 So I'm coming at this subject from a practitioner point of view as someone who has used data in their daily role, very much before I started working on them, using it now to encourage people in our organization, in operations, to use data to become more effective in their jobs. Perfect. So I have a couple of schematics here. And the top left there is a quote from Picasso. Computers are useless. They only give us answers. So again, it's the question that's important. An example of the absurdity of the answer being everything is the Douglas Adams quote there on the top right-hand corner about the answer to life, the universe, and everything is 42. What is the question is the important piece. So data with insight gives you the ability to make decisions with clarity. And if we look at the bottom, I think it's left-hand side there, that's taken from LinkedIn. And we talk about the value of data. LinkedIn and their graph, Microsoft considered that to be worth $26 billion, I think it was two years ago, to buy. And ultimately, they're buying the data that they're storing. So they look about insight, which comes from the data, and asking the questions of the data, along with the instinct of the professionals. And this is what's really crucial. It's not just the number. It's what we do with it, which is crucial. I mean, I think anytime you're given a set of numbers by a professional that came out of an AI engine or a bot told you this, the question you should ask is, so what? That's crucial. What does it mean to me and my role? So I'm sure many people have seen this Venn diagram before. It shows how the sweet spot for what we do in data analytics and data science is around, you've got the computing part, you've got the statistics, but then you have the domain expertise. So you need to have the ability to ask the questions of the data to get the actionable insight. So using a bit of cribbing here from some R code from the tidy verse, I'm sure some people are aware of that, data and then literacy and then insight gives action. So always holistically look at the bigger picture. It's not just the data, it's what you do with it. Data literacy is becoming so important that Gartner recently came out with a new model instead of using their people, processes and technology, they put data right at the center of that. With the quotation, data is useless without the skills to analyze it, so I think that's pretty self-evident and very much a theme of what I do on a daily basis. So why do we care about data literacy? Sure we can get people to tell us what the answer is. Well, we don't have the right question, we don't know what the right answer is. So the second dem in quote of the day is without data, you're just another person with an opinion, fair enough. So data will go to greater insight, better performance and it is the future. If you look here, we've got Sacha and Nadella there standing there and the cloud and we've got IoT and smart devices that are going to be more persuasive in our lives going forward, feeding it into cloud, massive amount of data, what do we do with it? How do we make it worthwhile to our experience? So that's the key elements of what I'm looking to do. So how do we improve literacy in our organizations? So we need, it's at all levels and this is my role in Microsoft currently is around data evangelism and developing a data culture because as a rule of thumb, how many people in a modern workforce are data literate? So I have a rule of thumb that I've been using, Microsoft get feedback from 300 to 400 million activated copies of Excel and my rule of thumb is if someone's able to do a pivot table in Excel, they're fairly data literate. So we now have 8% of those activated copies feeding telemetry back to Microsoft, which are doing pivot tables. So using that rule of thumb, only 8% of the working population who are using Excel would be, I would deem as being literate. So how do we improve that literacy? It requires leadership where any decision being made, any meeting being convened to make decisions. Senior leadership needs to look at what are the numbers tell us, prove what you're saying to us. Culture, every role should have a data element in it. Whether this is if we're setting annual commitments in place for our staff, some number that they're going to run against that they can say, look, I did a good job this year because I met 150 potential leads this year and that is up from last year. So some sort of number that people can run against is hugely important. In our organizations, we talk to, Luisa's talk there about this vast amount of data that we are sitting on. Is it redundant? Could it be used? Could it make us more effective? So how do we ensure that we treat data as a core business competency and a capability for us in the future? So it's not just sitting in a silo that is hugely difficult to access, that our people have the ability to self-serve and interact with this data and get what they want out of that data. So how do we remove the buyers to that data? Ensuring, obviously, that we have the right governance procedures in place. People, and this can be very controversial. We look at our existing workforce and you say, do we train them? And encourage them to do data literacy training. This year, for the first year in operations, we are going to do a level 100 data literacy course. So it's a very basic course. It's an introduction for everyone who will sit to the course and there's about 600 people who will do this course. It'll be online webinar type course. It's also from a cultural point of view that we as Microsoft are saying, guess what? We think this is important. We're prepared to invest money to develop this course and our people's time for them to take this course and achieve their accreditation on that. And then, so there is that, do we train people? But then, do we recruit for data competence skills as we continue to refresh our workforce going forward? So should there be a data element in all roles that we're recruiting for? We currently recruit a lot of, look, we have core competencies of language skills that are required, software skills, but do we need some evidence that people can understand data and use data in their daily lives? So this hasn't, it's quite controversial because you're asking HR to upturn their existing competencies that they're looking to bring in staff. And then data, we have to, itself, we have to make it accessible. And we have to train people that they can interact with the data. So, you know, we have in Microsoft a tool called Power BI which is really, it's the tip of the spear as I think Nadella said, it's the tip of the spear of our productivity strategy. He said that about three years ago. And really, it's a way that it's very simple and intuitive to use, it's very interactive, but we need to have it connected to the right data so people can say, why do I have this customer churn? Why are our partners voting us down in terms of satisfaction? Why did we have a dip in performance in July? And they're able to go and interact with whatever data we can provide with them and come to the solutions and the answers, ask the right questions and get the right answers. So this is quite theoretical at this point. This is the first year we're getting some data literacy training out there. But I want to, in this next slide, and this would be the final slide, what are the benefits of data literacy? One of the core requirements of the Microsoft operations function here in Ireland is to make sure that we are able to book all the revenue that comes into us on a quarterly basis. And we have a huge hockey stick of orders that come in, not just the last two days, but in the last six hours of every quarter. So how do we ensure that we ensure we have no revenue left on the table, which is a big no-no from our leaders in terms of their metrics to Wall Street to making sure that we are financially efficient and effective in how we operate our businesses. But how do we ensure we hit that number, which can be in the billions, but run our organization very effectively by only having the right outsourced staff levels required for each period of time? You know, we can't sit with a massive organization for 90 days. They're only busy for the last six hours of every quarter. So how do we ensure that we manpower resource ourselves that we are the most effective operation that we can be, but ensure that we hit this revenue left on table, metric being zero. So we used a very, we had some data scientists in Redmond and they came up with using our forecasting calculations throughout R and they ran a query that took about two days on a server to get this number, which said these are the staffing resources we need by day throughout the 90 day period. And we do that every quarter now. And that means that we have really fantastic vendor capacity planning now. We also ensure that we hit our revenue processing targets. So this means that Ireland and Microsoft operations are seen as a reliable, competent organization to process all this revenue for the larger organization. So that's a huge advantage in that, but we're also doing it in a very cost effective manner. And this was actually, took the award last year in the data science awards for enterprise. So that was recognized by your peers in the data science community as being a significant achievement by an organization. So I'll just leave you with that now. And just to sum up what I've been saying is the vast majority of people we work with, they're not going to be nerds and data geeks as such. They're going to be doing their job and they have other parameters and other important things to worry about. They need to be able to ask questions of the data and get the answers that make them be more effective, give them insightful and actual insight that they can move forward in their operations. So that's where I'm coming from. We don't need to be the best data people, but we need to make sure our data is usable by the companies that we support. Thank you very much.