2020. I know that these have been three full days, full days, but I hope that you enjoy them and that you learn new things that will be useful for your work. Before passing the floor to Tipsiana that is moderating the final session with two our guest speakers, I would like to announce here the winners of the best poster and demo competition. And I would like to thank all the people that spend time in voting online for this and all those that have submitted posters and demos for this EOSCAP week. So for the best poster, actually, so when we thought we only were thinking about the award, one of my EOSCAP colleagues suggested that the best award should have been to frame the poster and ship it to Karlsruhe that initially was the original venue of the EOSCAP week with the following message. I almost went to Karlsruhe for the EOSCAP week 2020, but all I got was the best poster award. Well, so in the end, we decided that the awards for the best poster and the best demo winners would be three tickets for the final EOSCAP event that we are going to announce at the end of this session. So then I'm happy and pleased to introduce the best poster winner. So the best poster award goes to the BI Enterprise Content Management. And I would like to invite Gergos Niemic, apologies for my pronunciation. Gergos is a solution architect at BI Insights, and I would like Gergos to briefly describe the content of the poster. Yes, so hello, good afternoon. First of all, a big thank you to all of you who voted for us. I have seen some of the competitors and I have to say it's a great honor for us to win here. I would also like to thank EOSCAP, the DIH and deep team, Marcin, Agnieszka, Elisa, Alvaro. I have a honor of presenting here, but it would not be possible without the team who stands behind the system that we designed. So I would like to name the hero. I need some support. It's a long list. So thank you Dorota, Magda, Malgosia, Mateusz, Vlad, Szymon, Piotr, Piotr, Krzysztof, Marcin. Thank you guys. So we designed a system that can help organizations manage their unstructured data, that is naturally hidden in the departmental silos and is not easily accessible to all the members of the organization. So the goal of the system is to create an information hub where the data from external sources is centralized in a central repository and the knowledge is extracted from the files automatically. It can be shared across the whole organization. So the system can connect to many popular data store types searching for new files which then go to a natural language processing pipeline and artifacts extracted automatically from the files are stored in a document database and in a search index. So now I will switch to a short live demo of the system. So here you can see a system that has been preloaded with almost 50,000 of documents generated by Polish parliament. These are acts of parliament. So we can search, quickly search for, sorry, the documents are in Polish so maybe you will not understand the content but the system is bilingual so the menus and all the labels are so I'm searching for a European Union keyword. This is only a European Polish so and if we go to the document details we will see the keywords generated by the system and all the all the metadata that was extracted automatically together with the summary and let's try find something about IOSC and we find one document but this is just by a coincidence because the system is correcting the user spelling mistakes but we can go and search in external data sources that are integrated into the system for example open air and here we can see something much more interesting and we can click on the link which will lead us to the source system where we can read all the all the details. So I will now switch back to the presentation and we are still improving the system we are planning to add new features like more text analytics and advanced image processing including search with picture and documents semantic similarity networks and we are almost ready to to launch a demo in the IOSC platform and we will load some English documents that would be more meaningful and maybe useful to the IOSC community and I think that's it from my side I hope I did not run over the time thank you it was perfect thank you very much congratulations once again and it was very nice okay so let's move then to the best demo so the best demo goes to the deep hybrid data cloud developing and deploying your deep learning application and actually the award goes back to Kershrui because we have here Valentine Coslog that is a researcher in the data analytics access and application department at the same back center for computing of the Kershrui Institute of Technology Valentine the floor is yours. Good afternoon everyone and thanks a lot for organizers for organizing this IOSC hub and it's not so easy times and of course a great thanks to everyone who voted for our demo and well the award doesn't go only to Kalshrui it goes to of course all the people with whom we work in the project and who developed the solutions which we are presenting together also with my colleague Lara from Spain. So in our demos and first I want to shortly introduce our project deep hybrid data cloud where deep stands for designing and enabling infrastructures for intensive data processing in a hybrid data cloud environment this is one of the European projects and where we have nine academic partners and one industrial partner and then the goal was to prepare a new generation of infrastructures with different generation latest generation technologies so leveraging also hardware like GPUs and infinite band to support deep learning and other intensive computing techniques to exploit very large data sources and we wanted and what we worked for is to make it easier for scientists to integrate their applications and transparently run them on different infrastructures exploring cloud environment and HPC environment that they can easily prepare their modules and share them with other people in the catalog or marketplace and that they also leverage DevOps approach and continuous integration and delivery so we have a few several deep learning use cases which cover different fields of science and this is biology and for instance plan classification this is also applications for classification of seeds fit a plankton this is also satellite imagery massive online data streams and for instance retina party to detect the level of retina party in patients then in fact our solutions are leveraged by quite a number of other communities and the whole list is beyond the use cases which we started this in the project and you can explore many of them in the hour in our marketplace if I now switch correctly just to show the marketplace where you can then for instance well go to an application and you have a possibility to deploy it immediately for training you get the information about the application itself you can also access the source code and the docker image of the application but you can also submit it for training then that's a big figure which is now loading with just to give you a short view on our architecture I hope the figure loads but so in the project we use different technologies and solutions there is a pass orchestrator there is a indigo orchestrate well which is based on a digger orchestrate there is a dashboard there are dashboards there is alien for cloud we also leverage one data storage solution from the xdc project which was presented also during this week we use u docker tool or we developed this tool actually to another that user without escalating privileges can run docker containers also in hpc multi-user environment and so on so sorry the the figure didn't load for some reason but then in short to what was presented in the demo that there are three let's say two different possibilities where people can deploy one of the deep modules hosted in the deep open catalog either in cloud or hpc environment so when we go to cloud we first can select the the module from the open catalog then use the dashboard to deploy it in our testbed and for instance one of the modules is actually deep development environment where you can start developing your own application and then you can do training and you can do inference and since this is based on docker images you can also do the same stuff in your local computer and then in the hpc environment we have two parts where first part where you can login to the hpc system using your odc token so not like login and password but we have also central im service and then we can basically use these odc tokens to login to to identify ourselves in different services and including hpc system and you can do very much the same so leverage a new docker tool you can also submit this deep development environment as a slurm job and then by doing ssh tunneling you can access this environment and work directly in the hpc system and then the second hpc part we do pretty much the same but using the pass orchestrated dashboard so the web interface where you configure your deployment and you submit that to the hpc system and there we also use one of the applications which is in our open catalog retina party and where the data for this application is hosted in one data and just by configuring through the web form you can make a deployment to hpc system slurm system and starts and immediately processes your data stored in one data so this is more less it and here i just show some links where you can find more information about the project the main web page of the project the marketplace also the open catalog we also provide a deep house training facility in the osc portal and there is one documentation and description of your docker tool itself thank you for your attention thank you very much valentine for this brief introduction to your work so i would like to thank once again valentine and jagos for this presentation and then i think it's time to move to the final session of the closing plenary focusing on the us cup contribution to societal challenges and the role of digital infrastructures so i would like to introduce the moderator of this session titiana ferrari managing director of the gi foundation and also your scab coordinator titiana the floor is yours good evening everyone it's a pleasure for me to to be here at the closing plenary for this virtual conference and also i'd like to thank many of you that i see are still connected i hope you are going to enjoy the interviews that we're going to have in this last hour our objective this closing plenary is to bring science in focus and and to show how concretely scientific communities through the european open science cloud through e infrastructures can address societal challenges so as the moderator of this uh of this last session i'd like to introduce our guests first is alexandra bonvan here representing structural biology as a scientific discipline alexandra is a full professor of computational structural biology with the chemistry department of the ultra university he's also scientific director of the day food center for biomolecular research in which structural biology and the study of interactions between molecules is central the second guest is uh richard lucas richard is holding a silcongry research chair with the earth observation and ecosystem dynamics research group with the department of the geography and earth sciences all the other is with university in wills and i've challenged my wash wash pronunciation here so with richard we will be exposed to a completely different sector we will look into uh sustainable development global goals and the ecology so i hope that you will enjoy this interview with our guests and also learn how we are in in daily life years to can support experiments and excellent science so i'd like to ask my first question to alexandra so alexandra in in a few words can you introduce your scientific activities and the we nmr community that you are representing here also first let me thank you for the opportunity to speak here at this plenary it's a real pleasure so i'm representing we nmr which is a virtual research community in the area of life sciences in general and structural biology more in particular we are linked to the instruct s3 project and also alexia s3 project and as we nmr we are serving a large worldwide community of researchers who are working on all kind of problem links to molecules and how molecules interact with each other and we are providing uh compute services actually thematic services under the yosca for for users to transform their data into models using high throughput resources so the portal that we are operating are operated from from uteric university but also from florans so there are different geographic geographical locations uh we have accumulated since now more than 10 years of operation basically under different projects more than 17 thousand registered registered users and these are generating a lot of computing jobs on the yosca resources those users are coming from europe but also the majority actually is from outside europe so from asia the us so we are covering more than 110 countries and in terms of also impact these days we see a large use of some of our services these days to fight covid actually because this is the area of structural biology or drug design yes we will see just in a few minutes from now how practically an experiment which is so actual will benefit from havoc in the demo so i like to to turn to richard so richard you have been i hear from your bio one of the main contributors to the aodism system and also through the eco potential project you have been contributing to aerogeo so can you say something about the project your activity and aerogeo you're muted richard yes thank you very much again for inviting me also and most of my experience is in is in quantifying understanding the response to terrestrial and coastal ecosystems environments to change through integration of earth observation data from multiple sources and we've looked at all sorts of methods looking from the scales range from trees to the globe and as part of the eco potential project we developed the earth observation data for ecosystem monitoring and that allows routine mapping of land cover and change based on evidence through retrieving environmental variables from satellite data so we don't classify really the images we bring the environmental variables together and we then combine these to produce land cover maps and also change so we're leading a project called living whales as well which is a follow-on and we're significantly advancing that as well basically what geo the group of earth observations is very as selected the united nation sustainable development goals as one of its engagement priorities and and eurogeo is the european regional component of geo with the focus on coordination and scaling up these user driven applications being developed in in europe and so i believe we have a video that can explain that a bit more to show is that right yeah thank you richard so as anticipated we have a short video that can give you a practical understanding of how important is addressing the societal challenges especially u n development goals so please fire risk depends on many factors including climatic conditions vegetation type and amount forest management practices in other socio-economic i think it's our eyes to fix the presentation layer so let's see if we can yeah fire risk depends on many factors including climatic conditions vegetation type and amount forest management practices and other socio-economic factors climate change projections suggest substantial warming and increases in the number of droughts heat waves and dry spells especially in the Mediterranean region italy's forest estate is one of the largest in europe preserving and enhancing forests is also important for society and economy for this reason local authorities are deeply committed to forest preservation and fire risk prevention on the 24th of september 2018 a large fire devastated the forests in the mountains nooks of pisa the mayor of calchi the closest city to the fire highlighted that it was difficult to estimate the extent of the fire but that it was surely a huge portion of the forest on mount serra earth vegetation helps monitoring the extent of burnt areas the eodesm model classifies land cover and land cover change eodesm runs through the virtual laboratory v lab which uses european cloud plough pumps including the copernicus diocese and the european open science cloud by using eodesm within the v lab the extent of significant change near pisa was mapped from central to images acquired before and after the fire v lab enables other scientific models too in particular trans dot earth model which calculates the un sdg indicator on land degradation eurogeo enabled the connection among the presented european capacities for forest fire and land degradation assessment so the the video shows how earth observation earth science modeling can provide knowledge for decision makers in particular for sdg 15 which is life on land and more specifically it shows how european capacities may contribute to geo european resources using the showcase are in a central data digital infrastructures including the osc and functionality created through the eo funded research innovation action with the v lab and eodesm thank you richard i like to go a bit deeper exactly on this very topic so looking at specifically how digital infrastructures and i would say also us cub have been enablers for the scientific activities of both we and mr and eodesm so i like to turn to alexander so alexander can you tell a bit how we in mr structural biology and also the research projects you are supporting in covet are depending on europe and the digital infrastructures today so if uh if you look at our thematic services we could not offer the the amount of services and the quality of services if they was not in infrastructure behind those and we are making extensive use of the high throughput compute uh infrastructure in eosk so that's the whole model or all jobs are in terms of thematic services are mainly not so much data dependent but we rely on a lot of computing for that and the high throughput computing is key for that another important aspect of eosk is to facilitate our task to dispatch all the computing to the distributed resources high throughput resources so as thematic service provider we are in between the end user who are using our services and the infrastructure and we have to communicate with the infrastructure so the tools like the dirac for egi are crucial for operations we use a bit of data but it's not that much so it's really the the access to the resource making sure that things are working getting the supports where things are not working because you know the world is not perfect so they are glitches from time to time so having a quick response to ensure that we are 27 uh in operation is also important and we could not provide these services if there was not an infrastructure behind it so i think probably we in mr with your application suite is a very good example of how the complexity of digital infrastructure can be mediated and made accessible to the to the researcher without a major it it but harddles so same question to you richard um because you're you've been very active we have heard about eosman it's actually virtual lab so how digital infrastructures and eosk hub have been supporting you yes so we're currently using eosk as a provider of the computing resources uh through its infrastructure as a service capability and specifically to allow execution of of the other thing the digital infrastructures allows the scientists decision makers through dedicated user interfaces which we have here is to run complex models of applications such as eodesm and without the need of sort of setting up you know their own infrastructures and so this really enables complex uh uh scenarios to be undertaken through collaborative research we can work with people reproducibility repliculability and uh reusability of the research outcomes so people can can access and use eodesm and for practical applications well through this system okay thank you richard um i think in my next question i will go into the the future challenges for both of you but before we have prepared a couple of demos so first alexandra we show through one specific application how scmpv projects can be supported and then we will go into the specificity of the specific cases of eodesm so we are planning live demos and we are very brave this evening so i hope technically everything is going to go smoothly and i'd like it to be the floor to alexandra to show how in practical terms how this works okay so i will share my screen and i hope indeed it's always scary to do a live demo you know murphy tends to to pop up in these times so i'm sitting in a dunes on an island on the north sea and i will show you how we can actually use eosk real time in real life at least i hope to okay so if everything goes right you should be seeing an empty screen right now my screen actually so the use case i'm going to demonstrate now is uh covid related everyone is of course aware of the current pandemic and what you are seeing on screen now is a structure of a protein from the virus the one that you see in a kind of violet light violet color that's a domain of of the virus a domain of a large protein of the virus the protein that you all know if you know the picture of the virus and like the spikes that are on the outside of the virus now the way the virus penetrates our cell is to interact with a receptor which is another protein which is sitting on our cell and the green ribbon representation that you show that you see here is actually the receptor on a human cell so this is the first step in the infection process by the virus so now we could think of a strategy to to combat the virus to compete with the receptor so for example see if i was to extract a small substructure from our receptor in this case an anelix and i could try to design a peptide that will bind stronger to the virus than our own receptors so if you could use such a peptide it will compete with the with our own receptors and prevent the entry so we are now in a field of say protein engineering which is relevant for covid of course but it's also used in in all kind of our life science application so one way will be for example and you see now a yellow part appearing to do a mutation in this peptide replacing an amino acid another question is is this a good choice is this the smart mutation will it lead to better binding of the peptide to this viral protein now that's a question that we can try to answer with one of our tools ad hoc in particular which is a tool we have been developing in uteride for almost 20 years by now and which has been offered as a web portal to our users using the eosk infrastructure for more than 10 years so i'm going to demonstrate you how this could be done so now i'm switching and so i'm a say user i've heard about eosk as a as a tool or european infrastructure so i'm going to search for eosk and i've heard about ad hoc as well now you see the first page and the first page i've all to do with eosk and ad hoc so that's good so we are well visible and actually the first link in this page points me to the eosk marketplace and ad hoc so let's go there and i'm getting this page which tells me okay i'm on the european open science clouds processing analysis as a service and ad hoc for the integrative modeling of biomolecular complexes so let's well i agree to get rid of this and now i'm accessing the service this brings me to like an intermediate step there's no need here in principle for for applying for the service directly from the eosk but users will have to to register first to be able to use our services so let's go to the service this brings me got it to the ad hoc portal and what you are seeing here is the ad hoc portal version 2.2 now for the sake of this demonstration i want to present you a brand new portal which is the ad hoc 2.4 portal which if everything is correct so this is the entry point in uterate for the thematic services the eosk we anymore thematic services in my laboratory so now i'm going to scroll and we see that we have actually quite a number of services that operating from uterates and the one that we are interested in is ad hoc now before being able to do any submission to these portals i need to to be registered which i am of course but i need to log in into the portal i click on the login window and you see another component of the eosk infrastructure which is the single sign one component the egi check-in so now i'm going to connect to the portal and the goal here is to use my university credentials to do that so i'm working at uterate and uterate university is here so this is bringing me to my university uh login page no typos fantastic so it's connected me to the egi check-in yes continue i'm fine and if everything goes right the portal now tells me you have been successfully logged in good so now i can go to the ad hoc portal and start trying to answer this question was the change i make in this peptide good or not so we're going to submit a new job by the way you see here also bio excel showing up so ad hoc is a core software in the bio excel center of excellence project so let's submit a new job and you will see that in these steps there is quite a lot of parameters that the user can can change so this is eosk demo i have two molecules i'm going to submit it so i'm need to upload a file which could come from the database but in this case it's my design peptide and i want to use chain a from that one so this was the the peptide from the human receptor so now i'm going to give it the viral protein which is chain b next so the data have been already validated uh the portal presents me with a number of options nothing needs to be changed for this particular use case in this case and now i'm getting into another page which offers a lot of options and parameters and i'm going to change them for for the use case that we want to do so we don't want to model from scratch the entire interactions we just want to basically refine this new mutated complex and define if this is a good solution or not you see a lot of menus that are clickable that expose all kind of parameters so while i'm changing the parameters to some things which is sensible i could probably do anything because there might not be many experts that are understanding what i'm doing but what you see on another component of basically running thematic services is that you need to provide training to your users users are not going to use the resource without the proper training so that's also a lot of investment in time in effort you need people to do that you need to put tutorials online so that people do the right things and you also need to be ready to answer a lot of questions so everything is ready i can submit my computation to the portal and now since the beginning of april we have added options to tag our jobs as being COVID related research so this proper window appears is this a COVID related research yes and now the job is being submitted so we see it has been successfully processed and at some point it's going to start running on the grid now this COVID tagging that we added in the last months basically allows us on the back end to send the jobs to sites resources that are specifically supporting COVID related research so we've with the so negotiation between EOSC and EGI we gain access since amounts now to the US open science grid resources and we have several high energy physics groups that are physics sites that are providing that are providing resource for this kind of COVID research so now we don't need to wait for for this job to finish because it will take some time so these are not question of seconds but i have some pre-calculated results we can go to so i'm going to the job information so this is the one i just submitted and here we see a previous run and this is bringing you to the result page what the user what the end user will be presented with now users also get notified by email so it's a very user friendly way of dealing with complex workflow complex computations which completely hide the complexity of high throughput computing to the end user so the user can look at these results we have some citations proper citation of the european projects and sites that are supporting us we constantly monitor the satisfaction of your users and then you are presented with results statistics in this case you see here a score and this score would have been to would need to be compared with the score of the wild type protein the unmuted one and actually i am not going to show you the comparison but this is better than the the receptor in our cells so yes we have improved the peptide and maybe we can do more mutations this is of course all in silico predictions we need to do experiments to validate those things another feature of the portal we can look at the structure directly online from the web browser you can see here this particular residue of course when i move my mouse connection might be a bit slow but this is the mutation that we introduce and yet it's there so now i mentioned in my introduction that we have a large user community so i'm going to bring you to a statistic page which is online so everyone can look at it so these are the number of of user requests that are actually active on our portal so we have two entry points so you see there's about 120 that are actually active there's a large number which is queued you can find here the total number of jobs that have been served on the portal since the portal have been running in 2008 so it's more than 270 000 and each job submitted to the portal might translate into a thousand high throughput jobs being sent to the iosca high throughput resources you can also see here that we are monitoring covid related research since since the months now and we can also have a look at the worldwide distribution of our users so you see a map let's sort the users so you see the total number of registered users for all of the we in a mathematics services is reaching almost 18 000 we have seen an increase in registration in the last months because of the covid pandemic so a lot of people are doing covid related projects all together europe aggregated is the largest community but you see that asia is following very closely in the us as well now finally i'm going to show you actually the real time statistics about jobs that are running now actually this is the statistics from last hour so this has been running on the iosca resources so we have about more than 2000 jobs and in this case it's about one third of the jobs that are running for the last hours where covid related one where have those jobs been running this is the distribution again over the last hour over sites so half of them have run actually at in calcruy which is good in the context of the iosca week we don't pre-define where jobs are running so we are using more an opportunistic computing model but dirac for egi does all the magic for us and distributes where there are resources so currently kit is giving us a lot of resource this is specific also for covid related research like the the high-energy physics sites in marseille which which put up resource specifically called covid but you also see the dutch resource very importantly represented there and finally the last bit i want to show you so this is the number of jobs that have been running on the infrastructure for the last 30 days the violet color that you see there is related to covid research so these are all jobs that have been tagged as covid over the last months and this represents a significant fraction of the jobs so with that i want to conclude this demo and i hope that i gave you a real life example of what the server or the thematic services can mean for users for also a real life uh important research question which related to covid pandemic thank you very much thank you alexander and i think today we've beaten the marseille completely so that's a good demo and thanks for this journey from science to infrastructures i think this is really nice and clear representation of the need of science in a specific domain i like to go to richard and we are again moving to a different domain with ecology and the climate and environments so richard we'd like to hear about how your science works and how elders is practically supporting it with use cases to you yes a completely different scale we're looking at the globe and basically one of the ideas is with the edes and is that we focused initially on national parks but as a result of the yes we've been and the facilities there we've been able to apply it um for any location in the world and there's still a lot of refinement that can be done with the with the technique but it's been very well developed over over the last um year so basically edes is a complex and expandable system which means you can keep on building on it so it's it's not something that's going to go away and it generates land cover and land cover change maps based on the food and agricultural organization land cover classification system um what i'm going to show you now is is some um demonstration of that and as i say it was originally developed for these national parks but um one of the things that we um tried to access was the the Sentinel-2 which is the satellite data from the Copernicus program and to try and access that archive directly and produce edes and using that data without actually having to download it so the classification and the change maps are generated that way the Vlab is a technology that implements um an orchestrate for automating the configuration coordination management of the online accessible systems and it's uh it's very um in the following demonstration i'm going to show the potential of edes and the virtual map to address some of the issues that are relevant to sustainable development for the goals example limit impacts and climate and how forest can be used and we've got examples of impacts of bushfires in australia's forests and how it can door in on little harvests and i'll also show you how um commercial harvesting of forest can be monitored using eodes and to support sustainable use using an example from the metang mangrove forest reserve in peninsula melasia um but first of all we'll show you how eodes and works in matthew um sanctor is going to drive the demonstration so um there we go so basically the first thing we can do is is uh we go to the website and we select our region and we originally i say we've looked at our mountains for example and we have a choice there of the the national parks that have been predefined um like grand parodies this is where originally focused our studies in eco potential so we choose grand paradisa national park we select the model and you can choose your region of interest as well the model is eodes and um and you then select the date and we're going to select the first of may 2018 and you choose the image from the archive and we have a nice we found a nice clear image here and then we do um the second date which is the first of may 2019 and again select the image so you've got two images and you're going to be classifying each of those and then looking at the change between those so you can review the um you select the platform are you these are the area of interest you select the platform this is the eos and then you can then run um the model so we've got this model it takes a while as with the other model you saw um previously um so we're going to go back through the history of what we've done before so what we'll do is we go to grand paradisa and this experiment shows the result of what we are running currently the main uh land cover in this particular type is water which appears as blue because it's it's it's actually snow but it's classified as water in the FAO um as you go into more detail and actually put it into water in a later more detailed version of this and the classification um so it's based on the FAO land cover classification system and there's eight classes which are mapped and you can see natural water and basically that's snow and there's eight classes there and uh forest and herbaceous vegetation not separated so you're not separating out forest in in this particular legend but that can be done at a later stage as well now the legend zero and legend one show you then the land cover um the legend for the two land cover classification for period one and period two and then there's another legend which is for associated with result two which shows you then the change in effect you have eight levels um three categories um the eight we talked about before and we basically compare them over time so um each of these are associated with a range of um drivers like climate economic and associated pressures like demand for land or increase in temperature and impacts on the environment for example if we have a change from natural terrestrial vegetation to bear or sparsely vegetative surfaces this can be associated with a loss of impact loss of vegetation which is the impact but the pressure is say deforestation but each of those squares actually represents a major change in the um in the land cover whereas on the diagonal actually it represents um no change in the land cover but internally you could have a change to say the canopy cover the canopy height and so on so basically that that change approach is very consistent and aligns with a lot of the political frameworks related to the SDGs as well so beyond the national parks we're taking out to the Australian bushfires and these were large and severe in in early 2020 and so we'll give you another example here um in the history section which goes to the Australian bushfires uh in New South Wales and they caused extensive damage to many forests in Australia and unprecedented loss of biodiversity and the rainforest areas were particularly badly affected um a lot of the forests like eucalyptus are are um you know they can adapt they're adapted to fire but the rainforest is not and so a lot of those were destroyed so between the 15th the 11th of February you can see now this big change from the green of the result one to the the um the complete uh loss of forest and results i'm sorry result one and it was basically saying it's converted a lot of that's converted to um sparse or no vegetation and the result two is the change which is your eight category change so if you go to results two and that shows the extent of bushfires and i was actually visited there in March and there the that is the a very good representation how massive that burn has been but what we can also do is see you know we saw a lot of recovery from the shrunks the tree trunks they sort of tend to grow up from the tree trunks so we look at what is called the recovery now so we've taken the image on the the later date which the 24th of February which was just after the fire and we just looked on the night of May which is like you know about a week ago and you can see now that it's actually coming back the cover is starting to restore and you're starting to get some hope now into that area where you know you could see that in the field and it's it's really nice to see it's not all coming back some will take decades but at least it's going greener uh the next example is um hurricane damage in the Bahamas and there's an interesting hurricane coming up now was a it's a it's a storm and the um you should now um are hitting uh anger and so on um we can look at that storm uh maybe in a couple of days time and see what the damage was so this is what we did with um hurricane um during in the Bahamas so on September the 26th of September 2019 a lot of the Bahamas was devastated so we look at the Bahamas before with our land cover classification and then we look at it afterwards and you can see that there's obviously a big change in the land cover and then the results which is the change and you can um and you can see the ledge in there that's number two results number two that shows that there's extensive flooding and there's damage to the vegetation in the urban area so there's been a lot of flooding in there and that actually aligns very nicely with NASA's assessment of where the flooding was but we've actually also captured more than just the flooding we've captured the change in the vegetation the 64 change categories are captured through that the um we can also um look at then what's happening in the recovery of those areas I believe we have an example in the history of the Bahamas recovery we just picked that up again you know a couple of a week ago and you can see now it's still pretty sparsely vegetated all there but there's actually more vegetation coming back there's there's a recovery of those areas so what we can do is is we can choose anywhere in the world anywhere in Europe or India or whatever we can change choose it we're providing a basic classification at the minute but we'll talk later on about how it can be advanced um but that's the that's the big thing we've gone away we can support the national parks we can support anywhere globally potentially with this and we're making significant advances in the higher level classification for this which we also want to run through as well the third example um is what should be the fourth because it's about the mangroves in the Tang um basically the mangrove forest reserve in the Tang has been in commercial operation about a hundred years so it's one of the oldest commercial forests in the world and we've been doing quite an extensive project there involving the virtual lab and showing them Malaysian people how it works as well and we can see how um they log on a 30-year cycle and we can see the logging coop so we'll be zooming in in a minute we've got to be able to see the individual logging coop so they they have the forest they cut it down for charcoal which interestingly goes to Japan a little bit but also for poles for construction and so on and you can see now then these these coops are appearing in the mangroves and they regenerate they're here about 30 years to regenerate then they clear them again so this this system allows the the forest managers to say okay what's being cleared you know this week if a clear image comes in what was cleared they can track it because they've got a lot of commercial operators there maybe they can check them with this for example so and also what they can then do is also track how the forests are recovering they can actually then use that to say well can we sustainably manage the forest is the forest being sustainably managed and so we can track all sorts of aspects in there as well so it's it's um very this is the very upper level part of the framework but there's a very deep framework going we should go much further in and use the power of the computing facilities that have been provided here to produce a sort of endless supply of land cover and change and contributions to looking at future environments and looking at sustainability so it's a very exciting way of doing things and the platform gives a gives a great way to allow people to just look at what's happening so so if you want to see the the impact of the cyclone tomorrow just go and have a look through this platform thank you Richard um was again another inspiring demonstration and I think with this second demo we can also see how scientific data can support policymakers and how impactful this is for all of us as citizens so yeah very nice presentation I'd like to encourage our audience to perhaps post some questions in the chat we are pressed by by time but while we wait for a few questions appearing in the chat I'd like to ask me to ask the question to both of you Alexander and Richard so the the question is um what is coming in the future so if you can say in few words what is the the biggest challenge and this would be useful input for the infrastructures and yes I hope to to define our future actions Alexander so well one challenge for us uh is to keep having software and services that are top of the line and up front in in terms of scientific science that are there they're covering because if you have something fantastic today you you still want to have users five years five years from now so it means that you cannot just put a service out there and and and let it go so you have to constantly improve it so that's not so much for the infrastructure maybe a part but projects like bioexcel are contributing to that further for us it's it's important to so I think in my demonstration it was clear it's more about the computing the data comes from the user and each user comes with a different set of data so we hear a lot about data in the context of the european open science cloud but without computing you cannot process or analyze any data so so so the computing is important it is it has to be there and it has to remain in the future should it be greed or cloud it doesn't really matter uh dirac for egi is ready to to to access both so far so life is easy what is also what is also important to realize I think in terms of European landscapes that all those nice services that are put by all the different projects to target the users they are not going to run by themselves you need an infrastructure to run them of course but you also need a manpower and people to to make sure that the service is running every day to answer user questions when things break down to contact the infrastructure provider when there is a problem and and this is not happening by itself so operating services does require manpower uh and this is something which is not always very visible in the current funding so we are speaking a lot about virtual access but we as service provider we cannot make our user pay for the access I think if tomorrow google is asking you to pay for each search that you are doing you're going to use a different service so we are sitting in between the infrastructure which has cost associated with it and the end users and we need to be able to to keep providing the services and I think also an infrastructure a fantastic infrastructure without usage is also completely useless yes thank you alexander I think the message was here we will make sure that you discover we bring this uh to to the commission but also to our partners and the future is bigger entity so Richard what is your uh your request for for the future and well I think one of the things is like you know you develop something like eosm and and um what you need and we've been successful to a point in uh taken out for what is to get the funding to support development of the system but also the associated platform you know you you you you develop something that works so for example in strictest form eodes and requires the use of environmental descriptors retrieved from satellite data and they can be continuous like canopy cover or you know thematic like plant species for example and um capacity needs to be put in place to support the routine retrieval of these which can be done through the eosk platform um and um and they um they could include things like snow hydro periods so for example if you classify snow every every um date you can actually over a year you can work out snow hydro periods so capacity for for doing that or machine learning to do plant species all these can be done some of them already in the vl um but again it's there's lots of elements that we've brought together to create something which is much bigger and much more powerful and and this is the sort of thing that eodesm is really good for so if you could do that and and make sure that infrastructure is maintained effectively get the funding coordinated um around this this concept and then the full um functionality of land cover and change mode could be realized and then also bring in data from other sensors you know like we've got central one land set as well as also a huge benefit so there's lots of lots of capacity due to expand it but what's nice about it is that you can get one grid work on hydro period one online bring in central one and so on and and it's almost like building a big system through the one system the the platforms that we're developing so eodes be designed with this in mind but continue to develop the system and the associated infrastructure is needed to create and deliver the advanced products to european but also the global community so um one of the things then is that um providing opportunities for continued and long-term funding of workable solutions is is also important is these often take time to develop and mature as you said eodesm is expandable it's a workable solution but it you know but but to have something which continue to relies on on small amounts of funding to to build something which is actually quite um large and how could have a real impact is is really needed so it's a combination of funding infrastructure as said before um people to actually be able to know how to use it to access it and so on so it's it's incredibly fantastic system but it needs feeding yeah i think you have in essence a similar message that uh science and policy making can be empowered by data and the complex infrastructures but this requires a lot of dedication and effort and sustained the funding to to make it work for the user so thanks for these comments i don't see more comments coming through the chat which is good because we are over time but um for everyone who is connected i hope you enjoyed as i did the science and the challenges for environment that we have discovered today with alexandra and richard and before i hand it over to sara for a final announcement i'd like to to just give a few things and first of all to our project management board with peroster leading it when we have discussed the conference and what to do with that they have been enthusiastically supporting the idea of a digital conference um this has required effort on trust at to put everything in place to the presenters and we were not too sure about how successful this was it was the first time i think it's overwhelmingly successful and we have enjoyed the large audiences in in many sessions so thanks for for these efforts for the support from the pmb and i'd like also to thank specifically sara and the team at trust at for the dedication for this conference to make it happen and making sure that all the technical part which is not visible would work seamlessly so thanks a lot for that for from us cab the consortium and everyone who attended these two days and with this and with a virtual applause for for the audience and for the speakers i'd like to give the floor to sara for roping up the session well thank you very much to you titiana you also were very much behind the scene and i also would like to add extra thanks to the communication team of eoscap the input julia rob diego and my other colleague at trust at of course and clearly i want to thank all the chairs and the speakers and the eoscap board package leaders as well for the huge work that they've done so before closing as i said before and titiana announced we have a final announcement that now you will see on the screen so we want to announce the final eoscap event that will be organized and together with the shock which is the cluster for social sciences and humanities and the freya project which is the project working on pids so we are working right now on the agenda the idea is to have a three-day event in amsterdam on the week of the 16 of november we will announce a save the date soon hoping that these covid situation is will be over otherwise i think after this very successful experience probably we can go virtual as well the idea is that the event will open with the shock stakeholder forum event so targeting very much the social sciences humanities communities where eoscap will also showcase the specific thematic services serving this community the second day will be mainly dedicated to the eoscap results and the third day to the freya results as freya is finishing in november say that i just want to remind you all that all the presentations are online and linked to the agenda of the eoscap website the recordings of the sessions will be also soon available online and in 10 days or a couple of weeks time we will also share with all the participants the latest edition of the eoscap magazine that will include an article a post event article and for that article it would be great so if we could have if we could take a group picture right now so if you all can turn on your videos so we will try to make this experiment and we will feature this in the eoscap magazine so i give you a couple of minutes to turn on your videos okay i see many faces there so i wait the green light from my colleagues are you taking a picture now so please smile oh nice to see all these very well known faces so this takes a couple of minutes because there are still many people connected so stay with us for another couple of minutes i think that so we are done so with this i wish you all a nice evening and a long weekend for those having one and see you in amsterdam in november hope so goodbye everyone bye thank you everyone i'm done bye


Travel restrictions being imposed to prevent the spread of COVID-19 have disrupted the movement of hundreds and thousands of people around the world. People seeking refuge from violence, pursuing new work opportunities, reuniting with their families or moving for any other reason have had their journeys halted. IOM's Displacement Tracking Matrix has been tracking the impact of these restrictions on human mobility using the innovative flow monitoring methodology. This tool allows people around the world to see what countries, territories or areas are imposing restrictions. It also shows what restriction a traveler may face depending on their nationality or area of travel departure. Since the beginning of March 2020, DTM has tracked tens of thousands of travel restrictions in more than 200 countries, territories or areas. DTM is providing accurate travel information to people on the move to make well-informed decisions. It also helps IOM and other humanitarian organizations design interventions to help people on the move who have now been stranded or whose safety and well-being are now at risk. No other crisis in modern times has disrupted our ability to cross borders and migrate so directly and at such a short time. The impact of COVID-19 on human mobility is simply put unprecedented. But even amid a pandemic, we are still a world on the move. Migrants, people displaced from conflict and natural disasters and other populations will continue to seek safety, opportunity and services in new places far and wide. So as the international community tackles the health implications of COVID-19, we will also continue to work alongside effective communities to protect the well-being and safety of people on the move with approaches that are informed with solid data and effective mobility tracking. We're all in this together.


We've got a double act coming up next from Etienne Zaninotto and Peter Britten. So Etienne is Program Director of the Agile Scale Transformation with Societés General Investment Banking. Prior to this appointment Etienne has led several various transformation teams and programmes within the Investment Banking Front Office and the Risk Department, lastly a COO of the Front Office CBA desk. Peter Britten is the Vice President of Fidelity and Architecture Chapter Area Leader for Technology and Strategy within the Fidelity Personal Investing Business Unit. Within that unit Peter has overall responsibility for the technology, architectural strategies and solutions architectures operationalised within the Domains, Tribes, Squads and Functional Areas which are the constituent components of the Personal Investment Agile Operating Model. Peter is a member of the Enterprise Technology Advisory Group. The TAG whose primary responsibility is execution of the Fidelity CIO Council Agenda combined with the establishment of broad technology strategies and standards across Fidelity. The TAG is comprised of all of the CTOs and Heads of Architecture. And as I say the presentation today will be on Scaling Agile Lessons Learned. Gentlemen, welcome to both of you. I will hand over to whoever is going first but a warm welcome from the Open Group virtually please for Etienne and Peter. Okay, hello everyone. Nice to talk to you today and thank you to invite me for this testimony of the transformation that we are still following at the investment banking of Société Générale. So today I will do a quick introduction. Then I will explain the transformation that we've made to move to an agile scale model and then a few lessons learned I want to share with you. Quickly first, who are we? Société Générale, I don't know if it's a French bank but the investment banking of Société Générale is made of around 22 people in 70 countries and within these environments we've decided to go for an agile scale transformation in 2017. I will just explain the history after a while but the agile scale transformation was done at the level of the support functions as you can see on the slide. We have the different business lines of the investment banking, you have the market activity, the financing, global transaction banking, security services, private banking and asset management and all the support functions that we call the IT department, the technology and the support functions that are called the other support functions that are in the banking called the back offices were the scope of this transformation. So we were talking about a transformation initially on IT, 7,000 people, we were talking about 7,000 people for the IT departments. First of all we need to recall why we went to this transformation because we did not go toward this long journey just because the other did, we thought that we had four main drivers. First of all we thought that it was a good way to enhance the value delivered to the business. Second we think that this model, the agile scale model is more effective to deliver either facing business or inside the IT department itself. Third it's important to have in mind that today we are in a competitive environment and we compete with the companies that are not banks to attract the new talents and today you need to have a model which is interested for the new talent to get new donors in the bank, top level new donors and of course we think that this model is a fit for purpose for innovation which is of course very important and more and more important today. If we take a step back we can go back to the agile manifesto just to recall on what we have based our target operating model. Here are the four value drivers of the agile, I think most of you know them. What we have done internally before to design the target operating model, we said okay how do we translate these values into things that will help us draft our operating model. So we've said first of all we want a business alignment of the super function and in particular on the IT to the business. Second we want all the decision to be valued driven which is something which is not obvious in the banking company. Of course we want to favor decentralized decision-making process and Société Générale has had an history of quite cascaded decision-making process. We want and we need a collective commitment and to do so we need 100% transparency of the activity of the IT and the support functions and of course we will work we will put everyone in iterative cycles which is the the basic of the agility. These are the following the principles we followed to draft our operating model. If we look at the journey we followed we did not start in 2017. Agility at Société Générale started we started by Dolin at the beginning of 2011 we started to have some project in agile. On IT departments on the operation side they continued to follow the lean management principles. On the IT we started in 2011 by this project in agile. We launched a very ambitious program in 2014 that we called a continuous delivery program to enable the platform to put in production the deliveries that we had that were more and more agile and more and more rapid and we've decided to go for an agile transformation in 2017. Then we started this transformation it's three years back and after one year one year and a half we switched to an organizational agility this is what I will present you today because agile at scale alone within the IT environment does not suffice. We have to embed that in a decision-making process which is compatible with agility and this is what we call organizational agility. So as you see the agile at scale program at Société Générale Investment Monkey was more a bottom-up program than a top-down program. It came from the IT then it has impacted all the support functions and through the organizational agility it is now impacting the whole chain of the activities. Okay so what is it about? It's about culture and mindset. It's about organization and governance and it's about methods and tooling. First of all we had to change the mindset of people around what is IT developments. We had to switch from a client-supplier-wish relationship to shared accountability mechanisms. We had to regroup two worlds that were completely independent the world of the project and the world of the production to have a full ownership by the business of a service which encompasses everything production and of course new investments and we had to switch to a project view or long term planning to iterative objectives and to a mechanism much more much more iterative. Then we had to adapt the organization itself to welcome an agile way of working. First of all we had to change what we call the GBSU the support unit governance so as to enable a delegation at every level of the organization. I will talk about it just after a while. Then we had to switch our way to steer activities. We are we are switching it's not yet finalized we are well advanced it's not yet finalized. We switch to a no-care approach. We want people to define properly and disclose their objectives rather than to indotify the outputs that they want. Then we had to change entirely the way we manage the demands to ensure that the BUN issue and support unit initiatives are prioritized and steered according to their value and their contribution to the strategy of course. And last element this can work at the condition that we the the resources are not resources are not a mechanism for adjustments. I have a new project I will adopt new resources. If you do that it does not work you go back to a project approach where you think that when you can pay you can have something which is of course not the reality on the ground. So we've switched to a capacity management approach and now we define the capacity at the beginning of the year and within this fixed capacity the business stakeholders they have to choose what they want they want to do. To do so we have to adapt entirely our tooling suites so as to have tooling to manage our OKR from the top of the investment banking because the OKR are impacting the business from the top of the investment banking to the tribes. We had to change our portfolio management tool. We had to extend and to reshuffle the way we were using the execution tool which is Gira in our context and we had to implement a tool which is giving a full transparency of on our costs which is called Apsio in our context. This is the all the environment of the of the transformation. In this context we what did we do? We first aligned the IT organization and the support function also IT and operations with the business value chains. We moved from a silo organization between tech and ops to an industrial digital service platform where you have the service platform serving the businesses that I mentioned just before. So you will find in the gray box you find the different activities the different support functions and these support functions are of course completely aligned with the business functions. We speeded a little bit on this one. We've put the client at the heart of our governance model and we we have we've we've nominated many POs from the business to help us to drive our activity. I will not spend time on this one because I want you to understand not this one but the following. If I go back to the governance what we did is what we call Russian Dole approach. This Russian Dole approach is a mechanism whereby the objectives are set at the IS level. This is what you see here. The GBSU support functions then you have the support unit then you have the tribe then you have the team. Each of the governance level they have their own OKR. They have what we call the emblematic KPIs which are the elements to follow the production, the quality of what is developed etc etc and they follow line per line a few what we call a few major initiatives. We could talk about APICS if we would talk in Nigel Warding but it was difficult to have this understood within the company by seven thousand people so we prefer to use the term initiative but take that as an APIC. At their level at the level of the GBSU support unit they only follow a few major initiatives then at the support unit major initiative as well and at tribe level they have a few major initiatives and this is what is the most important. They are delegations to manage a big part of their budget which is delivered through delegated initiatives and then of course at team level they manage their team backlog. So this cascading mechanism enables the delegation that I've talked about just before. So once we've done that we have the different layers. We have the cascading and delegation and how do we operate operationally speaking. We went for a model that we called at the beginning a spotty safe model. It's a mix of Spotify. We've redesigned all the teams in tribes, tribes made of roughly 100 people and made of several we don't call them squads we call them feature teams as you can see. So we went for a Spotify model and when we have a transversal big project we apply safe mechanisms. So first of all let's stop at the organization in terms of operating model. So with 7000 people we have roughly 70 tribes. It's a little bit less but roughly 70 tribes. All these tribes are split into feature teams made of seven to nine people. They are all aligned with what we call a product and they are referring to a product owner. Really an element which is key in our in our deployment is the fact that we have insisted to have the product owner from the business. There is no professional product owner in our model. So it's not Spotify by the book. Our decision was to say if we want a product owner to cover all aspects of what a feature team is supposed to deliver from new projects to the production, to the support etc etc they have to be in front of the business so they have to belong to the business. So in our model the product owner spends 20 to 25 percent of his time with the tribe and the rest of his time is dedicated to his real work. So first of all to work the business we have the product owner and second to steer the activity of a tribe it's done via a concept that we've taken out from from SAFE which is the concept of PMT, a product management team because we notice that in our environment in the banking environment we were not able to have a product owner leading a tribe of 100 people. We need different skills etc it's not possible to have only one person. So we put a few people together from the front office some trader responsible of desk people like that people from the back offices etc we put them together and we organize their work so that together with the tribe manager they can steer the priorities of the tribe. So the tribe is a Spotify tribe with vertical feature teams. Last element I want you to keep in mind in our model we don't have vertical management anymore of course we have the tribe manager who's the manager of the tribe but we have switched the management horizontally so it's the chapters that you have here that are managers of the people in the tribes and this is very important because when you do that you enable the feature team to be really autonomous and you have no intermediary between the business and the guys in the feature team. We need to speed up a little bit sorry I'm a little late so you have the key roles here but I will not have time to go through these key roles I have mentioned the two main from the business the product management team which is from the business and the PO and of course in terms of management the prime manager and the chapter manager just one word maybe to say that how do we manage the architecture consistency we have a league an architecture league which is organized which is a upper to several tribes and that's the same for the production aspects and this is this enables to manage all the priorities all the developments that are coming to a tribe directly that are flowing to a tribe this is how do we synchronize the activity we have a demand an initiative coming and if it can go to one feature team we call that the single feature team model so it's purely Spotify you have something coming to a product you have a product owner deciding the privatization etc it goes into the feature team but it does not work if you need to if you have initiatives that are cross tribes or that are complicated to deliver and in this case we have two models on the right hand side we can put in place when we have a lot of feature teams we put in place safe trains to coordinate the activity of all the feature teams working for given initiatives of course these feature teams they belong to a given tribe but they put together their forces to deliver something in the context of a safe train and of course you can have in all the intermediate level that you can imagine because between one feature team and a train off we have trains up to a 15 16 18 feature teams in the middle you can have initiative where you need a few feature teams to be coordinated in in this case we do what we call the light sync which is a kind of a scrum of scrum or sing or sing like that sorry to speed up because we are almost done with this model so spotty safe model applied to it organizational agility applied to the whole organization and to have all the processes compatible with an agenda scale model we found very excellent objectives in terms of business and it satisfaction in terms of time to market even if the time to market is always difficult to assess we have a very good time to market and where the dramatic increase of the time to market and we had a lot of efficiency gains as well because this model for sure reduces the number of intermediate layer the pyramidal aspect of the organization etc last the feedbacks and the lessons that you would like to share with you we are quite happy with the transformation that we've made we are already at 80 percent of the tribes are transformed today the whole organization has been transformed we think we've managed to do that around on more than seven thousand people because we had a structured program with a holistic approach from the beginning we did not launch initiatives of agility even if before to launch the program we had two of them but once we've decided to go we had a structured program then we've defined a set of clear objective of indicators of the benefits of the agility we have more than 17 indicators that are followed to see the progress of the of our organization and we started bottom up as I shown you by DIT but with the organizational agility we connected with the top-down approach so we think that there is does not matter if it's bottom-up or top-down approach we need what is important is to embark the business from the start we we did not do a top-down transformation but we embarked the business with the POS and the PMTs from the start the other element is that we don't have a one single model we have adapted the different model to build our own target operating model with people from a Société Générale working on the different patterns etc we have as I've shown you very quickly transform all the steering processes this is what we call the organizational agility all the steering process and it's a big part of the transformation and of course we this had an impact a huge impact on the people so we build a very important HR stream to accompany the deep managerial and cultural change in the investment banking I think I'm a little bit late sorry for that that's great Etienne thank you that that's fine I'm going to assume that Peter will take over and we are doing the the Q&A for this in a panel session after Peter's presentation so don't go away Etienne we have questions for you on the panel thank you thank you over to you Peter thanks everyone for all the you know financial services is a financial services firm in the US about you know nine trillion US dollars of assets on the management about 45 000 associates and the personal investing business unit really is the business unit that deals with the B2C part of the organization what I'm going to do is actually click down a little bit on on how we adapt it you know agile architecture as a part of our transformation so what's quite fascinating is and probably not unusual is a lot of the elements that Etienne spoke to very similar to our own transformational journey interestingly enough and I wasn't aware of this before we started actually in 2017 but that was as a result of a number of previous incremental attempts where we didn't actually do a holistic mashup of the business and technology we had elements like capability teams you know we were doing agile in technology but there wasn't the real comprehensive mashup between the business and technology along all of the the roles and responsibilities so you know we have come into 2017 with that learning and then we made a decision in about August of 2017 that we couldn't be half agile as a business unit so the personal investing business unit consists of about 19 000 associates 14 000 of those associates are in regional centers call centers and branches 5000 are what we call home office so this transformation is really in home office and that consisted of about 2500 associates in what we might call core business functions and then another 2500 in technology and so when I say mashup it was really taking 5000 people and fully integrating them into again interestingly enough the Spotify model and we made a decision actually that we would flash cut the organization and we feel we felt we had enough learnings so we you know we notified the organization in in August of 2017 educated the organization in September of 2017 that we were going to do this and what it meant and then in October November and December of 2017 we literally in three ways flash cut the entire organization into a set of domains domains simply align to the elements of our strategy tribes squads and then as Etienne mentioned before we likewise have chapters that are orthogonal to the tribe and squad structure so for example architecture is a chapter I'm an architecture chapter area leader I'm sitting below me or architecture chapter leaders and they are get aligned specifically with domains tribes and squads and that's where they live in those cross functional teams rationale for the transformation you heard a bit of that from Etienne very similar delivering faster value to our customers and really energizing and engaging associates much more holistically empowering our dedicated teams and then and then for us and you'll see this in subsequent slide we really recognize that there are some fundamental cultural changes we wanted to make in the organization as it's related to delivering customer value faster and that was related to a model of leadership that was much more empowering and multiplying and then of course you know to stimulate innovation I would say that you know because we had some prior experiences with this and I'm sure you know others have too I heard Brian mentioned that a little bit and probably Etienne the fundamental chasm we were trying to cross was the you know the semantic gap between technologies use of agile and the business seeing agile as a primary contributor to delivering value delivering value to customers very quickly and then alignment between IT investment and overall business strategy and so for us the whole issue of mindset was very very fundamental to what we were we were getting after self-directing teams absolutely it's part of this whole notion of empowering we wanted deeper collaboration that wasn't simply ad hoc and episodic but actually was baked into BAU you know business as usual standard modus operandi you know Brian focused a lot on the value stream and we wanted that those combined teams to really have clarity regarding the specific customer outcomes that were associated with the business strategy and then we wanted to integrate to a continuous learning improvement model into the organization so apart from you know instantiation of the Spotify model the associated tooling you know we we likewise use OKPR model OKR model with you know what we call OKPI's so we can measure in a very quantitative manner what's actually happening and we use what was formerly called agile craft it's now called JIRA line to deal with the holistic structure of initiative ethics etc and more importantly we like JIRA line because it links directly into JIRA so that there can be a very direct click down into the actual work being done by squads spring stories etc etc and there's no need to actually manually look at that but the other thing we've integrated is we've taken 20% of every week so we call Tuesday's learning days and you know all members of the organization are actually allowed to use that day either collaboratively or otherwise for specific areas of continuous learning so fundamental changes that we wanted to make in the culture this didn't exist before and then you know as I mentioned before for us really driving some leadership principles with holistic accountability right I think you know Etienne mentioned this also but we wanted to be very explicit about those principles and those principles actually are broad based fidelity financial services leadership principles but they're also baked in of course to personal investing so what happened after the personal investing business unit began this transformational journey Abby Johnson who is the chairperson of the company about eight or nine months and she made the decision that all of fidelity all of the other business units would would go into this this transformation this type of transformation on the right side of the slide you can see for us part of what we did in that flash cut model I mentioned in three months is we actually had everybody reapply for their jobs and so you went through a 360 review you went through face-to-face interview and then there was your profile regarding what you've contributed previously and what we were really looking for were really individuals who you know demonstrated multiplying behaviors as you see here and we were quite frankly trying to you know minimize I would say diminishes quite frankly right because we felt that this was really critical to actually operationalizing the transformation on the ground very similar you know Spotify model tribe squads chapters we do have center of excellence and those for us are constructs that are intended to look ahead in areas that we feel are complementary to our overall strategy and we what we use those those center of excellence is to accelerate the overall validation strategy standards that will subsequently then be utilized within the domains tribes and squads so for example within my architecture chapter area I actually have an architecture center of excellence their their current focus has been over the last 12 months we utilize amazon web services and they were front running for us along with an enterprise you know cloud computing organization our instantiation of azure because we want today a multi-cloud you know multi-cloud strategy chapters fundamentally you know there are squad members with similar expertise ie architects and they drive consistency and what good looks like into those chapters I will say that within our instantiation of the Spotify model we actually use squad leaders so the hierarchy is domain tribes squad squad leaders and then chapters and they provide the orthogonal resource so that a squad leader actually functions as the individual who is accountable for leveraging all of the resources within that squad to deliver the appropriate feature function that would be you know the component of what the tribe was was trying to deliver and of course what the domain is delivering now as part of our evolution we're seeing more and more that those squad leaders actually need to be either complemented with or they need competencies of product management competencies and that's that's part of an evolution that we're currently in the midst of sevens of excellence I think I've talked a bit about that so I'm not going to say much more and then you know chapter leaders they basically are responsible for the you know literally the care feeding and career development of members within that chapter and you know from an architecture perspective I hear some of the things that they're specifically you know responsible for in terms of the technical role of the architect their design skills their programming skills communication skills etc one of the things we found was that once we had actually instantiated this model we needed to make sure that there was clarity of vocabulary and semantics even though many of our colleagues who primarily were you know focused on on the business had had contact with architecture we needed now to be very very very clear about roles responsibility and meaning and so I'm a really big fan of the you know the two Peters the Eppels and Peter Cripps and they've got a very nice model in their book that speaks about what what does an architect actually do what is actually architecting and what is architecture and so we use this this particular model so that domain leaders tribe leaders squad leaders and chapter members have a very consistent vocabulary and understanding of the role responsibilities and activities of the architects and the architecture chapter members within you know within the constructs we also you know again go back to the notion of being very clear about collaborative behavior leadership behavior mindset we've articulated very you know very clearly the engagement principles upon which we along which we practice architecture right and again you know Brian in his first presentation on business architecture mentioned a number of these things right staying in the business context focusing on outcomes layering the conversation we you know we use the C4 model for simplifying architectural artifacts so that all of the constituent stakeholders in the collaboration you understand what's what's going on and then and then other elements of how we actually practice architecture but our fundamental learning is that we would like to infuse architectural thinking into all roles within the model clearly not everyone is an architect but we want architectural thinking infused because we think it's it's very fundamental to you know how one effectively leverages you know the model I think that that Brian showed right in terms of the business model the business strategy the associate business model right the business architecture and we view architecture as a necessary complement to that business architecture so we want architectural thinking infused in in all of the associates within the collaboration and then you know to go even further we get very explicit about what we mean by end to end architectural solution and you know great presentation first presentation today for Brian around business architecture we are particularly because of our key objectives around the evolution you know for financial services right which is clearly being driven by what we call the digital natives digital giants digital dragons there is the recognition that the experience that they deliver um really provides customers with a coherent set of journeys and those journeys are usually very strongly correlated across the various tasks within the journey vis-a-vis the customer's desired outcome so you know we want it to be really really explicit about that and so what you see in this slide is how we've is how we've articulated um we're three years in I think you know a lot of the okpi's or okr's have proven that our initial aspirations and objectives are being realized and so we are now in a phase where we are evolving our overall architectural strategy so for example we made a deliberate decision in the early days of the strategy not to perturb a number of the horizontal components right and so those components would be things like existing platforms or aggregations of capabilities that behaved like platforms and or underlying data architecture we have now been pushing along in those dimensions specifically with respect to data as we I would say drive very hard now for what we feel is the fundamental infrastructure pivot of the underlying architecture I mean I have a hypothesis that says everything below the experience layer will commoditize and the rationale for that is is really quite simple um you know I think that the CSP is the cloud service providers are providing managed services and fundamental technology capabilities are very very rapid rate and therefore there's the ability to leverage all of that to you know build architectures that in many cases you know we have to build very complex systems to actualize and so we recognize that a truly digital experience requires a fundamental data strategy right and Andres and Horowitz have got a very nice paper on emerging architectures for data infrastructure for modern-day infrastructure and they articulate three three particular models right they call one a multimodal model an analytical and ML model and an operational model and the multimodal model is a convergence of that analytic model and the operational model and so you know we are pushing very aggressively in that direction and and you know versus and the way we thought about data architectures before we think that there is a much more holistic perspective around business and technology you know clearly security cyber security legal risk costs and governments that need to be a part of that overall strategy and then manifested in the actual architecture itself as policies automated policies and not manual policies and procedures and so we've got a you know complementary data governance construct that's associated with that part of you know our transformation with all of the elements that you would would expect around accountability quality life cycle management etc etc and this slide is just really a model of how we've organized to to to actually you know accomplish those objectives you'll notice that when John read the introduction he mentioned this tag group and that's a grouping of all of the chief architect CTOs and firm and that has a working group called the tag data council by the way that construct is we do not have an enterprise architecture organization the firm previously had one our you know we operate in a fully federated IT model and what that means is that all CIOs report directly to the presidents of business units we do not have an enterprise CIO we have an enterprise CTO and they're dotted line to that individual and then we use the this tag construct to create coherence across all of the architectural activities going going on in the various business units and the rationale for that is we wanted much more control and prioritization driven out of the business units and no out of the center of the organization and then the final piece I think this is my second to last slide is that we we've now you know one of the most I think perturbing things to me is if you if you get into a conversation about platforms you can spend close to a year trying to define what is a platform and we would know different with respect to that but we recognize that you know for us quite frankly it's an aggregation of logical capabilities and the logic around the capabilities are what's the intended use of those capabilities and because of the kinds of managed services that are available now in cloud we want to we view this as a very critical construct in our architecture and so we wanted to be really really highly opinionated about this so that you know there wasn't a lot of swirl so this is now a big push in terms of our evolution going from really you know undefined and ambiguous to really being very clearly defined highly aligned capabilities very very clear product management around those platforms and then you know holistic platforms enabling end-to-end transformation with very tight alignment and clarity around their contribution within a value stream this is a generic model actually came from McKinsey paper that we use when we think of our platform ecosystem and then the connection to architecture is the following it's actually the last bullet on the left lower left there is you know once you get to this you know notion of platforms and and and you know hopefully one is addressing the issue of duplicative capabilities right and then the issue really then becomes what we call this city planning function which is we've extended our architectural accountability to now look across domains recall i'm talking about a single business unit and so the business strategy for that business unit personal investing is actually quite coherent and so we want the you know corresponding architectural strategy and its output to be coherent across the domains tribes and squads and so we've extended the architectural practice to actually include this notion of city planning so a set of my architecture chapter leaders with some tribe leaders now form this city planning function and their role is to make sure to ensure that we're thinking holistically across domains across tribes about our platform ecosystem and the associated capability and that's a that is actually an in-flight part of our evolution even as we speak that's it