 So thank you for your time. I think that's great. So as you can see, we had what we can say, connectivity, integration issue, which is great, because it's exactly what we will talk about, how company can really phase the integration issue by connecting and managing the data source together. So my name is David Telagas. I'm in charge of product marketing for data quality and data governance at Talent. So let's start, maybe, on explaining, first, who is Talent, because I'm sure that some of you don't really know who is Talent. So Talent is now a thousand employees, recognized as a leader over the last years by Gartner's. We'll explain it a little bit later. And with a really good focus, strong focus right now in the cloud. And we also have strong footprints. And the topic to do today is to explain to you exactly what we have done with some actors in this market in the digital farming industry. When we really start Talent, we really start with integrations. And that was the key things. So back 10 years ago, 12 years ago now. And then we had data quality. And we complete our approach with big data. And also, we were the first platforms to have native data quality in big data platforms and streaming platform as well. And then we come with the idea of unified platforms. And now we are developing personal-based apps to democratize that access to a lot of people in a company. Recognize as a leader. I think the first one is probably interesting because it's a big data forester. But the two are there as well. Not sure if you see everything on the screen. So we are the leader, a quadron, for data quality and data integrations. But being a leader, providing innovative technology sometimes is not enough. And the reality is that lots of companies don't really get it, are still not ready. And our challenge at Talent is really to help them to scale their data transformations. 60% of them are still behind in digital transformation. That's studies that come from forester. Harvard Business Review tells us that globally, 47% of companies still have data issues, data integrity issues. They cannot really connect data source together. And mostly, more than half of the data which is in the company data is not accessible. So transformational promise remains totally elusive for many. And the wall of talent is really to connect the dots between the source and storage from one side and analytics and apps through API on the other sides by really making sure that you can deliver trusted data inside it with the data, transforming the data from a whole state to a trusted state where the data can be insightful enough to be used for analytics, to be trusted in apps, and so on. So this morning that there was a kind of United Nations effort to help sustainable development to use better data, this is an idea where we are part of this challenge. It's not about sustainable development, but sustainable food productions. And the idea here is with the farming. So how can you deliver inside ready data at scale? So mapping all visions directly into the digital farming area. And I will start by explaining what are the challenges that all customers and also players are living when we are talking about farming and agriculture. The first figures is about agricultural land. It shows that now, based on global land area worldwide, we are just 35%. It was a little bit bigger previously, 45%. Now it's far less due to, of course, urbanizations and modernization of our societies. At the same time, two very interesting figures, the world populations, which is a percentage of total populations, which decreased by more than 10% from the 60s to the 2080s. And you could have told me, OK, that's OK, but it's just 10%. But the fact is that it's 10%. It's 46% of the total population, but this global population has really changed by being more than multiplied by 2 and 1 half in more than 50 years, which really put a challenge for talent customers, for the partners who are working in the digital farming industry, which is, how can we do more with less? But to be honest, nobody has weighted talents on other kind of players to do that. So if you look at how farming is evolving over the last 50 years, it's totally tremendous. This is a picture of a tractor from the 60s, and this is a picture of a New Holland tractor from the 2018. So it's a completely different model on the right side. The harvesters, one farmer in the US with such model can feed more than 150 people. So it's huge, and the food production has jumped by more than 60%. The question is, is it enough to feed the global populations? The reality is, you still need to do more with less. And coming back to this picture of the harvesters, so this is the interior of a New Holland harvester, you'll see it's not a tractor that we have in mind in terms of metaphor. There are a lot of connectors, a lot of sensors. The tractor also, the harvester has built-in machine learning capabilities to, based on farmer input, to anticipate the next passage in the fields, and really to optimize the adjustment and the yields for the farmers. So it's really impressive the way it's built-in with sensors and connectors. But not only, so if you should go a little bit deeper, you will see that you have also IoT's sensor in the equipments. So it's good because it improves productivities. And this is an example of how productivities and capabilities of sensors can really change the productivity model of a farm. But it's not only in the tractors or in the harvesters. You also have drones. There are lots of news about small companies that are starting to build their own adventures around drones. Drones can be used for crop monitoring at a very moderate cost. But also it helps to really visualize crops and combat droughts and other environmental factors, like weeds, or making sure that they can spot the right part of the fields, the right parcels, to make it, to canalize and focus some pesticide actions somewhere in the fields. They can also use 3D imagings as well. Thing is also 3D imaging is very consuming, data consuming as well. But that's not enough. So this is some pictures that come from Bosch. It's not one of our customers, but it's still a good example. They are using what they call smart preying systems. So what smart preying system is about is really to use camera sensors to distinguish weeds from crops and making sure that they can really focus the use of herbicides locally in the field. So the idea is not to use herbicides. The idea is to use the less possible herbicides in a field. And that's currently the challenge that the whole industry is right now facing. And these challenges, we see connectors, we see IoT sensors, tractors, everything believe that it's still not there. And you will have lots of farmers that would like to connect to use more and more data. I mean, if you are in farms, you want to make sure that you have satellite information. But you also want to make sure that you have your field information or crop monitoring information from the drones. So everything is coming together. And data will not stop there. Data source will not stop there. And there will be new challenges about what will come next in terms of data. And of course, the capabilities of embracing everything is limited. You will not have the capabilities to embrace everything at the same cost. So it's a challenge for the farmer who is not literate enough to use sometimes all the capabilities of the data that he needs to use. It's a challenge for the digital societies, the farming societies that need to get bigger and bigger data. And it's a challenge for us to make sure that we provide deeper and better integrations. And as it was not enough, farmers are really suffering from climate change. So the capacity to predict is really, really critical for them, probably worse than it was a few years ago. And then it created some challenges. First, collaboration, I would say, data democratization challenges for them. At one time, you had just one farmer using his fields. Now you can connect different farms together. You can, based on the region where you are working, you can really connect some farms together to share some productive field productivity models. There are some data that we know, but there are a lot of data that we don't know yet. Some new source coming from new objects, from IoT, from new source, from new satellites, that will come. And so that you will need to manage your own. Climate change, make sure that, OK, now it's a big change because it's not enough to proceed with batch processing. Now you need to have wind flight data processing. And real time is really critical for them to make sure that they can react quickly. And it's totally fantastic, the amount of progress that they have done over the last years. Fortunately, something has changed. It's about digital farming. So what digital farming is about is really the combinations of precision farming, so what we have seen directly into the previous slides, but also farm management software. So I will start introducing that in my next slide. And also the unique capabilities of an integration player like Talent, who can really bring some data ingestion capabilities to make sure that this one will be used by the farmers. And this is a promising market size for this. One picture is one slide from one of our partners who's really using it to explain the challenges that they are facing. And so I said, OK, let's jump into our use cases here. And this is one profitable farming. And people are not anymore talking about specifically digital farming. They are talking about profitable farming. By that, they mean that the value is not into the connectors, the value into the pooling of information and how you can make sure that you trust these information together. So that's the challenge that they are facing. And one of them that we will talk about is a company called Smag. What goes from Smag was to optimize the weed productions with the big data predictive modeling. So what is Smag? Smag is one partner of Talent. It's 200 people companies, more than 10 million euros. They are, let's say, the digital backbone for farming cooperatives in France, and specifically in the weed productions. And so they have a unique combinations of both 90 expertise, delivering lots of innovative solutions over the last 15 years. And also from farming expertise, because they also work on agronomic studies. So they have a unique combination of agronomic knowledge and at the same time, IT expertise. And Smag tried to consolidate what we have seen before, weather data. How can we integrate daily data from models, like from source, like weather news, weather measures, agriculture, but also map it with what we call INSI code. INSI code is like dots and brackets, codes where you can map a location of a satellite regions together with a company code. So that's one of the challenges that they were facing. So they had huge data stream on that. But they also had, and it captures, informations on plots, on parcels, like varietal informations, planting dates, but also soil types. And that kind of information can be huge. And at the same time, using satellite imagery, we can be huge, because it's not only taking pictures, like in Google Maps, taking pictures every day, every hour on the same region. So it's a tremendous amount of volumes. Globally, they are supporting weather systems for more than 36,000 communes or villages, towns, municipalities in France. And it's worth around 10,000 satellite images per year. And 1 million of data per hectare. And they are managing with 225 millions of hectares in France. So it's huge. They are covering the weed production in France. And they really want to make sure that they provide predictive models so that farmers can really be equipped with the right solutions on that. So what they did, they used talents as the real-time big data solution from talent on an adult data lake. And to create a model, which is called DataCrop, which is, I would say, digital farming platforms, which can help us to track the progress of the crop over the year, but also, of course, to public et al. So right now, I think this figure is very interesting. It's 80% of French agricultural plots are managed by this model. So it's huge. They provide that this model is working. And they make sure that it's working based on also talent. Why they use talents? Here, the combination of algorithmic processing and data integrations together, they train their models using machine learning capabilities of talent. They're using the big data capabilities of talent. But also, we provide them with the capability to transform agronomic data very quickly. I put this picture here. It's 10,000 high-definition image. So it's one crop year. They integrated that in a very short amount of time, directly into the model with talent. And what they also liked about is the orchestrations, the fact that they were not using only encoding tools, but also unique platforms to ease up the integration between teams together. So between what the developers were doing and what the data scientists were doing at the same time. Second use case that I will talk about is Bayard. So you are probably familiar with Bayard. Bayard has a team which, in what we were calling digital farming as well, they have a whole department around digital farming. And what they are doing with digital farming is that they are developing different kind of apps. They have a building apps factories just to spot the right needs of farmers and make sure that they are equipped them with the right tools, very easy apps, so that they don't need to have some specific data knowledge to use them. And making sure that the farmers can really feel comfortable using this data and capture this data together on different kind of subjects, of scenarios. So here the scenario where we helped is around the weeds. So weeds are really for the bans of farming for different kind of reasons. They are displaced native species, but also they compete with native plants. So it minimizes the farmers' productivity quite severely and it prevents them to be steady and to be sustainable over the long period of time. So the question was to say, okay, how can we help the farmers about these problems? How to accurately identify the weeds in their fields or different kind of natures of weeds? So how can you spot the right one from the bad one so that you can spot the right herbicide on the right weeds and avoid doing massive herbicide pollution in the fields? And also the other question was about the real times. How to know in which weeds is growing in your fields? And at the end, that's why we mentioned profitable farming. It's about minimized risk for improving profitability here. And they came up with an idea of an app which they call Weed Scoot, which helps us to identify very quickly how to spot the right weeds using these apps. So Weed Scoot has been used to equip the farmers, making sure that they can capture the data directly in the fields and so that the machine learning models will enable them to identify the weeds and say, okay, you have to use herbicide on these ones and not on the other ones. So they have plenty of apps like this one. So I tell them we are not building apps, but we are building also the data technology behind it. This app was quite popular because it resistors more than 250,000 downloads directly into the farmers, specifically in Germany. And what they are doing is that they are applying AI technologies to identify the weeds and making sure that it will change the way it was farmers manage their herbicides on their fields. So for that they are using also big data solutions, but they are using AWS solutions on the private cloud so that they can handle the data source differently in real times. And mostly right now more than 70 varieties of weed have been identified. It's more than 100,000 of photos which has been uploaded. You could say this app is not the app that we are using on a daily basis. But this is very functional, this is very productive and it's working and it helps the farmers to really improve their productivity so that we can consider that they like it. They were using talent before and the fact that as but not the big data solution but at the same platforms it was very easy for them to ramp up because they had the right tools and it was easy also to ramp up with new teams in their teams because of the easiness of the solution that they were using based on the knowledge and expertise that they had internally as well. So it's also about ingestion, it's also about time and I think that this one is very interesting because it shows that some concrete example of how you can combine the power of the big data solutions together with a digital company that has different digital farming expertise. And the very interesting thing about Bayer is that at the region Bayer is not a digital company. But they introduced, they had a digital farming department and we have, we see more and more companies developing digital services, digital departments that we need to capture the incredible amount of data that is producing by the ecosystem, by the apps, by the clients and together with the talent solutions. Summary on that, but I will have a little bit more slide to talk about but the value here is really about how you connect and by collecting the different source from the raw data whatever it's tractors, whatever it's a satellite imagery as we have seen in SMAG whatever it's drones also, whatever it's a sensor that you have in the fields or also between farms together because farms are also connected. They need to make sure that they are working on the same, they're working on the same ground and they are sharing the expertise locally so sometimes you need to share expertise together. And also connecting with analytics for sure, analytics that can be used in different apps, in different services that can be provided not only by companies like SMAG but also by producers, we've seen buyers here so as I would say to help distributing more efficiently RBC for farmers but look at the Holland model that I presented before so the Holland models and I can also quote the example of John Deere they also provide additional digital services to farmers so they really transform themselves by not only providing manufacturing equipment, farming equipment to farmers worldwide but they are providing more and more digital services so John Deere is providing remote support. You can also see even if you are not on your field you can see some monitoring activity from the harvesters that are in your field so you can manage remotely some data coming from different harvesters so it's quite powerful the way manufacturing companies change the way they embrace digital technologies and data so that they can provide added value to customers. But of course we are not only about digital farming so at Holland we have different examples in financial services but also in life sciences, recently we had a user conference where we talked about AstraZeneca but also in the energy distribution energy we had lots of examples that are I would say on the same kind of logic so they are producing lots of data but they don't know how to connect them together or if they don't know they don't have the right time, the right budget to monetize these data together and the value is around data monetization as well so they are using for some of them talent solutions and different models to make sure that they can value globalizer data I think that I stop here if you have some questions feel free to come and stop at TalentBoost we have plenty of demonstrations of use cases as well and lots of stories that we can talk to you around not only digital farming but other kind of scenarios thank you, even if you are not a farmer you can ask some questions so our booth is just over the corner so don't hesitate thank you