 So, our first and our keynote speaker this morning is Ron Toledo. He is a director on the governing board of the open group. He is based in the Netherlands with Cap Gemini. He's a senior vice president, Cap Gemini's group, CTO Network, and advisory and architecture lead with Cap Gemini's global insights and data practice. I did try memorizing that but it didn't work. So, as I said, based in the Netherlands, his interest areas are kind of the data landscape, cloud IT strategy, enterprise architecture, DevOps, application rationalization, disruptive technology, and above all, radical simplification. So, he's going to explain to us how the industry needs to reassess both its architectural ways and its reference architecture to fully benefit the new data landscape. So, talking about big data, a big deal for open standards, please give a warm open group welcome. Ron Toledo. I hope you can all hear me well, Dustin. Yeah, it seems to be working. Thank you for memorizing my role descriptions because I wouldn't be able to reproduce that anyway, particularly not this early in the morning. So, thank you very much for that. I hope I'll memorize a little bit more of what I want to tell you. I might have some interesting stuff for you. Today, as we all seen, also with the new memberships, there's quite a lot of people interested in the Open Platform 3.0 initiative, and that's a good reason for it. To be quite honest, it's also not a secret that Kev Jen and I, when we rejoined as a Platinum member of the open group, the Open Platform 3.0 was one of the main reasons why we were particularly interested in rejoining at that level, and we still are. Having said that, we also feel that, of course, when you are involved in that, it's also time to contribute every now and then a little bit, and what I'm introducing to you today might be an indication of the type of evolving open standards we might be interested in in the context of Open Platform 3.0. So, Ellen already showed you, of course, the snapshot, and hopefully you've all gone in detail through it already, and right now, or right after the speech, are producing your feedback towards the forum, but I'll be back a little bit on where the forum is going and where we believe that some standards might be evolving, particularly in the area of big data. Big data is a big word. It's a bit of a fuzzy topic, but obviously everybody very much interested in it currently, and it's clearly also one of the big areas within Open Platform 3.0. And I think it's a nice thing to say if all of these forces would come together, including, of course, big data, there might be something magic happening. And actually I believe it's the fuel, it's the driving force behind what we've started to call a more and more digital transformation. Now, I don't necessarily like this word digital. To me, maybe it's my age or something, but to me it has an association with the 80s, in which we played pong or something like that, 8-bit sort of gaming, I'm not sure. It all sounds a bit retro to me by now in 2015, but having said that digital transformation is a big topic I find with many organizations right now, and I am happy to share a few words with you about our findings in the past few years around what drives digital transformation and when we start to understand the success factors behind digital transformation, we also start to realize what type of open standards we need to further drive that success, to drive that evolution towards becoming digital enterprises. Because I think there's the key, and every now and then we need to remind ourselves within the context of the open group we're not here just to have fun, although it sort of helps, but clearly we're interested in standards that help us to create boundary less information flow, right? So we're interested in creating the ultimate platform to drive companies to becoming digital enterprises. Well, one of the things that we've been publishing lately was together with MIT. We've been very fortunate to work together with MIT in the past almost four years now, interviewing and querying many hundreds of different big enterprises and trying to assess what made them digitally successful or not. And mind you, these were all big enterprises, right? So we're not looking at the startup ecosystem and doing some disruptive type of research. We were interested in the rest of us, the 85% of us, that are working in big, you know, big enterprises, big organizational structures, maybe also in the government, trying to understand what it means to be successful in digital transformation. And it's very fortunate to work together with a company like MIT, with a research institute like MIT, because you can imagine if Kevdem and I would approach clients and ask them, hey, we want to talk in depth with you about digital success. They would be like, so what are you trying to sell, right? That's sort of what would result. And in case of MIT calling you, usually you're like, yeah, I would be interested in talking at least a little bit with them. So that's what happens. MIT did a tremendous job in surveying all of that. And there were some very interesting results out of it. By the way, this is the book. It has a very nice color being Dutch. I would say it's quite a well-chosen color. It's also very remarkable. You can't miss it in any bookstore, which I think was exactly the intention. It's not exactly Harvard's business press, but still it's very notable if you see it. And there were a few very interesting findings that I would like to share with you first before we dive a little bit deeper into the platform of 3.0, but also the role of big data in it. First of all, we all acknowledge that digital transformation means that there is a next wave of technologies that actually drives a transformation of businesses. People often ask me, even yesterday when I was involved in another session somewhere in Madrid, they asked me, so what's exactly the difference between digital and technology? Because, you know, it sort of sounds related, or maybe it's even the same. And at least my answer yesterday was, I'm not sure if it's accurate, but I would say it's these set of technologies that are next-generation, and I would say that all the technologies we reckon to be part of this open platform 3.0 effort actually would be the next-generation technologies that combine together, create an impact that we've never seen so far at the enterprise level, right? So all of these next-generation technologies, then you combine them. They create a force behind transformation that we truly could call digital transformation. So if we're talking about digital transformation, it's the way that companies radically improve themselves or fully transform themselves using these next-generation technologies. So that sort of, as a baseline, is what we're interested in. And then what we found is a very interesting one, and I think it's very relevant to almost any standards development as well. We found that there were two dimensions of success. So we simply measured a little bit in terms of how successful these organizations were financially speaking. I'll be back in a few seconds on that. It's a very interesting result there as well. But what we found is that there are two dimensions to that success. First of all, there's no substitute for embracing the actual technologies. So this is bad news for any management consultant that believes that they can do without understanding and embracing new technologies with bad news. You need to understand, appreciate, even embrace these next generation of technologies. There's absolutely no substitute. There is no escaping from mastering these technologies. And it's interesting, right? Because a lot of people believe, I'm in transformation, I can do without technologies. It's not true. In digital transformation, these things are intimately entwined. It's impossible to decouple them. So even for the hardcore management consultant that doesn't even like to start up her or his PC, need to embrace technology at a very intimate level and understand what the different technologies are that shape that transformation. So that's the what. And it makes sense, right? And to be honest, I see a lot of companies right now that embrace these new technologies and they set up their innovation labs. They have their separate digital units. They all go forward. But actually they're not necessarily successful, at least not of the mid-term and the long-term because we found that the how, or as we call it in the book leadership capability is equally important or even more important. And this all pertains to establishing a top-down, driven leadership vision. It's creating an organizational governance that is different from what you used to have in order to leverage all this fancy digital stuff. It means mobilization of people at the individual level. It's nice that your CEO has this vision, the individual at the shop floor, at the working floor, needs to be mobilized as well. And the fourth one, extremely crucial, you need a sustained business technology platform because nothing worse than having a very enthusiastic CEO and very enthusiastic people at the floor that all want to drive digital and want to move forward and then technology would say it's impossible. Sorry, come back two years from now. We're not equipped to do it. And by the way, we're waiting for the requirements to build this and three years from now, I guarantee you, you will have it. That's not exactly the type of business technology platform you would need in order to succeed. So it's a big success factor if you have that equipped as well. So the thing over here is it's not only understanding the use cases and what we've seen in Open Platform 3.0 so far is that these use cases are crucial. We need to understand how to create value from this nexus of forces as Gardner would like to call it. But also we need to understand what it takes in terms of platform capabilities to create a sustained impact. And here we're not talking exactly about what specific technologies we're using. How can we create a sustained platform that bridges business and technology and helps you to answer to the needs, also the needs that we haven't identified so far. And that might arise in a few weeks or a few months from now. So these two combined are crucial and what we find to be quite honest is that the house is often much more underestimated. So a lot of companies like to pick up all the shiny new technologies and nobody in the business could care less. That's literally what I've seen. Big retailer just a few weeks ago in Canada, they had all the shiny stuff. They had more stuff in their labs than IBM and HP together, right? They've got it all. They bought it all. They were experimenting with it all. Nobody in the business even knew about it or could care less about it. And they were generally surprised that it wasn't picking up. But they just forgot to create that sustained platform between business and technology to drive things forward. Also the CEO couldn't care less, by the way. So that was sort of another problem as well for moving truly forward. We, by the way, categorized these typical organizations a little bit. It's a nice thing. And by the way, if you're interested, just Google for a leading digital, you will find a quick questionnaire. It's, you know, consultants like these type of things. It's a self-assessment. And very quickly, you can score a company in terms of where it is. As you can imagine, companies could score very high on their digital appetite. So embracing new technologies is the what, right? They could score high or low on that capability. And they could score high or low on the capability to transform themselves and create sustained change. And the nice thing about it is, well, if you score them, you can sort of prototype them, stereotype them a little bit, right? So obviously, fashionistas is a very interesting breed. I see them a lot in practice, particularly, for example, in retail or in Telco. You see a lot of these type of companies or in high tech. So they're embracing all of the new technologies. But organizationally speaking, they're not really picking up. So these are fashionistas. And on the other side of the equation, we have companies that are extremely good at establishing a vision and mobilizing a company. It's just that they don't care too much about technology. And they don't want to innovate too much. They don't want to be leaders of the pack in terms of adopting these new technologies together. And you would sort of describe them as conservatives. Then, of course, on the top right, that's where we all want to be. At least in the Western world, we want to be top right. And you could call these companies digital master. They've done it all. They are combining these two different powers. And then, of course, they're always beginners. From a consulting perspective, they're always interesting because there's so much more to gain. There's so much more to achieve while you're over there. And one thing I really wanted to share with you, although very short, but just briefly, there is a direct connection between where you are in terms of your digital journey and your financial results. So we excluded, of course, government from this, but you can imagine that it would have an entirely same impact in terms of achieving more with less. It's all a matter of being effective and productive and being able to grow your services. So the interesting thing is it does pay off if you're a fashionista if you want to grow markets here because you would be growing faster than average in your markets. Only problem being, as you can see, that is not exactly profitable. So you would essentially losing more money than average. And that would be the price you're paying as a finisher fashionista for being a market leader or at least growing your markets here. And on the other side of the equation, we're finding the conservatives, as you see at the bottom right, they are actually more profitable than average because they have a very well-managed business. So they know how to keep the margins and be profitable, only tiny little problem. They're losing markets here on a daily basis. So their percentage of the market will be shrinking. But for the rest, it's very profitable shrinking part of the market, right? And some companies feel comfortable with that. They realize that that part of the business is in a shrinking market to sustain for a few years, still being profitable, maybe creating some other business elsewhere to compensate for that. Or if nothing else simply die very peacefully and slowly, right? In all glory. And only if you combine these two things, as you can see, you're both better in terms of growth than any peer, but also much more profitable. Now, of course, some of you, I know some of you, from the University and stuff, you would argue, or even study statistics, which I'm terribly afraid of, but you would argue, of course, how the effect is the other way around. So imagine the digital leaders of this world, the Nike's, the Starbucks, the Berberies of this world, you know, all the famous examples. Of course, they are very profitable and they have a huge market share. So they have a lot of money so they can spend it on digital, of course. But what else to do anyway? So why not spend it on digital? So it makes sense. They're successful financially speaking so they're just spending their money on IT and digital. My answer always would be who cares? It doesn't matter what the direction is of the causality because if you all agree that companies like Nike are very well managed because they have excellent results, you probably also want to learn from their digital management practices, they seem to know how you manage something and how you innovate something. We all agree, given their performance, their financial performance, they're an interesting benchmark. So even if the causality would be reversed, which I don't believe, by the way, I don't think so. I think digital leads to more financial results than better financial results still would be interesting to learn from the masters and understand how they deal with their digital strategy as part of their overall management strategy. Yeah, because every now and then we all feel, I'm sure many of you feel the same, we are usually in IT or in business technology and every now and then you ask yourselves why are we doing this in the first place? Well maybe because actually the performance of companies does improve as a result of digital which I think until now in many cases was just a hypothesis. Yeah, probably it's useful to do something in IT and do something with technology and digital. But I think we sort of prove backed up by MIT that there is actually some sense in applying technology to business, which is probably good news, right? So what I will not dive in today is the what in terms of leading digital although it's interesting but what we found is there are three, essentially three big areas of the what so there's a lot of emphasis nowadays on applying digital to the customer experience which makes, you know, we all will recognize that probably there's going on a lot of startups around enhancing and improving the customer experience so it's one big area where a lot of these technologies are coming together to create something better but also internal operations so everything beyond the customer experience is very interesting could be the supply chain could be employee environment and all sorts of other things behind the customer experience and mind you nothing worse than having a superior customer experience and then the back office would have a clue about what's happening at the front end so this is a very bad demotivator for many clients so they need to be in sync and the third one quite interesting is new business models so here we're talking about disruption one of the big words of course in 2014 and 15 but reinventing business models as a result of all of these digital drivers and thinking about your business in a fundamentally different way is a third big area where we saw successful digital transformation all of that is the what how do you apply technologies to create new business and what type of technologies do you actually apply to achieve it and then the second area I already mentioned to you these are the this is the how and you see for crucial areas over there top-down vision CEO driven you need it we have seen literally no single enterprise by the way that was successful in their digital efforts without top-down CEO leadership and I mean literally no single example of a company that did it bottom up I think this is quite important to realize because there are so many companies I see in practice that have it all you know they've got it all plotted out but there's one tiny little problem the CEO doesn't even know what they're doing or wouldn't be able to link it to to the vision of where the company as a whole is going there's no CEO leadership you will not have digital successes for sure and you may be a fashionista for some time and it might look good but but in the midterm might might turn into something terrible so vision individual engagement proper governance that reflects the new needs of the digital enterprise and then the sustained technology platform between business and technology that helps you to move forward also in areas that you couldn't identify right now so these are are crucial and if we map that a little bit to the third platform and particularly big data we start to see that we need to cover both areas I thought this picture was pretty neat I don't like that nexus of forces type of thing of Gardner to be honest I think this sort of sort of illustrates most of the drive before us right now so we see the social thing we see the cloud thing we see obviously the internet of things and anything mobile whatever wearable or whatever we would be involved in and then of course in the middle we see big data if you would have recognized the elephant by now in 2015 you probably have a credibility problem in this world so this has become some sort of a metaphor for big data some people argue nowadays that there would be a sixth driver which would be security and with it I mean security no longer a passion killer for digital transformation innovation which unfortunately it often is yes let's do something exciting digital security would say no way we have to study on it let's come back two years from now sort of a passion killer for digital transformation and imagine that it would be turned around imagine that security would enable you to do things that were deemed impossible before imagine you could walk in into a hotel room and you would press your finger on the door and it would open because it's more secure than getting a card over here at the reception desk with a fake ID imagine that the only way to open that door would be restriction it would be more secure than you could ever imagine then suddenly security would become a driver to business change that was unthinkable before and by the way that's my suggestion to the security forum as well when we start to realize that security could be much more than just a well defined open standards based passion killer but instead would be a driver to business innovation and transformation I think we start to realize it's actually part of this open platform thinking there are more drivers to do that transformation than we initially would think so but of course today second part of my presentation of course dive a little bit deeper in the topic of big data and how because it's a key component of the third platform we all realize that it's a combination of these of course that creates the magic probably that creates a real digital transformation but data I'm quite sure we all agree is quite pivotal to this is quite a key component in all of these forces coming together so it is it is in practice of course a primary driver for transformation and what I like about the efforts that we're currently seeing in the open platform 3.0 forum is that started to work a little bit on the use cases first because you do want to understand the world's right you do want to understand how data and all the other components working together could create that magical effect of digital transformation so just a few examples over here HMRC for example that would be able to use advanced analytics typical big data type of technologies underneath to uncover fraudulent tax VAT repayments and save I would say tons and tons and tons of money a lot as a result of that or of course UPS that were able to to improve their internal logistics of actually driving somewhere saving a lot of miles that are actually being traveled including of course a lot of fuel but also learning companies that would be able to analyze the way actually people learn and use it to improve their ways of working and create a completely different revamped type of educational platform all of these are examples of companies that use data as a primary driver to digital transformation it's one of the things I always check first with clients that want to create a big data strategy to what extent do you think do you believe already that data would be a primary driver to that transformation objective that you have and as I said it's a surprising number of cases there is no established link yet between these two and that's just about transforming existing business what is also very interesting of course is monetizing data a very nice example is of entravision which is a famous set of channels in North America some of you may be quite familiar with it they have all sorts of different dozens of radio and TV channels particularly focusing on the Latin American population in North America so they have all sorts of different shows running all sorts of different channels and they started to realize that they were collecting a lot of very interesting information based on the actual viewing and listening behavior of that particular population and they realized that it was worth a lot of money and they've been using all sorts of big data techniques including the ones that I will introduce to you in a few minutes and they realize that there was so much money in it that they could create a new type of company in this case it was Luminar and Luminar is a big data company that sells customer profile data to retailers and consumer goods companies or of course entirely anonymized as you can imagine they don't want to enter the creepy zone there in terms of privacy but they made a very profitable business as a result out of it and it has nothing to do of course with the broadcasting business TV and radio channels that they were originally involved in now they are a customer analytics company that sells profile data and they use actually if you think about it the radio and TV channel business as a way to obtain that data because it seems to be a more profitable business right now than broadcasting radio so it's a very nice example of completely turning around the business and maybe maintaining a certain part of your business and it would be profitable for some time but it's shrinking nevertheless and you would be creating entirely a new business based on technical capabilities that you've never seen before and that would enable you to go directions that you didn't impossible before and you were in the business of broadcasting TV and radio and now suddenly you are a customer profile data company and you're very profitable as a result of it just a few examples of how to use data to create that so let's assume we're all very enthusiastic about it and we're all seeing the big potential and we're seeing the potential of big data then of course there's a lot of questions if you have more data as well and I'm just mentioning a few over here one of the things that I find particularly striking that there's often a very clear lack of a business case so everybody sees the potential of it and you become quickly a fashionista because it's easy to buy your Hadoop stuff and your in-memory databases or your NoSQL and whatever type of technologies you would choose but you realize that in order to create a real convincing business case based on stuff that you've never explored before it's actually a little bit more than just some cost saving or return on investment we sometimes call this return on insight you're creating sooner or later insights from data and how do you create a return on insight that is actually compelling it's one of the biggest things that is keeping companies right now from moving forward and I think the use case work that we're currently doing in the Open Platform 3.0 certainly will help companies to understand where the potential application of big data-driven solutions might be right so it's still a very important factor towards this but there's also more ground to cover in the new data landscape because there's a whole new set of technologies and we start to realize that there's quite a lot of things keeping companies right now from going there we've done some research ourselves recently in this particular area as you can see the report is called big and fast data the rise of insight-driven business it's particularly geared towards what do companies need to move forward around big and fast data so it's not only big data as we all know by now but it's also data that's available in real time of course and we started to realize that among some of the critical success factors again is the ability of the IT department to create a platform a business technology platform that is able to deliver quickly the results that you need not two years from now or wait a minute we'll have to upload an enterprise data warehouse come back two months from now to execute your brilliant plan is not enough right so it's one of these big obstacles towards actually leveraging the promise and also of course we're starting to realize that the current data state that we typically have let's assume that most of the organizations we're involved in at least has an enterprise data warehouse nowadays let's assume that we found out a little bit about business intelligence and that we need to upload data in an enterprise data warehouse according to a certain structure so that we can analyze it and use it for all sorts of BI related purposes but then we start to realize that all of that is based a little bit on the business like we used to see it and the sources of data as we used to know them and now in the world of the Internet of Things and the world of social media the world of unstructured data we start to understand that that value of data often is far beyond the core transaction so we all recognize these areas over there right emails and documents often very unstructured but can contain a lot of very important crucial information that we want to make part of our analysis there's of course the Internet of Things so anything a machine and monitoring and centering could be a crucial input and then of course there's social media on interaction people might be tweeting about this session right now and we may want to be able to analyze that sentiment and use it for all sorts of different purposes when we created our enterprise data warehouse visions in enterprises in the past let's say two decades we didn't realize and we never designed it we never architected to deal with these type of information flows so unpredictable so unstructured coming from so many diverse sources that we've never seen before it's certainly not at that time as a result we're finding that the traditional approach of the enterprise data warehouse that we see in its glorious impact right there in the middle the EDW the enterprise data warehouse although many companies would have one and it's very expensive too nobody's using it in practice so there's a relatively low impact of it because we see that all the different business units the line of business units they might be creating their own little data warehouses or they might call it the data market whatever you want to call it they're creating their own perspectives on data and they're using their own sources often bypassing the central glorious enterprise data warehouse because they have their own needs they have their own sources they want to be able to combine it in different ways than central IT ever thought of so they might be bypassing the central enterprise data warehouse which costs a lot of money that thing and they are creating their own ad hoc quickly engineered agile if you like local data warehouses and data markets to cater their actual needs in this new world and it has high use it's of course not what you want because it costs a lot of money and it's clearly not coping with this reality so a lot of companies are still talking about well you know we just have to recreate our vision of the single source of truth we just need to you know expand the enterprise data warehouse buy a few additional racks over there expand it and still be able to enforce our top down vision of what data structures should look like to all of our lines of business which I think is more than it's less than an illusion I mean we all probably know this quote everybody claims that it's a Peter Drucker quote I'm not so sure a lot of people would say well we try to find where he said it he never said it but still it's a famous quote of course you can have a corporate strategy whatever you want would be nice but actually cultured so what is actually deep hidden into the genes of the company will eat that strategy already for breakfast so whatever you try to enforce in terms of my top down vision of the data warehouse that can cater for all the needs of the digital enterprise forget it it will already be eaten by breakfast particularly over here in Spain where they have a great breakfast by the way in this hotel did you yeah it's incredible isn't it I believe they even have sushi for breakfast what is that did you try I just had some coffee but anyway it's impressive and it's certainly culture is certainly eating a strategy for breakfast yeah I can tell you that's obvious so we need to do something about it we need to realize that you cannot change culture that local lines of business will have their own strategic ways but on the other hand we want it to be part of course of where we're going as a digital enterprise we want to be able to marry we want to be able to combine these two ways of looking at the world so I think this happens to be addressed by the 3.0 Open Platform 3.0 forum I think you already saw the mission statement of what it's doing right so no need to get back to that but let me go back a little bit more to some of the things that are interesting first of all we really see the world platform services being mentioned a lot and with it we mean imagine that we would create a platform that will combine all of these different technology drivers and would be the ideal bridge between business needs and what IT essentially could bring what type of platform services would be contained in such a new world what actually would the catalog of services look like that you would be presenting to your lines of business and that they could be using to create their own unique solutions based on the combination of all of these technology drivers but also I think a very important thing that we've always seen in the context of the open group is once we've established a vision of what such a model a catalog of services would look like you also want to be able to map it to the solutions of technology providers so that first of all you could discuss a little bit with technology providers in one and the same unified way you would tell them hey so what do you have in store for me and how does it map on the services that we've defined for this platform 3.0 so how does it fit where do you fit into this equation where do you provide the technologies that you're applying and also of course interact how would they interoperate how would we create a boundary less information flow although there might be different suppliers of technologies they all still would be aligning to that overall vision of what the catalog of services would look like and I think these two things are absolutely crucial to create and I think it's one of the key missions of the open platform 3.0 it's not only to create use cases to create these business cases so that we can move forward which by the way is an interesting one it's the what but I also think that the how in terms of how to establish a platform that will be a sustained foundation to move forward is a crucial one so if you look carefully at what it wants to achieve it's very clearly over there and the thing I would like to introduce to you is the notion of what we call the business data lake we've been pioneering that concept for quite some time now we've creating architectural models around it we've been creating it at clients it's based on the vision of the data lake which some of you may be quite familiar with we made our own let's say extension to it to make it a bit more at the enterprise level to be useful at the enterprise level and we call it the business data lake and what we're currently considering is to to contribute that architectural reference model and that way of thinking that catalog of services to the forum as a candidate as a candidate open standards one of the standards that I would like to introduce to you is the open platform 3.0 so essentially and again if you want to google this evening you want to find something nice to download which of course after a long day of conference you have this download urge I can imagine you must have something to download yes and to save as a pdf and have available on your tablet I just know you're craving for it by 5pm so I certainly would recommend you to look for big and fast data democratization of information I don't like this notion of democratization but PR people thought it would be nice so okay they would overrule anything so including this so but the big idea behind it is that we need to create a completely different vision which goes far beyond the idea of an enterprise data warehouse so what we're saying over here is enterprise data warehouse is a representation of the way we looked at information in the past and it worked very well for our purposes now as I hopefully have made it credible to you it's an entirely new different world it is the world of platform 3.0 the third platform if you like and we need an entirely different approach to do it we need to make access to information completely instantaneous but also we need to enable multiple perspectives because that's the culture that is eating strategy for breakfast we need to realize that there are so many different perspectives on data and should get away a little bit of this illusion that we could have a central grip on it and then force one in the same perspective to whatever line of business that is the essence of the business data lake it tries to improve two things first of all it tries to create what we call time to insight and it means a little bit if there is a certain insight created somewhere from a data point that has been created micro seconds before how long does it take to turn that into something that we could action on I'm sure many of you will recognize this insight itself means nothing unless you can have an action linked to it so you can actually do something and if that's a matter of micro seconds that is the time to insight you need so first of all instead of doing an ETL once a month of an enterprise data warehouse so once a month I will upload the data warehouse and then you can do your real time analytics stuff maybe we want to have it in half a seconds and that's the time to insight because two seconds from now it wouldn't make sense anymore we'll see more this morning about the internet of things obviously if we are in real time sensing and we are for example in a self driving car or something we're not really interested in an enterprise data warehouse that will be uploaded 30 days from now in order to respond to something that is actually happening in real time so we have a very important time to insight over there it's not much value anymore 30 days after the moment but also I think very crucial is to understand this notion of time to value how long does it take for an enterprise for a line of business for a business unit when they got this idea of how to create value from inside how long will it take for them to actually make it available for example in a data market in an in memory database embedded in an application mobile application whatever how long does it take for them to actually create it imagine you have this brilliant idea as a line of business I could do something with real time analytics and then you go to the IT department and they will say it will take us a year and a half to create that solution brilliant idea we'll start right away it's not exactly what you want so this time to value is another very important thing to realize so first of all we want action we want to be able to have an action a brilliant idea which is data driven we want to be able to create it much faster than we used to be able to do in the past these two things are the design principles behind the business data lake I don't really like this picture to be quite honest but the big idea behind it is nowadays the cost of storage of data is literally zero particularly with Hadoop of course a nice little elephant you're still wondering about the elephant and it's a technology that enables you essentially to not filter data anymore essentially you would be able to store any data points you might be able to gather and you wouldn't have to bother about the cost of being able to store it it's not a factor anymore let's consider it's almost zero cost to store a data point and that there's no limit to what you can store but also that it will not impose any type of structure on you at beforehand because that's another characteristic of that Hadoop style storage it doesn't necessarily impose a structure at beforehand on you you're supposed to do it later on so imagine that is the big driving idea behind storing data the moment it occurs and that would be in the data lake itself so ingesting it from different sources and then the big idea around it is that when you need it for a certain purpose and I think this is often poorly misunderstood it's not necessarily just diving into that Hadoop file system and trying to get it out because it was not designed to do it no you're creating all sorts of different distill points around that lake get it distill points so you're getting some part of that data lake and you use it for a very specific purpose for example for a local data market or an in-memory database if you want to have it in real time or just a normal data warehouse a local finance and administration is still using so there might be all sorts of different ways to distill the information from that data lake use it for different purpose bring it to the surface of the lake so you actually use it in practice you get much earlier exposure to what that data actually means and if it actually delivers insight and value and you can feed it back into the loop and the time to value will be drastically shortened because you're not facing any obstacles in terms of the technology underneath to create different perspectives on it or to show it in a little bit different way I'm terribly sorry Alan over here we're not using Archimate yet to describe the architecture of it I realize this is a big mistake we can work on it but this is just some fancy marketing stuff so I'm sorry for that obviously we will use Archimate to do that so we'll define the architectural models underneath in a different way so I'm sure you'll love it when it's there but essentially there's a typical layered architecture over here as I said the storage over here the dupfile system storage over there which enables us to store essentially whatever data point we want to store and then all sorts of different layers on top of it that enable you to create all sorts of different local perspectives, line of business type perspectives on data using yes or no in-memory databases using just a typical statical data mining type of perspective maybe using a no-sequel database all sorts of, maybe just a local data market all sorts of different ways to look at it maybe even do some simple SQL based analysis on the core Hadoop itself to be an idea, although often mistaken as the only way to deal with it that's the type of architecture you would be looking at it so the ingestion tier the data lake in the middle and then the insights tier on the right-hand side is a very important notion to understand over there they are essentially completely different and it's not a matter of just putting everything in the lake and then magically it will come out it's understanding all of the different distillation points that you will create around it to create the insights from it and many people would argue if you just have the data lake like it is and you put everything in it and then say to your business units hey, here you have a data lake, do something fancy with it it's not exactly what you want many people would call that a data swamp, by the way it's not a lake, it becomes a swamp, right and that's not what we're aiming for it's not what we want to do so just to summarize a few of the design principles that are underneath this business data lake so first of all, you want to land all the information you can gather even if you think it's not relevant right now this is particularly often the case with the internet of things, right it's going, we have a pretty good feeling that it will be very valuable pretty soon we don't know exactly how but we all know that we might lose a lot of money if we don't store it so we want to land on the information without modification and then we want to encourage lines of business culture, strategy, breakfast and we encourage them to actually create their own perspectives using this distilled philosophy instead of enforcing a top-down vision on the data perspectives they should be using and they might also have their own cost benefits rationale over there in terms of why they want to do it you really want to have this in-memory SAP HANA database it will cost you probably something to create it but maybe if the return on insights justifies that business case and again the business cases of Open Platform 3.0 will help us a lot help these lines of business to decide what they want to do that's fine you want to, again as you will realize there is a notion of central top-down governance will be definitely relaxed here you want to focus your governance a little bit on the areas where it actually matters which is not necessarily at the point of ingestion of data but might be further down the supply chain and also very interesting which is also often very nice to tell corporate people we understand you have a corporate perspective let's call it a corporate perspective it's nothing different from any other local perspective on data it happens to be the corporate perspective fine it used to be contained in the enterprise data warehouse why don't you keep using that enterprise data warehouse to create your corporate perspective and that's all fine it's just another way of looking at it and of course this also enables you to drive up and skill up and skill down depending on the type of needs you have which might be very temporary and you're not, again, forced to create one big solution for it that will last will have to last forever so just a few use cases to finish and I'm sure the Internet of Things will be more discussed later on this morning but obviously I'm not sure if you can really read this so there are of course a few things that I think are particularly interesting between the Internet of Things it's first of all the speed of data coming available and as I said if you are in a self-driving car for example or you are doing real-time predictive asset management you want to be able to have it available in microseconds rather than anything else and you want to be able to respond in real-time because otherwise there's no use there is the time to insight is already gone to share a volume of storing whatever comes from sensors or whatever other intelligence device we might have is something else and we have not designed our enterprise data warehouses to deal with that at all, obviously so we need an entirely different approach over there to deal with it these are some of the intrinsic challenges and opportunities you will see in the Internet of Things and I'm quite sure that later on today you will get quite some additional insights as I said the operational reporting is a very nice thing to do and we believe that again I've highlighted a little bit but it's very difficult to read over there but we think there is some local need some local recording and reporting needs that you might have that you want to enforce local business units with the local regulations for example and the local compliance needs you want to be able to cater for these without of course corrupting the corporate view on reporting and the funny thing is as I said they are no way different the corporate view is just another line of business view on that data lake that you have so you would be able to cater both for very regional needs, very specific maybe regionally different type of reporting needs and the corporate view that's another local if you think about it regional needs that you would be able to cater from that data lake oh okay and the last one over here anomalous behavior detection is just one other thing that might be applied both to the internet of things but also from social media flows and there's a lot of structured and particularly also unstructured information over there that you want to be able to analyze in real time and again as I said security is often considered a passion killer because people try to hide behind walls we all remember Jericho the metaphor of course they were hiding beyond the thickest walls that you could ever imagine it was completely unsafe it was an illusion that behind the wall you're safe so what you better be creating is awareness and if a data lake and all the technologies that come with it can help you to increase that awareness but also your responsiveness that would be a huge step forward also in creating security as an enabler as an innovation enabler instead of a passion killer please keep that one in mind because unfortunately we see it too much and it helps everything rather than enabling change and transformation so that's really what I would like to tell you I think mapping almost solution stacks is a very interesting thing that the open group is pretty good at I'm not already talking about certification over here it's not exactly a unique standard or something but I do believe that if we have reference models like this we could start discussing to technology providers and ask them to map their technology stacks on that business data lake reference model we start to understand where they fill in parts of it maybe they're good at the ingestion point or at the distill point or the management tier or maybe they fill in everything but certain different products we've already done this ourselves for example with Python and also with Informatica and next week as a little scoop we'll also introduce a mapping to SAP with their in-memory database their HANA technology all of that becoming part of one and the same vision of how to map that reference model on actual technologies so I think it's very suitable that way of thinking I think we'll need to create from the open platform 3.0 much more here's a reference model, here's a catalog of services let's see how all these different providers sort of comply with it not necessarily at the byte level yet but at least in terms of catalog way of thinking what are the services you're providing how does it fit into this overall vision of a business data lake and how would they be working together with the reference model the easier it would be again not necessarily at the technology level yet although sooner or later you could envision that it would be you know technically specified as well and then other stuff like the open data platform which is a a work in progress between several Hadoop suppliers particularly and they're focusing more on the technical details of what should a Hadoop installation actually look like is an interesting one to take into account to align with and to work together with or maybe sooner or later even embrace them in the context of the open group I wouldn't be surprised if sooner or later something like that might happen so that's really what I would like to tell you obviously the mission of the open of the open platform 3.0 is also to create that interoperability between the components so here's the data thing and it's one way of looking at it it's a business data lake a clear illustration of the new way of thinking and the way we're looking at solutions from a very different platform but obviously the same thinking will completely pertain to the internet of things to mobility, to mobile devices also of course cloud services and so on and social social media so thank you very much for bearing with me this early in the morning I hope I gave you some insight and where we're going I think this initiative might be a very good illustration of what we want to achieve within the open platform 3.0 I hope you agree with me by all means download whatever you want after today and to have a further look into it much of it already being defined in the public it's just not exactly ready to be submitted as an open standard but we will be very quickly working on that if all the stakeholders agree of course that this would be a useful effort to further accelerate the activities of the forum so thank you very much for your attention and I'm wishing you a very interesting day today thank you that was fantastic, thank you to ask the questions let me reintroduce Steve Nunn CEO of the open group and as you know CEO of the association of enterprise architects Steve thank you Ron that was great you'd have some interesting stuff and you did thank you a few questions here first one that came in quite early on what tactics are effective in moving to the top right of the digital masters quadrant what tactics so if you think about it and here's the consultant speaking often of course you first want to assess where you are and that often gives already an indication where you should be moving if you want to move to the upper right hand or vertically so some organizations I see quite a lot that are obviously fashionistas and we tell them did you ever speak to your business peers would you ever consider taking this to your CEO maybe or maybe some business people would have to agree with your splendid digital vision as well did you even realize that so we're moving a little bit towards creating a better governance and a better best business technology alignment which often I think is really a key diagnostic and people sort of forget about it they have a splendid innovation lab nobody in the business knows about it or they just see it and they don't even have a clue what's happening over there and yet some other organizations of course and they're fewer have a splendid transformation tradition they know how to create a vision and leadership and how to recreate a governance it's just that they don't care much for technology because they're conservative and they think it's scary and there you want to create a culture of actually enjoying technology and bit by bit starting to embrace some of the new technology drivers and often hands-on experience with technologies not as an innovation museum but more like a hands-on lab I see one of the more successful examples of organizations that start to do some hands-on work creating real-life business scenarios using some of the technologies that are actually available over there that's another clear one so if you're more in the conservative quadrant you may want to do a more vertical type of direction and these might be the tactical ones very, let's say, down-to-earth in terms of what you could be doing it always starts with an assessment of where you are and as I said, did I mention that there is a self-assessment tool just look for leading digital and Capgeminar or something you probably will find it very quickly you can do this fancy survey of doing your actual Facebook quiz your daily Facebook quiz what color am I or something what capital city am I so in this case you could be doing an actual useful quiz in terms of where am I as a company and that will give you some indication in terms of the tactics of where to move horizontally or vertically okay, thank you so it depends where you start another question it seems that Agile and DevOps are becoming a force in themselves do you have any comment on that it's very obviously we've even been talking already about something we call DatOps so DataOps because it not only pertains to development people that start to work together with operations people just for your information it's development people and IT infrastructure people, operations people working together and essentially what they're trying to achieve is create results have a live result of a modified version of the system not once every six months but six times every hour essentially, what would it take in terms of technology and industrialization but also of a unified team in order to create something you would be able to build a next version of a solution six times every hour I think to data that's so the time to value that I just mentioned is a very crucial one over there as well you might have a brilliant idea in terms of I can do something with this if I would have it real-time available and then if the development people say we'll start developing it and we'll do it agile scrum so we'll have it within two months which is relatively fast then operations would say what infrastructure do you need for that we'll have to work for another half year on it to get it up and running we'll have to order service we have to install it, we have to patch it and then we'll see if it works nice in your development environment but with this actual real-time analytics solution in terms of technology anyway would that work, we'll have to test it will probably take a year so nothing worse than having this brilliant idea and you are just very near to the technology that we'll be able to deliver it and then you're not able to bring it live because they are new technologies and they are risky in a certain way and they've never been tested before so you need a DevOps approach particularly in this environment as well and I would like to launch DevOps here claims it please somebody tweet it DevOps and it has been claimed over here in Madrid as the way to go forward so it's a very, very relevant question because without that thinking it all will die anyway and you won't be able to create the actual solution one more one more question, I like this one sometimes dead bodies end up in lakes and monsters how can we ensure the quality of the data we might use you know, metaphors are like buckets with holes in them they'll only bring you this far you think about that by the way so dead people end up in lakes, that's nice that's what you get with metaphors so yes of course data quality the thing of course is that also your perspective of what quality is is I think a local thing I don't think there is necessarily always one corporate view of what actually the quality of data should be so for certain purposes we might be quite fine with being very late at the still point and not bothering too much about the quality of it and still be able to do something useful for our local purposes with it and in other cases like financial reporting and we're a bank and we're doing our financial reporting probably want to be a little bit less relaxed about the quality of the data our definite claim would be that you want to to move much closer to the still points also for enforcing what you consider a suitable data quality rather than trying to enforce everything at the moment you ingest the data because when you collect from the data because again it's an illusion to think that there is one idea of what quality actually means and we should realize that it's yet another perspective and if the company the corporate perspective on quality is a certain level then we should realize that it's also just a local perspective that we want to enforce the moment we start working with it so the thing is we won't lose anything even if we think it's not the quality we're looking for maybe two months from now we would say oh damn I wish we would have kept that data point as well although at that point in time we deemed it a low quality but actually we are finding out that we should be using it for other purposes so it's just moving up more to the surface of the lake where we are actually distilling rather than somewhere down at the bottom right that's really the way of thinking