1 00:00:14,600 --> 00:00:17,760 Welcome to the APM podcast. APM is the childhood body for 2 00:00:17,760 --> 00:00:20,760 the project profession. My name is Emma David and I'm 3 00:00:20,760 --> 00:00:24,080 the editor of Project at APM's Quarterly Journal and your host. 4 00:00:24,760 --> 00:00:28,560 The subject of this podcast is data literacy, how to improve 5 00:00:28,560 --> 00:00:31,560 your own, what data-driven decision making looks like in 6 00:00:31,560 --> 00:00:34,360 real life, and how it can be used to make the delivery of 7 00:00:34,360 --> 00:00:38,560 projects more successful. The new APM Data Literacy Skills 8 00:00:38,560 --> 00:00:42,000 Framework was published earlier this year and is a practical 9 00:00:42,000 --> 00:00:44,640 toolkit for project professionals which has five 10 00:00:44,640 --> 00:00:46,760 competencies you need to get a handle on. 11 00:00:47,440 --> 00:00:51,200 These include managing project information correctly, 12 00:00:51,640 --> 00:00:54,280 possessing a foundation knowledge of data concepts, 13 00:00:54,480 --> 00:00:58,120 interpreting and influencing with data, data visualisation 14 00:00:58,120 --> 00:01:00,800 and storytelling, and decision making with data. 15 00:01:01,840 --> 00:01:04,920 Corporate members of APM can find a full copy of the report, 16 00:01:04,920 --> 00:01:08,120 while everyone else can find a shorter bridged version and 17 00:01:08,120 --> 00:01:14,440 other helpful resources on thewhatispage@apm.org.uk/resources. 18 00:01:15,520 --> 00:01:18,760 An adequate data literacy is a massive barrier to adoption of 19 00:01:18,760 --> 00:01:22,880 optimised data use in projects. Without it, project teams risk 20 00:01:22,880 --> 00:01:25,920 misusing data and AI tools or failing to recognise their 21 00:01:25,920 --> 00:01:29,040 value. It's a missed opportunity for 22 00:01:29,040 --> 00:01:30,600 all. So in this podcast, we're going 23 00:01:30,600 --> 00:01:33,920 to demystify data and give you a bit more confidence in 24 00:01:33,920 --> 00:01:37,880 navigating this brave new world. So let me introduce you to our 25 00:01:37,880 --> 00:01:40,760 two guests. First is Rob Lord, Director of 26 00:01:40,760 --> 00:01:44,120 Major Programmes at BT International and 2nd is 27 00:01:44,160 --> 00:01:47,160 architect of the APM Data Literacy Skills Framework, 28 00:01:47,160 --> 00:01:51,040 Gareth Parks, who's Head of Data and Analytics at Sir Robert 29 00:01:51,040 --> 00:01:54,400 McAlpine and one of the founding members of the Project Data 30 00:01:54,400 --> 00:01:58,080 Analytics Task Force. Why don't we start with each of 31 00:01:58,080 --> 00:02:02,080 you telling us a bit about your role and where your interest in 32 00:02:02,080 --> 00:02:05,080 data stems from? So first, why don't we go to 33 00:02:05,080 --> 00:02:07,440 you, Rob? Yeah, sure thing. 34 00:02:07,440 --> 00:02:08,919 Ever see? So, Mike, my name's Rob. 35 00:02:08,919 --> 00:02:14,120 Lord, last four years I've been director of major programmes at 36 00:02:14,320 --> 00:02:18,960 BT International. So I lead all of our most 37 00:02:18,960 --> 00:02:21,680 complex technology change digital transformation 38 00:02:21,680 --> 00:02:25,720 programmes into our our, our multi multinational kind of 39 00:02:25,720 --> 00:02:30,120 customers that really translates into accountability around 40 00:02:30,520 --> 00:02:34,040 governance, delivery, insurance, management of risk. 41 00:02:34,080 --> 00:02:38,240 And I guess, you know, my interest in data has really been 42 00:02:38,240 --> 00:02:42,880 kind of fine-tuned and amplified by the the needs I have for it 43 00:02:42,880 --> 00:02:45,280 to discharge my role successfully. 44 00:02:46,280 --> 00:02:49,000 Can you give us an idea of the types of programmes maybe if you 45 00:02:49,000 --> 00:02:51,240 can't give company owns, but it's the sorts of things so we 46 00:02:51,240 --> 00:02:53,720 can get more of a handle on what it is you're you're working on. 47 00:02:55,040 --> 00:02:59,640 They're very kind of impactful technology change programmes. 48 00:02:59,640 --> 00:03:03,960 They often relate to kind of mission critical functions and 49 00:03:03,960 --> 00:03:08,000 services. They can include critical 50 00:03:08,000 --> 00:03:12,840 national infrastructure, but air traffic control, for example, or 51 00:03:12,840 --> 00:03:17,920 leading multinational global banks, Oregon national financial 52 00:03:17,920 --> 00:03:23,160 institutions, global logistic kind of providers and also 53 00:03:23,480 --> 00:03:26,680 foreign governments. So we'll provide secure 54 00:03:26,680 --> 00:03:29,200 telecommunication services for foreign governments as well as 55 00:03:29,200 --> 00:03:30,880 UK government. Right. 56 00:03:30,880 --> 00:03:32,600 So I mean the pressure's on, right? 57 00:03:32,600 --> 00:03:35,640 You can't mess around with these kind of critical infrastructure 58 00:03:35,640 --> 00:03:38,200 programmes, can you? Exactly. 59 00:03:38,200 --> 00:03:40,480 And I mean, coming back to your, your points around kind of, you 60 00:03:40,480 --> 00:03:44,800 know, data, I mean decisions without evidence in that type of 61 00:03:44,800 --> 00:03:48,800 environment has a tendency to fail pretty quickly and it 62 00:03:48,800 --> 00:03:52,600 becomes quite public and quite messy pretty quickly. 63 00:03:52,600 --> 00:03:56,080 And without kind of naming and shaming between the three of us, 64 00:03:56,080 --> 00:04:00,320 we could probably come up with a quite a list of of of things 65 00:04:00,320 --> 00:04:01,920 that kind of have met that condition. 66 00:04:03,840 --> 00:04:06,240 Bit of a left field question, but what what do you enjoy about 67 00:04:06,240 --> 00:04:09,080 your job? What is it about it that you 68 00:04:09,360 --> 00:04:11,440 that that kind of really go gets you going? 69 00:04:12,480 --> 00:04:16,240 I really relish the the leadership aspect of it, but I 70 00:04:16,240 --> 00:04:19,839 also really appreciate the fact it is definitely not boring and 71 00:04:19,839 --> 00:04:21,519 it is definitely not kind of repetitive. 72 00:04:21,519 --> 00:04:25,960 Because whilst there are many kind of I guess common factors 73 00:04:25,960 --> 00:04:30,000 in how one manages complex major programme, indeed runs a 74 00:04:30,000 --> 00:04:34,000 portfolio programme programmes, how one manages risks and 75 00:04:34,000 --> 00:04:36,160 issues. Actually, there's always kind of 76 00:04:36,160 --> 00:04:40,160 variation in the kind of in the context, in the nuance in the in 77 00:04:40,160 --> 00:04:43,440 the customer perspective that brings kind of daily challenges 78 00:04:43,440 --> 00:04:45,880 and all those things in terms of how I conquer that really 79 00:04:45,880 --> 00:04:49,200 motivates me. Gareth, tell us a bit about what 80 00:04:49,200 --> 00:04:53,200 you do and a little about a little bit too about the APM 81 00:04:53,200 --> 00:04:57,040 Data literacy Skills framework, which is quite a mouthful to 82 00:04:57,040 --> 00:04:59,160 say. Yeah, it is, yes. 83 00:04:59,840 --> 00:05:04,560 Well, so I've been living and breathing data for, for several 84 00:05:04,560 --> 00:05:07,880 years now. But I, I guess it's something 85 00:05:07,880 --> 00:05:10,440 that I've been interested in my whole career. 86 00:05:10,600 --> 00:05:12,680 It just took me a little while to realise that that's what I 87 00:05:12,680 --> 00:05:15,400 was interested in. Have you always been in project 88 00:05:15,400 --> 00:05:20,840 management? No, no, I've, I've, I guess I've 89 00:05:20,840 --> 00:05:25,320 been adjacent to project management throughout my career 90 00:05:25,320 --> 00:05:30,480 and then have steadily moved closer and closer to it and 91 00:05:30,480 --> 00:05:34,360 taken on a more of a sort of hands on an operational role as 92 00:05:34,360 --> 00:05:39,440 as time's gone on. So I think that the data is all 93 00:05:39,440 --> 00:05:44,880 around us all of the time. And the way that I see this, 94 00:05:44,920 --> 00:05:47,760 almost anything and everything that people get excited about, 95 00:05:48,040 --> 00:05:52,280 data's involved in it somewhere. It's just that nobody names it 96 00:05:52,280 --> 00:05:57,240 or recognises that it is data. And because of that, we miss so 97 00:05:57,240 --> 00:06:00,400 many opportunities to build bridges and make connections 98 00:06:00,400 --> 00:06:05,240 between different things. And for me, what data does is it 99 00:06:05,240 --> 00:06:11,080 gives just an insight into the this incredible complexity that 100 00:06:11,080 --> 00:06:13,040 surrounds us in everything that we do. 101 00:06:13,440 --> 00:06:17,760 And being able to sort of see that and tap into that and 102 00:06:17,760 --> 00:06:21,600 understand it a little bit just makes everything so much more 103 00:06:21,600 --> 00:06:24,120 vibrant to me and so much more interesting. 104 00:06:25,480 --> 00:06:30,400 Well, that so cleverly segues into my next question, which is 105 00:06:30,400 --> 00:06:33,160 what is data literacy and why does it matter? 106 00:06:33,160 --> 00:06:36,600 So you're already highlighting that I guess every project is a 107 00:06:36,600 --> 00:06:41,840 data project now, and is that how we should think? 108 00:06:41,840 --> 00:06:45,280 It's just part and parcel of everything that that you're 109 00:06:45,280 --> 00:06:48,600 working on. So why what is what does data 110 00:06:48,600 --> 00:06:51,680 literacy mean? And why, if you're listening to 111 00:06:51,680 --> 00:06:54,520 this, should it be something you should be interested in and 112 00:06:54,520 --> 00:06:59,840 wanting to improve? So for me, it's, it's your 113 00:06:59,840 --> 00:07:05,160 ability to speak confidently about data and that then becomes 114 00:07:05,160 --> 00:07:09,160 a universal language that enables you to have all sorts of 115 00:07:09,160 --> 00:07:15,840 very kind of detailed cultured conversations as well as being 116 00:07:15,840 --> 00:07:19,280 able to zoom all the way out to, to see the big picture. 117 00:07:19,640 --> 00:07:26,160 And being able to tap into that and have that kind of, yeah, 118 00:07:26,400 --> 00:07:32,840 have that literacy breeds a confidence and a curiosity. 119 00:07:32,880 --> 00:07:35,680 And as I say, it makes everything in more interesting 120 00:07:35,680 --> 00:07:39,600 and richer and it helps people in in all sorts of different 121 00:07:40,240 --> 00:07:45,960 roles to, to achieve their goals and do their jobs and reach 122 00:07:45,960 --> 00:07:47,640 objectives much quicker and easier. 123 00:07:47,640 --> 00:07:52,120 So I think absolutely every project is a data project. 124 00:07:52,640 --> 00:07:55,920 It's just that most of us haven't realised that realised 125 00:07:55,920 --> 00:07:59,520 that yet. What I want to, whilst I've got 126 00:07:59,520 --> 00:08:05,840 you, Gareth, is the, you've kind of led the APM data literacy 127 00:08:05,840 --> 00:08:11,200 Skills framework, which is trying to give advice or it's, 128 00:08:11,200 --> 00:08:14,880 it's kind of like a practical toolkit for project managers, 129 00:08:14,880 --> 00:08:20,440 project leaders about the kind of skills they need when it 130 00:08:20,440 --> 00:08:23,640 comes to data. And I can only think that this 131 00:08:23,640 --> 00:08:25,960 is going to grow in importance. This is not going to go away. 132 00:08:25,960 --> 00:08:29,000 This is going to be a kind of core competency for project 133 00:08:29,000 --> 00:08:32,600 professionals going forward. So perhaps for those who have 134 00:08:33,159 --> 00:08:37,080 thought data is for someone else, that that data won't be 135 00:08:37,080 --> 00:08:40,720 part of everyone's role when it comes to kind of delivering 136 00:08:40,720 --> 00:08:44,480 projects. Could you just very briefly 137 00:08:45,040 --> 00:08:47,760 describe what those kind of, I think there were five, weren't 138 00:08:47,760 --> 00:08:49,280 there kind of? Five skills. 139 00:08:49,360 --> 00:08:54,520 Five skills and what that why it is that you need to think about 140 00:08:54,520 --> 00:08:58,960 these things and try and figure out how far along the kind of 141 00:08:58,960 --> 00:09:01,160 scale you are. It comes when it comes to each 142 00:09:01,160 --> 00:09:03,800 of those skills. Yes, yeah, of course. 143 00:09:04,640 --> 00:09:10,960 So the one of the reasons that I wanted to be involved in in this 144 00:09:10,960 --> 00:09:15,800 project from the APM was that I've been, so I've been doing a 145 00:09:15,800 --> 00:09:19,400 lot of work in this space with project delivery organisations 146 00:09:20,000 --> 00:09:23,240 across, across the project delivery sector, not just within 147 00:09:23,240 --> 00:09:24,760 our own company. There's various other things 148 00:09:24,760 --> 00:09:29,280 that I've been involved in across the, yeah, across the 149 00:09:29,600 --> 00:09:34,400 sort of the industry. And I say that we are still at a 150 00:09:34,400 --> 00:09:38,240 state of unconscious incompetence when it comes to 151 00:09:38,400 --> 00:09:40,960 data. What do you mean by unconscious 152 00:09:40,960 --> 00:09:47,040 incompetence? In so much as there's so much to 153 00:09:47,040 --> 00:09:52,800 know about it and lots of people either as you, as you said, 154 00:09:52,920 --> 00:09:55,920 think it's somebody else's responsibility to, to deal with 155 00:09:55,920 --> 00:10:02,760 this or to think about this or it's seen as the, the, the role 156 00:10:02,760 --> 00:10:08,440 of, of technologists or the IT department or it's, you know, 157 00:10:08,480 --> 00:10:10,320 it's somebody else's responsibility. 158 00:10:11,040 --> 00:10:18,040 And, and I think it's, it's very easy to yeah, to, to not think 159 00:10:18,040 --> 00:10:21,240 about it because it is it is complex and it is a, is a 160 00:10:21,760 --> 00:10:24,160 learning a new language. It's difficult to do. 161 00:10:24,800 --> 00:10:30,640 And I think whilst lots of organisations have invested very 162 00:10:30,640 --> 00:10:34,440 heavily in tools and visualisations and all sorts of 163 00:10:34,440 --> 00:10:41,600 other things, there is this unnamed issue that most people 164 00:10:41,600 --> 00:10:45,960 don't use many of those tools. They still rely on their gut and 165 00:10:45,960 --> 00:10:50,560 they still rely on other. They won't rely on the company 166 00:10:50,560 --> 00:10:52,440 spreadsheet. They'll rely or the, yeah, the 167 00:10:52,440 --> 00:10:53,800 company's platform they'll have had. 168 00:10:53,800 --> 00:10:55,280 They'll have their own spreadsheet. 169 00:10:55,280 --> 00:10:59,920 That's their version of of truth for for the things that they 170 00:11:00,320 --> 00:11:06,680 need to focus on And and that well, yeah, that starts then get 171 00:11:06,680 --> 00:11:10,240 into the the five skills that that we've introduced through 172 00:11:10,240 --> 00:11:13,080 this framework. So the first of those is 173 00:11:13,720 --> 00:11:19,320 managing project information. And I, I was challenged on why a 174 00:11:19,320 --> 00:11:21,400 project leader needs to know about this. 175 00:11:21,400 --> 00:11:22,880 Surely that's somebody else's job. 176 00:11:23,560 --> 00:11:26,920 But, but the fact is that if you as a project leader don't know 177 00:11:26,920 --> 00:11:31,560 where your information is, you don't have a handle on the, the 178 00:11:31,560 --> 00:11:36,040 methodology or the approach that you and your team are taking to, 179 00:11:36,880 --> 00:11:40,280 to truth, to trust to the evidence that you're going to be 180 00:11:40,280 --> 00:11:47,160 using for, for your project. Then it it, it makes life harder 181 00:11:47,160 --> 00:11:50,880 for everybody in your team to be able to do their jobs because 182 00:11:50,880 --> 00:11:52,760 everybody has their own version of things. 183 00:11:52,760 --> 00:11:56,000 They have to check, they have to search, they spend a lot of time 184 00:11:56,000 --> 00:11:58,720 trying to discover what the right, yeah, the the right 185 00:11:58,720 --> 00:12:04,760 answer is to things. And, and it is a it's a whilst 186 00:12:04,760 --> 00:12:07,240 it's a fundamental thing for many projects. 187 00:12:07,240 --> 00:12:12,880 We don't necessarily broadcast that as leaders to say actually 188 00:12:12,880 --> 00:12:15,280 information management is really important. 189 00:12:15,880 --> 00:12:19,640 It's, it's certainly not the most exciting topic, but 190 00:12:19,760 --> 00:12:23,080 actually once you've got that right, that gives you a really 191 00:12:23,080 --> 00:12:27,240 strong platform to, to unlock lots and lots of other 192 00:12:27,240 --> 00:12:29,480 capabilities. And that applies all the way 193 00:12:29,480 --> 00:12:31,360 through to artificial intelligence. 194 00:12:31,360 --> 00:12:34,920 Because if you're not putting in trustworthy data, then the 195 00:12:34,920 --> 00:12:37,600 outcomes that you're getting out from those tools become a lot 196 00:12:37,600 --> 00:12:40,120 less trustworthy, a lot harder to interpret. 197 00:12:40,800 --> 00:12:44,880 So that's the fair skill. Skill number 2 is then the sort 198 00:12:44,880 --> 00:12:48,080 of foundations of data. And really this is about the, 199 00:12:48,120 --> 00:12:52,880 the language of data and being able to, to distinguish between 200 00:12:53,400 --> 00:12:59,120 the, you know, the different averages, mode, median, mean, 201 00:13:00,360 --> 00:13:04,040 understanding the, the names of different charts that you might 202 00:13:04,040 --> 00:13:08,600 be looking at, understanding that in different situations, 203 00:13:08,600 --> 00:13:11,720 there are going to be different biases that will be introduced. 204 00:13:12,360 --> 00:13:17,800 Understanding a whole raft of other concepts that don't take 205 00:13:17,800 --> 00:13:21,280 long, and many of which are, are, are very simple and, and we 206 00:13:21,280 --> 00:13:24,440 use all the time. It's just that we, we don't, we 207 00:13:24,440 --> 00:13:27,440 aren't explicit about it. And what that skill 2 does is it 208 00:13:27,440 --> 00:13:29,760 starts to make some of those things more explicit so that 209 00:13:29,760 --> 00:13:31,840 everyone can relate to each other. 210 00:13:31,840 --> 00:13:35,440 And it, it means it makes life a lot easier, makes it a lot 211 00:13:35,440 --> 00:13:39,320 easier to communicate between leaders and, and technologists 212 00:13:39,320 --> 00:13:44,440 or technical experts. So the the third skill is then 213 00:13:44,480 --> 00:13:48,360 about interpreting and influencing with data. 214 00:13:48,680 --> 00:13:52,360 So actually being able to spot trends, being able to, to 215 00:13:52,360 --> 00:13:58,440 understand that, yeah, you may not be looking at all of the 216 00:13:58,440 --> 00:14:01,760 data, starting to question where that data is coming from, 217 00:14:01,760 --> 00:14:07,400 starting to, to spot that some data, if data is missing or if 218 00:14:07,400 --> 00:14:11,000 data is incorrect, then it might be throwing out your results or 219 00:14:11,000 --> 00:14:14,280 introducing anomalies. And, and it's that curiosity of 220 00:14:14,280 --> 00:14:18,240 asking why and, and then being able to, to, to know what to do 221 00:14:18,240 --> 00:14:22,720 about it. The, the 4th skill is then the 222 00:14:22,720 --> 00:14:27,000 one that I guess lots of people jump on and, and it's all about 223 00:14:29,080 --> 00:14:31,600 storytelling and visualising with data. 224 00:14:31,600 --> 00:14:35,640 And, and again, from a project leader's perspective, it's not 225 00:14:35,640 --> 00:14:38,400 necessarily going to be the case that you are going to be the, 226 00:14:39,000 --> 00:14:44,640 the, the expert that's able to produce incredibly detailed 227 00:14:44,920 --> 00:14:48,240 visualisations using some specialist tools. 228 00:14:48,960 --> 00:14:53,520 But as a leader, you need to, need to be able to articulate 229 00:14:54,120 --> 00:14:59,080 the, the story or talk about the storyboard and, and ultimately 230 00:14:59,280 --> 00:15:02,720 ask direct somebody to, to be able to create that for you. 231 00:15:02,960 --> 00:15:05,440 And if you've not got that understanding of some of the, 232 00:15:06,640 --> 00:15:09,880 of, of how those things get made-up, then it makes it much 233 00:15:10,360 --> 00:15:12,200 more difficult for you to be able to do that. 234 00:15:13,520 --> 00:15:17,160 And then the final skill, which is I think the one that most 235 00:15:17,160 --> 00:15:22,000 naturally sits with project leaders is then making decisions 236 00:15:22,000 --> 00:15:24,680 with data. How do you make those decisions? 237 00:15:24,680 --> 00:15:28,880 How do you and, and for, for so many of us, that's then making 238 00:15:28,880 --> 00:15:31,400 decisions under under uncertainty. 239 00:15:31,400 --> 00:15:35,880 And, and often people then do rely on their guts to to make 240 00:15:35,880 --> 00:15:39,760 decisions. And, and, and many leaders will 241 00:15:39,760 --> 00:15:42,880 say, well, yeah, I don't have the information at my 242 00:15:42,880 --> 00:15:45,400 fingertips, so I have to rely on my gut for these things. 243 00:15:45,400 --> 00:15:47,000 It's not, it's not available to me. 244 00:15:47,440 --> 00:15:51,440 And in many situations, that's fine. 245 00:15:51,600 --> 00:15:54,400 That's borne out of lots of different experience. 246 00:15:54,800 --> 00:16:00,760 But then those experiences, as project delivery gets ever more 247 00:16:00,760 --> 00:16:06,440 complex, our own experiences can only ever provide a sliver of of 248 00:16:06,920 --> 00:16:09,200 the answer in a sliver of the understanding. 249 00:16:09,560 --> 00:16:12,280 And without tapping into these other skills and these other 250 00:16:12,280 --> 00:16:18,560 tools, then you are limiting your your ability to continue to 251 00:16:18,560 --> 00:16:23,080 be able to make good decisions. So thanks, Gareth, that was a 252 00:16:23,080 --> 00:16:27,920 really, really concise way of summing these skills up. 253 00:16:29,520 --> 00:16:33,280 Rob, what is there anything you'd like to add about how you 254 00:16:33,280 --> 00:16:40,560 perceive data literacy and why it matters and also how what's 255 00:16:40,560 --> 00:16:42,360 it like within your own organisation? 256 00:16:42,360 --> 00:16:45,120 I want to ask both of you bring it back to the profession and 257 00:16:45,120 --> 00:16:48,240 how far on the what the profession needs, you know, to 258 00:16:48,240 --> 00:16:52,400 do to get up to speed on this. But yeah, Rob, I'd like to hear 259 00:16:52,400 --> 00:16:53,680 your thoughts on that. Yeah. 260 00:16:54,280 --> 00:16:57,600 So, so, so just in answer to your first question, I guess 261 00:16:57,600 --> 00:17:01,200 it's probably quite a lot of crossover with my thoughts and, 262 00:17:01,280 --> 00:17:03,720 and Gareth's thoughts. But I guess to me, kind of data 263 00:17:03,720 --> 00:17:06,920 and literacy is about, it's about the ability to ask the 264 00:17:06,920 --> 00:17:10,800 right questions of the data, which is something that Gareth's 265 00:17:10,800 --> 00:17:14,920 answers kind of explored. It's also understanding its 266 00:17:14,920 --> 00:17:17,040 limits. So again, kind of Gareth talked 267 00:17:17,040 --> 00:17:22,520 about incomplete data sets and I guess having 100% information is 268 00:17:22,520 --> 00:17:26,560 almost utopia if you like that you'll never quite reach. 269 00:17:26,680 --> 00:17:33,040 And it's absolutely about using it to make better decisions. 270 00:17:33,160 --> 00:17:37,120 It's quite easy for some folks to fall into the trap of being 271 00:17:37,320 --> 00:17:41,280 obsessed around perhaps data inputs and the process 272 00:17:41,280 --> 00:17:44,960 management around that. But much more important is the 273 00:17:44,960 --> 00:17:47,040 ability to answer the So what question. 274 00:17:47,360 --> 00:17:52,120 So what's the, what's the output or impact that's going to have a 275 00:17:52,840 --> 00:17:55,800 result, a positive result for you, for your customer, for your 276 00:17:55,800 --> 00:17:58,320 own organisation or for your supply chain, whichever 277 00:17:58,320 --> 00:18:02,240 stakeholders you're serving. So that's that's why it's so 278 00:18:02,240 --> 00:18:06,120 kind of important to me and I think it, it really is an aid to 279 00:18:06,120 --> 00:18:11,320 improve predictability assurance, especially when 280 00:18:11,320 --> 00:18:14,680 you're dealing with large portfolios of of work and to 281 00:18:14,680 --> 00:18:21,280 build trust through objectivity. What What do you both think 282 00:18:21,280 --> 00:18:24,080 about where the profession is along when it comes to data 283 00:18:24,080 --> 00:18:26,880 literacy? Like, is there a lot of room for 284 00:18:26,880 --> 00:18:29,280 improvement? What really needs to shift 285 00:18:29,880 --> 00:18:32,880 across the profession to to make this happen? 286 00:18:33,600 --> 00:18:37,760 I would say, I mean, I'm going to, I'm going to kind of base my 287 00:18:37,760 --> 00:18:40,400 answer closer to home, so to speak in terms of what I see 288 00:18:40,800 --> 00:18:42,800 across my own kind of organisation. 289 00:18:42,800 --> 00:18:47,720 I think it's fair to say it's a pretty uneven kind of landscape. 290 00:18:48,080 --> 00:18:54,040 So what is absolutely true is we've definitely made big bounce 291 00:18:54,040 --> 00:18:58,920 forwards in terms of common tooling and column common 292 00:18:58,920 --> 00:19:03,160 systems and common dashboards when it comes to data analysis 293 00:19:03,160 --> 00:19:09,160 and management information. But there's definitely, there's 294 00:19:09,160 --> 00:19:12,760 definitely a kind of gap perhaps in in how that's kind of 295 00:19:13,160 --> 00:19:18,080 interpreted and how we kind of pivot from analysis to action. 296 00:19:18,360 --> 00:19:21,600 I mean, some people are kind of masters in that, in that domain, 297 00:19:21,600 --> 00:19:24,400 but I think there's a lot of variation in, in skills and 298 00:19:24,400 --> 00:19:26,440 performance in, in, in that regard. 299 00:19:26,520 --> 00:19:32,440 And we definitely need to be a more challenging organisation in 300 00:19:32,440 --> 00:19:35,440 terms of the exploitation of that data management information 301 00:19:35,800 --> 00:19:39,520 don't kind of fall into track just consuming it kind of almost 302 00:19:39,600 --> 00:19:43,120 passively and make sure in programme management we're we're 303 00:19:43,120 --> 00:19:45,400 challenging kind of assumptions. We're really clear on 304 00:19:45,400 --> 00:19:49,560 definitions and we're really trying to drive quality from it. 305 00:19:50,600 --> 00:19:52,200 Thank you. That's probably going to be a 306 00:19:52,200 --> 00:19:55,400 common situation across many organisations that people work 307 00:19:55,400 --> 00:19:58,080 for who are listening to this. Gareth, what would you like to 308 00:19:58,080 --> 00:20:01,880 say on that? Well, so I mean that everything 309 00:20:01,880 --> 00:20:06,640 that Rob said I think resonates across our organisation and 310 00:20:06,640 --> 00:20:10,240 many, many other organisations that and, and for a lot of 311 00:20:10,240 --> 00:20:12,760 people, the first step is making sure that there is common 312 00:20:12,760 --> 00:20:16,960 tooling and that and that the inputs are starting to be 313 00:20:17,120 --> 00:20:22,280 standardised to a degree. And that's hard. 314 00:20:22,360 --> 00:20:25,160 That's, that's not an easy task in and of itself. 315 00:20:25,880 --> 00:20:32,120 But as, as Rob said, the So what can often get lost either either 316 00:20:32,120 --> 00:20:36,480 in terms of people aren't quite sure what they're asking for or 317 00:20:36,480 --> 00:20:39,400 what the insight is that they're looking for off the back of it. 318 00:20:39,520 --> 00:20:44,600 And we we spend so much time kind of doing the plumbing, 319 00:20:44,600 --> 00:20:47,800 putting together the pipes and the wires, Everyone forgets 320 00:20:47,800 --> 00:20:51,960 about the the water or the, you know, the fluid that's coming 321 00:20:51,960 --> 00:20:54,080 out the other end. What and what are we trying to 322 00:20:54,080 --> 00:20:55,800 do with it? How are we going to use it? 323 00:20:56,360 --> 00:21:02,240 And yeah, so we end up kind of spending lots of time trying to 324 00:21:02,240 --> 00:21:05,760 perfect things that that actually don't need to be 325 00:21:05,760 --> 00:21:07,600 perfected. It can be good enough. 326 00:21:07,880 --> 00:21:11,080 And I think often, yeah, the perfection side of things gets 327 00:21:11,200 --> 00:21:15,880 in the way of of just having good enough data or a good 328 00:21:15,880 --> 00:21:19,920 enough set of visuals to be able to answer the immediate question 329 00:21:19,920 --> 00:21:22,960 and then make the decision and move on to the next thing. 330 00:21:24,600 --> 00:21:30,400 So and and I think a lot of this is then amplified by AAI at the 331 00:21:30,400 --> 00:21:34,760 moment. There's there's lots of noise 332 00:21:34,760 --> 00:21:39,560 about it and there's so many positive things, but my fear is 333 00:21:39,560 --> 00:21:42,760 that without some of the foundations, we're at risk of 334 00:21:42,760 --> 00:21:48,160 trying to run before we can walk and that then that just sets us 335 00:21:48,160 --> 00:21:49,680 up for a four if we're not careful. 336 00:21:50,520 --> 00:21:52,160 Yeah, Yeah. You're not the first person to 337 00:21:52,160 --> 00:21:54,840 raise that issue. I think many organisations are 338 00:21:54,840 --> 00:21:58,080 worried about this. But it's really interesting what 339 00:21:58,080 --> 00:22:00,320 you're both saying about there kind of being pockets of 340 00:22:00,560 --> 00:22:04,920 probably higher expertise and understanding and, and other 341 00:22:04,920 --> 00:22:09,120 areas across an organisation where it's not that much 342 00:22:09,120 --> 00:22:11,880 interest or doing the bare minimum or misunderstanding of 343 00:22:11,880 --> 00:22:15,560 what it could do or like under appreciation of how it can help 344 00:22:15,560 --> 00:22:20,240 you in your work. What how so how should one go 345 00:22:20,240 --> 00:22:26,200 about improving data literacy? I guess within, I guess within 3 346 00:22:26,200 --> 00:22:29,120 parameters, there's an organisation who works with on 347 00:22:29,120 --> 00:22:32,760 projects as a project team and then as an individual. 348 00:22:32,760 --> 00:22:36,280 So the, the framework is brilliant across all of those, 349 00:22:36,280 --> 00:22:38,520 particularly for as an individual to understand. 350 00:22:38,520 --> 00:22:42,200 But honestly, like where do you, how do you go about improving 351 00:22:42,200 --> 00:22:44,000 this? Someone might be listening who 352 00:22:44,040 --> 00:22:47,880 is project manager, project leader realises the importance 353 00:22:47,880 --> 00:22:50,200 of this, perhaps not receiving that much support from the 354 00:22:50,200 --> 00:22:52,880 organisation. So how do you go about at those 355 00:22:52,880 --> 00:22:56,560 3 levels, team, organisation, individual to kind of really 356 00:22:56,560 --> 00:23:01,360 getting to grips with this and and learning about how to do it 357 00:23:01,560 --> 00:23:05,560 right? So weren't, yeah, as you say, 358 00:23:05,560 --> 00:23:10,840 that's that's exactly how we designed the the framework as a 359 00:23:12,560 --> 00:23:15,600 as a conversation starter as much as anything else at at 360 00:23:15,600 --> 00:23:19,560 those 3 levels. So it's, it's easy enough to 361 00:23:19,560 --> 00:23:24,720 pick it up and, and just run through and the, the, the 362 00:23:24,720 --> 00:23:29,600 categories there are sort of five levels of of skill and they 363 00:23:29,600 --> 00:23:33,280 tie back to the, the competencies from awareness 364 00:23:33,280 --> 00:23:39,280 through to expert in in aligned to APM competence frameworks. 365 00:23:39,800 --> 00:23:45,560 So there's, it's easy enough for somebody to pick it up and just 366 00:23:45,560 --> 00:23:49,000 take a look and, and give themselves 5-10 minutes to 367 00:23:49,000 --> 00:23:52,880 process and work out where they see themselves across each of 368 00:23:52,880 --> 00:23:55,680 the five levels. And, and from that 369 00:23:55,680 --> 00:24:00,040 self-assessment, work out which of those we have where they may 370 00:24:00,040 --> 00:24:03,640 have some blind spots. And there there's so many. 371 00:24:05,480 --> 00:24:07,880 I mean, it's so easy now to start Googling once you've done 372 00:24:07,880 --> 00:24:10,360 that, once you've got that. So hopefully that at the an 373 00:24:10,480 --> 00:24:12,840 individual level, what the framework does is it gives you 374 00:24:12,840 --> 00:24:16,920 that that. So it's like a checklist, isn't 375 00:24:16,920 --> 00:24:18,320 it? Like, what do I know? 376 00:24:18,320 --> 00:24:20,880 What don't I know? What do I need to find out more 377 00:24:20,880 --> 00:24:23,320 about? Yeah, exactly. 378 00:24:23,840 --> 00:24:28,120 And then? And then what, what you can do 379 00:24:28,120 --> 00:24:31,040 with it at the team level is use it at the beginning of a team 380 00:24:31,040 --> 00:24:36,760 meeting as a just taking five questions, one on each of them. 381 00:24:36,760 --> 00:24:40,160 Get get each of each of your team members to score where they 382 00:24:40,160 --> 00:24:43,040 see themselves against each of the five metrics. 383 00:24:43,040 --> 00:24:46,800 It's simple enough and, and there's a there's, it's easy 384 00:24:46,800 --> 00:24:49,840 enough to almost cherry pick the things that are then relevant to 385 00:24:49,840 --> 00:24:54,760 your situation, your project to be able to to put those into 386 00:24:54,760 --> 00:24:57,440 right. OK, well, for, for the level of 387 00:24:57,440 --> 00:25:00,480 competence, actually, that's what this means for us. 388 00:25:00,480 --> 00:25:03,360 And so make it quite specific to your particular project. 389 00:25:03,720 --> 00:25:05,920 And then is everyone reached that level of competence or 390 00:25:05,920 --> 00:25:10,280 actually are still people moving towards it and proficient but 391 00:25:10,280 --> 00:25:12,920 not yet practised or yeah, there's so there's, there's 392 00:25:12,920 --> 00:25:16,120 various different ways then that a team can do that. 393 00:25:16,440 --> 00:25:22,080 And and then from that very quick 5-10 minute exercise as a 394 00:25:22,080 --> 00:25:26,320 team, being able to to identify where there may well be some 395 00:25:26,320 --> 00:25:30,600 blind spots, where there may be some untapped, untapped 396 00:25:30,680 --> 00:25:35,480 expertise may well be that you have a somebody that's 397 00:25:35,480 --> 00:25:39,320 incredibly passionate about visualising data that that 398 00:25:39,320 --> 00:25:44,640 hasn't necessarily been yet or is is being underutilised it. 399 00:25:45,480 --> 00:25:49,160 It seems like an obvious thing to me that if you have a team 400 00:25:49,160 --> 00:25:51,880 where you decide that you say you have someone who's expert 401 00:25:52,000 --> 00:25:54,560 has more expertise or understanding one thing that you 402 00:25:54,560 --> 00:25:59,800 can quickly have a session, you know, like even a lunchtime 403 00:25:59,800 --> 00:26:02,040 session where people come together and say, I can show you 404 00:26:02,040 --> 00:26:04,080 this. I, you know, it's that sharing 405 00:26:04,080 --> 00:26:06,000 that it doesn't have to be a formal thing. 406 00:26:06,000 --> 00:26:08,520 It's like sharing the knowledge that people have already. 407 00:26:09,360 --> 00:26:11,880 Is that a good idea? I'm just thinking of how people 408 00:26:11,880 --> 00:26:13,520 could do something quite quickly. 409 00:26:14,280 --> 00:26:17,440 Yeah, absolutely. It's, it's and a lot of it 410 00:26:17,440 --> 00:26:19,760 because this is a, it's a soft skill. 411 00:26:19,760 --> 00:26:24,080 It's not a technical skill. And it's, it's not as hard as 412 00:26:24,080 --> 00:26:27,960 people think. It's because there are so many 413 00:26:27,960 --> 00:26:30,480 practical examples when you start thinking about it. 414 00:26:31,240 --> 00:26:34,440 And I think the challenge is that often data is seen as this 415 00:26:34,440 --> 00:26:37,160 sort of ephemeral conceptual thing. 416 00:26:37,600 --> 00:26:44,560 But it, it's, it's document management, our, our monthly 417 00:26:44,560 --> 00:26:49,280 reporting, our, yeah, feedback mechanisms. 418 00:26:49,280 --> 00:26:53,040 It's, it's, it's embedded within everything that we're doing. 419 00:26:53,040 --> 00:26:56,600 And then so you can have a 10 minute conversation or a, yeah, 420 00:26:56,880 --> 00:27:00,400 a lunchtime session to have somebody just show what they've 421 00:27:00,400 --> 00:27:03,880 been doing or what they've what they've learnt and, and speak 422 00:27:03,880 --> 00:27:06,600 about it. That's really great. 423 00:27:06,600 --> 00:27:10,000 And then so at the, I think so we've covered kind of individual 424 00:27:10,000 --> 00:27:11,560 project. What about an organisation? 425 00:27:11,560 --> 00:27:14,440 Because then surely I'm jumping in here. 426 00:27:14,440 --> 00:27:17,720 But if you have one team that's doing interesting things, it's 427 00:27:17,720 --> 00:27:20,280 about sharing the knowledge across the whole organisation. 428 00:27:20,560 --> 00:27:22,160 Exactly. Yeah, yeah. 429 00:27:22,160 --> 00:27:24,400 And, and then, and then there's different ways that it can start 430 00:27:24,400 --> 00:27:29,720 to apply between, yeah, inter team conversations and, and two 431 00:27:29,720 --> 00:27:33,080 teams picking up and comparing where they are all the way up 432 00:27:33,080 --> 00:27:37,160 to. It's, it's when we designed it, 433 00:27:37,160 --> 00:27:40,280 we were thinking that actually you could build out, build out a 434 00:27:40,280 --> 00:27:44,880 much more comprehensive sort of benchmarking exercise to, to 435 00:27:44,880 --> 00:27:48,640 assess the, the whole organisation or large swathes of 436 00:27:48,640 --> 00:27:54,800 an organisation or particular capabilities within an 437 00:27:54,800 --> 00:27:58,720 organisation to, to benchmark where they are and then 438 00:27:58,720 --> 00:28:04,040 introduce some specific measures to improve in, in certain areas 439 00:28:04,160 --> 00:28:07,320 and and then re rebaseline or reassess. 440 00:28:08,120 --> 00:28:12,520 Thanks, Gareth. Rob, how do you working within a 441 00:28:12,520 --> 00:28:16,320 massive organisation like EED? How, how do you go about 442 00:28:16,320 --> 00:28:20,360 assessing where people are on a kind of data literacy journey? 443 00:28:20,680 --> 00:28:24,560 And any advice or things that you've seen work when it comes 444 00:28:24,560 --> 00:28:27,600 to improving data literacy, understanding and skills and 445 00:28:27,600 --> 00:28:31,880 competencies at individual team and and kind of leadership level 446 00:28:31,880 --> 00:28:35,920 particularly interesting for you as a leader, like how one, how 447 00:28:35,920 --> 00:28:39,920 one makes it known that this is something people need to care 448 00:28:39,920 --> 00:28:43,920 about. I think there's so many sources 449 00:28:43,920 --> 00:28:47,720 of information, learning and knowledge out there that are 450 00:28:47,720 --> 00:28:52,160 available through open sources that there's no real excuse for 451 00:28:52,240 --> 00:28:54,400 ignorance on anybody's kind of part. 452 00:28:54,400 --> 00:28:57,880 I think even if you're, even if your company perhaps isn't as 453 00:28:57,880 --> 00:29:01,600 supportive as they can be, you know, folks can use obviously 454 00:29:01,600 --> 00:29:05,280 leverage the APM framework or LinkedIn training or other 455 00:29:05,280 --> 00:29:09,160 mechanisms to, to, to kind of build the kind of foundation of 456 00:29:09,160 --> 00:29:11,360 their knowledge. Then I think with that kind of 457 00:29:11,960 --> 00:29:15,760 foundation, you know, established, I think there's a 458 00:29:15,760 --> 00:29:18,160 lot to be said through kind of learning through lived 459 00:29:18,160 --> 00:29:20,840 experience. So try and put that learning 460 00:29:20,840 --> 00:29:24,880 into direct practise. I think some of that is almost 461 00:29:24,880 --> 00:29:31,320 about forming habits in terms of how we use data management 462 00:29:31,480 --> 00:29:33,960 discipline to drive good outcomes. 463 00:29:33,960 --> 00:29:39,720 So a couple of practical examples might be to establish a 464 00:29:39,720 --> 00:29:44,120 contract with yourself that you always triangulate multiple data 465 00:29:44,120 --> 00:29:47,600 sources. You don't fall foul of being, 466 00:29:47,600 --> 00:29:50,040 you know, biassed towards one data source. 467 00:29:50,040 --> 00:29:54,360 You use multiple sources and take a 360 perspective around 468 00:29:54,360 --> 00:29:56,760 the problem or the requirement that you're trying to kind of 469 00:29:57,440 --> 00:29:59,920 solve. You might also make a contract 470 00:29:59,920 --> 00:30:03,960 with yourself that you're always going to use data to inform 471 00:30:03,960 --> 00:30:07,680 your, you know, decisions, whether that be a decision 472 00:30:07,680 --> 00:30:10,880 internal to your team or a decision that's paramount to 473 00:30:10,880 --> 00:30:15,000 your kind of customer. So I think you know, kind of 474 00:30:16,800 --> 00:30:21,200 being a self learner, utilising those open sources, I think 475 00:30:21,640 --> 00:30:25,960 building on those foundations through learning by lived 476 00:30:25,960 --> 00:30:30,120 experience and then establishing some habits and principles that 477 00:30:30,120 --> 00:30:33,080 you contract to work by. I think all of those things 478 00:30:33,080 --> 00:30:37,160 would have a positive multiplying kind of effect to 479 00:30:37,160 --> 00:30:40,880 then a bigger kind of organisational level. 480 00:30:40,960 --> 00:30:47,160 I think it's really important that we see data info management 481 00:30:47,160 --> 00:30:51,320 as being very much kind of, you know, core to our business, core 482 00:30:51,320 --> 00:30:54,400 to how we operate. It's not something that sits on 483 00:30:54,400 --> 00:30:57,640 the periphery. It's not an adjacency that's 484 00:30:57,640 --> 00:31:01,640 only done by kind of specialist. It should be informing 485 00:31:01,640 --> 00:31:03,720 everybody. Now there's an onus that flows 486 00:31:03,720 --> 00:31:09,000 from that around the company establishing a strategy for for 487 00:31:09,000 --> 00:31:12,920 how that should be done because as Gareth said earlier, data is 488 00:31:12,920 --> 00:31:15,080 all around us. It kind of informs everything 489 00:31:15,080 --> 00:31:17,680 we've done. And actually if we don't have a 490 00:31:17,680 --> 00:31:21,920 strategy, we'll kind of lack structure and actually this 491 00:31:21,920 --> 00:31:25,280 thing will kind of grow very organically and could actually 492 00:31:25,280 --> 00:31:29,480 grow in quite a disorganised way that can't then be exploited to 493 00:31:29,480 --> 00:31:33,720 best effect. So, and also if you've got a 494 00:31:33,720 --> 00:31:36,400 strategy and you're clear on the North Star, what you're trying 495 00:31:36,400 --> 00:31:38,840 to achieve and how data management contribute, 496 00:31:38,840 --> 00:31:43,040 contribute towards that, it does make kind of investment 497 00:31:43,040 --> 00:31:47,080 decisions and also investment dilemmas perhaps easier to 498 00:31:47,080 --> 00:31:49,320 grapple with. And who's who does that sit 499 00:31:49,320 --> 00:31:50,560 with? At which level of an 500 00:31:50,560 --> 00:31:54,040 organisation should should this, who, who should be setting the 501 00:31:54,040 --> 00:31:55,880 strategy? I mean, this is going right to 502 00:31:55,880 --> 00:31:59,760 the top of the business. Yeah, right. 503 00:31:59,840 --> 00:32:03,880 I guess it differs business by business in terms of how the 504 00:32:04,000 --> 00:32:07,480 data and insight is represented, you know, at board level or 505 00:32:07,480 --> 00:32:10,720 perhaps one level down or or or two level down. 506 00:32:11,760 --> 00:32:13,440 And we, we're a technology company. 507 00:32:13,440 --> 00:32:17,480 So we, we, we tend to establish very, very close relationships 508 00:32:17,480 --> 00:32:20,960 with our CTIO function because quite often they're the 509 00:32:21,400 --> 00:32:24,920 mechanism apparatus, the plumbing, as Gareth said 510 00:32:24,920 --> 00:32:27,880 earlier, which enables this to be done at a kind of 511 00:32:28,000 --> 00:32:31,840 organisational level, a corporate level. 512 00:32:33,360 --> 00:32:37,480 So yeah, we're, we're, we're quite system orientated, CTIO 513 00:32:37,480 --> 00:32:40,000 orientated, but it differs by company. 514 00:32:41,120 --> 00:32:43,640 What can I ask both of you, what you think are the biggest 515 00:32:43,640 --> 00:32:47,440 barriers to data literacy at the moment and how they might be 516 00:32:47,440 --> 00:32:51,040 overcome? Maybe Rob, I think. 517 00:32:51,040 --> 00:32:56,120 That the the the greatest barriers probably learning 518 00:32:56,520 --> 00:33:01,480 knowledge, culture, mindset. I think all the component parts 519 00:33:01,640 --> 00:33:06,960 of this topic, it's not rocket science in isolation. 520 00:33:07,840 --> 00:33:12,360 It's not mega hard things to do, but actually it's how you make 521 00:33:12,360 --> 00:33:19,280 it a organisational wide kind of endeavour and bring some 522 00:33:21,080 --> 00:33:27,520 standardisation to that. Make it business as usual and 523 00:33:27,800 --> 00:33:31,000 put the enablers in place to achieve that kind of outcome. 524 00:33:31,440 --> 00:33:34,800 Be it like in the same way, cybersecurity Once Upon a time 525 00:33:35,000 --> 00:33:40,560 was this peripheral area of expertise that used to take 526 00:33:40,560 --> 00:33:43,040 place in a dark room which nobody really understood 527 00:33:43,040 --> 00:33:45,480 anything about. Whereas actually everybody now 528 00:33:45,480 --> 00:33:48,400 understands that cybersecurity and information security is 529 00:33:48,400 --> 00:33:50,960 everybody's responsibility. There's there's certain kind of 530 00:33:50,960 --> 00:33:54,200 parallels with with data management I would suggest. 531 00:33:55,240 --> 00:33:58,200 Gareth, what would you say are the biggest sort of barriers? 532 00:33:58,240 --> 00:34:00,680 Actually, Joe, what I wanted just to point out the way that 533 00:34:00,680 --> 00:34:04,680 you said that this is about, it's not a tech skill, it's a 534 00:34:04,680 --> 00:34:09,000 soft skill. That is a massive shift in 535 00:34:09,000 --> 00:34:12,320 perception because I imagine pizza, one of the barriers might 536 00:34:12,320 --> 00:34:15,280 be it's just techie. It's not. 537 00:34:15,320 --> 00:34:16,719 I don't want to go down that route. 538 00:34:17,239 --> 00:34:19,560 It's. Not that's really important to 539 00:34:19,560 --> 00:34:22,199 make that distinction. Very much so. 540 00:34:22,320 --> 00:34:26,440 And and I think there so some of the things that Rob touched on 541 00:34:26,440 --> 00:34:29,440 in terms of leadership and culture and habits. 542 00:34:29,440 --> 00:34:31,320 Coming back to something Rob said earlier. 543 00:34:31,800 --> 00:34:37,639 And the way that you almost need to look at this is that it is 544 00:34:37,679 --> 00:34:40,760 it's literacy, it is a language to learn. 545 00:34:41,120 --> 00:34:43,880 And anyone that's tried learning a second language will know that 546 00:34:43,880 --> 00:34:46,520 the hardest thing to do is to actually speak it. 547 00:34:47,199 --> 00:34:51,120 You can, you can be great at all of your yeah, that, that verbs 548 00:34:51,120 --> 00:34:53,520 and, and of being able to read the language. 549 00:34:53,520 --> 00:34:56,360 But until you can have a conversation about it. 550 00:34:56,360 --> 00:34:58,760 And that's, that's when you get the richness. 551 00:34:58,760 --> 00:35:05,360 And that's when your, your yeah, your fluency really starts to 552 00:35:05,360 --> 00:35:09,880 take off. And that's, it's, it's that sort 553 00:35:09,880 --> 00:35:13,600 of fear factor of, of I'm going to, I'm going to stumble over my 554 00:35:13,600 --> 00:35:14,800 words. I'm going to say the wrong 555 00:35:14,800 --> 00:35:17,440 thing. I'm what I'm going to say, I'm 556 00:35:17,440 --> 00:35:21,280 going to use the wrong terminology and yeah, and, and 557 00:35:21,400 --> 00:35:23,920 yeah, I'm going to fail. And so there's a real sense in 558 00:35:23,920 --> 00:35:29,680 and around this that's that's hard to overcome, but just start 559 00:35:29,680 --> 00:35:32,040 talking. And as Rob said, it's it's that 560 00:35:32,040 --> 00:35:37,200 habit of yeah, being able to find and it may well be on a 561 00:35:37,200 --> 00:35:42,960 daily report that you look at. And so today, tomorrow, rather 562 00:35:42,960 --> 00:35:45,480 than just skimming over it because it's what you've always 563 00:35:46,400 --> 00:35:49,040 kind of snow blind to it because you see it everyday. 564 00:35:49,720 --> 00:35:55,400 Stop and just take it, take a breath and look at the details. 565 00:35:55,760 --> 00:35:58,760 How old is it? What's the source of the data 566 00:35:58,760 --> 00:36:00,320 that you're getting that's on the report? 567 00:36:00,560 --> 00:36:03,200 Are there any biases that you can see from it? 568 00:36:03,680 --> 00:36:06,400 What's the story that the reports telling you? 569 00:36:06,400 --> 00:36:10,600 Is it the same story that was being told yesterday or yeah. 570 00:36:10,600 --> 00:36:13,240 Is, is there something that you don't understand or you don't, 571 00:36:13,320 --> 00:36:17,040 you're not quite clear on? And then ask the question, 572 00:36:17,080 --> 00:36:21,320 what's the, what does this mean? Why, why does this and this not 573 00:36:21,320 --> 00:36:23,400 equal each other today when they did yesterday? 574 00:36:23,440 --> 00:36:27,040 It's, it's those kind of things that you then start to see these 575 00:36:27,040 --> 00:36:31,560 examples everywhere. And, and it's that, it's, it's 576 00:36:31,560 --> 00:36:36,080 that individual habit that organisations need to then kind 577 00:36:36,080 --> 00:36:40,000 of, you know, three, leadership and culture and cultural change 578 00:36:40,000 --> 00:36:44,880 to, to be able to sort of embody and harness in the right way. 579 00:36:44,920 --> 00:36:48,600 Otherwise, again, as Rob says it, it grows organically and 580 00:36:48,600 --> 00:36:52,520 which is great, but it doesn't necessarily then align to the 581 00:36:52,520 --> 00:36:55,640 organisational objectives and the organisational priorities. 582 00:36:55,840 --> 00:36:58,760 And then there's a sweet spot to hit in and amongst all of that. 583 00:36:59,400 --> 00:37:01,880 Advertising with the way that people are experimenting with 584 00:37:01,880 --> 00:37:03,760 AI. So lots of companies like 585 00:37:03,760 --> 00:37:06,800 individuals going off and doing their own thing in certain 586 00:37:07,320 --> 00:37:10,800 departments going off and it all gets a little bit chaotic and 587 00:37:10,800 --> 00:37:13,400 messy and people doing stuff because they feel like they 588 00:37:13,400 --> 00:37:15,560 ought to. But what, why are you doing 589 00:37:15,560 --> 00:37:17,520 this? And let's let's share knowledge 590 00:37:17,520 --> 00:37:20,360 and think of it in a kind of more strategic way. 591 00:37:22,120 --> 00:37:25,600 So really the one of the most important questions I haven't 592 00:37:25,600 --> 00:37:28,360 asked you yet, although we have touched on it a bit is why 593 00:37:28,360 --> 00:37:30,800 bother? So what are the benefits that 594 00:37:30,800 --> 00:37:34,560 are kind of So what to, to cracking data literacy? 595 00:37:34,880 --> 00:37:40,680 And can you give me some examples, example or two where 596 00:37:41,120 --> 00:37:44,440 you've used it, you've, you've had good data literacy on 597 00:37:44,440 --> 00:37:47,760 particular projects or programme and it's brought better outcomes 598 00:37:47,760 --> 00:37:50,560 or benefits. So Rob, if you'd like to go 599 00:37:50,560 --> 00:37:54,320 first and then Gareth. So there's a, there's a number 600 00:37:54,320 --> 00:37:57,440 of things that this kind of drives and enables. 601 00:37:58,040 --> 00:38:00,840 I mean, one of the first things is, is kind of confident 602 00:38:01,280 --> 00:38:04,040 executive decisions. I mean what I'm involved in is 603 00:38:04,040 --> 00:38:07,520 always major programmes. So big, big technology change 604 00:38:07,520 --> 00:38:11,440 programmes that have big impact to our customer organisation and 605 00:38:11,440 --> 00:38:14,520 to their, their end users and their end customers. 606 00:38:14,520 --> 00:38:19,880 So data definitely, definitely, definitely enables and supports 607 00:38:19,960 --> 00:38:23,440 confident executive decisions. And often those confident 608 00:38:23,440 --> 00:38:29,080 executive decisions actually are a fuel and propellant for a 609 00:38:29,080 --> 00:38:31,840 programme. Because actually if you get into 610 00:38:31,960 --> 00:38:35,800 decision kind of paralysis because you haven't got good 611 00:38:36,160 --> 00:38:40,520 data, empirical inputs, that can be really, really dangerous for 612 00:38:40,520 --> 00:38:44,560 programmes. Health and success, perhaps 613 00:38:44,560 --> 00:38:49,720 building on that a little bit, good data management also assist 614 00:38:49,720 --> 00:38:55,080 greatly around risk management. So it's how we use it to drive 615 00:38:55,480 --> 00:39:00,800 early intervention in kind of failed delivery. 616 00:39:02,320 --> 00:39:09,960 And we are actually right now looking at how AI and predictive 617 00:39:09,960 --> 00:39:13,760 analytics can kind of further take us on the journey of kind 618 00:39:13,760 --> 00:39:18,520 of almost, you know, reactive to proactive to predictive and 619 00:39:18,520 --> 00:39:23,280 almost at the planning stage informed by AI and predictive 620 00:39:23,280 --> 00:39:27,200 analytics. Plan for success on day zero 621 00:39:27,200 --> 00:39:31,560 because you've got all of the data benefit of all the failings 622 00:39:31,560 --> 00:39:35,480 of projects and programmes that you've run before and the perils 623 00:39:35,480 --> 00:39:39,200 kind of associated with, with that, which if we can harness 624 00:39:39,200 --> 00:39:45,800 that will be extremely helpful in minimising any variation in 625 00:39:45,800 --> 00:39:52,560 our major programme performance. And probably lastly, just how it 626 00:39:52,560 --> 00:39:57,080 can help us almost in the visualisation and the 627 00:39:57,080 --> 00:40:00,040 articulation of confidence levels. 628 00:40:00,120 --> 00:40:04,000 I mean, I work in a world of IT and network, so kind of 629 00:40:04,640 --> 00:40:09,720 precision and reliability. Is everything we can put a 630 00:40:09,720 --> 00:40:12,200 number on pretty much everything as well. 631 00:40:12,200 --> 00:40:17,640 So being kind of empirically kind of enabled and be able to 632 00:40:18,040 --> 00:40:21,600 embed that in the confidence levels we can communicate for 633 00:40:21,600 --> 00:40:24,920 our storytelling is all quite key to to stakeholder 634 00:40:24,920 --> 00:40:28,480 management. I've been thinking just the 635 00:40:28,480 --> 00:40:33,800 headlines at the moment about a conflict, I guess that's what we 636 00:40:33,800 --> 00:40:41,680 call it, incursion into Iran and the global impact of that on 637 00:40:41,680 --> 00:40:46,880 markets, on energy prices. And every month there's 638 00:40:46,880 --> 00:40:49,960 something else that is disrupting the kind of the 639 00:40:50,520 --> 00:40:53,120 business as usual for many organisations, many 640 00:40:53,120 --> 00:40:59,120 international ones. Does data literacy give you a 641 00:40:59,120 --> 00:41:03,720 sense of kind of greater understanding of or prediction 642 00:41:03,760 --> 00:41:06,080 or stability? Is that something that is 643 00:41:06,080 --> 00:41:10,400 relevant to helping manage the ever greater uncertainty and 644 00:41:10,400 --> 00:41:13,960 volatility that certainly multinational organisations are 645 00:41:14,080 --> 00:41:16,080 are trying to get grapple with at the moment? 646 00:41:17,080 --> 00:41:22,360 As an international company that deals in IT and network services 647 00:41:22,360 --> 00:41:25,440 into multinational companies and we've got good representation in 648 00:41:25,440 --> 00:41:27,520 that region. The current situation is, is 649 00:41:27,520 --> 00:41:30,920 definitely a concern. I mean, I think it's also kind 650 00:41:30,920 --> 00:41:35,720 of Fair to say that we, you know, we are leveraging, you 651 00:41:35,720 --> 00:41:39,280 know, our existing kind of data management methods where it 652 00:41:39,280 --> 00:41:45,240 comes to service assurance. So in that sense how we operate 653 00:41:45,240 --> 00:41:49,280 is is kind of BAU, but what we tend to do is the wrapper around 654 00:41:49,280 --> 00:41:54,520 it is more our business continuity management, crisis 655 00:41:54,520 --> 00:41:58,280 management kind of arrangements. And you know, we have daily 656 00:41:58,280 --> 00:42:02,720 stand ups where we use quite specific data points to kind of 657 00:42:02,720 --> 00:42:06,360 confirm and assure that both our primary services, secondary 658 00:42:06,360 --> 00:42:11,920 services, resilient services are still intact and has no impact 659 00:42:11,920 --> 00:42:15,160 to the customer and is not presenting any further risk or 660 00:42:15,160 --> 00:42:18,960 threat to the to the customer. So this is something we're we're 661 00:42:19,080 --> 00:42:22,400 putting in practise kind of daily through those command and 662 00:42:22,400 --> 00:42:24,720 control arrangements. OK, thank you. 663 00:42:24,800 --> 00:42:28,920 And and Gareth, what why should we bother with data literacy? 664 00:42:29,360 --> 00:42:34,520 What benefits does it bring? I think so, as Rob touched on 665 00:42:34,520 --> 00:42:38,080 many of these things already in terms of the ability to speed up 666 00:42:38,080 --> 00:42:42,160 governance, reduce friction between people and across teams, 667 00:42:42,880 --> 00:42:46,080 win people around with stories that are backed up by evidence 668 00:42:46,080 --> 00:42:52,360 rather than yeah, yeah, leaving things to, to chance, bridging 669 00:42:52,360 --> 00:42:55,480 the gap between technical specialists and senior 670 00:42:55,480 --> 00:42:58,280 leadership, reducing the amount of rework. 671 00:42:58,280 --> 00:43:01,600 These are all things that that data literacy contributes to. 672 00:43:02,280 --> 00:43:08,480 But for me, the, the biggest benefit is that it turns your 673 00:43:08,480 --> 00:43:12,040 data and your information into strategic assets. 674 00:43:12,760 --> 00:43:17,440 And then the benefits and and the achievements that you can, 675 00:43:17,480 --> 00:43:21,880 yeah, that you can make happen with it are literally only 676 00:43:21,880 --> 00:43:25,040 limited by your imagination. I guess this comes all the way 677 00:43:25,040 --> 00:43:28,120 back to your first question as to why this excites me so much 678 00:43:28,120 --> 00:43:31,960 is that it is. And especially now with, with 679 00:43:32,160 --> 00:43:36,880 the the advances and the exponential advances in the, the 680 00:43:36,880 --> 00:43:41,640 quality of AI tooling, all reliant on good quality data, 681 00:43:42,400 --> 00:43:45,440 but you're literally only limited by your imagination. 682 00:43:45,680 --> 00:43:49,440 So risk mitigation, how we can get better at predicting and 683 00:43:49,440 --> 00:43:52,600 preventing risks and identification of variants, 684 00:43:52,960 --> 00:43:56,200 greater cost control, new service lines. 685 00:43:56,200 --> 00:44:00,880 And yeah, so whatever it is that you're trying to achieve, data 686 00:44:00,880 --> 00:44:05,440 literacy, data management and treating your data as a 687 00:44:05,440 --> 00:44:08,720 strategic asset will help you get there faster than you are 688 00:44:08,720 --> 00:44:14,120 doing guaranteed. Before you wrap up, because I'd 689 00:44:14,120 --> 00:44:17,200 like to ask you, each of you, if there's one takeover, one piece 690 00:44:17,200 --> 00:44:21,120 of advice you'd like to pass on what what that might be. 691 00:44:21,600 --> 00:44:25,240 But there's a kind of lot of thought going around at the 692 00:44:25,240 --> 00:44:27,720 moment. We haven't really talked too 693 00:44:27,720 --> 00:44:34,600 much about AI, but it I guess it was thinking about the soft 694 00:44:34,600 --> 00:44:37,640 skills that the skills you need as a project professional to 695 00:44:37,640 --> 00:44:40,400 thrive in the next 5 to 10 years. 696 00:44:42,200 --> 00:44:47,880 And AI is only going to increase in its importance in the way 697 00:44:47,880 --> 00:44:50,720 that projects are managed to run, delivered. 698 00:44:52,800 --> 00:44:56,360 If you are, if someone's listening to this, thinking what 699 00:44:56,360 --> 00:45:01,040 the skills I need to hone, part of that is a critical skill will 700 00:45:01,040 --> 00:45:07,960 be kind of fluency with data and as part of that AI. 701 00:45:07,960 --> 00:45:13,560 Would you agree with that? I would absolutely I would 702 00:45:13,560 --> 00:45:15,440 absolutely kind of agree with that. 703 00:45:15,440 --> 00:45:18,880 And I guess there is some very tight relationship come 704 00:45:18,880 --> 00:45:23,480 crossover between kind of data literacy and the the power and 705 00:45:23,480 --> 00:45:28,680 opportunity around AII mean in the world of project and 706 00:45:28,680 --> 00:45:34,200 programme management. I I don't see AI replacing the 707 00:45:34,200 --> 00:45:36,560 human in terms of project and programme manager. 708 00:45:37,760 --> 00:45:40,720 I see. I see kind of AI and data 709 00:45:40,720 --> 00:45:45,040 literacy as being mechanisms by which we can make the the job of 710 00:45:45,040 --> 00:45:48,960 those individuals easier. Certainly kind of automate some 711 00:45:48,960 --> 00:45:51,960 of the repeatable tasks when it comes to like programme control. 712 00:45:51,960 --> 00:45:55,120 So how we manage risks, how we manage issues, how we deal with 713 00:45:55,120 --> 00:46:00,240 assumptions, how we enable good decision support, so on and and 714 00:46:00,240 --> 00:46:03,240 so forth. And, and really kind of enable 715 00:46:03,240 --> 00:46:05,400 the and project to programme management. 716 00:46:05,400 --> 00:46:08,600 It could be, could have always like pushed up the, the value 717 00:46:08,600 --> 00:46:12,920 chain and give them the time and space to actually spend more 718 00:46:12,920 --> 00:46:16,680 time managing upwards and outwards rather than downwards 719 00:46:16,760 --> 00:46:20,560 and, and, and inwards. And some of the data will give 720 00:46:20,560 --> 00:46:24,800 them the kind of the, the confidence and assurance that 721 00:46:24,800 --> 00:46:28,360 their internal machine is working as expected and they 722 00:46:28,360 --> 00:46:32,760 won't have to spend unnecessary time checking and duplicating 723 00:46:32,760 --> 00:46:35,880 the work of others. So I see, I see all of these 724 00:46:35,880 --> 00:46:38,400 things coming together as a bit of a positive ecosystem. 725 00:46:39,160 --> 00:46:40,680 What do you mean by managing upwards? 726 00:46:40,680 --> 00:46:44,120 Can you just expand on that? Yes, yes, I can. 727 00:46:44,400 --> 00:46:49,400 I mean, I think again kind of reflect on on operations kind of 728 00:46:49,400 --> 00:46:55,160 nearer to home, we can find that our projects and programme 729 00:46:55,160 --> 00:47:00,760 managers spend too much time managing internal functions and 730 00:47:00,760 --> 00:47:04,480 teams and assuring that their performance that's contributing 731 00:47:04,760 --> 00:47:08,840 to the customer outcome you know is on track, is on plan. 732 00:47:08,920 --> 00:47:11,640 It's the right point of quality, it's the right point of cost, 733 00:47:11,640 --> 00:47:14,600 it's the right point of schedule, so on and so forth. 734 00:47:15,800 --> 00:47:18,600 Through kind of data management techniques. 735 00:47:18,640 --> 00:47:22,480 Some of that assurance can be done for them through system 736 00:47:22,480 --> 00:47:24,120 means. I actually just trigger 737 00:47:24,120 --> 00:47:27,760 interventions when strictly necessary rather than having a 738 00:47:27,760 --> 00:47:32,040 big kind of time consumption of ongoing kind of checking and 739 00:47:32,080 --> 00:47:35,040 assurance. And actually far more value can 740 00:47:35,040 --> 00:47:39,680 be given by actually working outwards and upwards into the 741 00:47:39,680 --> 00:47:44,160 client organisation, acting as their trusted advisor, helping 742 00:47:44,160 --> 00:47:47,400 them with their decision making that's going to lead to the 743 00:47:47,400 --> 00:47:50,720 joint success or failure of a programme, so on and so forth. 744 00:47:50,720 --> 00:47:53,320 So that's that's what I meant by that Quit. 745 00:47:54,080 --> 00:47:56,280 Thanks for a really interesting Gareth. 746 00:47:56,520 --> 00:47:58,200 What? What would you like to say? 747 00:47:59,080 --> 00:48:03,920 I'd say that there's not a magic bullet for this, that it's not 748 00:48:03,920 --> 00:48:07,760 something that you can, you can go on a single training course 749 00:48:07,760 --> 00:48:10,000 and all of a sudden you can, you can do all of these things. 750 00:48:10,240 --> 00:48:12,720 You have to practise it, you have to engage with it. 751 00:48:13,320 --> 00:48:17,240 And so I would encourage people to to take a look at the skills 752 00:48:17,240 --> 00:48:20,640 framework that we've produced, get a sense of where you are and 753 00:48:20,680 --> 00:48:25,720 where you might want to improve. And yeah, there are, there are 754 00:48:25,720 --> 00:48:30,480 then so many online sort of Open Access resources for people to 755 00:48:30,520 --> 00:48:34,320 to start Googling, start start making use of. 756 00:48:34,720 --> 00:48:38,520 And AI is a fantastic tool to just yeah, if you, if you don't 757 00:48:38,520 --> 00:48:41,400 want to have a conversation with, with another person, just 758 00:48:41,400 --> 00:48:44,640 start talking to one of the yeah, your AI tool of choice. 759 00:48:44,720 --> 00:48:49,880 And, and, and you will very quickly learn a lot more about 760 00:48:49,880 --> 00:48:54,800 this subject. I guess the, the, in terms of, 761 00:48:54,800 --> 00:48:57,680 as you mentioned, some of those kind of core capabilities for, 762 00:48:57,800 --> 00:48:59,680 for project leaders going forwards. 763 00:49:00,160 --> 00:49:07,240 And, and obviously, yeah, the, the, the piece around AI and AI 764 00:49:07,440 --> 00:49:10,160 is not going to replace the need for data literacy. 765 00:49:10,720 --> 00:49:16,240 What it's going to do is amplify all sorts of capabilities, but 766 00:49:16,240 --> 00:49:21,360 also potentially the mistakes and data literacy is one part of 767 00:49:22,120 --> 00:49:25,120 enabling you to be able to determine the difference between 768 00:49:25,120 --> 00:49:28,120 the two and, and avoid some of those pitfalls. 769 00:49:28,360 --> 00:49:33,400 Make sure that you are in a in a place to be able to understand 770 00:49:33,400 --> 00:49:36,920 the options that are that are being presented to you and and 771 00:49:36,920 --> 00:49:41,080 make the right decisions. Rob, is there one piece of 772 00:49:41,080 --> 00:49:43,640 advice take away you'd like to pass on to listeners? 773 00:49:44,480 --> 00:49:47,120 Yeah, it's probably, I guess it's probably more of a strap 774 00:49:47,120 --> 00:49:50,520 line than one piece, but definitely be self starter, self 775 00:49:50,520 --> 00:49:54,560 learner, be curious and put in practise would be it. 776 00:49:55,640 --> 00:49:58,360 So about mindset, yeah, OK, like that. 777 00:49:58,360 --> 00:50:03,240 Gareth, what would yours be? So I've spent the first half of 778 00:50:03,240 --> 00:50:07,760 my career focusing on unravelling digital pipes and 779 00:50:07,760 --> 00:50:11,320 wires and, and a lot around that kind of plumbing. 780 00:50:11,840 --> 00:50:17,240 And I, I want my second second-half of my career to be 781 00:50:17,240 --> 00:50:20,040 about making sure that the people at the end of those pipes 782 00:50:20,720 --> 00:50:23,720 actually know what they're doing with the insight that's flowing 783 00:50:23,720 --> 00:50:27,400 through them. And so my piece of advice is be 784 00:50:27,400 --> 00:50:31,840 constantly asking So what? And and it's it's the coining 785 00:50:31,840 --> 00:50:36,640 phrase that Rob used earlier. What is the action that this is 786 00:50:36,640 --> 00:50:39,000 going to drive? What's the value that this is 787 00:50:39,000 --> 00:50:42,840 going to drive? How can we make it real? 788 00:50:43,320 --> 00:50:48,960 Rather than, yeah, focusing on capturing more and more data 789 00:50:49,000 --> 00:50:52,560 with with little without that. So what without that purpose. 790 00:50:53,080 --> 00:50:58,600 So I guess, yeah, it's that sense of purpose and and 791 00:50:59,120 --> 00:51:01,480 forethought of what you're going into and why. 792 00:51:02,680 --> 00:51:06,920 Yeah, Know why you need to dip data you're after and what 793 00:51:06,920 --> 00:51:09,400 you're going to do and have that understanding, just being smart 794 00:51:09,400 --> 00:51:12,120 about it. OK, that is really fantastic. 795 00:51:12,120 --> 00:51:15,360 Thank you both. It just leaves me to say thanks 796 00:51:15,360 --> 00:51:17,520 for for joining us on the OPM podcast. 797 00:51:17,520 --> 00:51:21,000 Really appreciated. Appreciate you sparing the time 798 00:51:21,040 --> 00:51:23,000 and what an interesting conversation. 799 00:51:23,000 --> 00:51:25,480 So thanks very much. Thank you. 800 00:51:26,000 --> 00:51:28,960 Thank you very much, Emma. Thanks again to Rob and Gareth 801 00:51:28,960 --> 00:51:32,120 for joining us and to you for listening to the APM Podcast. 802 00:51:32,880 --> 00:51:35,400 If you're interested in finding out more about data literacy, 803 00:51:35,400 --> 00:51:40,080 please go to theresourcessectionoftheapmwebsite@apm.org.uk 804 00:51:40,440 --> 00:51:44,080 and look out for the Spring 2026 issue projects where we go into 805 00:51:44,080 --> 00:51:47,560 the subjects in more depth. Anyway, don't forget to look out 806 00:51:47,560 --> 00:51:49,720 for more episodes or to rate and reviews. 807 00:51:49,720 --> 00:51:52,960 Wherever you get your podcasts, we'd like you to get in touch 808 00:51:52,960 --> 00:51:56,480 with your comments, feedback and suggestions by emailing us at 809 00:51:56,560 --> 00:52:01,920 apmpodcast@thinkpublishing.co.uk. This podcast has been brought to 810 00:52:01,920 --> 00:52:05,280 you by APM, the chartered body for the project profession. 811 00:52:05,840 --> 00:52:10,000 For more information on APM, visit apm.org.uk.