 Thank you for that kind introduction. I think what it boils down to is I'm probably one of the biggest data nerds in the room. And what I'm here to talk to you today about is successful data management in the era of dark data. So one of the questions I get, particularly when I'm presenting at something quite forward-thinking like a data science convention like this, is, well, why are you still talking about data management? Sure, we've got that nailed. We have methodologies, we have frameworks, we have the data management body of knowledge. And the problem is, we're in a very, very different place to when those methodologies were invented 10, 20, 30, even 40 years ago. By 2020, the data universe will be 44 zettabytes in size. That is 44 sextillion gigabytes of data. Most of that is actually going to be unstructured, 80% of it. So if you cast your mind back to data management practices 40 years ago, most data was held in relational databases. It was very easy to manage, to control, to check the quality of and to store. We're not in that space anymore. We're in the age of big, enormous data and data lakes. A lot of you will have seen, and Paul alluded to the fact that data is growing at a huge rate. And even in the last couple of years, we've created more data than we ever had in previous history of time. That's only going to get worse. So by 2020, 44 zettabytes of data. By 2025, we're up to about 123 zettabytes of data. So this exponential growth is going to continue. One of the questions I get a lot is, why do we care? Data is the new oil. It's brilliant for us. The more we create and the more we consume, the greater insight we'll have and the better things will be. The problem is the ability of organizations to actually analyze the data is far, far less than their ability to gather and source this information. If you think about it, if you're pulling in all this data and it's your personal data, it's your commercially sensitive data, your intellectual property, your financial data, you really don't have the ability to analyze it the way you do to source it. So you have a huge, untapped resource and a vast wave of unknown data. And what we're calling this data now these days is dark. So what is dark data? So dark data according to Gartner coined the term is the information assets that organizations collect, process and store during regular business activities, but that they completely fail to use for other purposes. So for example, analytics, business relationship monetizing, or business relationships and direct monetizing. But it's bigger than that. Dark data isn't just this unknown massive data over in the corner. It's also an enormous source of risk for an organization. So dark data brings four major risks to it. There's a security risk. So if you think about, if you don't know what data you have, you won't know how to appropriately secure it. There's a non-compliance risk. I hate mentioning GDPR at the moment. I am GDPR out, but there's a huge amount of personal data in the dark data landscape and an organization. And you're probably not compliant with GDPR if you don't know where this data is and how to treat it. There's also the risk of inadvertent disclosure if you haven't protected the data appropriately. And then finally, there's a risk associated with the cost of storing so much data. This is one that tends to get soft-pedaled in an awful lot by organizations. I keep hearing, oh, you know, the cost of data storage is going down and down. It's much cheaper. It's much easier for us as data professionals to get more space for the data we want to hold. But cumulatively, across EMEA, by 2020, the storage cost per data is going to be €891 billion. It's an awful lot of money that could be probably better spent elsewhere rather than storing information an organization might not need. And the reason they might not need it is because it's probably very little value in it. So in a typical breakdown of organizational data, and this is from 2015, it's probably gotten a little worse since, 14% of data within an organization is business critical. 32% of it is the stuff that we know traditionally is rot, our redundant, obsolete and trivial data. The remaining 54% of data is dark. It could be business critical, it could be rotten, but we don't know because we don't understand it. And it could be anywhere. So it could be in our servers, in our ungoverned areas of data warehouses, in archive boxes off-site, in handheld or endpoint devices that people use, we just don't know. And that's an enormous source of risk for an organization. No more so than because most organizations still expect to use customer data to drive the majority of their sales decisions by 2020. How can you do that if you don't know where or what over half of your data is? Another challenge is that data is going to start being valued as a tangible asset by organizations. In fact, the accounting boards in the US are now looking at ways of actually valuing data on a balance sheet as a tangible asset rather than an intangible asset. Organizations that don't fully understand their data assets are going to be miles behind the curve here. We've also seen recently some data assets being valued for merger and acquisition purposes. They've been actually able to place a value on a customer database. So if you have valuable data lurking somewhere in your organization but you don't understand it or know where it is, you can't value it. And again, that presents an enormous amount of risk. So I'm not here to talk to you today about what we're going to do with the vast amount of data that's already been accumulated by organizations. But what I am here today to talk to you about is how do we stop this exponential growth of data that we may not need? So if you think back to the 44 zettabytes to 123 zettabytes, how much of that do you actually want to consume into your organization? And what value can it deliver? So there's a few different reasons why unstructured data is ballooning and structured data as well. But there are three that I've seen an awful lot in clients recently. The first one is omnichannel communications. The second is social media listening. And the third is the internet of things. I was going to drill into those a little bit. So omnichannel experience is the holy grail for the majority of organizations and particularly for the majority of clients I work with. So if you're a bank or if you're a retailer, you want customers of the same unified brand experience, no matter what channel they interact with. That could be mobile. It could be online communication, chat, email, writing letters. You should be able to perceive the brand the same way no matter what channel you use. But 78% of customers say that this isn't the case. Some of the main reasons that McKinsey have identified are siloed organizations and a lack of good customer analytics. But we have to make sure that the data strategy is aligned to the marketing strategy. And this is where all those data professionals come in. So if your organization doesn't have a data strategy, look at it and know why. If your organization does make sure it's aligned to the overall corporate strategy and make sure it's aligned to the marketing strategy as well. We need to know what channel's best work for our customers. Because bear in mind all of this information or the majority of omnichannel information is unstructured. And then how do we manage it and how do we use it to drive a better experience for our customers? The next one then is social media listening. So this is the real trendy buzzword, again, particularly in the banks. We're going to pick on a lot in this presentation. But social media listening is seen as the holy grail for understanding customers. So you can use that information to understand your customers, drive decisions about the next products you're going to create, how you engage with your customers, how you present yourself as a brand. But it requires a consumption of an enormous amount of data. And often there's no clear path for how to convert the data into opportunities or revenue. If you think about it at the moment, Facebook is running at about 3 million posts a minute. Twitter, even though it's going down a bit, it's still at about half a million tweets a minute as well. And if you're looking for a very broad range of hashtags or a very broad range of keywords, that could mean a huge amount of information coming into your organization. There needs to be a social media strategy. And we need to go back to basics with this one as well. We need to be educating the business around the data lifecycle. So don't bring it into the organization until we've defined it and by defining it, we mean what we're going to do with it, how long we're going to keep it, how we're going to get rid of it, and who we're going to share it with. It can't just be this enormous blind consumption of data with a clear strategy and a clear value proposal at the end of it. And the third one, and this is the biggie, is the Internet of Things. So by 2020, there's going to be over 26 billion connected devices. So everything from my wearable to the robotic vacuum I have at home to my absolute horror, the Amazon echo my husband brought home the other day. But at the end of the day, there's so much data being generated from these endpoint devices that we're actually having to invent new technologies to keep up. So the whole concept around say fog and edge computing, where you have what's effectively a combination of a server and a gateway that stores data in its own right is something that's being used more and more. And by 2025, 45% of the world's data is going to be moving closer to the edge. Most organizations I work don't even have a cloud strategy and the ones that do don't even have it enforced. So what we need to do is make sure that we're keeping abreast of all the new technologies and pushing that message and making sure that our stakeholders within the organization know the new technologies that are coming down the track and know how to prepare for them and which ones to leverage. Now, there is some success stories from dark data. So I don't want to leave you with the thought that all dark data is rubbish. There is some value in it. And like Susan was talking about earlier, there are some treasures that you can find. A really good case study is NASA. So NASA sent up a load of Nimble satellites into the atmosphere in the 1960s and they gathered a huge amount of environmental data, all held obviously in the old machine format, paper and so on. And this was nearly lost until somebody realized the value of it. And the value is that this has actually proved climate change. So all the aerial photos of the ice and all the different kind of environmental data they were gathering, sorry, Donald Trump, actually proved that climate change is happening and that it's speeding up. So this is an example of surfacing dark data and bringing it into the light. And it's a fascinating case study, if any of you want to read up on it, there's a number of white papers online. But what I want you to think about leaving this presentation is that for every one of these, there's a huge amount of rubbish sitting in your organization. And as we've talked about, and as a lot of the presenters have said, if you don't have clean data and you don't have good data and you don't know what it is, you're never going to get the analytics and the insight you want. Susan McKeever at the very beginning of one of the presentations said, the most important thing for an organization is to know thyself when it comes to data. And that's absolutely what we need to do. So I'm going to finish up with a call to action for you all. So all too often data has been seen as a supporting function in organizations. Data would be seen as IT, it's a second line defense role. It's not front and center at the table making decisions. That has to change. IBM had predicted that by 2015, 25% of financial organizations would have a CDO and by 2020, 75% of all organizations would have a CDO. In Ireland, we are maddeningly behind that curve. And given that Google described us as the data capital of Europe, that's a big problem for us. So we need to take a leadership role. Here's something I'd encourage you all to read, which is the leader's data manifesto. It's written by a number of leading lights in the data management industry, including John Ladly and Tom Redmond. And basically what it says is we can get so much from the information we have. It can change the human condition, it can improve healthcare, it can make us wealthier, it can make us happier if we use data the right way. But very few organizations know even what data they have back to the dark data and even less what to do with it. So as professionals, we should be the ones to provocate with regards to data. We should be pushing the agenda and saying, look, this is a new way of thinking about data. The old methodologies that we had don't work as well as they should. What can we do to improve the case? How can we turn data into a true organization last set? And once we do that, we're in a much, much better place. And as Paul said, only the truly data-driven organizations are going to survive and thrive in the future. And we all have a huge part to play on that. So what I'd like to do is leave you with, please read the Leader's Data Manifesto, have a think about the dark data within your organization and what you can do about it. And thank you very much. Be around for questions, except for GDPR in the break. Thank you.