1 00:00:00,320 --> 00:00:04,400 The Better Business Analysis Institute presents the Better 2 00:00:04,400 --> 00:00:14,360 Business Analysis podcast with Hi everybody. 3 00:00:14,400 --> 00:00:17,280 It's Ben Walsh from the Better Business Analysis Institute. 4 00:00:17,320 --> 00:00:21,640 And today we are going to be diving deep into data or data 5 00:00:21,640 --> 00:00:26,690 analysis. The, I guess the era or the 6 00:00:26,690 --> 00:00:29,450 capability or process we're talking about is data 7 00:00:29,450 --> 00:00:31,570 management. So if you're a BA who likes to 8 00:00:31,570 --> 00:00:36,850 think about things in terms of groups of processes, this is the 9 00:00:37,210 --> 00:00:39,570 whole area that talks about data management. 10 00:00:41,290 --> 00:00:46,650 Now as we've already noted in our Generative AI podcast and 11 00:00:46,690 --> 00:00:49,130 earlier on when we talked about different disciplines that have 12 00:00:49,130 --> 00:00:54,250 spun off from business analysis. Data is really important. 13 00:00:54,290 --> 00:01:01,130 So data science, data analysis in the true sense, I guess 14 00:01:01,210 --> 00:01:04,650 anything to do with machine learning and big data are 15 00:01:04,650 --> 00:01:07,610 probably words that you have heard. 16 00:01:07,970 --> 00:01:10,170 And if you haven't heard of those words, you may have heard 17 00:01:10,170 --> 00:01:13,880 of the tools that use. That new kind of data 18 00:01:13,880 --> 00:01:19,240 architecture which is ChatGPT for example, or actually 19 00:01:19,320 --> 00:01:24,840 anything these days that you that surface from the likes of 20 00:01:24,840 --> 00:01:29,160 Google or Microsoft or Amazon are using these technologies. 21 00:01:29,160 --> 00:01:33,120 So they are not new, they've been around for a while, but 22 00:01:33,120 --> 00:01:37,840 we're now at a point we are even smaller businesses, government 23 00:01:37,840 --> 00:01:40,320 departments are moving towards this. 24 00:01:40,980 --> 00:01:43,620 Cloud is usually the word that's in the front. 25 00:01:43,620 --> 00:01:46,420 It doesn't necessarily have to be cloud, but cloud data 26 00:01:46,420 --> 00:01:50,860 architecture, which means that the way we did things on premise 27 00:01:50,860 --> 00:01:54,900 or you know with database servers back in the day, it's 28 00:01:54,900 --> 00:01:56,580 starting to change for all of us. 29 00:01:56,580 --> 00:02:00,940 So it's important for us as Bas to really understand what that 30 00:02:00,940 --> 00:02:03,540 looks like. So we're going to take a bit of 31 00:02:03,540 --> 00:02:06,900 a holistic view, we're not going to go. 32 00:02:07,180 --> 00:02:08,820 Too deep. And I'm going to actually call 33 00:02:08,820 --> 00:02:13,980 out where I think the line is here for Bas versus those in the 34 00:02:13,980 --> 00:02:17,820 data specialized roles. So you know, you know how how 35 00:02:17,820 --> 00:02:22,500 much information you do need to know and then if it sparks your 36 00:02:22,500 --> 00:02:25,980 interest to know more than you can of course go below into the 37 00:02:25,980 --> 00:02:29,100 technology side and you know, understand what each component 38 00:02:29,100 --> 00:02:32,340 does. So yes, this episode is around 39 00:02:32,340 --> 00:02:36,900 data management and let's get started. 40 00:02:38,940 --> 00:02:41,500 Okay. So I guess there are three ways 41 00:02:41,500 --> 00:02:43,580 in which we could look at this topic. 42 00:02:43,580 --> 00:02:45,380 We could look at it from the bottom up. 43 00:02:46,580 --> 00:02:50,220 The one way of talking about that would be the architecture 44 00:02:50,660 --> 00:02:53,420 or the data model. That's one way we could look at 45 00:02:53,420 --> 00:02:58,980 it or we could look at it from the kind of how it. 46 00:03:00,330 --> 00:03:04,850 Is exposed to our users in terms of our processes and functions, 47 00:03:04,850 --> 00:03:07,170 in terms of what we call capabilities. 48 00:03:07,890 --> 00:03:11,610 Or we could talk about it holistically at the top from the 49 00:03:11,610 --> 00:03:13,690 data governance going down to the framework. 50 00:03:14,370 --> 00:03:18,370 Now as Bas, it's always best to start from the top down. 51 00:03:19,050 --> 00:03:23,210 Yes, bottom up or playing chess and working out what's behind 52 00:03:23,210 --> 00:03:26,610 the curtain is always useful sometimes, so we can test our 53 00:03:26,610 --> 00:03:28,750 assumptions. But at the end of the day, 54 00:03:28,910 --> 00:03:32,230 especially in this topic, if you haven't started from the top 55 00:03:32,230 --> 00:03:34,750 down, you won't get the results that you need. 56 00:03:35,510 --> 00:03:37,750 This is very, very similar to projects. 57 00:03:37,950 --> 00:03:41,310 When objectives or problems aren't defined, you know it 58 00:03:41,310 --> 00:03:45,190 becomes a mess, or you know the projects become less and less 59 00:03:45,190 --> 00:03:49,750 likely to be delivered if you haven't got that up front kind 60 00:03:49,750 --> 00:03:53,760 of managed up. Governance and framework in 61 00:03:53,760 --> 00:03:57,560 place before you start. So that is true of BA work in 62 00:03:57,560 --> 00:04:02,720 terms of the BA plan, but it's also true of the data side. 63 00:04:03,920 --> 00:04:08,760 So without going into boring detail around meetings and data 64 00:04:08,760 --> 00:04:11,840 governance boards and all the rest of it, which is part of 65 00:04:11,840 --> 00:04:18,160 this, let's talk about the fundamental elements of data 66 00:04:18,160 --> 00:04:23,090 management. Data management does incorporate 67 00:04:23,090 --> 00:04:26,170 what I've just said, the data governance layer, let's let's 68 00:04:26,170 --> 00:04:29,850 not go into that box, it's not very, I don't know, interesting 69 00:04:29,850 --> 00:04:31,890 to talk about. But there is a you need to have 70 00:04:31,890 --> 00:04:38,610 a governance structure and processes then in the kind of 71 00:04:40,090 --> 00:04:43,930 functional side or if you like the operational side, there are 72 00:04:43,930 --> 00:04:48,290 two main parts of data management. 73 00:04:51,780 --> 00:04:55,820 And you could say that they are defined in a data management 74 00:04:55,940 --> 00:05:01,540 framework. The two parts are the data 75 00:05:01,540 --> 00:05:05,740 management lifecycle. So the lifecycle in which data 76 00:05:05,740 --> 00:05:09,780 projects go through or a piece of data goes through, they're 77 00:05:09,780 --> 00:05:14,940 one and the same and they are a structured way, a lifecycle if 78 00:05:14,980 --> 00:05:19,810 you like, of. Defining the steps in which your 79 00:05:19,810 --> 00:05:23,970 data project should go through and you know, stage gate and be 80 00:05:23,970 --> 00:05:27,490 thought about and also a piece of data goes through this 81 00:05:27,930 --> 00:05:34,530 process and we're going to refer to some really useful predefined 82 00:05:34,570 --> 00:05:39,810 lifecycle Data management, Lifecycle terminology and 83 00:05:39,810 --> 00:05:43,250 journey and steps referencing data dot. 84 00:05:43,820 --> 00:05:48,940 Gov dot NZ, which is the New Zealand government data kind of 85 00:05:49,140 --> 00:05:52,660 experts. It is one way of defining this 86 00:05:52,660 --> 00:05:55,100 journey. You may have come across others. 87 00:05:55,100 --> 00:05:59,420 There's, I think there are a lot of data bodies of knowledge 88 00:05:59,420 --> 00:06:02,140 which define what this life cycle is. 89 00:06:02,550 --> 00:06:05,670 I'm going to refer to this one because I quite like it and it's 90 00:06:05,670 --> 00:06:08,630 relevant here in New Zealand. So if you don't have one and 91 00:06:08,630 --> 00:06:11,750 especially if you live in New Zealand, I would suggest just 92 00:06:11,790 --> 00:06:14,990 adopting this life cycle. So that's that's a big one 93 00:06:14,990 --> 00:06:17,950 straight away and I'll define the steps and what they meant in 94 00:06:17,950 --> 00:06:20,470 a minute. So we've got our life cycle. 95 00:06:20,470 --> 00:06:24,310 So this these are the steps in which data go through and your 96 00:06:24,510 --> 00:06:28,790 projects that have data in them should go through then. 97 00:06:29,610 --> 00:06:31,170 So if you like, that's the process. 98 00:06:31,610 --> 00:06:35,250 Then in order to support you doing that and in order to 99 00:06:35,250 --> 00:06:39,330 support data management, there are capabilities. 100 00:06:39,570 --> 00:06:43,610 There are different skills, human capabilities, but 101 00:06:43,610 --> 00:06:49,610 technical capabilities that need to be in place in order for you 102 00:06:49,610 --> 00:06:53,290 to have true data management. Now those capabilities would 103 00:06:53,290 --> 00:06:57,210 have been quite different 20 years ago or 40 years ago. 104 00:06:57,610 --> 00:07:01,570 So when we talk about these technical capabilities, these 105 00:07:01,570 --> 00:07:06,570 data management capabilities, they are the, I guess, the 106 00:07:07,650 --> 00:07:09,490 architecture, the data architecture. 107 00:07:10,210 --> 00:07:15,610 That exists in 2023, So if you have been involved in, you know, 108 00:07:15,610 --> 00:07:18,690 you've been a DBA or you've worked with databases, this 109 00:07:18,690 --> 00:07:22,730 might be an evolution of what you've already known. 110 00:07:23,130 --> 00:07:27,370 And some of these words will not be dissimilar to the standard 111 00:07:27,370 --> 00:07:31,250 pattern for managing data, but below the capability. 112 00:07:31,490 --> 00:07:36,330 When we actually look, you know, into the engine, the technical 113 00:07:36,330 --> 00:07:39,770 architecture, those technologies have definitely changed. 114 00:07:41,550 --> 00:07:45,550 So we've got our lifecycle and we've got our data capabilities 115 00:07:45,550 --> 00:07:48,630 and that's what we're gonna be focusing on today in this 116 00:07:48,630 --> 00:07:52,110 podcast. As I mentioned earlier, above 117 00:07:52,110 --> 00:07:55,590 all that is data governance. So that's the more management 118 00:07:55,590 --> 00:08:02,590 side of data in terms of committees and organization. 119 00:08:03,080 --> 00:08:06,080 And in terms of just making sure that you've got that governance 120 00:08:06,080 --> 00:08:10,560 across the top and then below all of this, below the life 121 00:08:10,560 --> 00:08:14,800 cycle and the capabilities, you should just have some data 122 00:08:14,800 --> 00:08:18,120 fundamental management and that is just best practice, things 123 00:08:18,120 --> 00:08:21,880 like data security classifications, just very basic 124 00:08:22,520 --> 00:08:25,500 ways in which people. Manage data. 125 00:08:25,500 --> 00:08:29,620 So they're not specific to the capabilities necessarily or the 126 00:08:29,620 --> 00:08:32,460 steps you go through. They go across all of those and 127 00:08:32,460 --> 00:08:37,100 they're just basic ways in which we deal with pieces of data, 128 00:08:37,580 --> 00:08:41,100 Okay. So you've got the foundational, 129 00:08:41,340 --> 00:08:43,380 if you like, fundamental capabilities down at the bottom. 130 00:08:43,740 --> 00:08:46,060 You've got data governance across the top. 131 00:08:46,420 --> 00:08:48,700 But what we're talking about is the middle, the actual when the 132 00:08:48,700 --> 00:08:51,860 rubber hits the road, which is the life cycle, which data goes 133 00:08:51,860 --> 00:08:54,420 through and which your projects should go through when they deal 134 00:08:54,420 --> 00:08:58,260 with data and capabilities. Now capabilities can be talked 135 00:08:58,260 --> 00:09:01,260 about in terms of from a BA perspective and that's what 136 00:09:01,260 --> 00:09:03,620 we're going to touch on. And then below those 137 00:09:03,620 --> 00:09:05,700 capabilities, you have the architecture. 138 00:09:06,420 --> 00:09:10,220 So we use capabilities as a way to talk about both the human and 139 00:09:10,220 --> 00:09:12,020 technology capabilities that are needed. 140 00:09:13,120 --> 00:09:16,240 And below that, that's the connection back to the 141 00:09:16,240 --> 00:09:18,600 architecture. Back to the solution, the Hal. 142 00:09:19,760 --> 00:09:26,280 OK cool, so let's get started on this data lifecycle now, the 143 00:09:26,280 --> 00:09:30,320 data management lifecycle. As I said, I am going to refer 144 00:09:30,320 --> 00:09:38,120 to data.gov dot NZ for those who want to reference and see what 145 00:09:38,120 --> 00:09:41,570 I'm talking about here. And I'll take these on on on a 146 00:09:41,570 --> 00:09:44,690 blog, on our website on the Better Business Analysis 147 00:09:44,690 --> 00:09:47,570 Institute, a website as well so you can reference it. 148 00:09:50,010 --> 00:09:53,650 Now when we go through this lifecycle, it's made-up of seven 149 00:09:53,650 --> 00:09:56,530 steps, so not too many for you to understand. 150 00:09:56,690 --> 00:09:58,890 Usually 5, somewhere between 5:00 and 8:00. 151 00:09:58,890 --> 00:10:03,490 Things are generally a good amount of steps, which is why I 152 00:10:03,490 --> 00:10:08,250 like this particular journey map, if you like. 153 00:10:08,610 --> 00:10:14,730 The first step is plan. So this is less technical and 154 00:10:14,730 --> 00:10:17,890 more around, you know the processes and resources that are 155 00:10:18,250 --> 00:10:20,770 that you need to manage the data. 156 00:10:21,570 --> 00:10:25,810 It's around the project goals for this data and just really 157 00:10:25,810 --> 00:10:29,170 making it really clear, you know, we can use something 158 00:10:29,170 --> 00:10:31,450 called the data management plan if we really like. 159 00:10:31,610 --> 00:10:33,690 It's where you create, you know how you're going to handle this 160 00:10:33,690 --> 00:10:36,890 data, what's the plan for this piece of data or pieces of data. 161 00:10:36,890 --> 00:10:38,530 So we're not just talking about the name. 162 00:10:38,930 --> 00:10:41,530 On a customer, we're talking about customer data or we're 163 00:10:41,530 --> 00:10:46,770 talking about moving a whole server that has a whole lot of 164 00:10:46,770 --> 00:10:49,930 data sets to a new technology in the cloud. 165 00:10:49,930 --> 00:10:51,810 So how are we going to handle that? 166 00:10:51,810 --> 00:10:54,570 What is the plan? So that should be, you know, you 167 00:10:54,570 --> 00:10:56,610 should work with your project manager on that and that should 168 00:10:56,610 --> 00:10:58,810 be very clear. But it's specific around the 169 00:10:58,810 --> 00:11:01,970 data side of things and it might be how you're going to migrate 170 00:11:01,970 --> 00:11:03,890 data. You know, what is the plan 171 00:11:04,010 --> 00:11:05,890 before you even start doing anything. 172 00:11:06,250 --> 00:11:08,530 So it doesn't necessarily involve any. 173 00:11:09,850 --> 00:11:11,970 Fundamental data management capabilities. 174 00:11:11,970 --> 00:11:15,810 At this stage it could just be pen and paper or a Google doc or 175 00:11:15,810 --> 00:11:18,690 you know, a Word document or a PowerPoint presentation, but 176 00:11:18,690 --> 00:11:20,610 you're really planning out what you need to do. 177 00:11:20,970 --> 00:11:23,770 So you know, you would start with writing plan and then you 178 00:11:23,770 --> 00:11:25,890 would list out what are you going to do with that. 179 00:11:26,210 --> 00:11:28,810 Now it's really important that your stakeholders, your product 180 00:11:28,810 --> 00:11:31,410 owners, your sponsors understand what the plan is. 181 00:11:31,410 --> 00:11:32,890 So it should be written in their language. 182 00:11:33,090 --> 00:11:37,170 A bit like a spec, but just the same as an agile BA plan that we 183 00:11:37,170 --> 00:11:38,530 talk about at The Better Business. 184 00:11:38,650 --> 00:11:41,410 Analysis Institute. This is a plan for your data, 185 00:11:42,650 --> 00:11:43,290 right? Cool. 186 00:11:43,890 --> 00:11:46,650 So you've got your plan and now you're going to execute on your 187 00:11:46,650 --> 00:11:49,130 plan. So the second step after plan is 188 00:11:49,130 --> 00:11:53,730 collect. So this is where data is 189 00:11:53,730 --> 00:11:57,450 gathered or generated. And this is the information 190 00:11:57,450 --> 00:12:01,450 that's in scope of your plan. So you need to be able to 191 00:12:01,450 --> 00:12:03,370 collect this information. And it's not. 192 00:12:03,370 --> 00:12:05,210 We're not. You may start. 193 00:12:05,580 --> 00:12:07,980 Your brain will be triggered and you will start to think about 194 00:12:07,980 --> 00:12:11,300 how that's going to happen. But at the moment just identify 195 00:12:12,180 --> 00:12:15,180 the different datasets which you've got in your plan. 196 00:12:15,500 --> 00:12:18,060 And we need to, you know, we need to collect information. 197 00:12:18,060 --> 00:12:22,780 So this could be information and other data stores within your 198 00:12:22,780 --> 00:12:25,140 organization. It could be accessing or 199 00:12:25,140 --> 00:12:28,100 consuming data from other locations outside of your 200 00:12:28,100 --> 00:12:30,140 organization. It could be multiple data 201 00:12:30,140 --> 00:12:33,090 sources. It could even be capturing new 202 00:12:33,090 --> 00:12:37,650 data through say survey forms. So it's all of that and you just 203 00:12:37,650 --> 00:12:40,290 need to define, you know, what are your collection points, how 204 00:12:40,290 --> 00:12:43,970 are you going to collect this data, what is that? 205 00:12:44,010 --> 00:12:46,570 And literally you need to collect first. 206 00:12:46,570 --> 00:12:49,570 That is the first you don't. If you don't have the data, you 207 00:12:49,570 --> 00:12:52,210 need to go and collect the data. So it's about gathering that. 208 00:12:53,710 --> 00:12:56,750 And you know when we get down to the capabilities, you know there 209 00:12:56,750 --> 00:12:59,590 is technical terms like ingesting that data. 210 00:12:59,790 --> 00:13:03,870 So how do you get that data into your particular data management 211 00:13:03,870 --> 00:13:06,470 platform. So at this stage you're 212 00:13:06,470 --> 00:13:09,990 collecting it, where it goes is important, but we're not talking 213 00:13:09,990 --> 00:13:14,510 about the how right now. So we've plans got a plan, we 214 00:13:14,510 --> 00:13:18,950 then collect now as the data comes in from multiple sources. 215 00:13:19,670 --> 00:13:22,910 We need to describe the data. We need to have some agreement 216 00:13:22,910 --> 00:13:26,710 around the metadata standards in our organization and we need to 217 00:13:26,710 --> 00:13:28,790 describe the pieces of data that we've got in. 218 00:13:29,110 --> 00:13:32,990 So you know, you may and your plan come up with a data model. 219 00:13:32,990 --> 00:13:34,950 So we're saying okay, we're going to collect information 220 00:13:34,950 --> 00:13:37,230 around our customers. We're a store. 221 00:13:37,230 --> 00:13:39,350 We're going to collect the information around our store and 222 00:13:39,350 --> 00:13:41,630 we're going to collect the information around our inventory 223 00:13:41,910 --> 00:13:44,390 and we might, I don't know, sell tshirts for example. 224 00:13:45,400 --> 00:13:48,760 So therefore you could have grouped that data into those 225 00:13:48,760 --> 00:13:51,800 three areas in terms of inventory, in terms of store and 226 00:13:51,800 --> 00:13:54,720 in terms of customer. And you could have mapped out 227 00:13:54,720 --> 00:13:59,800 what data you need to collect, only collect data you're gonna 228 00:13:59,800 --> 00:14:02,480 use and you would have defined that. 229 00:14:02,480 --> 00:14:05,160 So at this point, you may have lots of data sources you may 230 00:14:05,160 --> 00:14:08,060 have downloaded. I don't know, bought e-mail 231 00:14:08,060 --> 00:14:10,860 marketing lists with a whole lot of customer names who might be 232 00:14:10,860 --> 00:14:16,300 interested in your T-shirts. You may know what the inventory 233 00:14:16,300 --> 00:14:20,740 system fields are, so you know what those are and you may also 234 00:14:20,740 --> 00:14:23,420 know all the information you need to run the shop. 235 00:14:23,780 --> 00:14:26,540 So you've defined that you've run that down and that's what we 236 00:14:26,540 --> 00:14:30,940 talk about and it's important down to go the down to the data 237 00:14:30,940 --> 00:14:34,580 type level here, not database level, but just know that a name 238 00:14:34,580 --> 00:14:37,820 might be. Just a string or a piece of 239 00:14:37,820 --> 00:14:40,460 text, for example. Whereas if we were collecting a 240 00:14:40,460 --> 00:14:42,220 phone number, that would be a number. 241 00:14:42,980 --> 00:14:45,780 So it is worth, you know, describing that data at that 242 00:14:45,780 --> 00:14:48,900 level at least as a BA, and passing that on to. 243 00:14:49,720 --> 00:14:54,240 Those in the data team to maybe go a little bit further or for 244 00:14:54,240 --> 00:14:56,920 you to do that later on if that happens to be your role. 245 00:14:57,360 --> 00:15:00,320 So yeah, describe the pieces of data, what are they, What are 246 00:15:00,320 --> 00:15:04,040 they used for, what are the, what would they, where did they 247 00:15:04,040 --> 00:15:06,680 come from, what are they going to be used for? 248 00:15:06,680 --> 00:15:11,240 So input and then the output, not necessarily the process. 249 00:15:12,130 --> 00:15:14,330 Of course. So now that we've collected our 250 00:15:14,330 --> 00:15:18,050 data, if you like, or at least virtually collected our data and 251 00:15:18,050 --> 00:15:22,410 we've named and appropriately defined the metadata around that 252 00:15:22,410 --> 00:15:26,010 data describing it, we then need to store it. 253 00:15:26,450 --> 00:15:28,650 So the data needs to be stored somewhere. 254 00:15:28,970 --> 00:15:32,970 And data is digital these days, mostly digital. 255 00:15:32,970 --> 00:15:36,290 And if it isn't, we need to get it into digital form and we need 256 00:15:36,290 --> 00:15:38,810 to be able to store it in a digital repository. 257 00:15:38,810 --> 00:15:41,410 And that's the highest term, that's a very BA term for. 258 00:15:41,610 --> 00:15:45,130 Saying you know what you might think of as a database for 259 00:15:45,130 --> 00:15:48,890 example, or data like, there's a whole lot of terms of ways you 260 00:15:48,890 --> 00:15:50,530 can store information these days. 261 00:15:50,930 --> 00:15:54,290 But we are talking about at the highest level here, digital 262 00:15:54,290 --> 00:15:57,130 repository. And that of course needs to be 263 00:15:57,130 --> 00:16:02,040 secure, reusable. You know, we need to be able to 264 00:16:02,040 --> 00:16:04,440 protect the data we're collecting, especially if it's 265 00:16:04,440 --> 00:16:07,200 customer data. But equally if it's around our 266 00:16:07,200 --> 00:16:10,560 operations, around our store and our inventory, you know, it's 267 00:16:10,560 --> 00:16:12,680 not. If we, you know, don't store 268 00:16:12,680 --> 00:16:16,720 that appropriately, we put it into, I don't know, into save 269 00:16:16,800 --> 00:16:20,160 all of our information into a Google Sheet that's got a public 270 00:16:20,160 --> 00:16:23,600 link and that information is accessed, then you know that 271 00:16:23,600 --> 00:16:27,240 could have negative consequences both to our reputation but also 272 00:16:27,240 --> 00:16:29,600 to our business and to our store, so. 273 00:16:30,360 --> 00:16:34,400 We really need to, you know, think about how we're going to 274 00:16:34,400 --> 00:16:37,640 store information and that that quickly follows the collection. 275 00:16:37,640 --> 00:16:39,760 So you're collecting it. You've you've obviously got to 276 00:16:39,760 --> 00:16:42,120 put it somewhere. So this is and you might say, 277 00:16:42,120 --> 00:16:44,760 well, Ben, why are we just thinking about this now? 278 00:16:44,760 --> 00:16:47,240 Well, you're not, you've done this in your plan. 279 00:16:47,240 --> 00:16:49,840 You've already got an idea about where you might have stored it, 280 00:16:49,840 --> 00:16:54,200 but right now it's the action of storing the data somewhere. 281 00:16:54,440 --> 00:16:55,640 OK. And that could be multiple 282 00:16:55,640 --> 00:16:57,200 places. We'll come back to that when we 283 00:16:57,200 --> 00:16:59,560 talk about technical capabilities. 284 00:17:00,960 --> 00:17:03,480 Once the data's stored, so you've collected, you've 285 00:17:03,480 --> 00:17:06,319 described it, and you've stored it somewhere or in multiple 286 00:17:06,319 --> 00:17:11,640 places, you then analyze the data, Okay and so that is, you 287 00:17:11,640 --> 00:17:14,760 know, you explore it, you interpret it, You look at the 288 00:17:14,760 --> 00:17:17,720 data in different ways. It might be the number of sales 289 00:17:17,920 --> 00:17:19,920 for T-shirts in your store, for example. 290 00:17:19,920 --> 00:17:23,440 It might be the number of customers who visited the store. 291 00:17:23,980 --> 00:17:27,220 It might be the number of transactions, it might be your 292 00:17:27,220 --> 00:17:29,140 stock levels. So you're analyzing the 293 00:17:29,140 --> 00:17:33,340 information you're really looking for, grouping that 294 00:17:33,340 --> 00:17:36,340 information into useful. So when you're analyzing, the 295 00:17:36,340 --> 00:17:41,700 goal of it is to interpret it into useful data sets or data 296 00:17:41,700 --> 00:17:45,740 views if you like. So you might combine data from 297 00:17:45,740 --> 00:17:49,540 your customer repository. And your inventory repository 298 00:17:49,540 --> 00:17:52,220 and your store repository to have, you know one. 299 00:17:53,620 --> 00:17:57,860 So you are able to create a view where you see all customers by 300 00:17:57,860 --> 00:18:01,340 store, by inventory for example, where they bought their stuff. 301 00:18:01,340 --> 00:18:04,940 So the next step, so once you've analyzed the data and you should 302 00:18:04,940 --> 00:18:07,420 have an idea about how you want to use your data at that point 303 00:18:08,020 --> 00:18:11,460 and then you want to use it. So that's the next step. 304 00:18:11,460 --> 00:18:14,020 So we've planned, we've collected, we've described, 305 00:18:14,340 --> 00:18:17,420 we've stored it, we're analyzing and now we're using it. 306 00:18:17,700 --> 00:18:21,020 The data is used for a purpose. It was collected or generated. 307 00:18:21,300 --> 00:18:23,580 So you should have defined why you were collecting the 308 00:18:23,580 --> 00:18:25,300 information and now you want to use it. 309 00:18:25,300 --> 00:18:29,500 It might be a report that that's a general use case, is that you 310 00:18:29,500 --> 00:18:32,620 are collecting information so you have better insights on your 311 00:18:32,620 --> 00:18:36,140 customers and so this could be a report or you know, a business 312 00:18:36,140 --> 00:18:37,860 intelligence dashboard if you like. 313 00:18:38,340 --> 00:18:44,300 Dashboard is a very good use of data and you know the idea is 314 00:18:44,300 --> 00:18:48,620 that you could reuse that data also for for other purposes. 315 00:18:48,620 --> 00:18:51,540 You might feed it, now you've collected it and analyzed it. 316 00:18:51,740 --> 00:18:54,260 You might feed it into your e-mail marketing system. 317 00:18:54,260 --> 00:19:00,020 So using can can be consumed is another way of expressing that. 318 00:19:00,380 --> 00:19:04,820 It could be to present the data inside report, but it could also 319 00:19:04,820 --> 00:19:08,260 be make it available for use to be consumed. 320 00:19:08,340 --> 00:19:10,820 By other systems. So it could be, yeah, like I 321 00:19:10,820 --> 00:19:12,340 said, your e-mail marketing tool. 322 00:19:12,500 --> 00:19:15,820 So that'd be very useful to be used by MailChimp or you know, 323 00:19:15,900 --> 00:19:18,420 the meta platform or whatever marketing tool you're using. 324 00:19:19,140 --> 00:19:21,820 And the final step is to then save. 325 00:19:22,410 --> 00:19:25,610 Or destroy the data. So if you're going to keep that 326 00:19:25,610 --> 00:19:28,170 data for a long time, and you know this is where records 327 00:19:28,170 --> 00:19:30,970 management policies come in, this is where you really need to 328 00:19:30,970 --> 00:19:34,650 understand archival rules and disposal rules and how long 329 00:19:34,650 --> 00:19:36,450 you're allowed to keep that customer data for. 330 00:19:36,770 --> 00:19:39,930 You need to make a decision here in terms of saving it, which is 331 00:19:40,170 --> 00:19:43,490 could be archived because it might not necessarily need to be 332 00:19:43,810 --> 00:19:46,890 accessed all the time, or you need to destroy it. 333 00:19:47,210 --> 00:19:52,050 You need to take actions to safeguard that data for its long 334 00:19:52,050 --> 00:19:53,970 term. Viability and availability. 335 00:19:54,330 --> 00:19:58,890 So in practical terms, that data might not be, you know, saved on 336 00:19:58,890 --> 00:20:01,210 your desktop and might be saved in the database. 337 00:20:02,210 --> 00:20:04,970 So this is we really need to think about how we get a long 338 00:20:04,970 --> 00:20:10,490 term save our information. Now just to recap, when you've 339 00:20:10,490 --> 00:20:14,690 done your plan at your first planning stage, you should have 340 00:20:14,690 --> 00:20:17,930 these headers written down. Click describe store. 341 00:20:18,360 --> 00:20:19,320 Analyze. Use. 342 00:20:19,520 --> 00:20:23,720 They will destroy and you've already mapped out how you're 343 00:20:23,720 --> 00:20:27,040 gonna do those things, at least at a high level. 344 00:20:27,160 --> 00:20:30,280 You know high level business requirements level, not 345 00:20:30,280 --> 00:20:32,560 necessarily the detail necessarily. 346 00:20:33,240 --> 00:20:37,280 And as you go into each level, each step you may define the 347 00:20:37,280 --> 00:20:40,340 detail level requirements. And the solution you're going to 348 00:20:40,340 --> 00:20:45,540 use, so that is the data management lifecycle as defined 349 00:20:45,540 --> 00:20:48,500 by data.gov dot NZ. It is pretty good. 350 00:20:48,940 --> 00:20:53,500 And the other useful side of this particular plan is they do 351 00:20:53,500 --> 00:20:57,220 talk about digital capabilities. Now when I talk about 352 00:20:57,220 --> 00:21:01,980 capabilities, capabilities is a word that is used a lot you can 353 00:21:01,980 --> 00:21:05,400 talk about. It means skills, and you can 354 00:21:05,400 --> 00:21:08,160 also mean you know human capabilities or technical 355 00:21:08,160 --> 00:21:14,200 capabilities. data.gov dot NZ outlines the human capabilities, 356 00:21:14,240 --> 00:21:20,240 the processes or abilities that your particular team or people 357 00:21:20,240 --> 00:21:24,920 in your organization will need in order to kind of reach a high 358 00:21:24,920 --> 00:21:29,940 level of maturity. And so it's if you if you if you 359 00:21:29,940 --> 00:21:32,620 have that requirement. If your job is to audit where 360 00:21:32,620 --> 00:21:34,500 you're at, please go to that website. 361 00:21:34,500 --> 00:21:38,900 It's fantastic and it will it aligns those capabilities with 362 00:21:39,220 --> 00:21:42,620 the data management lifecycle I've just talked about. 363 00:21:42,620 --> 00:21:48,220 So your your job is is nearly done but because we live in an 364 00:21:48,300 --> 00:21:52,420 IT world and our job is to. Jump between the world of 365 00:21:52,420 --> 00:21:56,300 business and IT we we should be really talking about what I 366 00:21:56,300 --> 00:21:59,980 would say the technical capabilities that we now require 367 00:21:59,980 --> 00:22:01,620 in order to support that life cycle. 368 00:22:02,770 --> 00:22:07,090 And like I said at the start of this podcast, these capabilities 369 00:22:07,250 --> 00:22:12,450 have evolved over time. They to be honest these highest 370 00:22:12,450 --> 00:22:16,810 level capability names may well have existed 20 years ago, but 371 00:22:16,810 --> 00:22:19,570 definitely the techniques or some of the ways in which we do 372 00:22:19,570 --> 00:22:22,210 things inside these boxes have changed. 373 00:22:22,530 --> 00:22:25,450 So inside these boxes there are sub capabilities if you like, 374 00:22:25,450 --> 00:22:31,050 functions or features that these various systems allow us to do 375 00:22:31,370 --> 00:22:35,290 or do well. But at the highest level, we as 376 00:22:35,290 --> 00:22:37,050 Bas need to think about these things. 377 00:22:37,410 --> 00:22:39,490 They're not complicated and I'm going to read them out. 378 00:22:39,490 --> 00:22:48,410 Now this particularly are names that really Amazon AWS use to 379 00:22:48,410 --> 00:22:50,770 define these areas, but I think they're pretty common across 380 00:22:50,770 --> 00:22:54,890 whatever platform you're using, be that Google Cloud or Azure or 381 00:22:54,890 --> 00:23:00,090 even an on premise setup. OK, so these capabilities are 382 00:23:00,090 --> 00:23:02,810 technical capabilities. Just to recap, they need to 383 00:23:02,810 --> 00:23:07,410 exist in order to support that data management lifecycle I just 384 00:23:07,410 --> 00:23:11,090 talked about. The first one is data sources. 385 00:23:11,330 --> 00:23:12,890 So these are things. These are nouns. 386 00:23:13,010 --> 00:23:15,610 They're not just collect data or store data. 387 00:23:15,650 --> 00:23:18,650 They're actions. This is data sources. 388 00:23:18,650 --> 00:23:23,050 We need to have data sources. And that could be a database, it 389 00:23:23,050 --> 00:23:27,170 could be SQL Server, it could be a other systems like CRM 390 00:23:27,170 --> 00:23:29,250 systems. It could be devices these days, 391 00:23:29,250 --> 00:23:32,490 It could be small devices that feed in, it could be your 392 00:23:32,490 --> 00:23:34,970 contact center, it could be logs, it could be anything, 393 00:23:34,970 --> 00:23:36,570 right? It could be your social media 394 00:23:37,210 --> 00:23:40,930 marketing platform. But data sources need to exist. 395 00:23:40,970 --> 00:23:45,970 You need to have some way of storing, sorry of sourcing 396 00:23:45,970 --> 00:23:47,650 information. You need to know what those are 397 00:23:48,010 --> 00:23:49,610 okay, so data source is pretty easy. 398 00:23:50,640 --> 00:23:55,360 Then in order to use that information, we need to get that 399 00:23:55,360 --> 00:23:58,880 information into a platform or to a series of components. 400 00:23:59,080 --> 00:24:04,040 So it's useful for us and we call that function ingestion 401 00:24:04,440 --> 00:24:06,880 Okay. So we ingest data. 402 00:24:07,560 --> 00:24:11,000 If you like, you could say capture is probably the very 403 00:24:11,000 --> 00:24:15,640 high level, you know, holistic word, but it's not quite right. 404 00:24:16,290 --> 00:24:19,850 In this case, the reason why sorry, ingestion is really 405 00:24:19,850 --> 00:24:23,130 important is that we're actually taking the information and 406 00:24:23,330 --> 00:24:26,370 putting it somewhere now that's that's important. 407 00:24:26,370 --> 00:24:29,890 There's an important reason for using that word because it's 408 00:24:29,890 --> 00:24:34,010 suggesting you're taking a copy of that information and there 409 00:24:34,010 --> 00:24:35,730 are other ways of accessing information. 410 00:24:35,730 --> 00:24:38,690 You don't have to move it. You could report directly from a 411 00:24:38,690 --> 00:24:42,410 source system or you could use something called data fabric and 412 00:24:42,410 --> 00:24:45,850 data virtualization to kind of pull that information when you 413 00:24:45,850 --> 00:24:47,890 needed. It but not ingest the 414 00:24:47,890 --> 00:24:49,650 information. OK. 415 00:24:49,890 --> 00:24:52,610 So if we're going to, if it's really critical for us and we're 416 00:24:52,610 --> 00:24:55,250 going to add we want to tag it and we want to use it 417 00:24:55,570 --> 00:24:59,050 effectively and we want to process that information and we 418 00:24:59,050 --> 00:25:02,530 wanted to join it with others, then it is best for us to ingest 419 00:25:02,530 --> 00:25:04,890 that information. So we've got data sources and 420 00:25:04,890 --> 00:25:08,650 we're going to ingest at least some of that information into 421 00:25:08,650 --> 00:25:11,210 our system. We then need to store that 422 00:25:11,210 --> 00:25:12,890 information. So we've got storage. 423 00:25:13,570 --> 00:25:16,650 Now generally there are three categories within there. 424 00:25:16,650 --> 00:25:21,010 You've got you ingest yours saving what you've ingested and 425 00:25:21,010 --> 00:25:23,930 its raw form. So whatever format and what 426 00:25:23,930 --> 00:25:27,930 you've got it that we then then do a process of cleaning the 427 00:25:27,930 --> 00:25:30,010 information and we curate the data. 428 00:25:30,010 --> 00:25:34,210 So there are storage involves kind of moving information 429 00:25:34,210 --> 00:25:37,770 around. You know, processing that 430 00:25:37,770 --> 00:25:39,330 information. So there might be different 431 00:25:39,330 --> 00:25:42,730 levels of kind of copies of that information in various states. 432 00:25:42,970 --> 00:25:46,930 So we need to have storage now as I just mentioned, we need to 433 00:25:46,930 --> 00:25:52,850 have processing and this is where advances in data 434 00:25:52,850 --> 00:25:54,610 management have really taken off. 435 00:25:54,970 --> 00:25:58,610 So we can you know in terms of AI, machine learning and some of 436 00:25:58,610 --> 00:26:02,090 the tools that we've got available, the way in which we 437 00:26:02,090 --> 00:26:05,160 clean data. This is part of processing and 438 00:26:05,160 --> 00:26:08,200 we transform data. The tools out there are much you 439 00:26:08,200 --> 00:26:10,720 know are really advanced. And this is where you know the 440 00:26:10,720 --> 00:26:14,920 old ETL processes have really been replaced by using some 441 00:26:14,920 --> 00:26:16,680 systems that can do that automatically. 442 00:26:18,160 --> 00:26:22,240 So if we take raw data and our storage, we may process that 443 00:26:22,240 --> 00:26:24,960 data to clean it, transform it, and then we've got a clean data 444 00:26:24,960 --> 00:26:28,520 set. So regardless of what format. 445 00:26:28,780 --> 00:26:31,700 The data came in after we've cleaned it and transformed it. 446 00:26:31,700 --> 00:26:38,260 We get it into a clean, useful way, and then we go back and we 447 00:26:38,260 --> 00:26:41,500 compress this a bit around, aggregating information, 448 00:26:41,500 --> 00:26:45,220 segmenting it. We might have multiple. 449 00:26:46,800 --> 00:26:50,120 Different copies of the same piece of data. 450 00:26:50,120 --> 00:26:52,960 So it could mash that up and we can enrich the data and then we 451 00:26:52,960 --> 00:26:56,400 kind of have this saved back curated data. 452 00:26:56,680 --> 00:27:01,640 And again this is kind of a almost a modern day processing 453 00:27:01,640 --> 00:27:04,880 technique. So if all our data, if you think 454 00:27:04,880 --> 00:27:07,400 about it, all our different sources, we had 20 different 455 00:27:07,400 --> 00:27:11,200 sources, we inject ingest those, they're in raw format, we clean 456 00:27:11,200 --> 00:27:13,560 them and transform them so they get into our data model. 457 00:27:13,760 --> 00:27:16,520 We then enrich them and we. Segment them. 458 00:27:16,520 --> 00:27:18,520 So now it's in more than just our data model. 459 00:27:18,520 --> 00:27:22,520 It's in a useful format. Then we can start to use that 460 00:27:22,520 --> 00:27:26,680 information and we use that information in a couple of ways. 461 00:27:27,480 --> 00:27:31,920 One, we use analytics. So data analytics, this is where 462 00:27:31,920 --> 00:27:35,920 the term data analysis came from is when you're starting to use 463 00:27:35,920 --> 00:27:38,320 the information and you can think about that and through 464 00:27:38,320 --> 00:27:41,960 statistical data analysis, data science and dashboarding. 465 00:27:42,960 --> 00:27:45,960 The other way we could use it is we could share it with others or 466 00:27:45,960 --> 00:27:50,080 collaborate with other systems. So we were just doing this, use 467 00:27:50,080 --> 00:27:52,000 this platform for a part of our business. 468 00:27:52,320 --> 00:27:55,400 Then we could provide an API so it could be used by another part 469 00:27:55,400 --> 00:27:58,760 of the business or another tool. And we could also collaborate 470 00:27:58,760 --> 00:28:01,920 with other partners or stakeholders or agencies that 471 00:28:01,920 --> 00:28:05,530 want to use our information. So they're two consumption use 472 00:28:05,530 --> 00:28:07,650 cases. One is analytics, presenting the 473 00:28:07,650 --> 00:28:10,610 information and one is collaboration where we can share 474 00:28:10,770 --> 00:28:12,810 the information so other people can consume it. 475 00:28:15,090 --> 00:28:19,210 Obviously what we also need to think about is there are some 476 00:28:19,730 --> 00:28:23,130 more technical kind of access and security capabilities we 477 00:28:23,130 --> 00:28:26,290 need, which is roughly called technical data governance, but 478 00:28:26,290 --> 00:28:29,170 we need to have a capability for access rights. 479 00:28:29,750 --> 00:28:33,270 And in terms of controls and auditing and security and then 480 00:28:33,270 --> 00:28:36,510 there is another bit which is really important which is called 481 00:28:36,870 --> 00:28:40,750 cataloging where we assign, we have a kind of a metadata 482 00:28:40,750 --> 00:28:44,230 schemas and potentially data crawlers that will start to tag 483 00:28:44,230 --> 00:28:48,390 our data automatically so that they can be used more 484 00:28:48,390 --> 00:28:51,870 effectively and kind of self managed within the system. 485 00:28:52,390 --> 00:28:56,150 So just to recap, we've got data sources, we ingest those, we've 486 00:28:56,630 --> 00:28:59,350 got storage for those, we've got processing. 487 00:28:59,750 --> 00:29:03,670 Then consumer either through analytics or collaboration, we 488 00:29:03,670 --> 00:29:09,030 then have some kind of security access audit capability, which 489 00:29:09,030 --> 00:29:11,830 you could call technical data governance and then we've got 490 00:29:11,830 --> 00:29:14,980 cataloging. And if you happen to be a I 491 00:29:15,020 --> 00:29:18,820 guess a social marketing company or a or an advertising company, 492 00:29:19,060 --> 00:29:23,820 you may what we what we term activate that data. 493 00:29:23,820 --> 00:29:26,620 So you could use that specifically for advertising or 494 00:29:26,620 --> 00:29:29,980 real time marketing, but most companies don't have that piece. 495 00:29:30,500 --> 00:29:33,940 So those are the technical capabilities that need to exist. 496 00:29:34,780 --> 00:29:37,500 And if you have those capabilities and you've planned 497 00:29:37,740 --> 00:29:40,900 and you have a way of saving or destroying your data, then those 498 00:29:40,900 --> 00:29:43,500 middle capabilities that we talked about before, sorry, 499 00:29:43,500 --> 00:29:46,340 those middle process steps of collection, collecting, 500 00:29:46,620 --> 00:29:50,620 describing, storing, analyzing and using data will be fulfilled 501 00:29:50,620 --> 00:29:53,420 by those technical capabilities we just talked about, which were 502 00:29:53,900 --> 00:29:58,260 data sources ingestion, storage, processing, analytics, 503 00:29:58,260 --> 00:30:01,620 collaboration, cataloging and some kind of data. 504 00:30:02,180 --> 00:30:05,380 Technical data governance in terms of security and audit and 505 00:30:05,380 --> 00:30:09,300 access, OK, so those are the two main parts. 506 00:30:09,300 --> 00:30:13,220 If you have all of those pieces, the process and the 507 00:30:13,220 --> 00:30:18,140 capabilities, then you have a modern day, well, I guess you 508 00:30:18,140 --> 00:30:21,220 have at least a framework for data management. 509 00:30:22,420 --> 00:30:27,620 What then makes the difference here is what is doing or what is 510 00:30:27,620 --> 00:30:31,660 what technology is performing those capabilities. 511 00:30:32,440 --> 00:30:36,200 So I mentioned before the likes of AWS, there's Google Cloud, 512 00:30:36,280 --> 00:30:39,120 there's Azure, there are many others and they all have their 513 00:30:39,120 --> 00:30:43,440 own flavor of tools that carry out those capabilities we talked 514 00:30:43,440 --> 00:30:46,320 about. So for example. 515 00:30:48,150 --> 00:30:52,070 AWS in terms of their customer data platform, they will suck in 516 00:30:52,750 --> 00:30:57,190 many or any data sources and ingest those and it uses things 517 00:30:57,190 --> 00:31:05,030 like Amazon Kinetics and it uses Appflow or Amazon API Gateway 518 00:31:05,230 --> 00:31:09,630 and then stores it into buckets which called S3 buckets for 519 00:31:09,630 --> 00:31:12,710 example. It then uses step functions with 520 00:31:12,910 --> 00:31:15,110 the process and orchestrate the information. 521 00:31:15,110 --> 00:31:21,070 It uses AWS Lambda. AWS Glue for workflows and then 522 00:31:21,870 --> 00:31:25,390 pushes that information out to analytics through Amazon 523 00:31:25,390 --> 00:31:27,390 Redshift. It even has its own reporting 524 00:31:27,390 --> 00:31:30,230 tool. And for data collaboration it 525 00:31:30,230 --> 00:31:33,030 uses gateways again and cataloging. 526 00:31:33,030 --> 00:31:35,710 It uses AWS Glue for data cataloging. 527 00:31:35,950 --> 00:31:40,950 So Amazon has subcomponents, technical pieces of the puzzles 528 00:31:40,950 --> 00:31:46,510 or services that it. And allow which allows you to 529 00:31:46,510 --> 00:31:50,550 fulfill those capabilities. So Sba's, we don't necessarily 530 00:31:50,550 --> 00:31:56,990 care what those technical functions necessarily are, 531 00:31:56,990 --> 00:32:02,150 features or tools, sorry that we don't really care what they are. 532 00:32:02,150 --> 00:32:05,710 We just need to know that we have a way of accessing our 533 00:32:05,710 --> 00:32:08,790 sources, ingesting, storing, you know, processing that 534 00:32:08,790 --> 00:32:11,230 information and consuming consuming it. 535 00:32:11,740 --> 00:32:14,620 And cataloging it. So if we have those in place, 536 00:32:14,780 --> 00:32:16,580 then we're not. We don't necessarily need to 537 00:32:16,580 --> 00:32:19,900 understand these technologies. And most of the time, there's 538 00:32:19,900 --> 00:32:23,380 usually a hybrid approach where some of this information is 539 00:32:23,380 --> 00:32:25,900 still on premise and not in the cloud. 540 00:32:26,100 --> 00:32:29,140 Or you're using AWS to do part of this. 541 00:32:29,380 --> 00:32:32,700 And then when you do your reporting, you're using Power BI 542 00:32:32,980 --> 00:32:35,300 and it's connecting to this. So it doesn't necessarily mean 543 00:32:35,300 --> 00:32:38,820 you need to adopt one whole vendor to do all these things, 544 00:32:38,820 --> 00:32:43,890 which might become expensive. However, all those capabilities 545 00:32:43,890 --> 00:32:47,330 need to exist. So what does that mean for you 546 00:32:47,690 --> 00:32:50,730 as a BA? Now you understand that there 547 00:32:50,730 --> 00:32:54,770 needs to be this data management framework which includes the 548 00:32:55,290 --> 00:32:59,930 life cycle and these capabilities and means that you 549 00:32:59,930 --> 00:33:04,010 can actually start to write requirements, start to plan, put 550 00:33:04,010 --> 00:33:06,650 together a data plan and start to write requirements that use 551 00:33:06,650 --> 00:33:12,960 these features. So for example you might say as 552 00:33:13,080 --> 00:33:19,120 a user, sorry, as a Yep as a data analyst I would like the 553 00:33:21,720 --> 00:33:28,840 New Zealand Census household income data to be ingested into 554 00:33:29,040 --> 00:33:33,360 the data warehouse and therefore you can use these terms which 555 00:33:33,360 --> 00:33:36,080 means that the if you've grouped those under these kind of 556 00:33:36,080 --> 00:33:40,640 features and functional areas. Then the architect then knows 557 00:33:40,640 --> 00:33:46,000 that you've got a requirement to use those areas of components, 558 00:33:46,000 --> 00:33:49,000 and then it can start thinking about technology that might be 559 00:33:49,000 --> 00:33:53,560 best fit for that purpose. And of course, depending on how 560 00:33:53,560 --> 00:33:56,640 many data sets you have, how complicated they are, that could 561 00:33:56,640 --> 00:33:59,520 determine what kind of tools they use in those areas. 562 00:34:00,000 --> 00:34:02,840 You can ingest data, for example into Google Sheets or into 563 00:34:02,840 --> 00:34:06,560 Excel, and so you know that might be OK as long as you have. 564 00:34:07,120 --> 00:34:09,719 The appropriate data governance and security and access rights, 565 00:34:09,719 --> 00:34:12,320 which is why we don't really use Excel for that purpose. 566 00:34:12,840 --> 00:34:17,000 But that might be OK and that might be fine for us depending 567 00:34:17,000 --> 00:34:20,800 on the use case we've got. So I hope that that's given you 568 00:34:20,800 --> 00:34:24,400 some insights in terms of how data management works and what 569 00:34:24,400 --> 00:34:27,679 you need to think about as a BA. I'm sure there's lots of other 570 00:34:27,679 --> 00:34:30,960 subtopics we could go into, but I hope you have enjoyed this 571 00:34:30,960 --> 00:34:33,199 podcast and I'll see you next time.