Okay, there we go for the second talk a bit on the same subject that is this this will describe another way of partitioning better diversity using two families of coefficients that we have seen before. So again the beginning is pretty much the same as what we have seen so I will not go in detail through that. So what we were doing during the previous talk here I will only focus on the dissimilarity matrices I will use for the similarity matrices and we know we can compute total sum of squares and total beta diversity from that. So this does it in a very different way. The idea here is to partition the dissimilarities in a dissimilarity matrix like this into two components that are called replacement and richness difference. Now during the past five or six years two different methods have been proposed by opposing groups of scientists. One group is mostly Andres Bazalga and then a few people have joined him in publishing papers with him. He is in Spain and the other group is led by Podani who is a very prolific worker in numerical ecology in Hungary and he has been doing that with the various co-authors. Each group has proposed ways of partitioning. First the Jacquard and Sorensen indices for presence absence data into these components replacement and richness difference and then later the two groups added the quantitative forms of Jacquard and Sorensen. For Jacquard it is this coefficient called Rujiska that I discovered when I started studying in detail these methods and for Sorensen it is the percentage difference. So in this talk I will focus on the Podani approach but the function because I cannot have time to describe everything but the function that is available in the ADE spatial package that I wrote contains the coefficients from both groups. Now there was some sort of fight in the literature going on for couple of years saying my coefficients are better than your coefficients, this sort of thing. So I decided to look into these things and figure out if there was one type of coefficient that was definitely superior to the other. And I found that this was not the case and that there was room for both types of coefficients. I tried to re-establish peace between the two fighting functions with the paper that I published there and by including the coefficients from both groups in one R function that everybody can use and experiment with. Now the basic concept is quite simple. This is for species presence, absence, data. You may remember when we compute the coefficients for presence, absence, data between site, let's say if we put site one here, site two. There with presence, absence, presence, absence and the cells are called A, B, C and D. These are the A, B, C and D components. Well, essentially A, B, C and A, B and C because the D component and the record and serensin and disease are never considered, the double absence, the double zeros, they are never considered and we know why. So here are the components A, B and C. For two sites in which we have a number of species, these five species are present at both sites. They are the A component. B here are, well these are the other species from site one that form the B component and these are not present at site two. And these species are present at site two but not at site one. But they are different species. So when we want to compute this similarity, it will be B plus C divided by some denominator. And in Jacquard, the denominator is A plus B plus C. In serensin it is two A plus B plus C. But these two coefficients differ only in their denominator. They both take B plus C as the numerator for the disciplinary coefficient. Now replacement is the number of species that are found there that you can consider as having replaced the same number of species at the other site. So you take the minimum of B and C and you say this minimum here has been replaced at the other site by other species. So these three or any of those species would do. It does not indicate that these three specific species have a replacement zone. But that three species that are not found here have replaced three species there. And the replacement component would be these three plus these three. So two times the minimum of B and C. For the richness difference, well this site is more rich than that one because it has five more species that have not replaced those. But there is an extra group of five species that form the replacement component. So with that we will construct different indices. I will repeat this picture in the small version at the top of the next few slides. Okay, so the same picture here with the coefficients in written, with the components written in the column for presence, absence, data. Later I will have coefficients for abundance data in this column. But I will use the same presentation during the next few slides. So replacement is two times the minimum of B plus C. It is this thing here. And it can be computed in this way. Richness difference. Here it will be called abundance difference. But the richness difference is B minus C. That is here we have eight species minus three that go into the replacement. So the richness difference is five. And the disseminarity is B plus C. Okay, so this just repeats what we have seen. And now we will construct the coefficients for the Jacquard group. The denominator will be A plus B plus C. So we will apply the denominator to these numerators for the whole disseminarity. This is the numerator, this is the denominator. For replacement, this is the numerator and this is the denominator. This is what we have here. And for richness difference, this is the numerator and this is the denominator. Okay, so simple construction. At least it looks simple in this synthetic presentation. It looked much more complicated from the original literature on this subject. Now same slide, but for the Sørensen group. The only difference is that the denominator is now two ways plus B plus C in every one of these three components. Simple again. Now what do we do when we have abundance data? This same coefficient has to involve now the abundances. And the A component, which is what is common between the two sites, is explained by this example where I have four species and in each subgraph we compare site one and site two. So for this species at site one we have this many and at site two we have this many. So the portion in common is a component of A here, A1. And what is the difference here is a component of C from species one for site two. So C will go into this sum. Species two, we have these abundances with this many in common that will go into the calculation of A there. And here we have something, all these individuals that form the abundances unique to site two. So it will go in this sum for B. For species three it is only present at site one. So B3 is all of that and it will go in B. And then for species four the two species have equal abundances so A will go in the sum there. So we sum all the A's from there, there and there. All the B's from there and there and all the C's there is only that one. And that gives us A, large A, large B, large C that we will now use as measurements of this similarity, that this similarity will be B plus C while A is the similarity portion. And replacement will be two times the minimum of B and C. And the richness, this time abundance difference will be the absolute value of B minus C. And we will put that into coefficients in exactly the same way as before. In the Jacquard group, this was the Jacquard dissimilarity. Now we have the Rogiska dissimilarity constructed in the same way as the Jacquard dissimilarity but with the large A, B and C value there because the denominator is large A plus B plus C. And we will construct the replacement in the same way, two times the minimum of B and C, that is this component divided by the denominator. And the abundance difference is constructed with this numerator and this denominator. Very simple. So this is a new coefficient that I have not introduced yet. Now you will recognize the good old percentage difference, where the denominator is 2A plus B plus C. You obtain exactly the same value as with the formulation that I showed in my talk on Tuesday morning. But Tuesday morning the letters A, B and C meant something different than here. But the calculation result is the same. It is just that the abundances are assembled in different ways. So the percentage, for the percentage difference dissimilarity is computed like this. The replacement component is this divided by the denominator. And the abundance difference is this divided by the denominator. Some of these abundance coefficients have never been described. So I filled the holes that the original author here, Podani, and also for the Baselga group had never described. So I said, well, let's complete the list with all of these. Okay, the fish data, you know about that. And now what we will do is compute the dissimilarity and split it into replacement and richness difference. And by the way, I don't know if I pointed that out. In each case the replacement plus the richness difference amount to the dissimilarity. So this is a true decomposition of each value in the dissimilarity matrix. Hence this presentation where the dissimilarity is split between replacement and richness difference. If you add this matrix plus that one, you find again the dissimilarity. So it is a true decomposition into these two components. And we will see what we can do with it. Here for the fish data, I used the present substance data there. I could have done it also with the abundant data. The total beta computed as we did in the previous talk gives us a value based on jacquard of 0.32 and on certain of 0.27, let's say. Now remember that these are dissimilarities between 0 and 1. So the maximum value that can be attained is 0.5 of these two coefficients for total beta. So this divided by that is 65% of the maximum. And this divided by that is 53% of the maximum. So they are well diversified. Well, 53% is very close to what we had after chord transformation using the abundant data. I think it was 53% or 54%. Now if we look at the components of replacement, if we add all these dissimilarity of these replacement values and divide them by BD total, we find 28%. And it is the same value that we find there. And in the same way for all the richness differences, if we add them up, yes, we can add them up and divide by BD total, we find 72%. So this plus that is equal to 1, showing that these two matrices have really separated or split the total beta diversity into two component matrices. And this shows already an interesting result. That beta diversity in the river is dominated by richness difference, not by replacement. That is as we go down the river, we have difference in richness. That is richness increases as we go down the river, except at these three sides that are polluted. So we have more and more species as we go. And we lose few species. We lose a little bit because it is not purely richness difference. There is a bit of replacement. For instance, the brown trout disappears after the first three sites. But the main phenomenon is addition of species along the course of the river. This I think is an interesting result. Instead of focusing on what happens between pairs of sites, we can have a statement for the whole course of the river. Now how to represent that? I scratched my head for a while to determine how to do that. And here I decided to compare each of the sites. Of course you see here number 8 has been removed from the data because there is no fish that has been cut by Dr. Vernault. And compare each one to the last site, number 30. Because this has nearly all the species. It is nearly the most rich. The most rich is this one, site 29. But anyway, I compared all the other sites to site number 30. And look at the pair-wise dissimilarities. Black is the Jacquard dissimilarity. And the pair-wise then values of replacement and richness difference. So we see again that the curve has dissimilarity. Each time I compare the site to that one. So the dissimilarity is high because there is no species in common between site 1 and site 30. Here there is only the brown trout and here there is no brown trout. So the species composition is totally different also for site 2. Then the dissimilarity decreases a little because there must be one species common between site 3 and 3 and so on. Then it goes up again, becomes more different. And then the dissimilarity falls. And then at the three polluted sites, the similarity increases a lot. And then it becomes low here because these sites are pretty much similar. But we see that the overall dissimilarity is followed very well by the richness difference curve that follows the dissimilarity because this is the main component of the Jacquard dissimilarity. While the replacement portion does its things on its side there. And there is no great contribution to the total diversity. And here for reference I use species richness. That is pretty much the mirror image reverse to the picture of the overall dissimilarity. So that's one way of using these decomposed coefficients, these elements of the richness difference and replacement matrices and dissimilarity matrix in a graph that tells us what happens along the course of the river without focusing on individual species. That is just in terms of overall dissimilarity. Now we can use ordination of course for the circumcision dissimilarity. So it gives us a story like this with site one here at the top of the river. Then for a while we have about the same species composition. Then we go in the long loop here. Then we go to the polluted sites, 23, 24, 25, very different from all the others. Then we go back to some normality in species composition. And then if we compute the LCBDs from the Sorenson matrix, we obtain this with again these three sites having high values and these three sites having high values. So this is not surprising. It is like what we obtain with the quantitative data after chord transformation in the previous talk. Now what is new is that we can do the same thing with the replacement and richness difference components. We can use the whole matrix of replacement and do an ordination by principle coordinate analysis. In the appendices of the paper, there is at some point on page 22 of the appendices, I think. There is a table showing which one of these components produces a matrix that is metric or Euclidean, yes, either in its original form or in the square root form. So with the Sorenson coefficient, I think it is the richness difference that can be Euclidean, but the replacement is never fully Euclidean, even after square root transformation. But still we can do this little story here with site number one for the replacement portion. It goes like this. It goes to the polluted sites and this is what happens in the lower course of the river and this is what happens in the upper course of the river here. And we can compute LCBDs from this. We have this similarity matrix. We can do our centering, take the diagonal values and they are our LCBD indices. And they are just plotted here. I didn't do any test of significance and we see that we have high values at sites 11 to 15 here and high values, of course, in the three polluted sites that are there. So the LCBDs again are the squares of the distances between the centroid and each site and those that are farther than, let's say, this circle, these are the farthest sites, 11 to 15. They have big bubbles and these are far also from the origin, 23, 24, 25. They are there. So again, this shows us where are the sites where high replacement occurs. A funny thing happens with the richness difference. This is a mistake. This is the richness difference, not replacement, but copy and paste on my part. So we can take the metrics of richness difference and do an ordination by principal coordinate analysis and when we do it with the Sörenssohn index or with the quantitative form, the percentage difference, all the points are on an arch like this, a perfect arch. So this is an advantage of using the Sörenssohn or percentage difference compared to the Jacquard and Riziska in the C. Everything is perfectly in line here, but then I added lines to show the sequence from 1 to 2 to 3, 4, after 4, 5 must be here. From 4 we go to 5 here. 5 then come back to 6, 7, 8 is not there. 9 is at the same place. 8 has been removed. 10 to 13, then 14, 15, 16, and so on. And then after site 22, 23 goes there, pollution. So we lose most of the species. Where are 24, 25? Oh, okay, 24 and 25 are here. So fewer species than there, but we gain a few species. And then go back to normal there. So it is an interesting story to follow it there and we have it in schematic form by taking the diagonal values of the centered matrix and these are our LCBD indices showing these three sites. 1, 2, 3 as having big distances and 23 as having a big square distance actually from the centroid. So these four have big bubbles. So these are the interesting sites where richness difference is not smooth along the river but the sequence is violated and we have a very small number of species at these sites. What can we do? Oh yes, now I tried to see if we could do something more than what had been done in previous papers by Podani and Baselga in terms of using these coefficients to test the hypothesis that is to compare these component matrices to explanatory variables. And here the first explanatory variables that I use was to take all the environmental data in the numerical ecology with our book, all the explanatory variables for the river and I computed one clustering hierarchical classification and I took simply two groups a bit like what Daniel did when he determined three groups. Here I did two groups of sites, the upper course and the lower course. You know a rough classification into two groups and I tried to see which one of these components were related to that. So I must explain what I did here because it involves... it is like doing an RDA. In an RDA I would use the Y data against the environmental data. Environmental data X or here my environmental data have become classification into two groups one to 22 and 23. So I cut it here, group one and group two. So this is my explanatory variable. RDA works fine if we can take this similarity matrix and decompose it using principle coordinate analysis and put the principle coordinate into the Y matrix. This is the DB RDA approach that I showed yesterday in one of my concluding slides. So that works fine. But here we have at least one of the matrices that is never even if we take the square root. So there is another approach to doing the equivalent of RDA but from this similarity matrix here. This similarity matrix that can be D or richness difference or replacement. So we take the dissimilarity matrix against this or that and this method developed by Brian McCardell and Marty Anderson in... well, Brian McCardell is... they are both in New Zealand and in 2001 paper they developed an F test of significance of the RDA computed from this but it is exactly the same as if we were decomposing it using principle coordinate analysis and doing a normal RDA. But it can be done even if the matrix is not Euclidean. That's why we keep it in that form. So the mathematics is interesting and I think I have included that function into ADE spatial to die. Anyway, now it is in vegan. I sent it to Gary Axanen and he included that in vegan. So this is to explain that it was... the F test was done in a mathematically different way but it is the same as the F test of RDA. So I found that the whole distance, the Jacquard dissimilarity matrix, here I used the Jacquard. Yes? Yes, the Jacquard. It was significantly explained by the classification but replacement was significantly explained also by the classification but more significantly than that whereas richness difference was not significantly explained by the classification. So D contains this plus that so the component that is really related to the classification of the river in the two sections is the replacement component, not the richness difference component and you see that this P value is more significant than that one. So this is really the result that should be interpreted. Now I kept going and used the whole matrix of environmental variables. I must have done forward selection. So yeah, here I did that after transforming these data using principal coordinate analysis and four forward selection but then I did the test here using the MacArthur-Landerson test and found that the whole dissimilarity was explained by slope, hardness, nitrate and oxygen concentration. Replacement was explained by variation of oxygen concentration and richness difference was explained by the three other components found there. So that was, I thought, a very interesting result because splitting the dissimilarity into two components showed that the explanatory variables that were significant there were also split into two groups and related to these two different components that are orthogonal components in the story. So that is extremely interesting. It goes deeper than just analyzing the dissimilarity matrix. Here we really touch the ecology, what happens to the community composition data in terms of these two processes and how it is related to the environmental variables. Well, at least I like that result. In the original paper, there are other graphical tricks, methods of analysis that have been proposed by Podani and Chmera and by Podani and co-authors and I use them, for example, these triangular plots in the analysis of the two river data that I used in the paper that was published in the Global College in biogeography two years ago. Okay, so the conclusion of that part is that replacement and richness, difference in disease can be interpreted and related to ecosystem processes separately, as we saw in this example. And the innovation of this decomposition was that the index values can be summed across all pairs of sites to decompose total data diversity into total replacement and total richness, difference components, previous authors have not done that. So because of the mathematics, that was so simple. I thought if we can do the sum of the dissimilarities to obtain the total variance for these indices, remember these are indices that require to be square-routed to be Euclidean. So the total variance is the sum of the dissimilarities divided by n and then by n minus one. And each dissimilarity is the sum of component of richness, difference plus replacement. So these two other matrices can also be summed in the same way and divided by n times n minus one to obtain the total variance. And these sums of the replacement and sum of the richness difference component add up to the total data diversity. So that's another way of fully decomposing total data instead of only doing it side by side. Local contributions, the LCBD indices, can be done computed for these two components separately and they can be mapped as we have seen. Within a region differences among the sites measured by these indices can be analyzed and interpreted using explanatory variables, either a classification or a series of environmental variables as we have seen. Replacement and richness difference matrices can be analyzed by all methods of multivariate data analysis that are appropriate for dissimilarity matrices. And do I have anything else? Yeah, well, the reference. Okay, questions about that? I realize that this is new to you unless you had seen the papers and were already in this sort of literature, but for most of you this may be new. So if you don't have questions now, maybe you will have questions this afternoon where there are exercises where you can play with the function. There are examples at the end of the function and I will encourage you to run them and look at what you obtain and maybe you can understand more fully how useful this method of decomposition of the total beta diversity can be for your analysis. I hope it will be useful to some of you. And with the new package, ADE spatial, everything is available in there.


Jungle is an absolute depth trap. Everything out there can take you out. Dense. Rough. Wet. We're there for each other no matter what. In Tully, 1-1 Alpha was sent out to conduct the jungle warfare training, where we basically got sent out to the jungle for 6-7 days to learn what jungle operations are, how we can ultimately not only survive but thrive in that type of environment. Jungle definitely poses a lot of dangers. Not only in the enemy, but the terrain, the wildlife. Just anything in Australia can take you out at a moment's notice. Mentally, emotionally and physically drained throughout the duration of the training exercise.


Joe Tonneson. He enjoyed back with you and joined now by the new head coach of New York City Football Club and Nick first off Congratulations second house your preparation for this game been different as a head coach than when you were the assistant coach Firstly, thank you so much. I appreciate the support. Um, I Think for us, it's about being the same team as we've been all season, you know our league position Reflects the the work ethic and the the commitment of the team and you know, we've played some really good football this year And we've won some football games So for us the preparations being about continue to be consistent and continue to work on the things that have helped us and Contribute to us picking up so many points and getting us in the league position that we are Well, it's great to see you in charge now Congratulations from me also Nick and I know you have worked so hard to get to the stage to be in charge And now you're the man. So now you're the man that we're coming after as well These boys don't play well, but what I want to know personally from you your journey It was a big decision for you to bring yourself over here to the United States to commit to Major League Soccer and New York City Football Club now you're in charge. Was this always the vision for you that one day? Maybe you might be the main man? I Don't think it was come here and be the head coach of New York I think for sure it was come away from being a head coach within the group to Go into a phase of learning and developing go as an assistant in one of our clubs To contribute to try and get that club successful work with an experienced coach like Ronnie and work with high-level football players that will you know stretch me that will Make me you know get me out of my comfort zone and that will develop me as a coach So yeah, of course, I wanted to be a head coach and I came here to Become a better head coach Maybe the the role has come unexpectedly because of Ronnie's decision to go back to Europe and you know, I'm comfortable with that I'm I'm confident that I can continue the development of the team and I'm ready to be the guy that leads the team Nick we saw your resume the success you had with the Manchester City women What does that experience and what you learned from that? How does that help you here today? I think you know, this is an iconic stadium and you know We have an incredible following and we have really good players and that was no different that Manchester City women You know taking the team in front of 45,000 that Wembley and FA Cup finals and and Champions League semi-finals So I feel that I have the experience of of taking a team in a stadium like Yankee Stadium And you know, I've worked within the group for you know this mass 16 17th season in the group So, you know, I've been developed as a young coach here I've been developed as a head coach and and moving from being the assistant now into the head coaches role You know, I feel I've been prepared for this role and you know, I just want to get going now and get some games in Yeah, of course you've put in the hard work to get to the stage and this is an opportunity It's like gold us you're now in charge of the major league soccer champions And we all saw the song there a moment ago You're certainly creative off the field as well as you are on the sidelines and as well as you are on the training pitch But you also inherit a tremendous squad of players a group of players with great leadership And also a lot of young players who are still willing to develop and learn and they want to learn from the best But what I want to know from you Nick is the leadership How important is the the group of leadership that you have in that locker room and also out there in the starting 11th day going to be for you I Think it will be really really important and the thing, you know, we have I totally agree. We have some Firstly some really good football players, but we have some great leaders in that group And that was that was apparent last season when we went through the playoffs away You know three consecutive games and three consecutive wins to lift the trophy This week has been a really good week for me personally because those guys have shown me incredible support They have shown and continued the commitment that they have to New York City football club and to Continuing the work ethic that our football team has, you know, it's a it's a huge It's a huge benefit that we have to have those guys and for them to work as hard as they do because they're talented football players So yeah, we will we will we will lean on them We need them to show that leadership and especially in this period of change when a coach leads midseason You have to rely on your experience to make sure that maybe the inexperienced players Just continue to play football and enjoy their football You know me and Joe have the honor of the last seven years of being the voice for the fans But also sort of being in between the club and the supporters and it's been great sometimes for us to share our opinion Our voice, but what would your message be to the supporters who are not only going to be in attendance today? But also we are watching at home for the season ahead Continue to support us continue to be that that I know it's a cliche But continue to be that 12 player for us in those moments because this league is so competitive that of course we're champions But we're still going to have difficult moments in games because we're coming up against real quality whether it is home or away You know, we have a great group of away following fans that come and support us and just continue to be themselves You know, I love playing here at Yankee Stadium and when we're at City Field because the fans make incredible noise And and the players feel that the players love to play at home and the players feel it So continue to make noise continue to support us and in those difficult moments pushes over the line All right, Nick Cushing he's the new gaffer. Thanks for a few moments


If too many too many things are going to like go run with me today. Our entire lab is here in this hotel today, right? This and there's a large team which is supporting in the background You can see a team sitting there right there a bunch of guys here and we are completely suited So are we saying there are like tons of users on the virtual world? The answer is no, but there are tens and millions of people who are there in the gaming world right now in this particular session The thing to note is 5g is going to be a game changer for marketers so we are running a series of module on marketing in the 5g era Last week exactly at this venue i was running a session on drone marketing and these guys never allowed me to float a drone They took away my battery today we don't need drones but we are completely suited deep within so we are going to control whatever i do My metahuman does and guess what once i create my metahuman i don't need to be there somebody else can come in Control my metahuman and record content out there that makes it very powerful imagine top celebrities in India today if you render their character And if you get them get someone else like you use body double gone other days when Salman Khan used to come with a movie People used to get shocked saying that you don't know how to say I'm not a real life make you think right now look at this Everyone would have their own digital twin who would then multitask and do a lot of work what is making this possible So all the tech stack which are there as part of our r&d lab they are going to get live because of 5g When we did our first campaign using mocap which is group m essence media com along with iic disney there's a large team that worked on it And last year in one of the panels i was talking about how it was like a funny context but i meant it when i threw that use case Saying that if it's a cricket board meeting and Virat couldn't make it and there goes Virat he'll pop in there And there'll be conversations which are going to be running we actually made that possible by getting fans from various locations in India they popped into a live studio environment and that's how the fan meet was being done right how was that made possible that is 5g for you So 5g is not for video but it's going to enable so many such things which are getting suppressed because of bandwidth are going to get Invoked and are going to get live in the 5g and that's what you're seeing the entire infrastructure is getting geared for it Have you ever seen a vr device priced at 1300 rupees have you know right and there are tons of people who watched ipl cricket in Immersive form using a vr device which is geodive for 1300 rupee that is almost like democratizing a hardware which is going to go places And this is all part of the exact strategy with a lot of big brands and telecom operators are driving will come into picture they are Going to be 3d destinations where they would visit they are going to be avatars like this which they would control and that's Going to do show and tell in various pockets out there right so are you with me on 5g and mocap and metahuman which is Today's topic and how we leveraged it for one of the brands and today we also have one of my ex-colleagues Stigda with me she is a co-founder of this super duper bright start up which helped us release this campaign on etel Stigda is actually was with us and she had a very interesting story in terms of how it all started and where is she today Helping us collaborating and we are doing a lot of work together in this sci-fi era of ixar as we call it Stigda over to you. Thank you Neeraj that was quite an entry and my avatar I have just left it for workshops with the client back At office so I hope she is doing an amazing job. Thank you all for being here. It's truly exciting to be on the stage and to be Talking about metahumans and digital twins and alter egos it's a truly amazing journey that we are embarking right now Before we deep dive into the used cases and what all we can do for the brands and leverage it and all of those things I would just like to introduce myself a little bit so I'm Stigda I'm co-founder at IIC I handle the strategy side of things My journey with strategy started long back when I was in lift in bachelors and I got placed with top retail brands in India Shoppers store being human future group I soon realized that retail was not just one segment that I wanted to cater to And I wanted to cater to automobiles I wanted to cater to BFSI FMCG brands and all of those things and I knew if I had to continue my Dream and journey of catering multiple brands together the only way was being a part of an agency and I joined WBP So yeah I'm a product of WBP we used to handle a lot of amazing clients at Fitch which is now Landor and Fitch We used to cater Domino's we used to cater Wix we had very fun times during that time and I learned a lot Cut to today now me and Manish basically are running IIC I learned the things that I've experienced the things that I've learned I'm trying to apply it here IIC basically I would not go through the entire start up struggle journey that's for another session IIC basically is all things matic and we essentially humanize technology through design creating very impactful and immersive solutions As of now the focus and the sizzling thing that we are doing is integrating 5G, XR and AI into online interactions and Offline activations that is the core as of now parallely we are doing and working in a lot of technologies as well One of them being metahuman digital twinnings and all of those things so without further ado I'll ask Manish How is it all evolving like there was an interesting example you were showing me of Keanu Reeves and how accurate the render is Keanu Reeves, yeah So let's have a look at that Yeah, yeah, let's do Now let's load in Hi, I'm Carrie Ann Moss and I play Trinity in the Matrix films 20 years ago we asked ourselves how long it would be before faces and bodies could be changed as easily as we change clothes We wondered what would identity mean in a completely digital world And what would reality mean when a world we can build feels as real as our own So every time every time we show this AV to our marketers they say this is too English this must be all happening in Bollywood What's the state of heart when it comes to India right so let me just take you to our lab and show you our Indian prototype That how realistic is CGI is right let's just play that AV please let's come to India this is India we are building India Extremely photorealistic How is that this is India let's just stop it How is that guys need a big clap for this one yeah this is our output India is doing it please So now in the 5G era more and more characters are going to go photorealistic Let me invite Manish, Manish and me pretty much are geared with similar kind of a costume But before that we want to show you an AR experience for which I'll have to come here because we've done some level of mapping And this is something I wanted to do it with my very very amazing supermaverick client Mr. Amit Doshi from Britannia Britannia CMO he completely loves this space so I thought I will just create a quick render for him and please record this I want to send it to him over whatsapp alright look at this tap there you go that's an augmented reality Which is getting overlaid on top of a mocap powered metahuman it can be a brand character for all you know Now look at use cases for marketers all of us in the room we do lot of moll activations Can we get our face of the brand or brand ambassadors to pop into retail locations Me and Prashant we were having a conversation about how can we drive conversations with Zenzi outside the Burger King outlets Can we have a multiplayer gaming where your lead protagonist is in studio environment you don't even need him All you need to just render is metahuman and someone like us is sitting inside that mocap gear Which I am wearing which is completely loaded with sensors out here and then you can control an experience You can get them to play a game for all you know and that is what we intend to do right now Me and Manesh will show you a quick game and then Manesh will do the talking of how he creates all this magical stuff Let's go for it come on guys game time Okay, I'm loaded up in three two one shoot Okay, sorry my aim was really bad one sec three two one I'll now burst shoot okay in three two one Bus, bus, bus, bus, bus And bus audiences as well It's burgers bursting me Prashant You wanna play with us come over let's you just need to dodge this Manesh and Prashant come on guys Prashant Burger King Okay, then I think we should take a step back Prashant let me in center Are you guys both gonna hit me? Thank you guys Thank you so much for doing this Please Yeah And you are watching this and their burgers coming to you Okay, so I will You have to just dodge But it's still fine it's still fine Okay this is a three two one budge Budge here Okay, three two one once more Three two one once more One sec No sir I can bring it out this is real time this is okay sorry Three two one Three two one Okay you are going now Maybe I'll just come and Alright we got it Thank you so much Thank you so much Prashant We are coming to you now with the prototype since we worked so hard Come on guys this team has really worked so hard we haven't slept Have you do you see an event like this where the team puts up so much efforts right This is seriously crazy if you would have to do it we could have done it in the mall actually with some celebrity But the what we're trying to do here is to create an experience which exists in India It's independently can be deployed in India we have everything available What you are seeing is not some PPT which you clients usually see saying that I'm here This is India this is what we are collaborating with startup players using our creative thinking and deploying these kind of solutions right So we need to get into the drawing board and start thinking solutions for the 5G era That is what we call it marketing in the 5G era which is not just to watch videos but experiences Consumer CX which you can never ever forget that's what we are trying to create here right Are you with me all intense looks You are loving the feeling right so this is what we can do for you Over to you manish. Thanks a lot and thanks for having me here I was not quite feeling in my skin today so I just became a cyborg Now as you rightly said that I didn't have to be here I could have joined anywhere in the world in any shape or form This reminds me of a very recent case study of Adl and Disney star So we did 10 broadcast with them very recently and I would like to just bring up one clip What we were doing we were bringing different fans from different parts of India And they were interacting with Yes this is the last one they were interacting with celebrities in Stasworth studio And this happened in just recent IPL So can we play the clip of Ahana It's the last one Win this year but team RCB win or lose we are with you Which two players are here and your time starts now Well okay the first one is my favourite Virat Kohli I can't guess the other one Oh is it a captain? Paap Du Plesi Paap Du Plesi and Virat Kohli Ahana you proved that you are the ultimate fan We are sending you back home as fast as we can Alright thank you so much guys


From around the globe, it's theCUBE with digital coverage of AWS re-invent 2020. Special coverage sponsored by AWS Global Partner Network. Okay, welcome back everyone to theCUBE virtual coverage of re-invent 2020 virtual. Normally we're in person this year because of the pandemic, we're doing remote interviews. And we've got a great coverage here of the AP and Amazon Partner Network Experience. I'm your host, John Furrier. We are theCUBE virtual. Got a great guest from Tel Aviv, remotely calling in and videoing in Nimrod Vax with this chief product officer and co-founder of Big ID. This is the beautiful thing about remote. You're in Tel Aviv, I'm in Palo Alto. Great to see you. We're not in person, but thanks for coming on. Thank you, great to see you as well. So you guys have had a lot of success at Big ID. I've noticed a lot of awards, startup to watch, company to watch. Kind of a good market opportunity, data, data at scale, identification. As the web evolves beyond web presence, identification, authentication is super important. You guys are called Big ID. What's the purpose of the company? Why do you exist? What's the value proposition? So first of all, best startup to work at based on last door worldwide. So that's a big achievement too. So look, four years ago we started Big ID when we realized that there is a gap in the market between the new demands from organizations in terms of how to protect their personal and sensitive information that they collect about their customers and employees. The regulations were becoming more strict, but the tools that were out there and to the large extent still out there, we're not providing to those requirements and organizations have to deal with some of those challenges in manual processes. For example, the right to be forgotten, organizations need to be able to find and delete the person's data if they want to be deleted. That's based on GDPR and later on even CCPA. And organizations had no way of doing it because the tools that were available could not tell them whose data it is that they found. The tools were very stylo. They were looking at either unstructured data in file shares or windows and so forth. Or they were looking at databases. There was nothing for big data. There was nothing for cloud business applications. And so we identified that there was a gap here and we addressed it by building Big ID basically to address those challenges. That's great, great stuff. And I remember four years ago and I was banging on the table and saying, regulation can stunt innovation because you had the confluence of massive platform shifts combined with the business pressure from society. That's not stopping. It's continuing today. You're seeing it globally, whether it's fake news and journalism to privacy concerns for modern applications. This is not going away. You guys have a great market opportunity. What is the product? What is small ID? What do you guys got right now? How do customers maintain the success as the ground continues to shift under them as platforms become more prevalent, more tools, more platforms, more everything? So I'll start with Big ID. What is Big ID? So Big ID really helps organizations better manage and protect the data that they own. It does that by connecting to everything you have around structured databases and unstructured file shares, big data, cloud storage, business applications and then providing very deep insight into that data. Cataloging all the data so you know what data you have where and classifying it so you know what type of data you have. Cluster analyzing the data to find similar and duplicate data and then coordinating that to an identity. Very strong, very broad solution. Fit for IT organization. We have some of the largest organizations out there. The biggest retailers, the biggest financial services organizations, manufacturing, et cetera. What we are seeing is that there are with the adoption of cloud and with the success obviously of AWS that there are a lot of organizations that are not as big that don't have an IT organization that have a very well functioning DevOps organization but still have a very big footprint in Amazon in other kind of cloud services. And they want to get visibility and they want to do it quickly. And Big ID is really, Small ID is really built for that. Small ID is a lightweight version of Big ID that is cloud native built for your AWS environment. And what it means is that you can quickly install it using a cloud formation template straight from the AWS marketplace. Quickly stand up an environment that can scan, discover your assets in your account automatically and give you immediate visibility into that your S3 bucket into your DynamoDB environments into your EMR clusters, into your Athena databases and building immediately, building a full catalog of all the data. So you know what files you have where you know what tables, what technical metadata, operational metadata, business metadata and also classify that information. So you know where you have sensitive information and you can immediately address that and apply controls to that information. So this is data discovery. So the use cases, I'm an Amazon partner. I mean, we use the cube virtuals on Amazon. But let's just say hypothetically, we're growing like crazy. Got S3 buckets over here, secure encrypted and REST and all that stuff. Things are happening. We're growing like a weed. Do we just deploy small ID? Is that how it works? Is that use cases? Small ID is for AWS and big ID is for everything else or? So small ID is that you can start small with the small ID. You get the visibility you need. You can leverage the automation of AWS so that you automatically discover those data sources, connect to them and get visibility. And you could grow into big ID using the same deployment inside AWS. You don't have to switch, migrate. And you use the same container cluster that is running inside your account automatically scale it up and then connect to other systems or benefit from the more advanced capabilities the big ID can offer such as correlation, such by connecting to maybe your Salesforce CRM system and getting the ability to correlate to your customer data and understand also these data that you're storing, connecting to your on-premise mainframe with the same deployment, connecting to your Google Drive or Office 365. So, but the point is that with the small ID you can really start quickly small with a very small team and get that visibility very quickly. Nimra, I want to ask you a question. What is the definition of cloud-native data discovery? What does that mean to you? So cloud-native means that it leverages all the benefits of the cloud, right? It gets all of the automation and visibility that you get in a cloud environment versus a traditional on-premise line. So for one thing is that big ID is installed directly from your marketplace. So you could browse, find this solution on the AWS marketplace and purchase it. It gets deployed using CloudFormation templates very easily, very quickly. It runs on a elastic container service so that once it runs you can automatically scale it up and down to increase the scan and the scale capabilities of the solution. It connects automatically behind the scenes into the security hub of AWS. So you get those alerts, the policy alerts fed into your security hub. It has integration also directly into the native login capabilities of AWS. So your existing data dog or whatever you're using for monitoring can plug into it automatically. That's what we mean by cloud-native. And if you know if you're cloud-native you've got to be positioned to take advantage of the data machine learning in particular. Can you expand on the role of machine learning in your solution? Customers are leaning in heavily this year. You're seeing more uptake on machine learning which is basically AI, AI is machine learning but it's all tied together. ML is big on all the deployments. Can you share your thoughts? Yeah, absolutely. So data discovery is a very tough problem and it has been around for 20 years. And the traditional methods of classifying the data or understanding what type of data you have has been looking at the pattern of the data. Typically regular expressions or types of kind of pattern matching techniques that look at the data. But sometimes in order to know what is personal or what is sensitive, it's not enough to look at the pattern of the data. How do you distinguish between a date of birth and any other date? Date of birth is much more sensitive. How do you find country of residency or how do you identify even a first name from the last name? So for that, you need more advanced, more sophisticated capabilities that go beyond just pattern matching. And big ID has a variety of those techniques. We call that discovery in depth. What it means is that very similar to security in depth where you cannot rely on a single security control to protect your environment. You cannot rely on a single discovery method to truly classify the data. So yes, we have regular expression. That's the table state basic capability of data classification. But if you want to find data that is more contextual like a first name, last name, even a phone number and distinguish between a phone number and just a sequence of numbers, you need more contextual NLP based discovery named entity recognition we're using to extract and find data contextually. We also apply deep learning CNN capability. It's called CNN, which is basically deep learning in order to identify and classify document types which is basically being able to distinguish between a resume and a application form. Finding financial records, finding medical records. So our advanced NLP classifiers can find that type of data. The more advanced capabilities that go beyond a small ID and to big ID also include cluster analysis which is an unsupervised machine learning method of finding duplicate and similar data, correlation and other techniques that are more contextual and need to use machine learning for that. Yeah, and unsupervised also a lot harder than supervised. You need to have that ability to get that. What you can't see, you got to get the blind spots identified and that's really the key observational data you need. This brings up the kind of operational, you heard cluster, I hear, governance security you mentioned earlier, GDPR. This is an operational impact. Can you talk about how it impacts on specifically on the privacy protection and governance side because certainly I get the clustering side of it operationally, it's great, everyone needs to get that. But now on the business model side, this is where people are spending a lot of time scared and worried actually, what the hell to do? Yeah, one of the things that we realized very early on when we started with big ideas that everybody needs discovery. You need discovery and we actually started with privacy. You need discovery in order to map your data and apply the privacy controls. You need the discovery for security, like we said, right? Find and identify sensitive data and apply controls. And you also need discovery for data enablement. You want to discover the data in order to enable it, to govern it, to make it accessible to the other parts of your business. So discovery is really a foundation, a starting point that you get with small idea. How do you operationalize that? So big idea has the concept of an application framework. Think about it like an Apple store for data discovery where you can run applications inside your kind of discovery iPhone in order to run specific use cases. So how do you operationalize privacy use cases? We have applications for privacy use cases like subject access request and data rights fulfillment, right? Under the CCPA, you have the right to request your data. What data is being stored about you? Big idea can help you find all that data in the catalog that after we scan, we find that information. We can find any individual data. We have an application also in the privacy space for consent governments, right? Under CCPA, you have the right to opt out. If you opt out, your data cannot be sold, cannot be used. How do you enforce that? How do you make sure that if someone opted out, that person's data is not being pumped into glue into some other system or analytics into Redshift or Snowflake. Big idea can identify a specific person's data and make sure that it's not being used for analytics and alert if there is a violation. So that's just an example of how you operationalize this knowledge for privacy. And we have more examples also for data enablement and data management. There's so much headroom opportunity to build out new functionality, make it programmable. I really appreciate what you guys are doing, totally needed in the industry. I can just see endless opportunities to make this operationally scalable or programmable once you kind of get the foundation out there. So congratulations Nimrod and the whole team. Question I want to ask you, we're here at Reinvented Virtual. Three weeks, we're here covering CUBE Action, check out the CUBE Experience Zone, the Partner Experience. What is the difference between big ID and say Amazon's, Macy? I mean, let's think about that. So how do you compare and contrast? And Amazon is, they say we love partnering, but we promote our ecosystem. You guys have a similar thing. What's the difference? There's a big difference. Yes, there is some overlap because both small ID and Macy can classify data in S3 buckets. And Macy does a pretty good job at it, right? I'm not arguing about it. But small ID is slightly different. It's not only about scanning for sensitive data in S3. It also scans anything else you have in your AWS environment, like DynamoDB, like EMR, like Athena. We're also adding Redshift soon, Glue and other data sources as well. And it's not only about identifying and alerting on sensitive data, it's about building a good catalog of data. It's about giving you almost like a phone registry of your data in AWS, where you can look up any type of data and see where it's found across structured, unstructured, big data repositories that you're handling inside your AWS environment. So it's broader than just for security. Apart from the fact that it can be used for privacy, I would say the biggest value of it is by building that catalog and making it accessible for data enablement, enabling your data across the board for other use cases, for analytics in Redshift, for Glue, for data integrations, for various other purposes. We have also integration into Kinesis and you'll be able to scan and let you know which topics use what type of data. So it's really a very, very robust full blown catalog of the data that across the board that is dynamic.  Yeah, great stuff. I got to ask you a question while you're here. You're the co-founder. And again, congratulations on your success. Also the chief product officer of Big ID. What's your advice to your colleagues and potentially new friends out there that are watching here? And let's take it from the entrepreneurial perspective. You know, I have an application and I start growing and maybe I have funding, maybe I take a more pragmatic approach versus raising billions of dollars. But as you grow the pressure for app sec reviews, having all the table stakes features, how do you advise developers or entrepreneurs or even business people, small, medium-sized enterprises to prepare? Is there a way, is there a playbook to say, rather than looking back saying, oh, I didn't do with all the things, I got to go back and retrofit, get Big ID? Is there a playbook that you see that will help companies so they don't get killed with app sec reviews and privacy compliance reviews? It could be a waste of time. I mean, what's your thoughts on this? I think that very early on when we started Big ID and that was how our perspective is that we knew that we are a security and privacy company. So we had to take that very seriously out front and be prepared. And so security cannot be an afterthought, it's something that needs to be built in. And from day one, we have taken all of the steps that were needed in order to make sure that what we're building is robust and secure. And that includes, obviously, applying all of the code and CI-CD tools that are available for testing your code, whether it's SNEAK or checkmarts or these type of tools, applying and providing penetration testing and working with the kind of best in line kind of the pen testing companies and white hat hackers that would look at your code. These are kind of the things that that's what you get funding for, right? And you need to take advantage of that and use that. And as soon as we got bigger, we also invested in a very kind of a very strong CISO that comes from the industry that has a lot of expertise and a lot of credibility. We also have kind of CISO groups. So each step of funding we've used extensively also to make our kind of security posture a lot more robust and visible. Final question for you, when should someone buy big ID? When should they engage? Is it something that people can just download immediately and integrate? Do you have to have, is the go to market kind of and you target the VP level? Or is it the, how do you, how does someone get in know when to buy you and download it and use the software? Take us through the use case of how customers engage with that. So customers typically have those requirements when they start hitting and having to comply with regulations around privacy and security. So very early on, especially organizations that deal with consumer information get to a point where they need to be accountable for the data that they store about their customers and they want to be able to know their data and provide the privacy control they need to their consumers. For our big ID product, this typically is a kind of a medium size and up company with an IT organization. For small ID, this is a good fit for companies that are much smaller, that operate mostly out of their IT is basically their DevOps teams. And once they have more than 10, 20 data sources in AWS, that's where they start using a count of the data that they have and they need to get more visibility and be able to control what data is being stored there because very quickly you start losing account of that information. Even for an organization like big ID which isn't a big organization, right? We are 200 employees. We are at the point where it's hard to keep track and they keep control of all the data that is being stored in all of the different data sources, right? In AWS, in Google Drive and some of our other systems, right? And that's the point where you need to have start thinking about having that visibility. You know, all growth plans start big, dream big, start small and get big. I think that's a nice pathway. So small gets you going, you lead right into the big ID. Great stuff. Final question for you while I got you here. Why the awards? Someone's like, hey, big ID, this is cool company. Love the founder, love the team, love the value proposition, makes a lot of sense. Why all the awards? Look, I think one of the things that was compelling about big ID from the beginning is that we did things differently. Our whole approach for personal data discovery is unique. Instead of looking at the data, we started by looking at the identities. The people and finally looking at their data, learning how their data looks like and then searching for that information. So that was a very different approach to the traditional approach of data discovery. And we continue to innovate and to look at those problems from a different perspective so we can offer our customers an alternative to what was done in the past. It's not saying that we don't do the basic stuff. The REG-X is the connectivity that is needed, but we always took a slightly different approach to diversify, to offer something slightly different, more comprehensive. And I think that was the thing that really attracted us from the beginning with the RSA Innovation Sandbox Award that we won in 2018, the Gartner Cool Vendor Award that we received. And later on, also the other awards. And I think that's the unique aspect of big ID.  And again, I don't think the problem's going to go away anytime soon. Platforms are emerging more tools than ever before that converge into platforms. And as the logic changes at the top, all that's moving on to the underground. So congratulations, great insight. Thank you very much. Thank you for coming on the queue. Appreciate it, Nimrod. Okay, I'm John Furrier. We are theCUBE Virtual here for the Partner Experience APN Virtual. Thanks for watching.