 --> 00:03:38.360 in the form of a table. Right you have rows and columns and the 47 00:03:38.439 --> 00:03:43.960 data somewhere between, and you have multi dimensional data of course that is three 48 00:03:44.000 --> 00:03:47.840 dimensional or even more dimensions than three just depending upon the particular use case. 49 00:03:49.280 --> 00:03:53.199 But when companies are trying to get value from data, you do have to 50 00:03:53.240 --> 00:04:00.560 go through this process of distilling the information you have, understanding their relationship between 51 00:04:00.599 --> 00:04:03.680 the data points, and then going ahead and building out the model. And 52 00:04:04.199 --> 00:04:08.039 that has changed somewhat in the recent years. You know, we talk a 53 00:04:08.039 --> 00:04:13.960 lot about Snowflake and how Snowflake really saw an opportunity to allow companies to tear 54 00:04:14.000 --> 00:04:16.240 down the data warehouse, redo the schema, and then spin it back up 55 00:04:16.240 --> 00:04:19.720 again quickly, because in the old days that would have been extremely difficult. 56 00:04:19.759 --> 00:04:23.399 I mean, once your data warehouse is running, if you want to start 57 00:04:23.439 --> 00:04:27.800 adding tables and things and adding dimensions is like, oh no, I think, 58 00:04:27.879 --> 00:04:30.040 really, are you sure you have to do that? And that's changed. 59 00:04:30.040 --> 00:04:32.600 But if you would just kind of walk through a little bit what it's 60 00:04:32.720 --> 00:04:36.759 like to look at the data and to understand the relationships, to start to 61 00:04:36.759 --> 00:04:40.639 map out that data model, what does that actually look like? What are 62 00:04:40.680 --> 00:04:47.279 the aha moments? So I noticed that in the recent years the strictness with 63 00:04:47.360 --> 00:04:55.279 which we designed solutions kind of disappeared. And it's not free for all, 64 00:04:55.439 --> 00:05:02.720 and you still have to understand lot. That didn't change, but in terms 65 00:05:02.759 --> 00:05:10.519 of how to implement and how you know how to how to deliver a solution 66 00:05:11.040 --> 00:05:15.399 piece by piece by piece. I think that this is what you're talking about. 67 00:05:15.519 --> 00:05:18.759 This is what what changed because the speed of delivery, Like, nobody 68 00:05:18.800 --> 00:05:26.120 wants to wait until a big, huge data warehouse is done finished for you 69 00:05:26.199 --> 00:05:30.040 everybody. It's an iterative approach. It's it's an agile at Gail game, 70 00:05:30.920 --> 00:05:36.199 and that kind of works in with the technological changes that are happening. Nobody 71 00:05:36.240 --> 00:05:43.879 wants to wait for a big solution. You know. You you develop understanding 72 00:05:43.920 --> 00:05:48.279 in one area and you deliver it. You develop additional understanding when the when 73 00:05:48.319 --> 00:05:53.839 the project is delivered. That's that has been my experience, and then you 74 00:05:53.879 --> 00:05:59.199 add to it. So a lot of meaning that I was talking about, 75 00:05:59.199 --> 00:06:02.000 I like to talk about meaning because this kind of this kind of I understand. 76 00:06:02.040 --> 00:06:08.439 I think that this elevates the conversation a little bit because people like to 77 00:06:08.519 --> 00:06:13.639 understand, people like to have meaningful discussions, people like to speak the same 78 00:06:13.759 --> 00:06:19.800 language and collaborate. So I feel like modeling creates this type of conversation. 79 00:06:20.600 --> 00:06:29.879 But when I was talking about the hard structure about on delivering a project, 80 00:06:30.160 --> 00:06:35.279 that seems to be dissipating, and people feel much more empowered and flexible in 81 00:06:35.360 --> 00:06:44.600 the way they work and in a way they approach the overall the overall objectives. 82 00:06:46.639 --> 00:06:50.519 There's a lot of data right now. Companies have a lot of data, 83 00:06:50.920 --> 00:06:57.160 so they have a lot to work through. So jumping at everything at 84 00:06:57.199 --> 00:07:01.800 once is not practical anymore. So you have to step back and identify your 85 00:07:01.800 --> 00:07:06.399 priorities in jump where it is most needed, or you can pick up a 86 00:07:06.480 --> 00:07:12.720 low hanging fruit and jump there. Yeah, you said several really interesting things. 87 00:07:12.720 --> 00:07:15.480 One I love that line where you said people like to understand, right, 88 00:07:15.519 --> 00:07:20.800 people like to have meaningful conversations. And when you don't understand, that's 89 00:07:20.839 --> 00:07:25.639 when you're still in discovery mode. You're still asking questions, You're trying to 90 00:07:25.639 --> 00:07:28.079 wrap your head around something. You don't get it, and then when you 91 00:07:28.079 --> 00:07:30.680 do understand, it's like, oh, I get it now. And that's 92 00:07:30.720 --> 00:07:34.000 what you're able to do with data. And I think the really interesting point 93 00:07:34.000 --> 00:07:39.519 you made is that there's so much data right now. I think like observability, 94 00:07:39.560 --> 00:07:43.120 I mean, observe ability exploded in the last few years, which is 95 00:07:43.160 --> 00:07:46.160 for good reason. I mean, there's reasons why these things happen, but 96 00:07:46.480 --> 00:07:47.879 you have all this other data to make sense left, So how do you 97 00:07:47.959 --> 00:07:50.480 do that? I mean, so I'd like to just throw that back at 98 00:07:50.560 --> 00:07:55.079 you and say, I think it makes a lot of sense. How do 99 00:07:55.120 --> 00:07:58.319 you start with with low hanging fruit, especially for a data model. Do 100 00:07:58.319 --> 00:08:01.560 you build that one corner of it and then get that nailed down and then 101 00:08:01.639 --> 00:08:05.800 start to incrementally move out from there to build out a bigger model that really 102 00:08:05.800 --> 00:08:11.439 represents the business. How does that actually work? Usually you identify an area 103 00:08:11.120 --> 00:08:18.480 where it would be practical to begin, even though even though on your roadmap 104 00:08:18.600 --> 00:08:24.279 there could be much much more. You have to start to focus, to 105 00:08:24.319 --> 00:08:30.439 focus the effort in this area and to start developing. It's almost like prototyping, 106 00:08:31.199 --> 00:08:37.879 you know, you start developing the structures that you start developing the understanding, 107 00:08:37.320 --> 00:08:43.159 You start developing a way to deal with the group with the requirements. 108 00:08:43.440 --> 00:08:48.039 So all of this goes into the into delivering the project, into delivering the 109 00:08:50.480 --> 00:08:54.440 you know, the plate platform as as it is called now. Nobody we 110 00:08:54.600 --> 00:09:01.559 rarely speak about data warehouse as it is now? Did you notice that because 111 00:09:01.639 --> 00:09:07.240 we speak about data platforms and this is platform is the only thing that can 112 00:09:07.639 --> 00:09:11.679 resolve you know, the problems that people are having with the data because they're 113 00:09:11.840 --> 00:09:18.480 they're so universal and they are so broad, and I noticed that they're pretty 114 00:09:18.559 --> 00:09:24.639 they're also pretty standard. You know, everybody, everybody is dealing with the 115 00:09:24.720 --> 00:09:30.559 amount, like sheer amount of data everybody is dealing with, you know, 116 00:09:30.600 --> 00:09:35.679 whether data quality is up to the level everybody is dealing with common creating common 117 00:09:35.799 --> 00:09:45.360 understanding around around major concepts in the business. So the more so. Actually 118 00:09:45.399 --> 00:09:52.159 the technology reflected this trend, I think, because you know, there are 119 00:09:52.159 --> 00:09:58.480 many data catalogs that exist now so so, and like, honestly one better 120 00:09:58.519 --> 00:10:03.200 than the other. You know, you take your pick. They're great, 121 00:10:03.320 --> 00:10:09.720 and I understand and every client has something needs something different, but you can 122 00:10:09.600 --> 00:10:18.279 have your choice at this point. But what it gives is the common creating, 123 00:10:18.360 --> 00:10:22.759 the common understanding, the creating the common language so that it is easier 124 00:10:22.799 --> 00:10:26.879 to reach the consensus and it's easier to build out the structures that solve the 125 00:10:26.919 --> 00:10:33.000 problems for like more than one group of people. Essentially, Yeah, that's 126 00:10:33.120 --> 00:10:37.399 very interesting. I really like this approach that you've pagan because you've come along 127 00:10:37.480 --> 00:10:39.399 this journey and you've seen the changes, right, I mean, there's so 128 00:10:39.480 --> 00:10:43.440 much more data, right, now it's coming at people so quickly. And 129 00:10:43.879 --> 00:10:48.240 the question with design and the data model is to understand the most efficient way 130 00:10:48.799 --> 00:10:54.600 to capture the data, to potentially refine the data, transform the t for 131 00:10:54.720 --> 00:10:58.759 example, transform the data and deliver it to a solution that can be used 132 00:10:58.799 --> 00:11:01.080 for operations or brand analytics. I presume a lot of what you do is 133 00:11:01.120 --> 00:11:07.879 geared around analysis, is that right? Absolutely? We're building the platforms to 134 00:11:07.960 --> 00:11:16.639 facilitate the advanced analytics, to facilitate data science, so to know. In 135 00:11:16.759 --> 00:11:20.200 order to along the way, along the joy, we pick up a lot 136 00:11:20.200 --> 00:11:28.000 of things like you know, glossaries and governance and creating additional processes around the 137 00:11:28.080 --> 00:11:31.799 data and data management in general. Yeah, and governance. You know. 138 00:11:31.879 --> 00:11:37.360 I was talking just this morning about privacy and last week earlier this week, 139 00:11:37.399 --> 00:11:39.519 I should say, we did a show two about privacy and I was like, 140 00:11:39.559 --> 00:11:43.960 you know, what is privacy really And when you get down to it 141 00:11:43.000 --> 00:11:50.039 in the data world, it has to deal with the appropriate accessing of information, 142 00:11:50.279 --> 00:11:52.679 right who should have access to this information? Who should not have access 143 00:11:52.720 --> 00:11:56.519 to this information? Certain things are private, Certain things you only want certain 144 00:11:56.559 --> 00:12:00.159 people to see, but you do want those certain people to see it. 145 00:12:00.559 --> 00:12:03.879 You don't want no one to see it, right, So you have role 146 00:12:03.960 --> 00:12:07.480 access role based access controls, for example, and you want to be as 147 00:12:07.639 --> 00:12:13.519 as dynamic as possible because if it's very fine grained, then some person has 148 00:12:13.559 --> 00:12:16.240 to manually switch things on and off and that can be a pain. You 149 00:12:16.279 --> 00:12:22.600 start to think about organizational hierarchies and how challenging it is to get the right 150 00:12:22.639 --> 00:12:26.480 access for the right person at the right time without disrupting the access right because 151 00:12:26.519 --> 00:12:28.759 the people who need the data, you want them to have the data and 152 00:12:28.799 --> 00:12:31.559 not to have to jump through hoops to get it. So that's really what 153 00:12:31.639 --> 00:12:35.480 it comes down to with respected data governance right is figuring out what are the 154 00:12:35.519 --> 00:12:41.919 ideal policies, what are the appropriate choke points where we can require someone to 155 00:12:41.000 --> 00:12:46.200 log in, where we can require some to validate who they are without disrupting 156 00:12:46.240 --> 00:12:48.320 the workload, without disrupting what we want them to be doing with the data. 157 00:12:48.399 --> 00:12:54.559 Right. Absolutely, absolutely, And this is one of the major areas 158 00:12:54.799 --> 00:13:01.080 of data governance where everybody, you know, everybody thinks about roll based based 159 00:13:01.120 --> 00:13:05.840 access, everybody thinks about data classification. So to implement that role based access, 160 00:13:07.399 --> 00:13:11.519 uh, there are a lot of there are a lot of tools to 161 00:13:11.519 --> 00:13:16.080 to facilitate security. But also, you know, a big part of an 162 00:13:16.200 --> 00:13:22.799 initiative such as security is the consensus, you know, and I think that 163 00:13:22.919 --> 00:13:28.000 implementation is a big piece of it. And there's technology to choose from. 164 00:13:28.080 --> 00:13:33.399 Again, but you know, jumping in and uh, you know, classifying 165 00:13:33.440 --> 00:13:41.639 the data elements and developing the definitions for the roles is the step that takes 166 00:13:41.679 --> 00:13:46.799 the longest. There is a little bit of inertia in people in general. 167 00:13:48.039 --> 00:13:54.200 Everybody everybody has it, but companies do too, So you know, I 168 00:13:54.240 --> 00:14:03.399 think that the compliance requirements actually forces people to those areas, so they have 169 00:14:03.519 --> 00:14:07.360 to address it. But it's also it's also logical and the right thing to 170 00:14:07.440 --> 00:14:11.960 do, given that the data sources that we're dealing with are incredibly important and 171 00:14:13.399 --> 00:14:20.080 incredibly expensive and incredibly vast. And I'm just guessing here because I haven't done 172 00:14:20.080 --> 00:14:24.200 all these projects myself, but I've been around them. I'm guessing that the 173 00:14:24.240 --> 00:14:28.080 compliance which is a driver. There's no question in compliance as a driver, 174 00:14:28.399 --> 00:14:31.799 but what you really want is for the business to take more of a proactive 175 00:14:31.799 --> 00:14:35.919 approach, more of a positive approach about used in the information, and in 176 00:14:35.919 --> 00:14:41.000 that sense, the compliance can be a nice driver it can be a nice 177 00:14:41.440 --> 00:14:45.840 mechanism of action to get the business to pay attention and then build a value 178 00:14:45.879 --> 00:14:50.039 around that. So it's that the value isn't just being in compliance. That's 179 00:14:50.039 --> 00:14:54.200 not the value. That is a value, But the bigger value is knowing 180 00:14:54.240 --> 00:14:56.440 how to use the data, knowing how to leverage the data, and I'm 181 00:14:56.440 --> 00:15:01.600 guessing that you figured out years ago and you use it as an opportunity to 182 00:15:01.720 --> 00:15:05.759 explain to the business the value they can be getting from this data while being 183 00:15:05.799 --> 00:15:09.039 in compliance. Right, you don't want to just be defensive in pasture. 184 00:15:09.360 --> 00:15:13.879 You want to be offensive and you want to be creative in using the established 185 00:15:13.960 --> 00:15:20.559 rules to drive change but then bring value to the business. Right. Yes, 186 00:15:20.639 --> 00:15:24.000 I think the compliance frameworks such as GDPR for example, it is a 187 00:15:24.039 --> 00:15:31.679 framework and there's a benefit, there's a benefit of to tapping in into a 188 00:15:31.720 --> 00:15:37.879 framework that's already made. You know, it does it does require an investment 189 00:15:39.559 --> 00:15:43.440 to develop all of this in an organization, But I feel like I feel 190 00:15:43.480 --> 00:15:48.000 very positive about it because you know, there's a lot of thought that has 191 00:15:48.120 --> 00:15:52.840 been put into those framework and you know, I feel like we're the beneficial 192 00:15:54.320 --> 00:15:58.240 beneficiary of this thought process, which is you know, this is where the 193 00:15:58.240 --> 00:16:03.240 time goes, you know, to develop, you know there's time that is 194 00:16:03.279 --> 00:16:10.200 spent in even adopting it in the company. Sure, but I feel like 195 00:16:10.399 --> 00:16:15.960 I feel like having having a framework to work with is always always fit, 196 00:16:15.080 --> 00:16:22.200 saves a lot of time. Not to mention in lawsuits. Right, Well, 197 00:16:22.240 --> 00:16:26.240 you have your guardrails already, you have the framework, so that at 198 00:16:26.320 --> 00:16:30.639 least limits the discussion, that limits the free play of the scenario, if 199 00:16:30.639 --> 00:16:33.559 you will, and it kind of focuses people and keeps people untasked basically, 200 00:16:33.600 --> 00:16:36.960 But folks, don't just that down. Will be right back. You're listening 201 00:16:37.039 --> 00:16:48.279 to Inside Analysis with Allah zeke In from Athena Solutions. Stand by, Welcome 202 00:16:48.320 --> 00:16:55.720 back to Inside Analysis. Here's your host, Eric Tabanac. All right, 203 00:16:55.759 --> 00:17:00.080 folks, back on Inside Analysis talking to a lah zake In of Athena Solution 204 00:17:00.240 --> 00:17:03.200 and we're talking about data governance and data modeling and processes. And you had 205 00:17:03.200 --> 00:17:07.519 a great quote that's where the time goes, and it's really in working through 206 00:17:07.000 --> 00:17:10.759 first of all the objectives of the business. What do you want to accomplish 207 00:17:10.839 --> 00:17:14.839 with this data and how can you do that while remaining in compliance, which 208 00:17:14.839 --> 00:17:18.400 of course is very important. But what value can you get from the data 209 00:17:18.440 --> 00:17:21.000 that always has to be on the mind, and I'm sure it's always on 210 00:17:21.039 --> 00:17:26.599 your mind, is thinking how do we align these practices and programs with some 211 00:17:26.880 --> 00:17:30.839 value creating initiative to feed people to the data that they need at the time 212 00:17:30.920 --> 00:17:34.680 they needed and what sources are available. So I wanted to talk about data 213 00:17:34.720 --> 00:17:41.079 catalogs because to me, business colossies have been around for well, quite frankly, 214 00:17:41.160 --> 00:17:42.640 for thousands of years. I mean, if you get all the way 215 00:17:42.680 --> 00:17:48.079 back to Mesopotamia, they had symbols that they used for managing things and someone 216 00:17:48.119 --> 00:17:51.160 had to learn those symbols and know what they meant and build to do the 217 00:17:51.200 --> 00:17:53.400 math in real time as they're trading brain and other things like that. So 218 00:17:53.480 --> 00:17:57.400 this is not new. It's been around for ages, but now there's so 219 00:17:57.559 --> 00:18:03.160 much more data and the business glossary, the data catalog can be such an 220 00:18:03.279 --> 00:18:10.680 enabler by fostering business literacy data literacy, and that all feeds into helping people 221 00:18:10.759 --> 00:18:14.799 understand what are we trying to accomplish here and then use the data to get 222 00:18:14.839 --> 00:18:19.440 things done. Talk about data catalogs and how you use them to help clients 223 00:18:19.559 --> 00:18:27.000 figure out what they need to get done for their information systems. So there 224 00:18:27.119 --> 00:18:36.319 is there is I think an image at this point. There's so many there's 225 00:18:36.319 --> 00:18:47.079 so many different products to choose from for dat catalogs. The clients always, 226 00:18:48.400 --> 00:18:52.079 you know, there is always a desire to have it. Well, that's 227 00:18:52.079 --> 00:18:56.920 what I wanted to say. There's always a desire to organize and there is 228 00:18:56.960 --> 00:19:02.839 always a desire to understand the data. People are a little bit shy of 229 00:19:03.240 --> 00:19:11.400 purchasing sometimes a data catalog because everybody understands the investment and the you know another 230 00:19:11.640 --> 00:19:17.160 it's basically another application to maintain. And I am starting with this because in 231 00:19:17.200 --> 00:19:22.640 my experience there is always this hesitation. You know, we have the spreadsheets, 232 00:19:22.680 --> 00:19:26.079 like in the Mesopotamia, right, we have the spreadsheets. Why do 233 00:19:26.119 --> 00:19:30.400 we need to buy a tool and then maintain a tool? Okay, but 234 00:19:32.559 --> 00:19:37.480 you know right now, the need for creating meaning out of the data is 235 00:19:37.519 --> 00:19:44.599 so so strong, and so with meaning, the value is being drawn out. 236 00:19:45.519 --> 00:19:52.519 So the data catalog actually does that, and it works in different ways 237 00:19:52.599 --> 00:20:02.440 in different situations. Sometimes an organization wants to invest in data governance and they 238 00:20:02.519 --> 00:20:06.960 choose a data catalog as basically their platform for data governance, which can be 239 00:20:07.000 --> 00:20:11.160 done and it's a it's a it's a great platform for data governance. It 240 00:20:11.599 --> 00:20:18.519 allows room to, you know, to accumulate business data. It allows the 241 00:20:18.599 --> 00:20:27.480 room to to accumulate metadata. So the information that is being stored in one 242 00:20:27.519 --> 00:20:33.519 place and used by the data stewards, by technical data stewards, by laid 243 00:20:33.519 --> 00:20:37.440 people in a company, by business analysts, by data scientists. So you 244 00:20:37.519 --> 00:20:44.480 open up this meaning to a variety of audiences that before that went and used 245 00:20:44.519 --> 00:20:49.400 the spreadsheets here, and the spreadsheets here and Microsoft Access databases there. So 246 00:20:51.240 --> 00:20:55.720 big, big, huge step forward. And another thing that I think is 247 00:20:55.759 --> 00:21:02.200 a benefit is again the framework. You know, each of the data catalogs, 248 00:21:02.599 --> 00:21:04.200 you know, you bring in a new tool, you bring in a 249 00:21:04.240 --> 00:21:11.440 framework. So you have to choose wisely. And the initial stage of choosing 250 00:21:11.480 --> 00:21:15.880 a tool for your organization is very very important. Proof of concepts are very 251 00:21:15.960 --> 00:21:22.880 very important because every organizations has different needs. But once you committed to a 252 00:21:23.000 --> 00:21:27.279 framework, I think it's a benefit. Yeah, you bring up really, 253 00:21:27.400 --> 00:21:30.119 you bring up really, I just want to dive in this. You bring 254 00:21:30.200 --> 00:21:33.240 up a really good point, which is that and I've seen this with all 255 00:21:33.279 --> 00:21:37.400 sorts of different technologies, you really need to play around. That's why I 256 00:21:37.400 --> 00:21:41.799 proove a concept is important because each tool will have its own first of all, 257 00:21:41.799 --> 00:21:45.160 its own model underneath for how it works. It'll have its own array 258 00:21:45.200 --> 00:21:51.720 of functionality. It may have a particular bend toward a vertical financial services insurance 259 00:21:51.759 --> 00:21:57.240 for example. Some need very complex higher r keees, some don't, And 260 00:21:57.319 --> 00:22:00.680 if you don't need these complex higherarchies, then you don't need to go down 261 00:22:00.720 --> 00:22:04.119 that road. So you do have to kind of understand what are you trying 262 00:22:04.119 --> 00:22:07.200 to do with the tool, what is the business problem you're trying to solve. 263 00:22:07.599 --> 00:22:11.839 Then be able to talk to an expert like yourself and understand what's the 264 00:22:11.839 --> 00:22:15.480 difference between Calibra and Elation for example, or data dot World and any number 265 00:22:15.480 --> 00:22:21.119 of other solutions. And once you wrap your head around what's available and the 266 00:22:21.119 --> 00:22:26.759 functionality and kind of how companies, how individuals will use the technology, that's 267 00:22:26.799 --> 00:22:30.240 when you can make the hard decision. And like you said, you have 268 00:22:30.319 --> 00:22:33.720 to choose wisely because you know, I mean, you know, we talk 269 00:22:33.759 --> 00:22:37.000 about this in the cloud all the time. I've talked about companies that moved 270 00:22:37.000 --> 00:22:40.000 off a part Ot onto HubSpot and then a year later they go back to 271 00:22:40.079 --> 00:22:44.160 Partot. You're like Oh, the engineer is like, all the people who 272 00:22:44.160 --> 00:22:47.079 had to do all that work are just like, are you kidding me? 273 00:22:47.599 --> 00:22:51.640 Like, we have to go through the gaination side. Oh, there's nothing 274 00:22:51.680 --> 00:22:56.160 more painful than doing a job the second time because something messed up at the 275 00:22:56.279 --> 00:22:59.079 end. I have I mean I have a fear of that, Like I 276 00:22:59.119 --> 00:23:02.160 can't stand Like if I in the old days, you'd write an article and 277 00:23:02.200 --> 00:23:06.319 if you lost it because the floppy disk broke or something, you're just like, 278 00:23:06.440 --> 00:23:08.680 oh, no, I have to rewrite this thing. We've now baked 279 00:23:08.720 --> 00:23:12.240 in auto save to all these tools, right, so you don't have that 280 00:23:12.319 --> 00:23:15.680 problem as much anymore. But nonetheless, the point is you have to be 281 00:23:15.759 --> 00:23:19.720 careful in that decision, and that's where a trusted advisor comes in handy. 282 00:23:19.799 --> 00:23:26.079 Right. Absolutely, there is so much power that modern technology gives us so 283 00:23:26.119 --> 00:23:32.400 that we can move with like much greater speed, but we might move in 284 00:23:32.440 --> 00:23:40.680 the wrong direction. So now this this decision becomes even more important. Yeah, 285 00:23:40.720 --> 00:23:44.960 And when you work with clients on that particular subject area, I mean, 286 00:23:45.000 --> 00:23:48.039 I'm guessing it's a series of meetings where you sit down and you try 287 00:23:48.079 --> 00:23:51.799 to understand what is the nature of this business you know of course, where 288 00:23:51.839 --> 00:23:56.119 does your revenue come from? That's always a major topic to understand where is 289 00:23:56.119 --> 00:24:00.519 the revenue coming from? And then how do you leverage data for marketing purposes? 290 00:24:00.519 --> 00:24:03.839 How do you leverage data for reporting purposes? How do you leverage data 291 00:24:04.400 --> 00:24:08.880 for product design and service design? These are all the kinds of decisions that 292 00:24:08.960 --> 00:24:14.839 can be really aided by having the right sets of data curated in a data 293 00:24:14.880 --> 00:24:18.240 catalog. Perhaps, so just kind of walk us through what it looks like 294 00:24:18.279 --> 00:24:19.880 in the early stages. What are some of the questions you ask, and 295 00:24:19.920 --> 00:24:25.400 how do you sort of shape that journey to choosing the right data catalog? 296 00:24:29.920 --> 00:24:36.759 I think the journey to implement a data catalog, I guess, and to 297 00:24:37.160 --> 00:24:44.559 implement any project actually starts with understanding what doesn't work now? All right, 298 00:24:45.319 --> 00:24:53.079 yes, so I would I would say that this has to be that that 299 00:24:53.119 --> 00:24:59.559 has to be the first step. But then you need to balance it out. 300 00:24:59.680 --> 00:25:03.960 What what are the aspirations in the business, you know, where where 301 00:25:04.000 --> 00:25:07.400 they see the improvement? You know, sometimes we ask a question, in 302 00:25:07.440 --> 00:25:11.519 the perfect world, you know, what would you like to see if there 303 00:25:11.519 --> 00:25:15.039 are no limitations? You know, what would you like to see? And 304 00:25:15.559 --> 00:25:22.559 sometimes this is this conversation is very telling. Then you get into the requirements. 305 00:25:22.640 --> 00:25:26.920 Like I said, the requirements for the data catalog everybody has slightly different 306 00:25:26.960 --> 00:25:33.200 situations. The you know, the need is there to In some cases business 307 00:25:33.279 --> 00:25:38.480 glossary needs to be so you know, developed and so precise. In some 308 00:25:38.559 --> 00:25:42.279 cases, the technical side of it, the said technical metadata side of it, 309 00:25:42.319 --> 00:25:48.039 and they have availability of that metadata has to be you know, carries 310 00:25:48.119 --> 00:25:52.359 much more weight, so you have to you have to take this into consideration 311 00:25:52.519 --> 00:25:59.440 when you're choosing a tool. Also, is it is it an older company 312 00:25:59.440 --> 00:26:03.839 that has mostly on prem Is it a company that is cloud with the high 313 00:26:03.839 --> 00:26:10.240 security requirements that you know need to be taken into consideration, and that because 314 00:26:10.519 --> 00:26:15.240 because you are, you will be exposing some meaning, so you have to 315 00:26:15.279 --> 00:26:21.839 take into account the technical architecture that exists in the organization as well as the 316 00:26:21.880 --> 00:26:27.559 compliance requirements. So all of this becomes a long list of requirements, and 317 00:26:29.039 --> 00:26:33.559 I think the you know it worth it is worth it investing a little bit 318 00:26:33.640 --> 00:26:41.799 more in developing these requirements before jumping into the pool of different data catalogs. 319 00:26:41.559 --> 00:26:45.400 Right, Well, no, and I'm guessing that here's how my mind works, 320 00:26:45.440 --> 00:26:48.359 right, I'm always trying to kill three birds with every stone. I'm 321 00:26:48.359 --> 00:26:52.319 guessing that if you go through the process of getting to know the company, 322 00:26:52.720 --> 00:26:59.720 you are understanding bit by bit what makes it special. Because every company has 323 00:26:59.759 --> 00:27:02.880 their own own DNA, and this is why templates only go so far. 324 00:27:03.039 --> 00:27:07.160 You know, insurance companies may have a particular focus or a particular area of 325 00:27:07.200 --> 00:27:11.000 specialization where they're very good at things, but what they're good at requires additional 326 00:27:11.079 --> 00:27:15.960 data sets. And what you said is you know what's what's not working now? 327 00:27:15.079 --> 00:27:18.319 Or I think maybe the better way to put it is where is their 328 00:27:18.440 --> 00:27:25.400 friction right now? Where is something difficult that should be easy, And especially 329 00:27:25.480 --> 00:27:29.559 in terms of data being fed to someone right sometimes you can feed one more 330 00:27:29.640 --> 00:27:33.759 data point to someone and you just solve the whole array of problems. And 331 00:27:33.839 --> 00:27:37.960 I'll give you an interesting example. I remember there was a company when I 332 00:27:37.000 --> 00:27:41.799 was just when I was learning about process mining, which is fascinating stuff, 333 00:27:41.240 --> 00:27:45.480 and they were looking at the processes as they actually occur in the business because 334 00:27:45.519 --> 00:27:49.799 they've instrumented all the different information systems. And what they saw is that there's 335 00:27:49.839 --> 00:27:56.039 this big bottleneck on a credit check and like, huh, so every time 336 00:27:56.160 --> 00:27:59.039 someone requested something they had to go through a credit check, and that was 337 00:27:59.079 --> 00:28:02.400 the biggest part of the whole process in terms of the time it took, 338 00:28:02.759 --> 00:28:04.599 in terms of the difficulties and all these things. And they realized, Hey, 339 00:28:06.119 --> 00:28:07.680 for most people, we don't need to do this credit check. For 340 00:28:07.759 --> 00:28:11.119 our established accounts, we don't have to do the credit check, So let's 341 00:28:11.119 --> 00:28:14.920 just cut that part out and only use it when we need to. And 342 00:28:14.960 --> 00:28:18.000 they would not have ever known that had they not looked at the process and 343 00:28:18.079 --> 00:28:21.119 looked at the data and understood what was happening. And I'm like, wow, 344 00:28:21.359 --> 00:28:25.720 let's just do this. We'll check and see is an established account with 345 00:28:25.839 --> 00:28:30.400 credit already with us? And then skip that part and they're like, Holy 346 00:28:30.480 --> 00:28:33.759 Christmas, it's sped up. Like a whole department got like three times as 347 00:28:33.839 --> 00:28:40.119 much work done because they weren't wasting time on an unnecessary process. So I 348 00:28:40.200 --> 00:28:44.599 throw that out to you because I guarantee as you're on site working with clients, 349 00:28:44.599 --> 00:28:47.519 there are little bits and pieces that you pick up in conversations where you're 350 00:28:47.559 --> 00:28:48.920 like, hey, wait a minute, there's a different way to do this, 351 00:28:49.759 --> 00:28:53.720 And that's what a consultant is supposed to do, right, is recognized 352 00:28:53.759 --> 00:28:57.640 where there's a process that is not efficient that can be greatly improved, and 353 00:28:57.680 --> 00:29:00.079 a lot of times it just takes some of the outside come and say, 354 00:29:00.079 --> 00:29:03.480 hey, you thought about doing it this way, and there's someone who goes, 355 00:29:03.680 --> 00:29:07.480 yeah, I suggested that last year, right, and it's like, 356 00:29:07.519 --> 00:29:15.240 but that's that's exactly you know, someone like it is. It is so 357 00:29:15.440 --> 00:29:22.359 true, very very often you get in to an organization and revive the efforts 358 00:29:22.720 --> 00:29:27.000 that were you know, done and forgotten, and you you know, you're 359 00:29:27.039 --> 00:29:32.039 able to kind of bring it back to life and get somewhere with it. 360 00:29:32.599 --> 00:29:41.200 But you know, another interesting like I think another common example of it's more 361 00:29:41.160 --> 00:29:48.799 more over not a process mining, but I guess it's related to the data 362 00:29:48.960 --> 00:29:53.200 and to the definitions. And this is another area where I feel like there 363 00:29:53.240 --> 00:29:59.119 is a lot of benefits benefits and of course the data catalogs they are storages 364 00:29:59.160 --> 00:30:02.720 of the definitions. You know, metadata is nothing more than a technical definition, 365 00:30:03.000 --> 00:30:08.839 and then you bring business definition definition definitions. So differences and definitions, 366 00:30:10.519 --> 00:30:15.279 which is very benign as it seems, create big differences in reporting. And 367 00:30:15.359 --> 00:30:22.319 this is when the hours go when people try to figure out the differences and 368 00:30:22.359 --> 00:30:27.680 they investigate and there could be you know, literally weeks of time of the 369 00:30:27.720 --> 00:30:33.119 most you know, valuable people and the teams that go into that. So 370 00:30:33.119 --> 00:30:37.200 so this is usually one of the problems that people cite, and this is 371 00:30:37.279 --> 00:30:42.480 one of the problems that you know, you you try to address sooner rather 372 00:30:42.519 --> 00:30:47.680 than later. Either you approach it with the catalog or you get into the 373 00:30:48.240 --> 00:30:55.279 you know, the architectures the database designs. But but somehow you need you 374 00:30:55.319 --> 00:31:00.160 know there, you know, at the at the end when the day that 375 00:31:00.319 --> 00:31:04.359 is being analyzed, it needs to show consistency results. Right. No, 376 00:31:04.440 --> 00:31:08.000 you bring up a really good point about, for example, what is an 377 00:31:08.079 --> 00:31:14.559 active customer versus the passive customer. So if someone wants to report on all 378 00:31:14.599 --> 00:31:18.039 of our active customers and how much revenue they have, if you haven't defined 379 00:31:18.119 --> 00:31:22.640 that uniformly across an environment, especially where there are mergers and acquisitions and you 380 00:31:22.680 --> 00:31:26.160 have different systems that are being pulled in. Now, that can be very 381 00:31:26.160 --> 00:31:29.920 misleading because it can look like the number is low and then you realize, 382 00:31:29.920 --> 00:31:33.920 oh, wait, we've miscategorized twenty percent of the companies in this report and 383 00:31:33.920 --> 00:31:37.119 they shouldn't even be in here. Bring the numbers down. This guy only 384 00:31:37.200 --> 00:31:40.839 wants to know what the active clients are doing. Those are not active clients. 385 00:31:40.880 --> 00:31:44.079 So these are the kinds of definitions you're talking about right where you get 386 00:31:44.119 --> 00:31:48.640 into what goes into the reporting that gets delivered to your sales teams or your 387 00:31:48.680 --> 00:31:52.160 senior management or whatever it is. What are those definitions and are they applied 388 00:31:52.240 --> 00:31:56.839 evenly in all situations. That's a system's issue, right, And if you 389 00:31:56.880 --> 00:32:00.960 don't get that right, then numbers are going to be wrong and you're gonna 390 00:32:00.960 --> 00:32:02.359 get unhappy clients. So folks, don't touch up that. I'll be right 391 00:32:02.400 --> 00:32:06.680 back. We're talking to Allah zake In from Athena Solutions. You're listening to 392 00:32:06.680 --> 00:32:19.359 Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric Tabanac. 393 00:32:22.000 --> 00:32:23.680 All right, folks, back on Inside Analysis, talking to a Lah 394 00:32:23.839 --> 00:32:28.799 zake In of Athena Solutions, and we're talking all about data governance and data 395 00:32:28.839 --> 00:32:31.599 catalogs. Made a couple of really good points. Allah wonders that a data 396 00:32:31.640 --> 00:32:37.039 catalog is a good mechanism for data governance. And you know, I've been 397 00:32:37.079 --> 00:32:39.720 around long enough to remember that back in the old days, data governance either 398 00:32:39.759 --> 00:32:44.759 occurred at the database level or at the application level. Like either you have 399 00:32:44.799 --> 00:32:46.559 access to the data or you don't. Or you have access to the application 400 00:32:46.680 --> 00:32:52.920 or you don't. And there's a very archaic and very difficult and challenging and 401 00:32:52.039 --> 00:32:55.319 manual way of dealing with things in which you always wanted was something in the 402 00:32:55.319 --> 00:33:00.319 middle. And that's what a data catalog can provide because it can be your 403 00:33:00.359 --> 00:33:04.720 source, your marketing area for definitions what do these things mean? But those 404 00:33:04.720 --> 00:33:08.079 definitions can come into play in reporting and analysis that we're talking about a minute 405 00:33:08.079 --> 00:33:13.839 ago. How how you define an active customer, for example, or a 406 00:33:13.920 --> 00:33:17.079 prospect. In the marketing world, what's a marketing qualified lead versus a sales 407 00:33:17.160 --> 00:33:22.759 qualified lead? How do you push someone over the edge from MQL into SQL 408 00:33:23.079 --> 00:33:29.759 and actually not structure career language sales qualified lead and actually get them into the 409 00:33:29.799 --> 00:33:32.640 hands of a salesperson. That's a big thing, and that's a definition that 410 00:33:32.640 --> 00:33:37.400 probably changes a lot these days because it's all based upon the data that's coming 411 00:33:37.440 --> 00:33:40.160 in and it used to be based on just gut instinct. You would say, 412 00:33:40.200 --> 00:33:43.160 oh, I don't know the salesperson, go oh, I think he's 413 00:33:43.200 --> 00:33:45.519 a marketing qualified lead. He's a sales qualified lead. Really, like, 414 00:33:45.640 --> 00:33:49.880 what is the data say about that? And when you can get into using 415 00:33:49.920 --> 00:33:53.680 the data that's much more accurate, that's much more reliable than someone's gut instinct. 416 00:33:53.799 --> 00:34:02.440 Right. This is the different between the old ways and the data driven 417 00:34:02.440 --> 00:34:07.719 culture that a lot of organizations trying to establish in their in their companies. 418 00:34:08.239 --> 00:34:13.599 And the way to get into this data driven culture is to start the data 419 00:34:13.599 --> 00:34:20.760 governance program because it goes into the areas which improve this maturity, improve the 420 00:34:20.840 --> 00:34:29.440 data literacy, improve the compliance, create overall across the board improvement and not 421 00:34:29.679 --> 00:34:35.280 to jump in at everything at once. But at this point we're very lucky. 422 00:34:35.559 --> 00:34:38.880 Again we have you know, we have a lot of we have thought 423 00:34:38.880 --> 00:34:44.719 about data governance a lot. You know, I think as as a humanity, 424 00:34:45.079 --> 00:34:49.480 we we thought about it a lot. And right now, official and 425 00:34:49.719 --> 00:34:54.719 unofficial data governance frameworks they look similarly. You know, everybody is talking about 426 00:34:54.719 --> 00:35:00.800 metadata management, everybody is talking about data quotes, everybody is talking about compliance, 427 00:35:01.079 --> 00:35:06.760 everybody is talking about data architecture, give and take some other categories. 428 00:35:06.840 --> 00:35:09.719 But this is what this is what the focus is, and this is what 429 00:35:09.760 --> 00:35:16.880 we can talk about in organizations, and in essence, the conversation is not 430 00:35:17.000 --> 00:35:22.119 completely new. So we are very like in terms in terms of like in 431 00:35:22.119 --> 00:35:27.800 comparison to what it was ten years ago when people started talking about data governance, 432 00:35:28.679 --> 00:35:35.159 But people understood it so differently that the conversation like went much slower. 433 00:35:35.280 --> 00:35:37.440 There was a lot of people. You know, you would have to get 434 00:35:37.480 --> 00:35:43.960 a buy in and sell it much much more and the results of your selling 435 00:35:44.360 --> 00:35:52.480 would be much more modist right now, and also the amounts of data organizations 436 00:35:52.519 --> 00:35:59.239 are dealing with is increased so significantly that and everybody feels the pain so acutely 437 00:35:59.440 --> 00:36:07.280 if something doesn't work that it is almost it is almost easier to get into 438 00:36:07.320 --> 00:36:12.880 it and start conversations and start converzations on multiple levels, on the executive level, 439 00:36:13.079 --> 00:36:17.480 on the department head level. You know, you pull people into data 440 00:36:17.519 --> 00:36:23.320 governance organization that would be focusing on this type of work, and people are 441 00:36:23.920 --> 00:36:29.400 you know, willing and excited and you know, hopeful, I want to 442 00:36:29.440 --> 00:36:35.320 say, because nobody wants to spend you know, fifteen days on debugging or 443 00:36:35.360 --> 00:36:38.519 reconciling two different reports that are equally important. That's I mean, you hit 444 00:36:38.559 --> 00:36:42.360 the nail on the head there, and I think that's one of the selling 445 00:36:42.480 --> 00:36:45.719 points, right, is that people realize that there are things that can be 446 00:36:45.840 --> 00:36:50.599 automated and once you've realized something that can be automated and something that can be 447 00:36:51.239 --> 00:36:53.760 instrumented so you can see what's happening, you know. I mean, I'm 448 00:36:54.039 --> 00:36:59.320 old enough to remember the earliest days of email marketing, and it was nineteen 449 00:36:59.400 --> 00:37:02.400 hundred and nine nine when I first use an email marketing platform and I could 450 00:37:02.440 --> 00:37:07.199 see who opened the emails and who clicked on the links, who and how 451 00:37:07.239 --> 00:37:10.639 many times they clicked And I was like, wow, that just changed the 452 00:37:10.679 --> 00:37:15.480 game, because before you just have to call everybody and hope that someone cared, 453 00:37:15.519 --> 00:37:19.320 and that's very bad for morale. But when you know who cares because 454 00:37:19.320 --> 00:37:22.639 it's in the data, then you call the right person and that person is 455 00:37:22.719 --> 00:37:27.119 interested. And you know, consulting is a lot like sales. I mean, 456 00:37:27.199 --> 00:37:29.760 yes, you have to sell the service, but then you have to 457 00:37:29.800 --> 00:37:32.440 sell yourself every engagement, every time you're talking to the client, in a 458 00:37:32.480 --> 00:37:37.559 way, you're selling your awareness and your knowledge and your ability to ask good 459 00:37:37.679 --> 00:37:42.519 questions. And one of my soapbox topics is morale. And when morale is 460 00:37:42.599 --> 00:37:45.559 high in an organization, really good things happen. When morale is low, 461 00:37:45.920 --> 00:37:51.159 good things do not happen like bad things happen. And when you can get 462 00:37:51.199 --> 00:37:54.880 the right data to the right people and give them that observability and give them 463 00:37:54.920 --> 00:37:58.880 the meaning that you were talking about, then it gets back to what you 464 00:37:58.880 --> 00:38:02.360 said earlier, which is people like to be understood, They like to understand 465 00:38:02.440 --> 00:38:07.280 things, they like to have meaningful conversations, because that's when stuff gets done. 466 00:38:07.599 --> 00:38:10.599 That's when you can convince someone in a different department, hey, we 467 00:38:10.679 --> 00:38:15.679 really would love to get that returns data that you have earlier in the process, 468 00:38:15.840 --> 00:38:20.559 because you know, if a pocket, if a package isn't delivered on 469 00:38:20.679 --> 00:38:25.119 time, if we can tap into UPS's feed for that for their tracking service, 470 00:38:25.440 --> 00:38:30.239 that helps us know which products to bring in the other side for manufacturing 471 00:38:30.280 --> 00:38:34.559 for example. That's just one example of how the right data to the right 472 00:38:34.639 --> 00:38:37.320 person can make a huge difference in the business to keep morale high, to 473 00:38:37.400 --> 00:38:42.800 keep things humting, to keep profit going. So it's usually these days boiled 474 00:38:42.840 --> 00:38:45.760 down to getting access to one particular data feed, getting it in concert with 475 00:38:45.880 --> 00:38:50.199 other data so you can see it and understand it. Because that's why you 476 00:38:50.239 --> 00:38:52.719 need the two screens right, because you can't see it all on one screen, 477 00:38:52.800 --> 00:38:57.119 right. You have to see the modeler see and the wrong over here. 478 00:38:57.599 --> 00:39:00.599 And there's a lot to see and absorb, and so that's I'm kind 479 00:39:00.599 --> 00:39:05.960 of rambling here, But the point is when when you're in this business and 480 00:39:06.000 --> 00:39:09.400 you realize how much difference one more source of data can make, that's a 481 00:39:09.400 --> 00:39:17.199 magical moment, right I would say, I would say, there's I think 482 00:39:17.239 --> 00:39:25.039 that what you talked about is trust. You know, people are afraid to 483 00:39:25.079 --> 00:39:30.880 trust the data. And I started talking about data driven culture, and this 484 00:39:30.039 --> 00:39:37.920 is exactly the definition of it. It's the culture where people are not afraid 485 00:39:37.960 --> 00:39:43.159 to trust their data. They when they faced with the choice of relying on 486 00:39:43.199 --> 00:39:46.440 the intuition and relying on the results, they can trust the results more. 487 00:39:46.920 --> 00:39:51.880 You know. Nobody said that you don't meet your business knowledge like and you 488 00:39:51.920 --> 00:39:58.559 know the years of your business experience, it's not true. But often the 489 00:39:58.639 --> 00:40:04.199 results that they that that the organizations get in the data are so weak. 490 00:40:05.039 --> 00:40:08.800 So the process is to get them stronger, you know. And so this 491 00:40:08.880 --> 00:40:15.719 is where the data literacy helps and the data governance helps all of these things. 492 00:40:15.719 --> 00:40:21.639 It's it's almost you know, you you it's a it's a complicated and 493 00:40:21.760 --> 00:40:24.639 big problem, but you and you don't address it all at once, but 494 00:40:24.719 --> 00:40:32.239 you address it from many places, and as long as the direction is as 495 00:40:32.239 --> 00:40:37.199 long as the movement is in the right direction, this is what This is 496 00:40:37.239 --> 00:40:42.800 what's important, and this is why people talk about levels of data maturity. 497 00:40:43.519 --> 00:40:47.639 Ah. I love that this is the problem. You know, it's not 498 00:40:47.679 --> 00:40:52.679 one level of maturity. There are many levels of maturity, and there's a 499 00:40:52.119 --> 00:40:57.079 there's a way. This is a nice way to estimate where you are and 500 00:40:57.159 --> 00:41:01.039 where the progress is made and where more progresses. Yeah, that's a really 501 00:41:01.039 --> 00:41:07.519 good point too, because understanding your maturity will help you know what is possible 502 00:41:07.599 --> 00:41:10.360 right now. And you know, for example, we talk a lot about 503 00:41:10.440 --> 00:41:15.079 artificial intelligence, and AI loves data. It consumes data. It needs data 504 00:41:15.079 --> 00:41:19.079 to be able to train models, and then it has to be curated over 505 00:41:19.119 --> 00:41:22.079 time. You don't just turn it on and walk away. But the point 506 00:41:22.159 --> 00:41:24.400 is, if you don't have your data house in order, you better hold 507 00:41:24.440 --> 00:41:28.119 off on trying to leverage too much AI. Right, It's like, don't 508 00:41:28.480 --> 00:41:31.800 you have to crawl walk run? Don't go from crawl to run because you're 509 00:41:31.840 --> 00:41:36.119 going to fall over and cause some problems. And that's where the data maturity 510 00:41:36.119 --> 00:41:39.039 comes into place. And that's a combination of things, right, it's understanding 511 00:41:39.280 --> 00:41:45.440 what information do you have. It's understanding how how skilled are your people, 512 00:41:45.840 --> 00:41:50.840 how data literate are they? All that goes into understanding your maturity right now, 513 00:41:50.920 --> 00:41:57.559 right, just one more minute, go ahead. Yes, people's side 514 00:41:57.599 --> 00:42:02.679 of data maturity is how well people understand their role in organization and how well 515 00:42:02.719 --> 00:42:09.239 they're able to follow processes associated with the data. So that that is another 516 00:42:09.320 --> 00:42:14.239 aspect that you know that goes into data maturity, which is not data at 517 00:42:14.280 --> 00:42:19.519 all. This is this is people. This is organizing, uh work essentially 518 00:42:19.719 --> 00:42:23.119 right, day to day day to day work, whether you you know your 519 00:42:23.159 --> 00:42:29.760 actions actually improve the situations or you keep patching the old problem and it will 520 00:42:29.760 --> 00:42:32.559 never go away. Right right, That's that's it. Well, folks, 521 00:42:34.039 --> 00:42:37.000 Podcast Ponus segments up next, stand by, and we're talking to Allah Zak 522 00:42:37.079 --> 00:42:43.480 and of Athena Solutions. Standby, right, folks, back here on Inside 523 00:42:43.480 --> 00:42:47.119 Analysis with Allah Zakin Athena Solutions, having a great conversation about data governance, 524 00:42:47.559 --> 00:42:52.679 data maturity and data catalogs and how to get the most value from your data 525 00:42:52.960 --> 00:42:57.119 and allow you reminded me of one of my favorite cliches. It's a Russian 526 00:42:57.159 --> 00:43:01.559 proverb that just gives me chills. It's says there is nothing more permanent than 527 00:43:01.599 --> 00:43:07.559 a temporary solution. Oh, isn't that a good one? It's like, 528 00:43:07.719 --> 00:43:12.880 oh no, let's just do like this for now. It's like, no, 529 00:43:12.880 --> 00:43:15.719 no, be careful to be like that forever. And you have. 530 00:43:15.239 --> 00:43:21.000 But that is a good clue to understand a the maturity and be understand all 531 00:43:21.039 --> 00:43:22.800 right, what are the next steps? What can we take in terms of 532 00:43:22.800 --> 00:43:28.559 steps now in light of your situation. When you see what's broken and you 533 00:43:28.639 --> 00:43:31.360 have to talk to the people you got to talk to sales maybe marketing, 534 00:43:31.440 --> 00:43:37.039 administration, where do you find the frustration? I think that's really the heat 535 00:43:37.199 --> 00:43:42.880 map for where to focus. I mentioned friction as a term earlier. Where's 536 00:43:42.920 --> 00:43:45.079 the friction? Where are the rough spots? What are people complaining about? 537 00:43:45.639 --> 00:43:50.400 And then you look to see what data do we have available that can fulfill 538 00:43:50.519 --> 00:43:53.320 this gap or fill this gap and get people happy again, right, because 539 00:43:53.360 --> 00:43:55.760 you want your marketing, you want ever want to be happy pretty much in 540 00:43:55.800 --> 00:44:00.559 your company. You want them to be working hard and focus, but you 541 00:44:00.599 --> 00:44:05.880 don't want them frustrated and anxious and untrusting, right, And the problem with 542 00:44:05.960 --> 00:44:07.679 data is that if they don't trust the data, it's hard to get that 543 00:44:07.719 --> 00:44:13.039 trust back. But I'll just throw that over to you. The temporary solutions 544 00:44:13.079 --> 00:44:15.559 you got to watch out for that, but they are good signals to know 545 00:44:15.679 --> 00:44:20.400 where to focus attention in the near term because you have to do these things 546 00:44:20.400 --> 00:44:22.840 incrementally, right, You have to sort of tick off the boxes and climb 547 00:44:22.920 --> 00:44:27.000 the mountain step by step before you can get up to there and use this 548 00:44:27.039 --> 00:44:30.840 great AI. You got to do the blocking and tackling necessary first, and 549 00:44:30.000 --> 00:44:36.599 that's going to revolve around the frustrations. Is that about right? I agree 550 00:44:36.679 --> 00:44:39.239 with you. One thing I want to say, don't trip off the band 551 00:44:39.280 --> 00:44:46.280 aid until you're ready, until the wound isn't healed, right, So we 552 00:44:46.440 --> 00:44:55.719 can't really rush things too much. I wouldn't. I want to say that 553 00:44:55.760 --> 00:45:01.079 it is important to be respectful to the existing cult in the company, and 554 00:45:01.719 --> 00:45:07.800 existing platform and existing data architecture. You know, the project has to start 555 00:45:07.880 --> 00:45:15.119 within this framework because everything can be changed at once. However, you know, 556 00:45:15.199 --> 00:45:20.559 it's important to keep track. It's important to keep track of the you 557 00:45:20.559 --> 00:45:25.199 know, the old patched wounds that exist and they a fester. You know, 558 00:45:25.320 --> 00:45:30.880 you you have to you have to give it a thought. You have 559 00:45:30.000 --> 00:45:37.760 to see where you know where it could work too, you know, where 560 00:45:37.760 --> 00:45:46.000 the improvement could work, what could help it. Ah. However, I 561 00:45:46.039 --> 00:45:54.079 think that the detailed understanding of the current environment goes hand in hand with the 562 00:45:54.440 --> 00:46:00.719 creating a vision. You know, this is this is where, this is 563 00:46:00.760 --> 00:46:05.360 where the morale, like you are talking about the morale. And I really 564 00:46:05.440 --> 00:46:10.480 like that idea because personally, for me, it is very important to have 565 00:46:10.679 --> 00:46:16.639 the vision and have have like kind of the next steps outlined for myself and 566 00:46:16.760 --> 00:46:22.679 you know, see that what I'm doing is beneficial. So creating the vision 567 00:46:23.159 --> 00:46:30.840 together with the top management understanding the business goals, understanding goals for each department, 568 00:46:30.960 --> 00:46:35.320 maybe on a little bit more of a more detailed level, allow to 569 00:46:35.760 --> 00:46:39.920 marry the two, you know, when we when you know the current situation 570 00:46:40.239 --> 00:46:46.679 is understood and the vision is developed, it's easier to bring them together. 571 00:46:47.440 --> 00:46:52.280 Yeah, that's great. That's a great way to end our conversation too. 572 00:46:52.320 --> 00:46:54.840 All. I love your energy about this stuff, and I love your little 573 00:46:54.880 --> 00:46:59.000 pithy quotes, and I think you're exactly right that you have to have the 574 00:46:59.079 --> 00:47:02.639 vision. You got to know where going and then really understand the different stepping 575 00:47:02.679 --> 00:47:06.800 stones to get there, because it is kind of like stepping stones across the 576 00:47:06.920 --> 00:47:08.800 river, right, you have to be careful and hit those stones properly. 577 00:47:09.079 --> 00:47:12.599 Don't want to slip off of them. You don't want to wind up swimming 578 00:47:12.599 --> 00:47:15.440 in the river. You want to get across the river, and it's the 579 00:47:15.480 --> 00:47:17.559 stepping stones that get you there. And sometimes you have to build a little 580 00:47:17.559 --> 00:47:21.559 bridge for example. There are different things you'll have to do. But when 581 00:47:21.559 --> 00:47:23.800 you can see the vision, and this is why you talk to the clients, 582 00:47:23.840 --> 00:47:27.920 when you can see where things need to go, and then understand the 583 00:47:27.960 --> 00:47:30.840 steady state. And probably one of the most important things you said is respect 584 00:47:30.880 --> 00:47:37.639 the culture of the company, respect how they do things. Now, having 585 00:47:37.679 --> 00:47:39.920 some big bang approach is probably never going to work. So you really have 586 00:47:40.000 --> 00:47:45.000 to figure out what is your access point, who's flexible and who's not, 587 00:47:45.639 --> 00:47:49.599 and be very careful about how you move forward. Because at the end of 588 00:47:49.599 --> 00:47:52.880 the day, when you get the catalog delivered and they start using it, 589 00:47:52.320 --> 00:47:57.400 that's when the business changes. That's when really cool things start happening with your 590 00:47:57.559 --> 00:48:00.239 prospects, with your customers, with your partners. They're all on board, 591 00:48:00.239 --> 00:48:05.599 and that's when morale stays high. Right, yes, yes, no, 592 00:48:05.679 --> 00:48:09.239 you're right, Eric, this is something that is always important to consider. 593 00:48:10.239 --> 00:48:13.639 Yeah, well, Alla, thank you so much for your time today. 594 00:48:13.639 --> 00:48:15.519 It's so fun talking to you and folks sending an email. If you want 595 00:48:15.519 --> 00:48:19.679 to learn more about what's going on here, info at insideanalysis dot com. 596 00:48:19.719 --> 00:48:23.920 We'll talk to you next time. You've been listening to Inside Analysis KCAA where 597 00:48:24.039 --> 00:48:32.400 every day is a great day, KCAA Loma, Linda and now the voices 598 00:48:32.519 --> 00:48:37.760 of ACAA was an exciting announcement. Want to hear NBC News or k c 599 00:48:37.840 --> 00:48:42.360 AA anywhere you go, Well, now there's an app for that. 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If you've been thinking about broadcasting a weekly radio 703 00:57:15.679 --> 00:57:21.760 program on real radio plus the internet, contact our CEO at two eight one 704 00:57:22.000 --> 00:57:27.079 five nine nine ninety eight hundred two eight one five nine nine ninety eight hundred. 705 00:57:27.119 --> 00:57:30.519 You can skype your show from your home to our Redlands, California studio, 706 00:57:30.559 --> 00:57:34.599 where our live producers and engineers are ready to work with you personally. 707 00:57:34.760 --> 00:57:39.039 A radio program on KCIA is the perfect work from home advocation in these stressful 708 00:57:39.119 --> 00:57:44.800 times. Just type KCA radio dot com into your browser to learn more about 709 00:57:44.840 --> 00:57:47.559 hosting a show on the best station in the nation, or call our CEO 710 00:57:47.719 --> 00:57:52.280 for details. Two eight one five nine nine ninety eight hundred. Listening to 711 00:57:52.360 --> 00:57:57.559 KCAA Love Melinda at one O six point five FM, K two ninety three 712 00:57:57.599 --> 00:58:04.480 cf Brito Valley, NBC News Radio, I'm Chris Gragio. Israel is vowing 713 00:58:04.480 --> 00:58:07.599 to respond to a rams drone and missile attack. An aid to Prime Minister 714 00:58:07.679 --> 00:58:12.000 Benjamin Netnyaho said no final decisions have been made on the type or timing of 715 00:58:12.039 --> 00:58:15.280 the response. The official made the comment after Israel's war cabinet met for several 716 00:58:15.360 --> 00:58:21.400 hours Sunday. Meantime, urgent diplomatic efforts are underway to avoid further escalation of 717 00:58:21.440 --> 00:58:24.159 the conflict. President Biden spoke with the leaders of the G seven during a 718 00:58:24.280 --> 00:58:29.480 video conference today from the White House. The leaders condemned the attack while pledging 719 00:58:29.480 --> 00:58:32.760 to work toward an immediate ceasefire in Gaza and the release of all hostages held 720 00:58:32.760 --> 00:58:37.719 by Jimas Jerry's selection starts tomorrow in New York. In the hush money criminal 721 00:58:37.760 --> 00:58:43.239 case against former President Donald Trump, Scott Pringle reports Trump is fighting felony counts 722 00:58:43.280 --> 00:58:46.960 accusing him of falsifying business records to hide a previous affair with adult film star 723 00:58:47.000 --> 00:58:52.599 Stormy Daniels right before the twenty sixteen election. The trial is expected the last 724 00:58:52.639 --> 00:58:55.480 six to eight weeks. Trump does plan to testify. He has pleaded not 725 00:58:55.559 --> 00:59:00.639 guilty and tried numerous times to delay the trial. The New York Young Republican 726 00:59:00.679 --> 00:59:04.840 Club is planning a morning pro Trump rally outside the courthouse in Lower Manhattan as 727 00:59:04.880 --> 00:59:08.400 the trial gets underway. Michigan Governor Gretchen Whitmer SA's abortion will be a top 728 00:59:08.440 --> 00:59:13.840 issue for voters in the upcoming presidential election. The only person who should be 729 00:59:13.920 --> 00:59:16.639 making a decision about whether and when to have a child as a woman, 730 00:59:17.159 --> 00:59:22.679 the people she loves and trusts, and her doctor. Period. Politicians need 731 00:59:22.679 --> 00:59:25.639 to get out of the way, and the American people have over and over 732 00:59:25.639 --> 00:59:31.920 again told us that their exact expectations. Governor Whitmer told NBC's Meet the Press 733 00:59:31.960 --> 00:59:37.719 that there's a real possibility of a national abortion ban if Donald Trump is elected 734 00:59:37.760 --> 00:59:42.360 president. The Democrats said she believes the issue will motivate Americans to show up 735 00:59:42.360 --> 00:59:46.199 to the polls come November. Her comments come as the former president recently said 736 00:59:46.480 --> 00:59:51.159 he would not sign a national abortion ban into law, and it should be 737 00:59:51.280 --> 00:59:55.119 left up to the states to decide abortion laws. I'm Chris Garagio, NBC 738 00:59:55.159 --> 01:00:04.119 News Radio, NBC News, KCAA Lomel sponsored by Teamsters Local nineteen thirty two. 739 01:00:04.280 --> 01:00:07.639 Protecting the Future of Working Families Teamsters nineteen thirty two. Dot org