00:00:13

Thank you. Gus Paul, the next speaker is Sade Heming, who is head of data products at Virgin media. Oh two, the entity. That is the result of the merger of two of the largest UK companies with over 18,000 employees and 46 million UK customers. When their new chief data officer was brought on in October, 2020, his challenge to the organization was to find new ways to be disruptive and Sade jumped at the chance to contribute. She will be co-presenting with Richard, he head of data services on how their team of now over 130 people are creating data platforms and capabilities to help solve important business problems across the Virgin media, oh two enterprise. They will be talking about specific data technology choices. They've made patterns they've chosen to invest in as well as the philosophies and the engagement models they use to help elevate the use of data into the organization. One of my favorite parts of this talk is the fabulous testimonials they've gotten on the work they are doing. They were recently awarded the Google cloud DevOps award, which resulted in a pretty special congratulations message to the team. So here is Sade and Richard,

00:01:29

Thank you so much, Jean. We're absolutely delighted to be here representing Virgin media O two in the journey that we've been on in the last 12 months. So let me tell you a little bit about Virgin media. Oh two, for those that don't know. So we were born in 2021 combining the UK's largest mobile network with a broadband network offering the fastest speeds. Now, just because I mentioned 20, 21, don't think that's simple. So Virgin media goes back, um, to 2006 through a merger of many legacy companies. And O two goes back right back to like 1985 through the, um, the merger of many different companies coming together. We're actually one of the UK's largest businesses now, and we've got 46 million customers and around 18,000 employees across the UK and within our group that we're gonna focus on today. We've got hundreds of technologists working with us and making the dream come true.

00:02:19

Thanks a lot, Nate. Uh, I'm Richard and I look after the data platform and product engineering team in virtual media too. So, uh, I've been here since May, 2021, uh, and we, where we kind of started this amazing journey, uh, just look at more about myself. So it' kind of myself has been spent maybe 15, 16 years. Uh, at least in the, in the, as a software engineer in the industry started with this OnPrem, uh, you know, services and then worked with different cloud providers. Um, and then, but in the last five years I've been specifically focusing on, uh, helping large organizations, uh, uh, or tech companies to transition into the Google cloud. Um, that's been quite amazing journey. Um, I, what you resonate.

00:03:04

Thank you, Richard. Yeah, so quite similar to Richard. Um, but I joined this team a couple of months earlier, so I joined in, in March, 2021 and following maternity leave. So for me, like for the last Sunday, like four or five years, I've wanted to move into data. Um, I've been at virginity for five and a half years, um, and working in product management with software engineers, um, for like the last, over a decade. Now, when I came up from mat leave, I was kind of like looking for other things to do. And then an opportunity came up to basically set up a product management function within advance analytics and data science working on the Google cloud platform. And it couldn't have come at a better time. So within advanced analytics and data science, we've got about 130 people now and we're going rapidly. Um, within this group, we've got people that have been with Virgin media for, for some years, many years, um, and others that have come from external companies.

00:03:55

So hiring talent from all across the UK, um, I guess like we're very much remote. Um, we're going into the office for purposeful time. What that has done is tapped into other people that probably wouldn't have applied for particular roles before. Um, the journey that we've been on has been mega exciting and it kicked off really, um, like may last year, but taking a step back to that just before I went on maternity leave in 2019, um, we had a chief digital officer join us, and that was the first time we had that representation on this, on the C-suite. So when I came back from Matley, I saw like a massive shift, um, in the behaviors of the leadership team and kind of like the commitment, um, and the journey that we're on basically. And that's why we're here. So I guess the business problem we're trying to solve is we're bringing a legacy company with very complex systems into today's world, into the cloud.

00:04:47

And there's, you know, why are we doing that? Customer's expectations are changing faster than ever you. And I know that speed at which ideas can be executed is faster than ever. Competition is in many places and retaining and hiring the best talent is really, really hard. So there's just a couple of experiences that I wanted to share where we kind of use these, these examples to kind of, I guess, like inspire and encourage this kind of like mindset where technology speed and convenience can help us win, um, and solve the jobs that we want to do seamlessly. If I think about this example, like blockbuster, blockbuster, massively could have seen what was gonna happen in the future, if they leveraged the data that they had available to them and actually took that seriously. So rather than using the data of like sales and being like, yeah, like we're doing great.

00:05:34

People are still renting videos from us. They didn't look at other trends and opportunities to predict what was going to happen. And you only have to look at the streaming industry. Now it's absolutely on fire and not list, not just Netflix. There you've obviously got Disney, Amazon, and many others playing in that space. Another example, there would be like the music industry. So going from the right back to the cassette and flipping to side B right through to the likes of Spotify. Now we've got things at our fingertips, um, from a data perspective, I absolutely admire Spotify and what they're doing, using many different data products to build this wonderful product. Um, and thinking about data products, the like the recommendations. So if you, if you do like the recommendations they give you amazing, um, there's a little thing that they do there, where if you skip the track before 30 seconds, they kind of learn.

00:06:22

So that goes back into the machine learning model and there's says, don't show ADE, something like that again. And then another example, we kind of share often with people around the company to get 'em thinking about a technology and how it could do wonderful things is, is banking. Um, and I think one thing that I've massively seen is shifting, you've obviously gone from like a, a physical bank through to telephone banking, through to digital only banking the data and the insights that you can get now through the likes of, um, styling, the Monso is incredible. And you start to see, um, legacy, um, banks like Nat west coming through with those features, not quite as fast, but they do eventually get there. So that's kind of like a bit of a summary of some of the brands and the experience that we talk about when we're trying to show having a commitment to technology and data where it really can pivot you to in the future.

00:07:09

So onto a little bit about our team. So we built a winning team, as Jean said, um, delighted that we received a Google DevOps award, um, for 2021 having been so early in our journey, um, we're around 130 people, as I mentioned. Um, and I would kind of say, we are like the rebellion. We a bit big to be the rebellion from the uni unicorn project, but really we are challenging the way that we're working today. We're doing things differently and we're showing people around the organization, what can be done when you decouple yourself from that legacy, um, platform that rich is gonna talk about and do things in the cloud. I think it's been a phenomenal journey and the feedback's been awesome. And hopefully we can give you a little bit of insight into that and the journey that we've been on today. So I mentioned briefly about data products.

00:07:55

Um, what we're doing is we're unlocking value through the creation of data products and a data product really is, um, a product that facilitates an end goal through the use of data, quoting DJ Patel there. If you don't follow him, he's awesome. Give him a follow. Um, but basically what we're doing is we're using all of the data from around the organization to unlock experiences. It could be recommendations to a customer. It could be predicting churn. There there's many different things that it's doing, but we're using data in real time in production. Whereas historically companies like ours and many other companies are still doing this are just using data for reporting to look back at what happened and not to predict what could happen. So that's the journey we're gonna kind of take you on some of the examples we're gonna, we're gonna show you so over to you, Richard.

00:08:41

Thanks a lot. I think it's also, uh, it's important to talk about some of these technology choices that we're make, uh, we're making you, you can see on the screen, there's, uh, quite a lot of these, uh, blue boxes. There's actually Google cloud focus services. Um, the most important reason for Google cloud that we, we, you have a primary focus on is because it's data focused. So, uh, we've got experience in different areas using on premise services, other cloud services, but none of these other cloud services has a focus on data. The other reason that is really important is Google cloud is actually behind the scenes and more, um, open source focused. So as you can see some of these product services and behind the scenes, they're not actually just, you know, Google for Google clouds sake. They are actually, uh, based on, uh, open source, uh, services, um, that sorry, open source product, uh, or open source framework that you can use to deploy to other cloud as well.

00:09:40

But Google is actually the, the cloud providers actually making it run really well, uh, especially, uh, the last point. Um, but not least is the, a lot of the Google services that are fully managed or even serverless. So those product and services that Google offers that really kind of give us the ability to focus on not just the technology, because technology is just a tool, right? The most important thing is how quickly you can actually drive your business forward, but delivering value. And that's the key thing we're focusing on. So just to dive into some of this, uh, this, this details. So first of all, like in order to get, uh, started in a cloud journey that you obviously need to collect data, right? So cloud pops up is a message queue system that you, you, you can see as like, uh, a Kafka or something kind of equivalent, but it's fully managed that you, you don't, again, you don't have to manage any services or service, his planning scale.

00:10:34

You just send the messages into that and you start, start sending it to downstream system, then collecting them. So it, it is really important to, um, to focus on event the event, first approach. That's why I mentioned pops up in here specifically, um, as organization, you can collect data in batches, you can have ELT jobs getting this data into big query, for example, but what I want to emphasize that have a message queue service in your organization to start collecting a lot of the data based on events, right? Something happened in the different kind of systems. It really gives you the capability to not just, it's not just about reporting analytics. It's also about building downstream subscription services based on those messages to, to be event driven, to be, um, using those events to drive, for example, sending communication to customers or, uh, changing some of the status is updating, you know, customers details.

00:11:26

So you can do a lot of things in real time based on the message system, which gives you a really powerful way to integrate, uh, with Bevan, many teams working together, uh, obviously after data is your data gets collected. Um, we need to process it, right? You need to, in many cases you need to even convert the data format from one to another, um, that you need a fully managed slide service to be able to process those data in very live volume. This is where cloud data flow comes in. But again, as I mentioned, the open source first approach data flow is actually just a rapper of the Apache beam framework. You can run, um, Apache beam anywhere on different cloud on-prem, but Google gives you a really kind of serverless way that you can basically track your workload into there. And you don't think about it.

00:12:14

You always have to just give it to CPUs the Ram. How, how, how do you want it to scale? And then it does the drop for you, right? It's really important to focus on just the delivery, uh, in terms of, uh, you know, deploy your services. Um, but the open source call that gives you control. And then, because that is not something that you just have to run on Google cloud as well. Um, but where it comes down to data in the next steps is, is also about modeling the data, cleaning the data and making sure you can actually use the data that has has good quality, right, has good meaning than to unlock other business values. So this is where the I'll probably say the most important thing in the entire Google cloud ecosystem is cloud BigQuery. So BigQuery is a fully managed, uh, I will say just analytical database, but it's typically a choice of where you can put all of the data in there permanently.

00:13:05

Uh, what that means is you have all of your data in a central place. Uh, this helps many processes, including the GDPR processes. DBT is the open source tool again, and then you can simply integrate with the big query to do your data model. Uh, what's important about DBT is gives you a way to actually visualize and model your data based on a, like a lineage diagram, right? You can each box in the lineage diagram is like a, just the simple sequel query. So it takes not much time at total, typically like a week or two for your team to learn these things. And then they can be, you know, start working on production system in no time. And this is extremely powerful and that you can actually enable this, um, especially as a data platform engineering team to, uh, to build the tooling for the initial, for the, for the individual analytics team to be self-sufficient and building those data, data models themselves, which extremely powerful to, uh, introduce this more like a self-service culture, uh, in your organization in terms of unlocking more values, many organizations kind of stops at the, uh, you know, reporting and maybe, maybe a bit of kind of batch data processing, but it's really important to also unlock the value by building product and services.

00:14:19

This is where cloud run and cloud data store comes in. Um, in a nutshell is basically cloud run gives you a service way to build APIs, right exposed to other services. I will talk about why building APIs is very important later on in this, uh, you know, the cloud transition, uh, uh, process and also data store is where you have is basically a document database where you can store the data and therefore very fast concurrent lookups that for your cloud run services, you obviously can't do that with big analytical database, but it's nothing stop you from storing the data in a goodly shaped modeled way and ship that into data store as the API backend. And in addition to that, you can also, uh, use some other services such as vertex AI to build, uh, training pipelines. And then for after the training pipelines is built.

00:15:05

Now you can actually reinforce your learnings by putting these back into cloud run APIs with the scores in data store to actually do predictions. So it's extremely powerful, uh, ecosystem when you connect some of these things together. Also, let's not forget, forget about reporting. Lu is one of those services that really allows you to have a, the teams themselves like analytic teams, uh, maybe product teams and also the, uh, the sales teams, right? They can use this thing called the semantic layer, which they don't have to understand SQL. They don't have to learn anything about SQL, but they get the same power to be able to build, to explore data and build dashboard themselves. And again, if you think about, you can enable your entire organization to be self-service and self-sufficient and building their own dashboard. And then that is going to shift the transition into a data-driven decision organization, uh, and is this is the really serious stuff.

00:15:59

Um, the few other things I wanna talk about, uh, in the kind of supporting stack is the cloud composer is a service. Uh, many of you probably run, you know, ETL jobs in, in other areas or ELT jobs. In other areas, I typically call this like orchestration workload. So it gives you something called, uh, concept of a dag that stands for directly esthetic graph, which means the dependency graph with no circles, right? This is really important if you design your, your pipelines, right, orchestration pipelines, you have circles. That means things goes backwards. You don't know where things are coming from or where things goes to anymore. And then it kind of gets really mixed up. So it's really important to have a service that can run, uh, managed by a cloud provider. Um, then you don't have the variable infrastructure, but again, behind this is, is an open source framework called Apache airflow.

00:16:51

And that is actually the core foundation of the directed graph. Google just gives you a rapid to run it, but they do it pretty well. So it saves my team like our team a lot of the time. So we don't, we typically spend almost like compared to some of the other areas I worked, uh, where we didn't have a service like this. It saves like 90, 95% of the time. I'm not getting, it's like 95% of the time in terms of cluster management, all of this infrastructure side, you just don't have to worry about these things anymore. Also, let's not forget about the, uh, sensitive data, right? In a large organization, especially in enterprises. This area is extremely important. And this is yet another reason why I say Google is a data cloud. There's no equivalent services. Um, as far as I know, that does a good job as cloud DT, DLP, which stands for data loss prevention.

00:17:40

And what it can do is even with actually recently, a recent offering called automatic DLP, that you can basically plug it into your organization. It actually, uh, use the machine learning algorithms tool provided to give you the, uh, detection of what the category of sensitive data are. What's more importantly, you can then tag those data. What's called PI policy tax into your permanent storage. In this case is query, right? Then you can layer your, uh, your sensitivity of data that based on the tax and give different user groups, different privileged access based on, uh, the tax. So this is extremely powerful on unlocking. This is not only about, you know, protecting the data so nobody can use it. This is about protecting the data that the right people can get access to it in a way that is safe and is efficient. It's quick. Uh, this is really important, I think in an organization that not only you get the protection, but you get the speed as well.

00:18:35

Um, last, not the least. Let's not forget about monitoring loyalty. So this is where we keep everything together. The tooling that is, you know, it's very kind of standard. You've got like monitoring stuff, you've got logs, you've got metrics. These can be imported into again, the big query, uh, storage engine where we can actually analyze all of this stuff. But at the same time, when there are, uh, issues with the systems, we have multiple slack channels, which is categorized as such as U T alerts, production alerts warnings, and also security, right? This different kind of stuff happens in your, in your data products. And then you want to be able to monitor these in real time that give you, give you, give you alerts. If something isn't quite right, like something is going wrong, but why did I put Lu in there as well? Right?

00:19:20

Looker is not just, uh, for analytical reporting. It's also very, very powerful for operational insights. So let's say if you have an API you deployed and then there's different kind of errors happening, right? So you can use Looker to map into those API logs. That's been imported into big query. They give you representation layer, or you can explore those things, or with dashboard, even setting up alerts based on the analytics metrics, based on rolling windows of what is actually happening with your data based on aggregated insight. And this is extremely powerful to, rather than spending two, three hours, which is what typically happens in the, in the old world, um, of developers trying to crunch through the log messages to see what's going wrong. You can transition yourself into this data driven, uh, debugging in the, in the development teams. So instead of taking two hours, it will take like few minutes for you to get straight down to the problem you can fix that, uh, which is really important.

00:20:19

So the journey moving to the cloud is not simple at all, especially in a large organization where you may have even, you know, systems been there for 15, 20 years, and that is not a joke. Uh, so, and many of these systems are actually, you know, priority one operational system, transactional system, they're dealing with people's orders, right? Dealing with, uh, when the, when the next truck is gonna be sent or with the engineers to fix people's, uh, people's issues, um, at their homes. So you cannot just replace all of these overnight or move all of these things into the cloud. And it is really important to have to collaborate and working together with other teams in the organization and to, to then come up with good ways at which I will talk about a, in a little bit, and in order to transition organizations, uh, by bridging the two worlds together.

00:21:12

So as you can see, you've got in this, uh, picture, you've got the legacy part, which is the thing I was representing earlier on the, on the on-prem. Uh, and you have the new part, which is on the cloud, right? So in many scenarios that, uh, you cannot, as I mentioned, just to get rid of everything, but what you can do. And it's very important that you have this capability now is to build your product and services with better data quality, with better scaling capabilities in the cloud. So once you've got that, you can adopt this thing. It's maybe a little bit buzzword, it's called microservice based architecture, right? But you simply just put it in a way that you can have, uh, APIs exposed, but actually all your product services is built in the scalable architecture with modern ways of working serverless in the cloud.

00:21:59

Then you expose those APIs to your legacy systems where it is struggling to handle those workload. So it is in my opinion, never a good idea to try to do a lot of this lift and shit without really thinking about what is that, what is the important thing about delivering value? So many areas, especially on OnPrem, the reason this system exists for 15, 20 years is not necessarily mean they all just need to be get rid of tomorrow is because the agility, the ability to develop and deploy is very difficult, but by utilizing what you have in the cloud with all of this modern tooling and native cloud tooling, um, you can use the, the, the APIs to actually connect the two worlds together that while working with the teams and have really good domain knowledge in those areas, which is extremely important. And then, then you can actually start solving these really critical problems in your, uh, on-prem or legacy environment by using cloud technologies. So over time, this might take like month or even years, but, but you continuously delivering value, right? And over time you start transitioning more and more of these things and lifted them lifted by space into the cloud to deliver the most valuable stuff. So it is really important to think about how do we work with other teams, uh, to kind of bridge this together, uh, to move on, um, with the same vision, uh, how, how to get transitioned into the cloud over time.

00:23:24

Thanks, Richard. I think that's such an amazing explanation there. I think of that, I think just to bring that to life a little bit, if I think back to the times Prego cloud in this company as a product manager, it was almost like you were a little bit in the dark and there were many handshakes with other people. Whereas now we can be having alerts come into slack, we can pop into a dashboard. And as a product manager, we can instantly see what the problem is. And within like five minutes, we know the issue in somebody can be working on it. And I guess it's that notion of focus, flow, and joy. We can see the change, we can make the improvement, we can ship it without having to go to other people and fill the forms in. So I just wanted to cover a little bit on like how we work.

00:24:02

So we've taken a leap into various areas in the company, and there's kind of, there's probably like three areas that I probably talk about in, in where we work. So where data hasn't been used to drive decisions is, is an area where we've, we've jumped into to help those areas of the company start to use data better where data is being used, but it's historic data. Um, like what, how did we do not like predicting what is the next thing that could happen? So we've been shifting to like a world where we've got real time decisions in production that could be from everything from like pricing or, um, recommendations, and then other areas where you think we've grown over time. Like, there's a lot of incumbent third parties that we work with. And whilst we didn't have this Google cloud platform in the company or this amazing talent, they did things that we can now do.

00:24:50

So we're starting to look at some of those areas and see, where can we actually like reduce cost and bring that work in house and excite our people with these brilliant problems to solve some of the things like in terms of like how we work, it adapts for the work. So here's some of the things that we do, like as a team and these won't, you know, you'll all really doing these things anyway, but like in one instance, we could be working on a data product. That's got like that build, measure, learn, um, lean startup approach to it, where we're constantly iterating and learning. And in another sense, we could be working on a project so we could work with an area of the company. Let's say, I dunno if we were working with like the network area of the company. So deal with, with all like the fiber and everything that goes under the, under the streets, into people's houses to give them wifi, we could be working with those people, like a project for like two months, for example, that project could be around, like, how can we predict X, Y, Z, and put tools in the hands of others to be able to, you know, get to things faster, or it could be, um, to spin up a pro um, like a proof concept.

00:25:49

So if you think about us, we all around the UK, what we're trying to do is get the company to use data and then test what we're building in like regions. So we could choose to work in Birmingham. Um, we could choose to work in London and we could break that up into areas and start to run. Um, some of the data products we're building and test them, um, out in the wild, if you will cause that's where you're gonna learn the best. And then we could scale up. So I won't go through all this stuff, but like, I think the really, really massive thing for me is collaborating with the expert. So on Richard's side, it's more with the people that are deep down in the on-prem systems and the data for my team. I guess it's about being translators between the technical teams, also the company.

00:26:30

So spending time with those commercial managers, spending time with those people in the operations that are on the front line, talking to customers and getting out in a truck from time to time and visiting our customers. Um, we work in cross functional teams and we have particular problem areas that we focus on or, or domains of the business. So we typically have like a data product manager assigned to a domain with, um, a cross-functional team of like data science, data engineering analyst, et cetera, that focus on a common problem. And they could be working on a product that's basically always on, or they could also take mini little projects in as well. We speak often and we prep plan frequently. Um, we get together quite a lot, whether it be remote or in person. Um, and we kind of in the last like 12 months, trying to bring that product, thinking into advanced analytics and data science and getting people thinking about like the north style, like where do we wanna get to?

00:27:21

And then how do we take baby steps to get there? Because often I think if you don't set that kind of like north star, you end up veering off to the right and not necessarily going in the right direction in terms of our outcomes. So I guess what have we achieved? We've achieved quite a lot in a short time, which is awesome. Um, it can be tiring at times. Um, we've definitely got less handshakes. Um, there's less handoff to other teams and it's the first time in the company where I've seen the delight of like a data scientist when they've had an idea for something they've coded something. And then one of the engineers is like, it's been deployed within like hours. It's like, it's just like amazing to see. Um, there's less forms to fill in that there are still forms, but there are less forms to fill in, um, in certain areas where you're working with the business.

00:28:09

Um, and we're learning faster than before and, and, and gaining trust. And I think that's a really, really big one for us. Like at the beginning of the journey, there was a lot of work. Um, me and my team had to be around at hearts and minds. And because we'd not done some of this stuff within the estate before, it was like, yeah, okay, you've done that in another company, but we are not that company. So we had to kind of like start small release things, get trust from people. And now, like we've got people queuing up at the door to do things with us, which is, which is awesome, but scary as well. Um, there's the stuff we've been doing around our product recommendations, which has been really interesting, um, and kind of like driving towards that data democratization around the organization to get in everybody in every corner to feel comfortable with data, but to get them to feel comfortable with data driven decisions.

00:28:57

If you think about pricing, for example, that's quite a sensitive area. And if you're starting to have real time decisions happening, you've got to get the humans to kind of step away a little bit. So you've got to try and get them to trust you in what you're doing. And you can do that by proving the quality of your data and the results that you're achieving. I wish I could tell you more, um, the company views it as a valuable competitive advantage, um, which is a fantastic, um, sign that we're creating value for the people who matter, um, always open for like conversations outside of the conference and stuff. So, you know, do get in touch if you wanna chat about anything in particular, your experience, any of the problems. So I guess, like talking about the value they create and the impact that we're having, um, here's a little note from our CEO looks, um, he's an absolute legend.

00:29:42

Um, and for the team to see this like across, like, I think it went across our workplace, it was literally everywhere. You could look, it was, um, and having this like outward communication, even in like LinkedIn, we don't really post much there, but the company was shouting about this stuff, which is great for our hiring, right. But looked sent a message around to us. And he was like, congratulations team. This is a shining example of high performing team play and a huge achievement for all of us at Virgin media. O two, for me, this is proof that we are becoming a game changer in the industry using innovative technology and new tools to support our millions of customers. We're on a mission to upgrade the UK and become a digital first company. So this recognition for Virgin media, oh two and specifically the teams involved is incredible.

00:30:23

I mean, this went down like great. Like we're quite, we're relatively flat and we've got really open leadership team, but I don't care what anybody says. I guess if the CEO comes and congratulate you as a team, that's like a massive win. Um, so that was awesome. And we've had tons and tons of other feedback from around the company where the work that we're doing is having a really great impact, whether it's making someone's life easier, giving them things better, faster, or they're learning more it's yet been pretty incredible. So I'm gonna hand over to Richard, Richard's gonna talk to you about some of the obstacles that still remain on this journey, cuz we are only at the very beginning, I would say.

00:31:00

Yeah, thanks lot, Nate. Uh, I can't agree more so it's not like things already been done. Uh, all this fancy stuff is out of the way. Then we, we, we, you know, we, we are moving to the cloud journeys or finished. It's not like that. It's we are we're as Ette. You mentioned, we are at the beginning of this journey, we're embracing more adoptions and trying to, uh, gather, you know, get a lot more, uh, teams onboarded working on this together. And then one of the key things I wanna mention here is, is very difficult, but it's so rewarding at the same time is to scale the whole company. So obviously some of the teams are involved in here are kind of more pioneers and trying, failing a bit more at the very beginning, but it's very important to, uh, to scale the whole company to do this.

00:31:44

And that is like a really diff difficult process. Um, so because there's lots of new ways of working, it's not just about the technology we choose. It's also about the way we work. And I think, uh, one of the really toughest things I think many would agree is hiring. It is not easy to hire at all. Um, and what is even harder is retain the people you hire. So that is, I think pretty much like challenge. I hear from many, many different friends and companies in different areas that is really, really difficult to do. Um, so, but I think at the same time, it's also important for us to think, okay, uh, we must be doing something right, right. When people kind of joining us, uh, and then, but it's more important to work with, you know, the people you hire as, as permanent employees in the company to grow them.

00:32:35

So when working with, uh, with, with, with, with people, you kind of in the same team, you, it's not all about, you know, just get something delivered and then one followed by the next one. It might feel like you kind, you keep hitting deadlines, but it's not very, uh, efficient way to grow because people need skills, right? They, they work on things, but they also the time to calm down and do some trainings and to, to take a break, to have her hackathon together, to bring people together, to in be innovative, right, try something, you know, sometimes when you don't have time, maybe not have enough time to solve this problem with normal work time, but then you bring each other together in a, in a, you know, hackathon in an innovative day where you bring so much more ideas together to solve problems, then you probably even wouldn't even imagine you would've solved on a daily daily basis. Um, so I think that's, that's really kind of the, the, the key part. And there's also, um, part of the job is kind of to influence a much wider group of people to change together, to work together. I think as we mentioned, we are at the very beginning of this journey. So there are quite a lot of difficult obstacles to overcome, but I can only say we're very confident and we enjoy it. <laugh>

00:33:47

I think Richard's on that one. I think it, it feels like, it means so much more when you're in a big company. Um, and it's hard and you deliver, it's like big cheery moments. Um, I just wanted you to end on, this is one of my favorite quotes of 2021, I think came across this one. Um, and it's from Simon Sinek and this is kind of the ethos that we kind of stand by. When we starting to look at building something, we kind of say to ourselves, what's the fastest simplest thing we could do with the highest probability of success, build that. And we kind of enforcing this into the teams rather than trying to boil the ocean. Like how do you get to market the fastest with the simplest thing? And it might not, not be the most amazing thing you've ever built in your life, but you get in there and you're learning.

00:34:29

And like Elon must say is like, just get me the data and let me learn. So I think for us, like we are working within the current business model, but we're also looking at other ways we pivot on that business model. Um, and I think some really great examples, we try and inspire people around the company with that is you've got Tesla with the cars, but now there's batteries to power the house. You've got like Netflix replace the DVD. Um, and now they're making award winning content. And then finally Amazon like came online to be a bookstore. And now look at them, they've got like grocery stores and everything, um, and like prime delivery. So all these companies have used technology and data to kind of pivot themselves and they've gone to market with something that's got a great product market fit, and then they've evolved it over time. So I think when you're operating as a tech company, you benefit from speed to market pivoting, quickly try new things, and your company has the ability to, to go past the unimagined vision. That's what I wanted to leave you with there today. And yeah, just thank you so much for listening. Um, we'll be on slack. Um, we're also on other channels. You can hit us upon like LinkedIn and Twitter, but yeah, we'd love to, um, carry on the conversations.

00:35:37

Yeah. Thank you very much. I think it'd be, it was amazing, like having this opportunity to share our journey and share experiences. Thank you.

00:35:46

Thank you, Sade and Richard. So shortly after they recorded their session, they asked their CEO Lu Schuller. If he'd be willing to record a video that we could play for this conference <laugh> and here is a result

00:36:02

Congrat relations team winning the Google death ops award, such a proof for high performing team play here in verge media too. We so proud about it. And for me, that is a proof that we are really transforming to a digital first customer using state-of-the-art technology, different ways of working and doing so much great stuff for our customers. We are on the way to upgrade the UK. We are changing quickly and winning this award makes me so proud and so confident we are on the right path. See you soon.