 Ευχαριστώ πολύ για να παρακολουθούσατε τη δημιουργία μας, σε αυτή η σημερινή σημερινή. Είμαι Γιάννη Στοιιτσής, είμαι ο κ. Αγρονο, και σήμερα θα μιλήσω πώς μπορούμε να καταλαβαίνουμε τα δημιουργία της δημιουργίας, by introducing a new solution that will catalyze the access to food safety data. Θα ξεχωριώ με τον σημερινό μου ένας πραγματικός, μια δημιουργία που είχα ποτέ ώστε time με ένα κουραγία της δημιουργίας της δημιουργίας της ευρωπαϊκής Ευρωπαϊκής, εμείς είμαστε παρακολουθούντας για την δημιουργία που η δημιουργία της δημιουργίας πρέπει. Ξεκάζεται ότι η δημιουργία θα πρέπει να μην καταλαβαίνει όλα τα δημιουργία με το κλεικ της καταλήμπτης. αυτό δεν σημανωθεί με την τεκνότητα back in and which ventos make the solution and by accessing such critical data, were will be able to prevent food safety and fraud incidents he told me which are increasing during the last years causing financial significant financial loss. και αυτό είναι κάτι πολύ σημαντικό. Υπάρχει για πολλές χρόνια στην κατασκευήκη, με τα δέτα προβλήματα στην κατασκευήκη, και τα φωτοσύκταση, έχουμε δημιουργήσει ότι έχετε τρεις τρεις φορές, όταν έρχεται να εξετάξει όλα τα δημιουργία, τα εξαιρετικά δέτα, τα εξαιρετικά δημιουργία, τα εξαιρετικά δέτα, τα εξαιρετικά δέτα, τα εξαιρετικά δέτα, και τα φιλοσυκτικά δέτα. Πάμε να πούμε τις ισχύεις των αυτών. Πάμε να ξεκινήσουμε τα εξαιρετικά δέτα. Στο κατασκευήκουμε και αυτό είναι κάτι που μιλάμε, όταν μήνες τρεις σηκώδες, ότι το φορές είναι τραγουμένοι, να παίρνει πιο και πιο στιγμή. Αυτό είναι ένα σημαντικό amount of data that is generated by different entities and at different stages of the food supply chain. Data from the food production, data about the weather, environmental data, records data, laboratory testing data, production data and many more. Data that are stored in local databases in information systems that are disconnected. And this leads to data that are stored in data silos and are completely disconnected. So every time that we need access to the global food safety data, we are frustrated that we cannot use all this data to assess the risk and to predict the risk. The second obstacle category of obstacles is the internal ones. It needs a lot of time and resources in order to set up internally and to build the know-how that you need to deal with all these data sharing challenges. So you need resources, you need to restructure the team, you need to change workflows and processes. And we should not forget that in all this internal change there will be a cultural resist to change. So you need also a lot of effort for the change management. And my favorite is also philosophical obstacles. We had a lot during the last three days that together we stand, we fully, we very much agree to that. If we are able to involve all the key stakeholders and to collaborate and sync efforts in data sharing, then we will be able to create high impact in the food supply chain and we will be able to prevent incidents. This is what we believe, but it's still difficult. There are many obstacles and it's difficult to set up such synergies and collaborations and to create such impact. And we have authorities that are suggesting and that are pointing out that it's very important to create public-private data trusts, large volumes of data that can strengthen preventive measures, but still such collaborations are difficult to be set up and there are obstacles that we need to address and to need to overcome. However, we should mention that there are some interesting cases like the case of Food Industry Intelligence Network, where more than 30 food companies are actively involved in sharing in a secure way the results of the laboratory tests that they perform in a way that is secure and also anonymous. And there are very well-defined processes in order to perform the data submissions, to perform the analysis of the data and to create the reports that are shared among this network. But it's still difficult to scale up such approaches and to bring also the public sector into this picture and to make the data sharing between the private and public sector, but between all the involved stakeholders of the food supply chain. We at agronau help food companies to prevent food safety incidents and we care a lot about your data sharing challenges. That's why in every work that we are doing, we are starting from the business challenge, from the business question. We understand very well the business question, we define the data that should be used to solve to address this business question or the different data types that they should be used. We use the technology as an enabler and we provide a solution to this kind of data sharing challenges. We also understand very well the data silos and we innovate to break them. So this is why we have been invited every time that there was such a challenge in the past, such a data sharing challenge in the past. So this was the case of aggregating scientific information in FAO AGRIS, which is the most important bibliographic engine for the agri-food sector. This was also the case of how we could aggregate and harmonize training information in global food safety partnership. This was also the case of aggregating and harmonizing all the available open data in the agri-food sector in the global open data and agricultural nutrition initiative. And using all this experience, we are now aggregating and harmonizing and combining global food integrity data to enable risk prediction. We innovate, we test and we introduce new technologies in order to facilitate the data sharing and the secure data exchange in very critical processes, like for instance the certification process. So this is the case of this BFSM project, which is an initiative that focuses on how we could share data in a secure way using the blockchain technologies in a way that will allow the travel, the secure travel of the data from the certification bodies to the food producers and vice versa throughout the different stages of the supply chain. We also share our knowledge and our experiences by writing about data sharing challenges. So in the block of agronome, you will find several articles about the data sharing challenges, about the technologies, the standards that can be used in order to address these challenges. And today we have a plan to better on how to better understand the emerging risks and to protect consumers by combining processing and extracting meaning from as much data as possible. That's why we introduce for the first time an online solution that will catalyze the access to the global food safety data. Risk data is the solution that provides the world's food safety data at your fingertips. It unifies and simplifies the access to million of food data records, data records about suppliers, hazards, recalls, border rejections, inspection results, scientific information, laboratory testing data, even production and trade data. Data that come from many different data sources and data that cover the majority of the regions in our world. The risk data is based on a data engineering on the data processing engine that does all the heavy work of processing, analyzing, enriching the data. So we apply big data processing techniques to collect and process the unstructured data. We apply artificial intelligence technique and text mining techniques to harmonize the data. We translate the data, we enrich the data and we also apply technologies to exchange the data when it is required also to integrate data that are sensitive and where we need to have security and privacy. And this data engine is open so it can also connect and it can also collect any new data type that will be required. There are only three steps that one should do in order to get access to all this data. First to visit riskadata.com then to go and select the type of the data and the format of the data and the delivery mode of the data that he needs and to the third step is to receive the data offer with the data that he needs in order to improve the risk assessment and the risk prediction. Not having and leveraging the right data will lead to invest serious times and a serious time and resources in order to set up internal data engineering and data science team to set up all the new processes. And this will focus and this will make all your resources to focus on solving the data problems rather on focusing on the risk assessment and the predictions. And until you achieve all that to set up and restructure everything there may be critical information that will be missed both for the ingredients in terms of hazards for the ingredients that you are using, but also in terms of incidents for your suppliers. So having and leveraging the right data can help data science departments that need data to improve risk assessment and prediction models. And they can also it can also help the food safety experts that need specific data in order to assess suppliers and ingredients, or they need to receive to create very specific reports in order to make some decisions, some critical decisions. Using such an online solution for risk data, you can establish a data driven decision making approach by getting access to live food safety data streams that are collected every few seconds to get access to data that are connected and that are not disconnected. So this data are combined and with one query you can get information from different types of you can get the answer from different types of data. You can streamline processes and food safety systems connecting all this data and you can select in which was or which way or which mode and format you would like this data to be integrated. So you can start by visiting the riskadata.com by telling us what type of data do you need, and in which delivery format, and we will get back to you with the data offer with the main goal of helping you to achieve your goals in terms of risk assessment and prediction. Of course, you can visit our booth and we can continue this discussion about the data. Thank you so much for your attention.