 Thank you very much for that, Cronin, and good morning, everybody. I think I'm honoured to have the first speak, so thank you very much for that. As Cronin mentioned, so I'm Kleene Meany and I'm head of subscriptions and reader insights at the Irish Times. That's a role that wouldn't have existed four or five years ago, really, in our industry. And that's really the theme of what I'd like to talk about today. The media industry in particular is one that is undergoing and continues to undergo a lot of change. And it's really about how we introduced a culture of data into the organisation to help move that change process along. In terms of the industry itself, our audience base has really moved from this. So some of you probably got the bus or the train or the Lewis in here this morning. Back in the day, we would have seen much more printed newspapers in use, in circulation, in readership. And when you came in this morning, it was probably more like everybody on their phones, everybody on their devices in that sense. So in terms of that industry, we're really looking to move along with where our audience is and what they're looking for in that sense. But I think what's core is that the core of the Irish Times is quality journalism. And that remains the same, so our core offering is the same and it's really about how we reach the audience and how we can continue to reach our audience and add value in that sense of it. A little history of the Irish Times. It's almost 160 years old and it actually was one of the first newspaper companies in the UK and Ireland to launch a website in the 90s. So we do have a bit of a history in being a trailblazer in that sense of it. But what we started out about four years ago now, the area of big data and media was really hotting up. And we set about looking at what data we had available to us and how we could put that to use. One thing that was key for us was that we did not want to be jumping on the big data bandwagon for the sake of it. We didn't want to be analysing data for the sake of it. It had to drive insight for the company. It had to add value in that sense. So for us it was really about giving people in the organisation the information they needed to make informed decisions. So that was really our mission, was giving them that information for that purpose. Before we set up the analytics function, I suppose just to give you a sense of the Irish Times back then, the company was very much focused towards the printed newspaper and publishing that every day. So the structure of the day and the organisation and shifts was led towards getting a printed newspaper out at the end of the day. So we published all our online articles on IrishTimes.com. The majority of them were published at one o'clock in the morning after the newspaper had been put to bed and then the content went online. The first editorial conference of the day was on at 12 noon in the afternoon. And the editorial conference is the first meeting of the day where the editors of the various departments, news, business, sports get together and discuss the big stories of the day and how we're going to treat them as the day goes on. We didn't have an analytics function. Everything was done probably more so on a monthly basis. All content on IrishTimes.com back then was freely available. And we didn't know how... We knew readership figures, but we didn't know, say, who was reading this article on the top left corner of page five or how many people were reading them. So we didn't have much insight in terms of the content readership at that point. So when we set about building the analytics function, we looked at the data we had and how we could structure it. That was just a technical challenge to overcome, but really it was about what did people need, what information did they need from us? The stakeholders for us and the customers of our department, the analytics team is a centralized function in the IrishTimes. It's not just editorial, it's senior management, it's customer care, it's marketing, it's advertising. These are all our stakeholders and we wanted a company-wide approach to analytics and what data was needed for that. So a couple of things that we did that I would recommend, I suppose, in that sense. One is to ask the questions of the stakeholders of what decisions are they making? What decisions are they making on a daily, weekly, monthly basis? It's not about asking them the technical questions of what data do they need. It's really listening to what it is that decisions that they're making and how we can help them in that sense. What leads on in core to that is hiring the right people. So the Holy Grail is hiring technical analysts who have great communication skills. And sometimes you need to build a hybrid team that has different people with different skill sets because communication is absolutely key when bringing people along and having conversations with less technical stakeholders in the organization. Also key is finding the evangelists within the organization. So a couple of people say on the editorial side who got the data, who understood how it could be put to use, and they can help spread your message. So analytics shouldn't be a silo. It can't work in isolation. It needs to be working with other departments. So find your evangelists to help spread that word. We also at the start got a couple of quick wins. So it's not really by going to the organization and say we need all this investment, we need to set up this analytics function and this technology. You need a few quick wins to show people, to win people over and finding what would peak the interests of various people. Find a little data and know good for them and that'll help win them over because there can be skepticism around the use of data and how it can be put to use. Building trust with the stakeholders is also key and again, this might be more specific to the media industry. But we're talking about journalists and their own personal work. This isn't a product on shelves. There can be skepticism around data. Data can be the stick to beat us with. So it's very important to build that trust with the organization and build relationships with them in that sense. And the last thing I'd say just on our side, the mission for us was to be a data-informed organization and not data-led and that is key that the editorial integrity of the Irish Times is key. And say if we were a data-led organization, the Irish Times homepage that you'd see would be completely driven by an algorithm of the most popular stories and that's not what our readers come to us for. They come for our editorial judgment on the important stories of the day. So that's where we aim to give people the information they need to make the important decisions and they can choose how they use that information then. So the organization now, after all of that, I would say is reader obsessed, but in a good way and in a healthy way. We put the reader at the center of everything that we do organization-wide. So in terms of who our journalists are writing for in terms of our advertising, our marketing and other departments. So we're really looking at who and where are our readers? What are they reading? When and what devices? And for what the data can't tell us, we enhance that with research. So asking them the whys, why do they do that? What do they want? What would they like to see more or less of? So really is that reader at the center of everything that we're doing. So a couple of examples of the data in use, a couple of top-line examples from the editorial side. When we look at when and where our readers are coming to our site, this graph has shown a typical day. The blue line is desktop traffic and the red line is mobile traffic. So what you'll see on that is that earlier in the morning, mobile traffic starts coming through. So this is as soon as people wake up, they're reached for the bedside locker, they're on their phone straight away. They're commuting into the office on their mobile and then they're hitting their desk onto desktop, lunchtime spike on desktop, going home in the evening. And again, we have this evening lift, lift that usually peaks about 10 o'clock at night. You can see from that that when we were publishing all our articles at one o'clock in the morning, that wasn't the right time to maximize our audience and our readership of that content. So this helps us with our strategy around when we publish and promote our content and what content people would like at different hours of the day. A typical Saturday is very much mobile-driven, morning spike and then people go about their business. And on a Sunday, similar morning spike, but then we have this evening lift as well and this is what we call the Glen Row Factor, or when you don't have your homework done and you hear the Glen Row team tune, getting ready for the week ahead. And we find things like healthy eating recipes work at this time because people have had their takeaway on the Saturday night or they've gone all out and they're like, I'm gonna be healthy next week, this is the start of it. So that kind of works well on a Sunday. So again, this is content that we would have anyway, but it's about how we maximize that content, putting it to the right audience at the right time, right place in the format that they needed for. Another quick example of it in use in terms of real-time monitoring of content as opposed to it being monthly or weekly or daily. An announcement was made last year from Buckingham Palace that Prince Philip was standing down from public engagements. This was of interest to our readers, but it was a sort of a worn and done story when you read it, you knew what the story was. We could see that that was doing well. So another story then, an article was published with some of the more controversial remarks that Prince Philip has made in his time. And how that manifests itself for our newsroom team is the top graph here is showing the traffic and the sources from that first article, which was very much early in the morning, you knew the story and that was it. The second graph then shows how we continued on that story throughout the day. I knew that that was of interest and followed up with it. So that helped to maximize the life of that story. And another example then, again, with this changed mindset from a print focus with print, it's very much a one-day story and then you're moving on to the next and that doesn't work in the same way from an online context. Example here of the Pope's visit to Ireland and everything you need to know. We published that article about two and a half weeks before the Pope visited. The graph on the right shows the daily traffic to that article. So that article had continued readership right through from the two and a half weeks beforehand, peaking to the time of his visit. It would have been the wrong decision to just publish that the week that he was coming. So it's really about knowing and seeing what is of interest to readers and feeding that need for them in that sense. So as opposed now, we're really an organization where we still have the printed newspaper. It's very important to us. But as an example here, when Enda Kenny stepped down or said he was stepping down from leader Fina Gail, we would have on the right here, the mobile homepage or optimized, newly developed mobile website that is optimized for speed. We would have our political journalist doing a piece to camera about the leadership contest. We would have an inside politics newsletter, an inside politics podcast discussing that, social promotion, so Facebook and Twitter posts. We also had a leadership tracker where we asked the TDs who they were voting for out of Simon Kovni and Leo Radker and we had this ongoing running, which is old school journalism asking the TDs, but we did it in an online context and kept this going for our readers. And all of that alongside doing a four page supplement and print, which we would do. So we are asking a lot of our journalists now that they do more than writing words in that context. And we as an analytics function need to be there to tell them what's working and what's not working. We learn from this with the presidential election coming up and we'll be learning, taking the insights from these things as to what works well in that sense as well. So I suppose now as an organization from that shift to where we didn't have an analytics function, we had 12 o'clock meeting in the afternoon about editorial and we had freely available content. Now it is what I would call the democratization of data. So we have daily reports going to the entire organization company-wide to keep everybody in the loop informed as to what people are reading and the different patterns like that. In terms of democratization of data, I don't believe that analytics should be the key, just have the keys to the data. I feel that it's important that it is shared out and that people don't feel that it's a big secret. The importance there is putting it in a structure and accessible format that they can access it and that they can understand it without them being overwhelmed by it. That 12 noon conference, editorial conference is now at eight o'clock in the morning. So our motto is win the morning and own the day. If we have a good start of the day, we're on to a good thing and the analytics team leads out that editorial conference around how yesterday, how this morning is doing. And that's a big shift in mindset in the organization as well. We've also developed reporter dashboards so that our reporters themselves can see the performance of their own content and giving it to them again in a way that is accessible for them and we're about to release an app in the next couple of weeks on that as well. And I suppose most importantly, we launched a subscription model just over three and a half years ago. So in that time and with these changes, we have managed to grow, consistently grow our traffic, our readership to IrishTimes.com while implementing a paywall, a metered paywall model. And we now have 85,000 subscribers on IrishTimes.com as well. So the result for us is continuing and growing our user base, having a subscription service with 85,000 subscribers in it and really turning around that culture and building a culture within the organization that's 160 years old where there is a serious appetite for data. Some might say too much. My team are inundated with requests for data but it's a really positive take up of people of the use of data and how it can help them to do their job. So that's it from me. Very good, thank you very much.