SDSW23: Harnessing the Power of Data

tl;dr

The speaker, Mark Humphries, discusses the UK government’s digital and data roadmap as an example of the growing significance of data. He emphasizes the shift in attitudes towards data quality and the increasing focus on harnessing the potential of data.

The challenges of implementing data strategies, such as data skills and culture, are highlighted, along with the necessity of promoting data literacy. He shares an experience from the Clipper around the world yacht race to illustrate the complexity of leveraging data in decision-making.

The importance of finding success stories to promote a data-first approach is emphasized over using punitive measures for encouraging data governance.

Transcript

Mark Humphries: So as John introduced me, so I'm actually the vice chair of DAMA UK and I stepped down after six years of leading DAMA UK, and handed over the reins to Lisa Adam. But that's quite a recent development. Harnessing the power of data. I'm going to limit this to a certain extent to talk about a couple of, things which I think are relevant. And I'm going to start with, the CDDO’s data road or digital and data roadmap. The reason I've chosen this is, in my day job, as the central government consulting director at Civica, I deal with a lot of central government, organisations they’re our main customers. And so I follow things like this very closely.

And I think this is also a very interesting example of, a the change that we're seeing, across everything at the moment in terms of data. So Claire was talking about data first. Now, I've been working with data for over 30 years, and for a long time, I think data professionals were very much, sort of like shouting into the wind and saying, we need to we need to worry about data quality. We need to worry about data architecture. We need to get data right because everything else depends on it. And it kind of felt like a very lonely position for a long time. That's changed. and the rest of the world is now coming back to us and saying, okay, you've been banging on about data for all this time. What are we going to do with it? How are we going to get this right? And the sheer scale of the ambition of the roadmap of the UK government, I’ll put the statements on there in terms of their three, priorities and the six missions.

When you step back and you think about it, there's actually there's a lot going on there. And the plan is to deliver this by 2025. And I think if they do deliver all those six missions by 2025, I for one will be very impressed. It's backed up by 75 individual initiatives. So different government departments have put their foot forward there, first initiatives that they're tackling in order to meet this plan. And one of the things which I think is interesting when you dig into the detail of that is, nearly all of those 75 initiatives, they are all very interesting. Very, and they sort of meet those high level plans. but they're all point solutions.

So one, for example, at the DVLA is to improve the speed at which some driving licences are issued and renewed. That's great, but that's something that DVLA can do entirely on its own. There's a huge change, there's a cultural change. There's a whole lot of security issues which people need to look into. And, you know, this is one of the bigger discussions which going on in the background is how, for example, that is going to work in practice. Then you've got things like better data to power decision making. Again, you unpick that again, it becomes, you know, what do we mean by decision making? There's the data literacy side of things. There’s skills which go with that.

And there's the trust. Claire talked a lot about, data quality and that being first. When we start to unpick things like decision making, using data, the data quality that underlines that is really, really important. And if I pick the fifth one, the data skills at scale, that's also a theme we're seeing again and again and again. data skills. And again, when you start to unpick this, data skills are huge and, I think with the Ministry of Defence, for example, one of the things we've defined there is it's these three very distinct communities. So there's leaders and we don't mean data leaders, we mean, operational leaders. So generals, admirals, you know, air marshals, that sort of thing. But also then you've got the data professionals and all the DDAT skills which go behind that. Then there's the whole workforce. What are the skills that everybody needs in their day to day work?

So, there's a lot going on there. Like I said, it's huge and, if you’re thinking about data first, I mean, think about the scale of what the UK government is trying to achieve here. I think it is a really good example of, what we mean by data first. and I think the really big challenge here underpinning all of this is data sharing. And data sharing when people start to say, look, you know why, especially in government, there's a lot of data which is duplicated. One of the conversations we have on a regular basis is, why should we duplicate this data? Why can't we share the best data we have? As you unpick that one, one of the things which people are really worried about when it comes to sharing data is, oh what happens when we expose our data to other government agencies and then they realise that there's holes in it. They realise our data quality isn't as good as we would like. And again, this is linking back to that whole data quality issue. So everything is going round and round in circles in a positive way. In terms of there’s this feedback loop. Coming back to the same sort of things. So and this is just one example.

So there's other examples outside the public sector. But it's a growing trend that we see. So picking into that a little bit, so you know, with if we need to figure out what's going on in the world of data, one of the things we do on a regular basis or an annual basis, at DAMA UK is to poll our members. And we do this for two reasons. One, because we're a, you know, we're a membership organisation. So we want to see what interests our members, what brought them to DAMA in the first place, and what they want from DAMA in return. But it also gives us a little bit of insight. So we've got 1500 data management professionals. So how do they see the world. And we can reflect that in terms of so how does how does DAMA UK see the world.

And you know top three I guess part of this is, you know, our heritage. These are the things we're strong at: data governance, data quality, data strategy. But these are things which you see which are important. And the nice thing about data quality is, you know, just as Claire was alluding to, data quality and understanding of data quality and the need for data quality has now become a mainstream issue. And for someone like me who's been very much a data geek, to see that this is now a mainstream issue it kind of, it makes me very happy. Because this ties in, this survey result ties in with a similar survey that, civil service world did recently, across senior civil servants and funnily enough and this surprised me pleasantly that even for them, data quality is a number two item in terms of, you know, actually achieving data strategy plans.

Interestingly AI machine learning, right down at the bottom for data management professionals, which is an interesting sort of result in terms of, you know, the buzz that AI and machine learning get. But you know, as Claire was alluding to, data quality underpins that. The other thing was in terms of challenges. So if those are your priorities, what are the challenges which, you're actually facing in implementing, those priorities and following those priorities?

Culture and skills right at the top. And this sort of links back to that whole thing about what do you mean by data skills and data culture? And that's about moving the dial, moving the dial from, you know, talking about the shiny new technology, talking about the day to day business, but actually understanding what data goes in there, you know, how do you actually move the dial? You know, again, Claire talked about the data quality and how you present that, getting the buy in from the bosses. That is very much part of that data culture. Data skills, everyone is, it's a huge topic right now, is how do we improve, those data skills? What training to people need? You know, and this is, as you know, in my day job, we're constantly being asked, what training offerings do we supply? What training should, what should the learning paths be?

Again, splitting into those three groups of leaders, of data professionals and whole workforce. So that's a recurring theme. I mean, of course, the business case, again, this is about the buy in for the bosses. This always been a challenge, I think, for for data management professionals is, making the case. and I completely agree with what Claire was saying about making that, you know, turning that into what is the what is the return on investment. And I think if we can't do that as data management professionals, then we're kind of letting ourselves down, but also letting down our customers, our organisations that need us. I would now like to sort of dig into that in a little bit more detail and give, a real life example of what it means to change the culture and to increase the levels of data literacy.

And we do that, by talking about a recent experience I had. Now, I know I'm talking to a bunch of geospatial people. So I know you like maps. this is a map, from the Clipper around the world yacht race, which is something I did recently. I took two months off. I sailed across the Atlantic from Portsmouth to Punta Del Este in Uruguay. And one of the things I noticed as we were doing this was, the volume of data which the skipper and the skipper and the first mate have to deal with. Now on this race, they basically the skipper and first mate are the only professionals on board, and there's 11 identical yachts. Each of the two professional sailors on board and 20 amateurs. So I was one of the amateurs. And they have some very advanced software called Time Zero, which they use to take the weather forecast. And they use what I call polar coordinates, which is the different performance, that they can expect from the boat and the different wind angles and different sail configurations and what speed you’re going to track, to effectively work out an optimum course.

Now, the interesting thing here was I'm looking at this through the views of oh look interesting data. So the polar coordinates - that’s master data. The forecasts - that’s dynamic data. You've got an optimisation engine in there which is trying to find the best route between two points, given lots of variables. And it all the result is going to be underpinned by A. the quality of that master data. So your polar coordinates, the performance, characteristics of the boat. But also the quality of the forecast. Now this all makes sense to me as a data professional, but sitting in the middle of this and watching this happen for real, these guys have an awful lot of other things they have to do. You know, the skipper, for example, he sells 30,000, yeah 30,000 miles a year. He knows everything there is to know about sailing. You've got to understand tides. He's got to understand weather. He's got a strong, he's got a really big leadership challenge. After all, he's got 20 amateurs on the boat. We've got some big seas got, you know, he's got to be able to deal with any emergency the line throws out including medical emergencies. So he's got a series of qualifications and skills that he's had to build up, over the years. and then on top of that, we're throwing some more data at him. You know, okay, you've got great, you know, you've got great tools, you've got great information which can help you optimise your route.

But he's now in a competition and all the other skippers have got that as well. So he's got to learn a whole set of new things. And I think this is really important for us as data professionals to understand what we mean when we say we need to, people need to increase their data literacy skills. They need to understand more about data. we're often talking about people who've already got a whole series of very valuable skills to do their day job, and we're adding something on top of that. So just banging on and saying, you need to become you need to understand, data is really important. Is I think we need to have a little bit of humility sometimes and understand just what burden people already have already, and how it doesn't necessarily just make life easier in some ways.

It leads to better decision making, better optimisation possibilities. But it comes at a cost and it comes as a sort of burden of new skills that these guys have to learn. And I'm watching the skipper and first mate effectively learning as we were going. The other thing the interesting thing about this is this is the race viewer, it’s from a couple of days ago, this is where the boats are right now or they were I think on Tuesday. On the boats themselves we went and received updates every six hours that we got to see four times a day, where we were compared to the rest of the fleet. Those of us sitting at home watching this now, get an update every hour. So in many ways, those people watching the race have a better, more insight into what's going on.

One last thing here so when things go slightly wrong, we took an optimisation decision as we're going through the doldrums. And we decided we looked at the weather forecast and we thought that okay, if we go further to the west than the rest of the boats, then we should pick up some, some tailwinds coming out of the doldrums and that will give us an edge. And that went very well, as we were going through the doldrums, we had better winds through the doldrums and then the rest of the fleet, and we got up to second place. And so we find ourselves beating into the wind for the next two days, whereas the rest of the fleet then overtook us over to the east. And interestingly that links back to the wisdom. Because the wisdom of sailors who have raced these waters in the past say, don't be lured into going too far west as you come out of the doldrums. so the wisdom that actually overrode the data, because it turned out the forecast wasn't as accurate as it might have been. Right. So that's hopefully I've kept my 20 minutes, and given you some thought in terms of, you know, the challenges in terms of harnessing data.

John Hartshorn: Brilliant. Thank you. You ok to answer some questions?

Mark Humphries: Yeah.

John Hartshorn: Yeah. Thank you very much, Mark. That was fantastic. A hugely relieved room when you showed a map there. I noticed the engagement level, suddenly oh theres a map! Great. Everyone loves a map. Does anybody have any questions for Mark? Got to be at least one. I mean, I have one. Yes, sir. I think we've got a microphone coming in here.

Tim Shepherd: Hi I'm Tim Shepherd. I work for J Murphy and Sons Infrastructure Company. Construction it's quite a late adopter when it comes to leveraging our data. In your experience, what is the what's the best carrot and the best stick in your experience for, encouraging our governance through adopting data first approach? Does that question make sense?

Mark Humphries: It makes a lot of sense. First of all, I would say I'd go for the carrot every time rather than stick. So in terms of getting people interested, I think find a good example of, find a good example of how you can improve somebody's problem, or you can improve the data that they need to do to do their day job. And ideally, if you can find someone who's receptive, then that's good. Then what you've got then is you then create a success story. Okay. and if I can, so if I go back. So my, years ago, I was a data manager for a utilities company. And one of the things I've managed to achieve huge success out of the blue, as I was banging on about data quality was, to present to the billing manager, a list of people who had, gas tariffs for electricity contracts and electricity tariffs for gas contracts. I said, look, we've got 150 of these. What's the impact to you? And her reaction to me was, that's not possible. We've got processes that prevent that, look, here's a list of 150. I promise you, this is what we found. she went to give that to one of her BA’s. They went away and investigated and said, oh not only can we not bill these customers when that happens. But we don't even get an error, so we don't even know we can't bill them. And suddenly for her, because one of her main KPIs was unbilled revenue. So for her, this was gold dust. So suddenly she was really, really interested and she came back to me. And once they realised that and they’d done the root cause analysis and she came back to me and she said, how many more of these have you got? What else can you tell me about what's going wrong? So that's an example where you just find an example that makes a difference to someone in their day job. Okay. Then you've got a success story. And then they'll start telling everyone else. Then everyone else wants them. So that was the approach that I would recommend.

John Hartshorn: Now my question, which would be the last one is when you're actually out on the ocean.

Mark Humphries: Yes. Are you ever in sight of other competitors? Yeah, we were we were on a couple of occasions, and sometimes it's really freaky, you think wow we’ve sailed 5000 miles and there's another boat just over there.

John Hartshorn: I've always wanted to ask sailing questions, so thank you very much. Brilliant thank you very much, Mark. Thank you