 Good morning everyone. I guess I'm competing against the downloads outside. So it's it's a very tough Competition, but thanks for being here very excited to to be here today. So let's get started I travel all the time and and I love spending time with my clients and When I get them talking to me about BA you business are useful kind of all the alarms come to me because I think we can say that Business today is is everything but but usual so many changes which I'll try to To explain to explain today Actually, it's probably good to put a bit of a context about what's What's the situation and and why I'm saying that businesses or should be kind of everything anything but but usual eight almost seven eight out of the ten largest companies in the market Tech companies at the moment and we can say that they are fast 50 times more efficient Which you know if we break that down that number is kind of a bit scaring actually to compete with a company that Use one resource to do the same as as what you do with 50 people, right? It's quite a scaring Companies where the cost of IT every IT transaction is a seventh and where the ratio of capital consumption Is a third right? It sounds a bit scaring. It sounds a bit like the future, right? Is is out of control because if any of these companies decide at any point in time to get into your business You're probably out at least you will struggle So I think what we will be talking today is about how to regain control and what does the role of both? Technoscience and trusted intelligence to regain back control on your future so Realities that these companies are not stop everybody's under transformation. Yeah, but it sounds like it seems like the efforts Are not working because by the time they finish a transformation program the gap It's even bigger So it's not decreasing the situation is getting worse and time matters time is of essence now We can say that data and technology are Consolidating as a disruptive business driver So let me explain a bit of why that gap is increasing and why from a new paradigm is Needed because with this paradigm if we don't change the paradigm this situation will get worse and worse Now not everything is about technology so I'll talk about three Main factors, of course, I think the audience of this event It's more likely to you know get into the details about technology But I want to maybe make a step back and I'll talk about three things operating model culture and technology And I make a stop and I'll come back to that in a moment about the architecture of the future Enterprises right because under the concept of an enterprise, right? We are what we see is a massive simplicity on these technology companies versus extremely complex Organizations under an enterprise. I can say that there are hundreds or thousands of enterprises below that Which has an impact around speed and as I said before Speed is of essence. It's like trying to you know with that heavy bag Trying to get into a race or with the crutches that they want I would probably not do that probably better play chess rather than anything else But reality is that the concept of the enterprise the architecture of the enterprise or traditional business need to change the second element is around Technology as I was saying and I think We need to talk about Distributed age technology versus the monolithic age Technology were probably 95% of the market somehow is dealing with What's the difference between? Gathering data trust the data from every single transaction and customer interaction and use that to learn by the second Versus organizations that don't learn anything. I Mentioned before A gap in terms of efficiency of 50 times Yeah The paradigm was wrong and I will try to explain why in our views the paradigm was wrong That's separation in times and space Between the operations where the customer seats etc and the information the analytics the brain That separation in times and space is no longer valid because that's makes Organization and capable of learning by the second and that's massive legacy so as I was saying before this This approach has a huge impact in terms of efficiency speed and efficiency now we are very excited about The strategic partnership that we have made with the strategy Because we are going to change the state of school And we are committed with that we strongly believe that we can do because I think why what we do is transformation, but now we're bringing Into we're joining efforts and we are bringing probably the best technology We have seen in the market to change that status cool and to Help companies operate as Google does as a same as if they had kind of this distributed technology So let's see how we do that. Well, first I'm saying that and I'm I'm fully confident that we will change the status cool Because I mean I see why we are almost 300,000 people and we have access to 200,000 clients We can get anywhere So what's the value proposition that we are taking to the market today, right? We believe that As I was saying before will change the way companies leverage data in AI to regain back control on their future It was a bit of an intro but and I mentioned the complexity of the architecture of the enterprises as well as the point around why Companies are so slow and also kind of that gap is becoming bigger and bigger and Let me introduce two concepts the concept of techno science and trusted intelligence Because we believe this will be an act as the core of the future enterprise We always talk about AI right and AI is kind of the headline Yeah, but the reality is that as the conversions of multiple technologies beyond AI and The combination with science What will shape the future? If you think about the architecture of the future at least the way I said The architecture of a future enterprise will be fundamentally different as what it is today And I think we need to talk about for instance Just put an example the convergence of blockchain in AI and I can see the future Architecture of an enterprise with ledgers of blockchain around finance around operations around HR Etc in AI sitting on top of those So the headline I think the market should evolve and the headline of AI to me is It's getting a bit shorter because it's that conversions 5g the IOT the quantum computing Combined with the science the algorithms that intelligence the trusted data what will Somehow change when when companies will will operate So I was talking about the stream complex on how Organizations and again behind an enterprise. There are hundreds or thousands of enterprises Pro and probably one of those constraints And it's not trivial One of the elements that make that complexity actually is data Because there's a lot of constraints dependencies for any application for any business solution on the technical data We need to think about a future enterprise as an exercise of a stream Simplification of a stream simplification of the current structures So how do we see that? architecture in the future This is a very small very simple picture Companies from a technical standpoint will have cloud tech computing layer We'll have a business data fabric a trace a trace the data for a fabric at the core of the future enterprise And this is essential for the rest of the talk. I will I will explain that concept in more detail We need to talk about intelligent process The new data to me is the process itself People talk to me and tell me what's matter. What is what matters now? Is it data or is it the algorithms? What is it what matters? I don't know if you have an answer to that. I have an answer. It might not align with your views to me somehow the concept of ontologies What gives what is relevant today? Because an anthology as I will explain later later Gives meaning to data, but not only that it explains the relationship And we live in a connected world Relations and networks become more and more important To me is the meaning of the data and the relationship of that data what matters Intelligent process I was saying before That's an opportunity to send companies as the company the enterprise of the future will move from static processes to life processes The new data to me is the process itself That's probably the next gen of of AI As when you can learn from the experience where process become life So it's about embedding the reason why These tech giants are so efficient. I said before they learn by the second They get smarted by the second they have AI embedded as the core processes processes are not static but they are life and Then signals and experience with every interaction you change the way you interact the enterprise changes the way they interact everything is as I would say power by techno science and trusted intelligence Somehow we need to move from building solutions to configuring solutions to come on kind of speed up and and building trust Trusted intelligence based on trusted data, so we'll see a way to to get into into that So the trusted or business data fabric for us become a disruption and the reinvention of IT How do we close the gap? With those digital giants as I was saying they operate in a fundamental different way They don't split between the operations and the informations. They don't build data lakes Yeah, which is what we have been doing for Data warehouses data mouse data like whatever everything is the same Yeah, we were actually bringing all the inconsistency and then non-trusted data of the operations We were bringing all that into these lakes which Were nothing else and more than another data silo. They were not solving the problem So the first way for you know how to make kind of all these companies with a lot of legacy system to get smart and to operate as Google does the first thing is to Put a platform on top of that right a single Data platform where we are able to somehow self-discover all the data When I said trust the data You will probably think about quality data and that will immediately take you to clean data But trust the data is more than clean data Yeah, because you need to think about compliance. You need to think about security. You need to think about regulation, etc etc, etc, right So I can't tell you that the data lakes that we had in ages were not based on trust the data So first thing that's platform. Yeah to Put kind of to self discover the data and start first step to start giving meaning to data a Second disruption and by the way no need to Split to create an informational environment. Yeah second thing moving from human land data management to AI land data management, yeah Data data management cannot be solved by humans full stop It's just a problem that because of cardinality more than 10 million colons more than 50,000 Business terms they match in between the technical dictionaries and business term can't be managed by humans full stop Companies have been trying to do it manually But reality is that they never catch up never by the time They get may take two years three years by the time they finish the problem is even bigger So how To get that problem sorted and we introduced with our partner of the strategy That's the technology that they bring the technology to create data on two logis describe as a formal as the relationship formally structures quality rules and Relationships between different structures. Yeah So we can do the matching The automating matching we can put an ontology and attribute Into and then and the quality rule kind of into a vector and do the automatic matching Think about any attribute of I don't know the address The balance of an account, etc It might not be done 100% by AI, but it will reduce the human effort 50% 60% 70% and the thing is the system will get smarter and smarter These ontologies will immediately create what we call the business data layer Which is the beginning of what we are saying as the what we define as the Reinvention of IT at the new age of IT Because basically what as I was saying before one of the reasons why companies are so slow It's because there's for any the constructions to build out of any application That's a lot of dependency the constraints are always on the data model, right? So for a company, it's impossible actually to build a central unit of intelligence Think about a large bank or you know a large retailer They can't create like at the same or the one thing crime solution for instance for everywhere. Why? Because the data model in the UK is different than In in Germany the US Brazil, etc. They have to Create Multiple applications the cost of IT gets multiplied but as many times as countries, right? Again, one of the reasons why organizations today are so complex Arpecals of that dependency and the constraints on data This business data layer will decouple somehow the development of any application from the The the technical and from the technology from technology and will give business users the possibility to really build AI Applications, which is at the end of the day the stage where we want to get the full democratization of AI Because reality is that business are trying to compete against those digital Giants in the technology playground and they are losing and they will lose The only way to regain to regain control on the future Is to give the business users? access and control on the technology because that's where They can't really that business knowledge is What any business in any industry has and it's these technology companies they don't have that's what we call IT for business and again that speeds up and reduces cost of For and in in the development kind of any any application Now that data ontology is already disruption because somehow What we are talking about is defining the future data standards not in an enterprise level But at the sector level Which become a massive? accelerator So we are at the moment together with the strategy of defining these business data layer these ontologies for retailers For banks We believe that we have the best open banking solution for health For the agro business Etcetera Etcetera and one use case. I think is what brings things to life, right as a bank The development for a global Global trading platform across 14 countries what we are talking is moving from 36 months development and a cost of 42 million to a development of eight months and Less than 8 million and why is that? Because what before was developed in 14 different countries 14 different platforms The cost of maintenance is multiplied by 14 What we are doing that now is building things once in one place and then rolling that out Across the rest of the countries and that business data layer as I was saying is what is the coupling and enabling us to do It's a bit like the concept of data Data adapts to the algorithm and not the other way around so again that constraint of of The data the data structures we are removing that and that's why we're saying it's a bit like the the next age of the revolution So it's a as I said before it's about moving from building solutions to configuring a solution The way to it per escalate linear growth doesn't cope with this Russian. How do you grow in a way that is exponentially? What you have to leverage you have to build Artifacts you have to build components. This is like a Lego and accelerate the way you build solutions You have to add the reusability the concept of reusability becomes essential That's what these digital jaguars do kind of all the time and Everything underpinned by trust and intelligence and trusted data It really composes what is a solution? Because on the enterprise I mentioned about these four layers the cloud tanks the data fabric the smart processes and The signals and experience Across to that. It's all the solutions And we can say that or any solution will have a component of a data ontology will be based on that We will be built on a data ontology. We'll have a component of Machine learning. We have the business logics the amp the value We are bringing trust in every single component of these artifacts That's why again the combination of techno science and trusted intelligence will become essential to develop the enterprise of the future Now why I Think I highlighted a bit why this is a game changer Again, we have a very very powerful technology now And we have extremely we have access to anywhere in the world. I mentioned 200,000 clients 300 100,000 people What we are doing is We are reimagining business What if it's like what if you guys were 50 times more efficient How would you value chin? What will be your value proposition? How would you change? Let's do that? Let's make it real We are transforming entire industries. I said this is not This is not an enterprise-wide solution. This is a sector. Isn't it right? right We are reimagining we are rebuilding the open banking solution has never before With this technology And I didn't mention but we are improving communities And I know this was the second day of two days conference, and I'm sure you've seen a lot of use cases I Selected today a few use cases which are More relented with somehow improving communities as well because to me we also have the commitment and responsibility around supply and tech for good and I I think I think actually I was not competing now just against the donuts But I was competing again. There was a tech talk about someone from the United Nations. So Maybe that's that's that's why Now so with this technology And the business knowledge around transformation how we are helping clients change The start of school I mentioned we are going to change the way companies use Data in any eye we are Reinventing we are helping them catch up and and grow exponentially as the others do. How do we do that? There are four paths. There are four ways. One is is get the problem on data sorted Yeah, just get the problem around data governance sorted. It's something I can't tell you any CEO Any CDO and everybody will be happy to get that sorted For the regulators for the clients compliance purposes, etc. But it's actually the best the fastest step For the rest Because getting the problem data right means giving meaning to data gives means Having trusted data Which again is essential is the basis for the rest The other thing is another path is where we are not talking about a big bang things do not happen in general in life Like a big bang. Yeah, actually if we think about what are the core processes of a company? It's less than 20% This is where we need to put a focus This is what matters. These are the processes where we need to embed and we have trusted data Trusted intelligence and build AI into those process. Don't get crazy. We don't need to change everything We need to change that 20% and that's where the focus is I was with the company the other day in New York Re-inventing the entire agro business. That's fascinating that industry, right? You know, I think but it's like well If you could learn by the second How would you change your relationship with the farmers? with the providers With the end users with the end customers. I mean you could build the best e-commerce platform Why not? What we are talking is about actually a competitive advantage and I can't tell you that the market will use this technology The opportunity window is 24 months. I Said before Speed is of essence speed is of essence now. Of course you can Stop you can decide to adopt this in a few years, but that opportunity To have a green field will be gone and that's actually one of the patterns Same as Google Google for instance, right? They they are entering now in the UK in The banking yeah with the bank with a Google account you can open a Citibank, right? They are just learning they are in the process of learning, but you know they Anything where they want they just get it Like what if your own business could be in that situation because this is what we are talking about The difference between Human land data management from AI land data management makes a whole difference and again, this is a competitive advantage now probably not in five years time and and the other path where we are helping our clients is on the entire IT transformation a Company's I was saying before They are still using the mainframe the horse the technology of 50 years can we wait for a minute and think what? Was the technology 50 years ago? Well, I still think that is amazing today We could go to the moon But think about the technology 50 years ago really This is what this is how the clients how the market is operating now This is impossible to compete as I was saying with the one platform native. Yeah So we are helping somehow to define a road map to the progressively decommission From the old technology and bring things into into that platform I've taken two Two videos actually is one of my colleagues here. I think I know I mentioned I think I saw him And that again What we are trying is to to have an impact and I'm sure you probably don't think that he why We don't think that he why is doing anything like this. The first use case is about Preventing on breast cancer We are using actually empowering using the the technology and the platform of Astratio to build Not just to build a solution in algorithm itself, but to change the entire operation about how Radiology is kind of managed kind of this process we believe that we can Through a pattern and image recognition. We can identify Signals of breast cancer two years before what any radiologist can do at the moment Let me put that video Breast cancer is one of the main cause of death in women were wild Every year more than 1.7 million women are affected by this pathology In the developed countries every woman from 55 to 75 years old has to take one Mammography every two years in order to prevent this pathology In the non-developed countries the problem is huge because they don't even have Radiologists to inform this kind of test Analyze and improve the image in order to mark the zones with possible injuries classifying the types and to have a diagnosis of the images Identify a digital biomarker by using artificial intelligence Where it will allow the prediction of possible occurrences of malignant tumor in mammograms with benign and diagnosis In the future the mammography is sent to the new cloud solution which will analyze and improve the mammography to diagnose the image also mark the zones with possible injuries and To facilitate the radiologists which detect with much more accuracy the types of injuries in the study It indicates with high precision if these injuries are benign or malignant and marks all the affected areas identify a digital biomarker Allowing the prediction one to two years before of possible occurrences of malignant tumors in mammograms without any suspicion Also, this new solution can be used to analyze other medical images such as dermatology or ophthalmology Actually, but the studies here So that was the the first use case And the second one and I'll finish with with this I'm just conscious of time and It's one solution that I'm very committed with personally, which is about child protection which is about identifying situations of Child abuse at early stages So basically Million kids suffer From violence at home. There's a huge problem. We're talking about from an economic also an economic perspective 7 trillion dollars and about 2,000 kids die every year because of Violent what we are our child protection solution is gathering information from multiple sources from the police hospitals the courts, etc. And we are moving from We are moving from an early Detection yeah in action. We are changing the entire cycle Detecting applying analytics to detect early signals of violence. We are using use special analytics to identify Situations in boundaries kind of of different jurisdictions and identify when someone is close to a child and that might be at risk We are also using network analysis to identify the different relationships And and just to protect so at the end of the day we are creating a solution That moves from being reactive to being productive The solution has been applied in Australia in Canada And it's now being ruled out in Brazil and we hope again we can have some impact to solve and to save the lives of thousands of kids So that's all thank you very much. I really enjoy being here. It's been a pleasure