Good afternoon everyone. My name is Christel van Steen from the University of Liege and Keuleuf in Belgium. Our next speaker is Laura Furlan and she's an associate professor at Universitat Pompeu-Fabra, UPF, and is also a miguel servet researcher at the Hospital Gellmar Medical Research Institute in Mim. She's heading the Integrative Biomedical Informatics group of the research program on biomedical informatics in Barcelona, Spain, and she's really combining her background in biology and bioinformatics to develop bioinformatics approaches to identify and understand molecular mechanisms underpinning human diseases, possibly in relation to drug response. She's particularly active in systems medicine and systems toxicology, text mining, and knowledge management. She has a long-standing record in EU project involvement, covering the former FP7 framework program of the EU, but also the current age 2021, including LXE's accelerate. So Laura, the floor is yours. Thank you Christel for the nice introduction and good afternoon to everybody. So we'll start my presentation of today that is titled Enabling Comorbidity Analysis from real-world clinical data. So as you maybe already know is that we are facing an increasing aging of the population worldwide, and this has consequences from the health perspective. So we are also observing an increasing prevalence of chronic diseases and this also has an impact on the increasing prevalence of disease comorbidities and this can in a way impact how patient management has to be achieved and also has an impact on the treatment of the diseases that are involved in comorbidities. But let's start having a clear idea of what we talk about when we talk about comorbidities. So there are different definitions in the bibliography and this depends on how we approach the problem of comorbidity. So traditionally this was approached from a perspective in which we can see the one principal disease and the other concomitant diseases as secondary or less important and this first definition is more in the spirit of this perspective. So traditionally in the earlier studies of disease comorbidity the focus was on the index disease and then the other diseases were considered as concomitant or secondary to this principal disease. And then there was another perspective that is more focused on the patient, the individual patient and all the diseases that occur in each individual patient without putting a special importance to one disease over the other and this has a more holistic perspective and in some way is the approach that we use for our analysis. Notwithstanding this for simplicity I will sometimes refer to the term comorbidity although we are studying more than two diseases so different diseases that may co-occur in a given patient. Another important aspect we need to consider when studying comorbidities or multimorbidities are the chronological aspects. So in which time window we are looking at the whole patient's history to define the multimorbidity or comorbidity because as you can imagine and this is illustrated in this figure you can observe the two disorders or at the same time in the in the patient so they coexist, the individual patient may have several diagnosis that coexist in time or they can be they can be separated by a certain time frame and in this scenario you might also have situations in which one disease is the consequence of the other. So there are different combinations but the important thing here is that you need to have into account these chronological aspects when you design a study and you define what you mean with comorbidity or multimorbidity and not also this is the fact that in order to analyze or identify comorbidity or multimorbidity patterns in a population the time is really a key factor and this is what one of the main messages that I want to convey to you today. So why is it important to study this problem of disease comorbidities nowadays? So I already presented that we are facing an 18 of the population and this has increasing the prevalence of comorbid diseases and this of course has great impacts in the healthcare so there is a need to address this problem in a proper way in order to to reduce cost and this is from a healthcare perspective but also to improve the quality of care that is given to these patients and it's also important to to note that the comorbidity is not an exception but most of the patients currently have more than one disease when they encounter the clinical the healthcare system. Another important aspect also to consider is that when we study comorbidities we can gain a better understanding of the etiology of the diseases that are in place we can also identify patients subpopulations that have similar characteristics and even define new diseases types and this is also very important when we come to to the problem of disease classification and also has consequences for trying to find out more effective preventive approaches and even designing better and safer treatments. So I think the reasons why addressing this important problem in health are quite clear. So in our group we from some years ago we are working towards helping addressing this problem by developing different approaches in also bioinformatic tools that can be used for from one side to identify and and assess the degree of comorbidity that is available in patient level data and for that we exploit real-world data and also different types of omics data sets. So we are in a moment in which we can access to different information from the patients that describe their molecular features like genomics, protonics and so on and this can have the access from different initiatives and on the other side it's important to consider data that is captured from the healthcare systems and this is what I mean from real-world data. So data that is obtained from the encounters of the patients with the clinical system, the hospital and that is recorded in the electronic health records and other types of records that are collected during the clinical practice. So it has been demonstrated also in other applications. All the information we can extract from this real-world data and how can be applied for research of course but also for translational applications. So with this spirit in our group we have developed a series of tools to from one side identify comorbidity and multivormidity patterns from real-world data and these are listed here and I will provide later a more complete list with our URLs and also some tools that help gaining insight on the molecular basis of the coexistence of diseases. But today I want to focus on our approach for analyzing temporal patterns on disease trajectories. So a little bit more about using real-world data to mention some aspects that need to be taken into account. So from one side this data is very rich and can provide a lot of information about the real patients but when working with this data you have to be aware of some barriers or aspects that require special attention. One is that you need to know that this data has not been collected for research proposals so it's different from a cohort database that you might collect with a lot of attention to collect very well specific information about the variables you are studying. So this is a completely different scenario so you are working with data that has been collected for other purposes for of course patient management but also in sometimes billing. So the idea is different and from this knowing this you have to deal with data incompleteness sometimes you will find errors or bias in the way data is registered that you need to know in order to control in an appropriate manner. Other things that you need to be to take care of is how the data is represented. Sometimes the information is encoded in different using standards but depending on the data you work with they will use one standard or other and also different standards are used for different types of information diagnosis medications laboratory measurements and so on. So you need to be aware of all these aspects to process your data set in an appropriate manner and also is the issue of how the database you are working with is structured and also this is important when you plan to combine data coming from different databases or different hospitals. So let's then move on once I have talked a little bit about working with real-world data how we approach the problem of finding these these trajectories. So first let's see how we can represent the information that we can obtain from an electronic health record of patients from a hospital for example in which we have encoded the different diseases so the different diagnosis that a certain patient can have at different time points and this is something you can find in an electronic health record or a patient registry. So the idea is to use this information to represent a single patient and we are interested in how these diagnosis are assigned in time. So we can define these disease trajectories as disease history vectors in which each diagnosis is is ordered according to the time of diagnosis. At the end we have a time series so order sequences of diagnosis and this disease vector represents an individual patient. So we can if we have a database of patients we can represent each patient with this approach so each patient will be represented with this disease history vector and then what we would like to do is to find for instance patients that are similar among the whole database or even group of patients that share similar disease trajectories in our case. So for that what we do is to perform pairwise comparisons between our disease vectors that represent individual patients and we with the goal of finding what we call the common disease trajectories. These are the trajectories that are in which the diseases are shared among a minimum number of patients. Let's say we set the limit to 10 patients or 100 patients. So in this way we can identify these common disease trajectories that represent sequences of ordered diseases that are shared by a number of patients. So if we perform this procedure for all our database we will end up with a collection of trajectories and the next step then is can we identify common patterns between in our population and for that we will represent a clustering algorithm that is based on the dynamic type warping approach that allows us to identify patterns of commonalities between our data our disease trajectories and assign them in different clusters. Note that our trajectories may vary in duration and in the diagnosis and the order of the diagnosis and the clustering algorithm is able to assign trajectories of different duration different length in the same cluster and considering the similarities in the in these individual trajectories. So let's talk a little bit about dynamic time warping. So dynamic time warping is a powerful dynamic programming algorithm that is used for measuring similarities between signals or time series that can have different lengths or speeds and it has been successfully applied to different domains from speech recognition and gene expression and many other domains and we propose that this can be used to identify disease groups and especially for identifying common disease trajectories. So dynamic type warping is a global alignment method and what it does is to calculate an optimal path between the two sequences by minimizing the total distance. So the way it does it is to try to compute an optimal path, the minimal path between the two distances and in this way it achieves a more intuitive way of aligning the two sequences. So in a way it stretches or compresses sections of the sequence in order to find this optimal alignment and this is done despite these sequences might be somehow out of phase in the time axis and that's why this is a really suitable approach to apply to these disease trajectories that I already shown to you. So let's look at an example with the disease trajectories to see a little bit more how the algorithm works. So we have from one side two patients represented by the disease vectors and so what we want to obtain is first this common disease trajectory that is obtained by comparing in a per-wise manner all the diseases that are described in it trajectory and we do that also by using a local distance matrix that displays the similarity between the two sequences, the two trajectories for each of the elements, so for each of the diseases and the distance metric that we can use here for the diseases can be obtained using different approaches that I will explain a little bit later. So we compute this local distance matrix and the dynamic time working actually works by trying to find an optimal path that is shown here with this red line that minimizes the distance between the two trajectories. So here in the local distance matrix the lower values indicates the more similarity elements of the trajectory are so what the what we try to do is to find out this path that goes from the that minimizes the cost of comparing the two sequences. So what we do is actually to obtain from the local distance metric a total accumulated distance matrix and the value of the last element of the column actually is the distance between the two sequences and is the similarity metric that we will use later on in the clustering and we subsequently, sorry, we subsequently calculate this optimal path between these two sequences and we aligned them to using this working path. If you are interested I can give you some pointer this is a nice example and very illustrative to to show you how actually the local distance and accumulated distance metric is calculated if you are interested in exploring a little bit more about dynamic time warping. So once we have obtained our common disease trajectory sorry too fast we end up with trajectories of different lengths so trajectories of length two three four five six and maybe more so these are sequences of diagnosis that are shared among a certain number of patients so this represents our database and what we want to do now is to answer the question if there are similar patterns of trajectories that we can identify and for that we apply this unsupervising clustering approach based on dynamic type warping. So let's go to explain how it works so we have our collection of common diseases common disease trajectories and we will process iteratively to obtain our clusters. So the first trajectory of the collection is assigned to to first cluster and imagine that we repeat the process that we will go through several times and we are in a step of the process in which we already have three clusters and as you can see each cluster has a set of common trajectories that can vary length but that they share some common properties like diagnosis and combinations of certain diagnosis over time. So we want to cluster the next trajectory so what we do is we compute our using dynamic time warping our metric of similarity between my common trajectory and each trajectory of a cluster and we repeat this procedure for all the clusters we already have. We then take the average distance between the trajectory and all the trajectories of each of the clusters and we identify the minimum distance so we identify the clusters that might be more similar with my trajectory and then we use a threshold that has to be heuristically defined in order to identify to decide if we assigned the trajectory to this cluster so if the the average distance is lower than the defined predefined threshold or on the other side if it's higher we will end up by creating a new cluster with this trajectory. So this process is repeated several times and until we are able to allocate all the trajectories to the clusters. So as I mentioned before the threshold has to be selected and this is an iterative process in order to find a configuration in which the clustering is not very fragmented but also we don't end with clusters that are very big and contain a lot of trajectories and therefore do not provide a proper separation of our data set. So we analyze all this data we are applying the dynamic type warping trajectories so I explain how we compare the trajectories but at some point we need to define how we will compare diseases individually so what metrics of similarities we use for the diseases. So in this regard we can relate diseases from different perspectives so because diseases themselves can be defined or expressed in different ways so we can in the clinical setting the diseases are described and identified by science and symptoms and then ultimately they receive a code a diagnosis code so we can use this information and the diagnosis code have some meaning and are organized according some terminologies so we can use this perspective the clinical perspective. We can also describe diseases from the genetic perspective by using the information that we know about their genetic entertaining and we can also describe diseases from the science and symptoms by which they are manifested so the phenotypic manifestations of the diseases so in our work we use these three different perspectives of course there are other perspectives that we can or other data sets that we could use to describe diseases. So for the perspective that use the diagnosis so the definitions of the diseases we derived a similarity metric between diseases that is based on the terminologies so the hierarchical organization of the concepts in these terminologies and for this we use the unified medical language system that contains a metatosaurus of concepts that cover all the domains of interest in biomedicine but in particular they have some soup domains that pertain to diseases and they have a really large collection of disease terminologies included and they organize each concept that would represent a single disease in a hierarchy of ISA relationships in a manner that we can have a particular disease and this is related to a more general disease description and it also to similar disease description so we can exploit this organization of the of the terminology to address in a more semantically aware way the similarity of the diseases so there is that if you we are comparing for instance two diseases that belong to the cardiovascular system we will assign them although they are different diseases we will assign them a higher similarity value than if we compare cardiovascular disease with neurological disease and this is a representation of these concepts in the hierarchical structure provided by the UMLS and we apply metrics that have been implemented to exploit the topology of the ontology the terminology in order to assess the similarity between the concepts using information contact metrics and and the and explores the the depth of the of the ontology so the other metric that is based in the genetic information is based on obtaining a list or sets of genes that are associated to the diseases that we are studying and for that we use the degenerate platform that is a platform developed by our group that integrates information on on genes and variants associated to human diseases and in this way we can obtain for each disease of the trajectory a list of genes that have been studied in association to to the disease and what we do then is to apply a jacquard index to assess the similarity of the diseases based on these gene sets. Brought for a question that is asked by several members yes so it's about for finding common disease trajectories do you incorporate similarities between unequal diseases and how do you handle the time difference between the diseases? Well yes so so I'm explaining now how we compute the similarities so yes we we use these three different metrics that I have to explain now the third one so and and then you will decide if two diseases are similar or not and this is defined by this metric and the time distance is so the time factor is considered when we aligned the entire trajectories so the the distance between diseases is used to compute to to make up the local similarity matrix that compares to disease trajectory and then the the time dimension is used when we compute the the similarity between common trajectories and this is used for the clustering. Okay thank you so I hope this clarifies the questions if not we can then yes comment more on this so I have explained that so maybe to clarify we have so we have all all this process to compute the to obtain the common disease trajectories and then once we we have the common disease trajectories we have let me see if we can go here so okay so I will continue from here we have this once we have this we want to cluster them but a very important point in fact is this how we define the similarity between diseases so we we implemented three ways to to compute the similarity between diseases so we have this based on diagnosis and the meaning of the diagnosis we have the one based on the genes and now we also use one based on the phenotypic descriptions of the diseases so so for that we use the human phenotype ontology as this is a resource that provides descriptions of phenotypes for diseases so each disease can be represented by a vector of of phenotypes and at the end what we assess is the similarity between the two diseases by comparing the the phenotypes and we also exploit the the in fact the ontology that organize these phenotypes according to ease a relationships when we compute the similarities between the diseases and for that we use the best much average approach for combining these two pieces of information so to sum up at this point we by by what I explained before we we we obtained the common disease trajectories and we use dynamic time warping to for the clustering and we use the three different metrics to obtain the similarity between each individual diseases and this then are are approaching the problem from different perspective from the the meaning of the disease the genetic information and the phenotypic information so what I want to show you now is an example of the application of the methodology so how it looks like and what what we can actually achieve so we have an example of for indicating area of cancer in particular prostate cancer and we did a study to I try to identify which are the comorbidities of prostate cancer and for that we use a health registry from a regime of Spain the first step is to identify those patients these are our main patients that are diagnosed with prostate cancer and this is the actual three level digit code according to ICD-9 terminology that is the one used in the registry and we end up with 21 000 patients diagnosed with prostate cancer and then we applied our approach to identify those common shared disease trajectories so this is a summary of the trajectories that we identified from this registry so we identify trajectories of length two so that have two diseases three four five up to six and these are the number of trajectories that pertain to each category and this sum up to a total of a little bit more 2000 trajectories and these are the set that we then used for the clustering algorithm using the three metrics that I have already described for the dynamic time mapping and this is a little bit of summary of the results that we obtained so for the semantic or clinical we obtained 86 clusters for the genetic 41 and 82 for the phenotypy and these are the mean number of patients allocated per cluster and the mean number of trajectories allocated per cluster in each configuration and let's explore some of the results that we obtained so here I show some of the more populated clusters in terms of patients so the first one is the one that we call the metastasis cluster that has is formed made up of by 6 500 patients this cluster contains trajectories mainly of length two two sorry as you can see starts with a prostate cancer and then progress to metastasis two different organs and systems and we can see here that this trajectory is the one that is most populated in in the cluster with a larger number of patients another interesting cluster was the one that we named the COPD the chronic obstructive pulmonary disease cluster that contains a diagnosis the first diagnosis of prostate cancer followed by different diseases that are part of the COPD definition and again these two diseases were the one that the trajectories that were more populated in this cluster is important to know that COPD is a disease in which smoking is a respiratory disease in which smoking is a risk factor and for prostate cancer it happens also that smoking is a risk factor and also this COPD is a predictor of mortality for prostate cancer patients the next cluster is one that has diseases related to neurodegeneration and this also was quite interesting due to the large number of patients that were allocated to to these trajectories and cluster and finally there was another cluster in which we find several different types of cancer that then lead to prostate cancer and the one that started with bladder cancer was the one that the trajectory that was more populated so this configuration or these clusters were extracted from the cluster configuration in which we use the clinical the semantic information of the diagnosis so it's a way to you know you can see that we can organize the different cluster the different trajectories in different clusters and identify different patients subpopulations that follow a different temporal order of diseases another example that I wanted to show before we finish is different clusters that have similar diseases that were obtained using the different measurements so the clinical the genetic and the phenotypic if you see for instance you compare these two clusters they contain similar diseases and these were obtained using the different metrics but there are some diseases that are not shared and this has several explanations it has to do with the genetic description of the diseases or how these diseases codes are defined and organized in in the hierarchy but the fact that some associations are found using the different configurations are also supportive of the of the associations that are found and also it's interesting to note that when you use the phenotypic similarity metric the cluster is larger in the sense that it contains different trajectories but most of them are also associated somehow with kidney problems so to summarize the conclusions of of what they presented today or the main conclusions is that we have developed these systems medicine methodology for identifying disease patterns from patient disease trajectories this approach study associations or similarities between diseases from three different perspective and it's also important that we introduce this the factor of the dimension of time into consideration for the clustering algorithm I also want to stress that this approach for clustering using dynamic time warping that as you could see is an unsupervised learning approach it can be used not only for for the case that I presented for disease comorbidity and disease trajectories but to classify an heterogeneous patient population in different groups by by using different types of data that we could have associated to to this patient population and in this way I encourage you to to try it to use it by your own applications so all the work that I have presented has been published two years ago and this publication is mainly this work is mainly done by a postdoc in my group Alexia Giannone and also I have presented some results that are published it's a manuscript under review and the code is available in this github repository and finally we have other tools as I mentioned before that can be used for the study of comorbidities that are listed here and with that I I finish the presentation and I'm happy to take more questions thank you very much are there questions from the mlfpia we do have a few questions via Slido so does the warping method assume that the two trajectories being compared spend the same time frame or can gaps be introduced at the starts and ends and the trajectories do not need to have the same length and this is really important point so you can cluster or compare trajectories of different duration and in fact the algorithm is able the dynamic type algorithm dynamic type warping algorithm in fact is able to deal with these differences in length and differences in the elements in in our case the elements would be the diagnosis but can be different elements of that make up the signals and it does this by stretching or compressing the the signal so you you actually do not introduce gaps as in other algorithms but actually really stretch or compress the signal in order to align them but yes it is actually very good for these kind of signals that differ in length yeah and does the algorithm allow for multivariate time series so multivariate responses well this is a good question and we haven't tested but I think there are examples in other areas that have been applied for yes yes but we haven't tested but this is really a very interesting question yeah indeed I think there is also a question from Thomas Gumpsch yes hello thank you um thank you for the talk I have a question about the representation of the disease trajectories um you mentioned is a time series so um as if I assume that a patient is in a hospital and there are several disease codes for that patient during the hospital stay and then there's a gap and then there's another hospital stay then comparing this disease trajectory with another patient the last disease code that this patient got from the first hospital stay would it be then forward filled for the entire uh gap because then this disease code would have a higher weight compared to the other disease codes the patient got during that hospital stay you get I don't I don't think I get the question so we so each so we don't assign weights to the diagnosis so it's uh the only thing we we actually do is uh if a diagnosis is repeated over time in our method we make an assumption we only take the first time other diagnosis is made so if this diagnosis is then assigned another time then and this happens we only take the first time the diagnosis was made but I think this doesn't answer your question so yes so I think I've understood that from my understanding if you're compared to uh time series with dynamic time warping then um the time difference also matters so there's a time difference between the last disease code of the first hospital stay and the first disease code of the next hospital stay that is also is also part of the dynamic time warping difference to a different uh patient trajectory yeah yeah so the order at which the disease codes during the first hospital stay are assigned matters in that sense right but maybe maybe this is too too much detailed I'm sorry no no this is important it's important and this is something we we also discussed internally so the algorithm at the end tries to match the the diagnosis irrespective of the time gap between them and sometimes this time gap is something that is interesting uh yeah it it tries to map them irrespective of the time of the time duration uh yeah but I yes all right okay that answer is an interesting point thank you right I have here another question um so this warping algorithm struggles with performance in big data could you explain how you managed to handle it just well we didn't have a lot of problems with the big data so uh we yes the this example is not really large database but we in the in our publication we have a population of 500 uh individuals 500 patients and we we managed to compute the the trajectories in a normal work workstation in a reasonable time so the the step that is more demanding computationally is the first step of comparing per wise the individual disease trajectories but this is yeah with actual computers not a problem yeah we have also a question from Zuki Lee Zuki uh yes yep hi Laura hi thank you for the in-bar insights go talk so my question is do you consider integrating the three similarity metrics and if not if not how do you choose in practice all of these three metrics well this is a really nice interesting question and something we are really struggling how to combine them because this would be really the interesting thing to combine in a single uh clustering configuration the three or even more but actually we haven't come out with a nice solution but this would be the best because now we have this yes three different cluster configurations and no one is uh is better than the other and each one has their limitations so it would be good to combine actually to overcome the biases and limitations of each of the metric yeah okay yeah thank you has it been investigated to what extent the ordering in a trajectory has an impact on the final results so the actual question was does the outcome of the working clustering depend on the ordering in which the comparisons are carried out we haven't thought about this no no no no no no karson do you like to say something thank you crystal laura thank you for a very interesting and stimulating talk um i have one question about this clustering on different modalities or different views of the data so clusterings often end up in local optima clusterings are very parameter sensitive so while i fully trust in your results i think if if the field at large does these kinds of analysis there's a danger that people tune the parameters tune the the the the the initial configurations of the clustering until there is some overlap between the different modalities um here so do you see like a systematic way or a proper way how to avoid this kind of fitting the clusters i would yeah thank you for the question i i i didn't show some interesting thing that that has to do with your question because of time but we actually implemented clustering evaluation metric in order to assess the homogeneity of each of the cluster in terms of the trajectories that are actually uh end up in the cluster uh and we compare them uh according to the how similar are the trajectories among them so the trajectories that are in the cluster and we have a similarity metric in this sense and another similarity metric that is more focused on each trajectory in order to find out if in an individual trajectory the diseases that are there have something to do or not uh but uh yes we implemented a metric to actually evaluate the clusters and uh in fact we select the threshold for the clustering algorithm taking into account these uh metrics for each of the configurations but as you know clustering and so provides clustering so something quite tricky to fine tune the parameters but we we implemented this metric in order to in a manner more objectively select the threshold of the clustering and also evaluate the homogeneity of the clusters that we obtained thank you okay thank you very much um thank you again laura for this excellent talk and to all who participated in you know asking questions it was a nice discussion i think thank you very


Hi, everyone. Welcome to another fireside chat. I am here with Yunan, who is the co-founder and VP of products at Wiz. Yunan, thank you so much for joining us today. Thank you for having me. So, yeah, let's dive right in and get started. I'd love to know more about your personal journey in the tech industry. How did you get from where you started to VP of products? OK, so I was born in Tel Aviv. So I actually started my studies as part of the Air Force Elite Programme, called Tel Piot in the Hebrew University. I learned studied math, computer science, physics, and I started my journey as a cyber specialist. And basically, after completing my service, I joined Adelon. Adelon was a startup. It was a very new startup in the cloud security space. We were barely like 10 people and I was thought can be the VP of product. It was very small and I basically took over the product role and ran for a very fun ride for two years until 2015, where we were acquired by Microsoft. We were a Cosby product, SaaS security. At Microsoft, I actually had the privilege to take the product from being a startup to enterprise scale. So taking it all the way from all the way to one and a half billion dollar across hundreds of millions of users. And then two years ago, the team we left to found Wiz. Wiz is a cloud security company. And again, same old VP of product. Excellent. So I'd love to dive in and ask you some questions about cybersecurity. But let's start with talking about Wiz. In products, we're always talking about being obsessed with the problem and not with the solution you're trying to build. So can you tell me what problems you're obsessed with at Wiz and how you're solving them? Yeah, of course. So basically the problem that I'm obsessed on is cybersecurity, right? But I think that it's much deeper than just cybersecurity. When we look at cloud security, so let's start with cloud as a whole. Cloud is obviously a very new space when talking about technology. It's here for, let's say, a few years, a decade, but only the past few years. I think we have realized the benefit of cloud. So we have seen massive migration to the cloud. So any organization is either now born cloud native or now migrating to the cloud. I think cloud is advancing in a phenomenon faster than any other technology we have seen in the past by and offers like really all the cool stuff to the developers so they can run as fast as possible. On the flip side, doing security for cloud is actually a huge problem or a challenge, I would should say. First, I think that the complexity of the environment that organizations need to secure is, let's say, much more complex than what they used to have. So multiple clouds, multiple architectures when you think about containers, VMs, past services, serverless functions, all of this operating, and you need to secure all of them. Thousands of technologies that are being put into place. So everybody wants to use the latest and greatest. So that's around the environment. The other complexity is around basically the risk. So understanding like, what is risk in the cloud? Is it just vulnerabilities, misconfigurations, or is it something much deeper? For instance, around understanding exposure path, understanding permissions. So actually cloud security is much more complex than what we were used to and lots more options. And the third problem is actually around how do you do it at scale? How do you do it at the scale of the business, at the pace of the business without blocking the business? So I think when you take these three complexities, they really create a very problematic environment for cloud security teams that basically want to enable their businesses to run as fast as they can to develop, to modernize, to actually become digitally transformed and do more, but they need to secure this. So that's the challenge. One more data point, by the way. I think that interesting to see that according to Gardner by 2025, more than 50% of all IT spent in the world will be around cloud services, like cloud functionality. So you can see like how you can really, we are moving to this space, but we're still not really equipped to secure it. So definitely not small challenges that you're facing or small problems that you're solving. This is big stuff. And apparently you're doing it very well because I think was it CNBC, that was it CNBC and Business Insider named you guys the fastest growing security company. Congratulations on your success. That's amazing. I feel quite lucky that as a co-founder, you may be able to give us a bit of the inside scoop of how you guys did that. How did you manage to build the fastest growing security company? So yeah, we are actually running super fast. So we were founded actually tomorrow. It's going to be two years since we were founding. Then we are running super fast, right? So I think we raised we are topping like 300 employees, more than $600 million in revenues and $600 million in funding. And I think that the nicest figures is actually that we have more than 20% of the Fortune 500 using the service already today in just like two years since inception. So I think that this is a really fast growing. And when you look at the story of Whiz, so really Whiz is a result of 20 years of teamwork. So Whiz is a team. The same actually my story, the story that I told you earlier on is the same story of like the core team of Whiz. So more than like 20, 30 different folks on the team that we went together for the past 20 years building cloud security products. So I think that one is the teamwork. And when you think about teamwork, actually it's about the ability to work together in a very harmonious way. So think about two years ago where was the word? So it was just COVID started. We were all in quarantine, but we worked seamlessly together because we simply know each other and we know like how to operate together. It's true for the team. It's true for the founding team. So actually I think that having a very strong team at its core is the most fundamental way to run fast. Second, I do think that we are experienced in multiple senses. It allows us to do less mistakes, okay? In the sense that one, we have built to scale at Microsoft. We've been in cloud security for a long time. We have done a startup.  So getting all of these angles actually build a very solid experience that allows you to know what is good, what is bad and so you can move fast. And we are, as you mentioned, in a burning space right now. So there is a dire need. There is actually a void of solutions right now. All of the solutions are, I would call them like fast generation. They're not really hitting on the pain point. And I think that it's a space that is in dire need for a better solution. And fourth, I think we have a really good culture in the end. I think that when we build a company, the culture is one thing that sticks. And a culture that basically helps you be successful. So very customer first approach. We'll talk about it. I'm sure it's part of the product, but customer first approach, the ability to have a very supporting environment, asking questions, collaborating, being very inclusive of other ideas and glorify trust, I would say. So trust is first of everything. So I think all of these makes us like fun to work with. So it makes people want to move faster. And I know we're talking about cybersecurity as one of those spaces that's really growing. I know that every time we talk about cybersecurity in our community or anytime someone brings up that they work in cybersecurity, there's always so much buzz around it of people saying, I want to learn more. I really want to get into cybersecurity. This is the career for me. What are some of the unique challenges of working in cybersecurity that maybe product managers in particular should prepare themselves for if they're thinking about moving into the space? So product actually being product in cloud security is really interesting because I think first the level of complexity of understanding what we call the red side, which is the threat actors. So the attackers understanding the blue side, which is the defenders. And then again understanding the technological landscape. The environment, you need to do all of these together in order to like go into and be successful, I think as a cybersecurity product manager. So you actually have to get a really solid view of the multiple parties and then you need to build a product that needs to face with huge amounts of data, a lot of noise, a lot of different personas, different levels of expertise with the teams. I think enterprise building it's mostly like enterprise software that needs to adapt for like, you know, be ready for enterprise adoption. So there are lots of challenges actually in cybersecurity that require you to be really in the details on the flip side. I think that cybersecurity is also going through and cybersecurity is like in a huge talent shortage right now. It's not about training so many like I think cyber specialists. It's actually about consumerizing the knowledge of cyber. So same thing that we became very aware with phishing. And now everybody gets a phishing email. We expect them to know that they, you know, they got phished and don't click on that link and so on. So I think in general we have a lot more awareness to cybersecurity and actually having security a bit simplified. I think this is one of the biggest challenges we have today. So it actually gives I think a different type of challenge for PMs that are going into that space. How do we simplify? So it won't be just for the very few that know cyber very well. And what would you say are some of the big mistakes that companies make when it comes to security and basically protecting themselves and their businesses? Okay, so the big mistakes. I think that the big mistake is not having. So if I am a company, the biggest mistake for me is not to be able to prioritize correctly. Where do I spend my effort? So let alone in a startup, we always say focus, focus, prioritize. But even in larger organizations, there are so many things to do. I mean, even when you look at security, you can improve yourself to like day and night. The question is, am I doing the most important things right now to secure my cloud environment? And I think that prioritizing it is what is very challenging for customers. So be ruthless about prioritizing your security like endeavors or initiatives. On the flip side, I think that visibility is like maybe the first biggest challenge because we may be thinking we're securing our environment. But in fact, we are securing a very small part of our environment. So I think visibility is in the core of like every security person, like am I seeing everything that I need to be secure? Because if I'm not seeing it, then I cannot protect it, right? So I think that visibility in the cloud, it goes to a new extreme because anybody can spin up and down new resources. Anybody can get like a cloud account or something without going through central security. So it makes it very, I think challenging to gain visibility and then prioritizing would be the next. So these are the two things that I would recommend. Make sure you have really good visibility to the environment on the flip side. Also make sure you prioritize the right things. Super useful tips there. Good, great advice. And as you've grown in your cybersecurity career, what do you think are some of the skills that have really helped you to get to where you are today? So it's a good question. I think that first the ability as a PM, right? So communications I think is key. I think communications team work in the end as a PM. You work with customers. You work with your sales organization. You work with engineering and your goal is really to be able to communicate back and forth like what, how should we think about the problem? What, how should we think about solving it? What is good? What is bad? And being able to communicate it to all of the different stakeholders is in the end key. The second thing is being able to listen and basically get the information for all of the teams and have a really good analytical skill in order to understand what is more, what is the real truth? So what's, what's the real reason? What's the root cause for like, why are customers having a hard time doing this? Why is, this is a challenging task. Why is it hard to do today? So when you start really understanding the root cause and have a very analytical approach to it, data-driven, analytical in the sense that understand the details, why it happens. So there is this Y exercise that I actually do like, I know it's a cliche, but asking yourself, but why, why is this the case? I think that having this ability to have an analytical analysis of a problem really gets you to the place where you're saying, okay, I get it. I know what I need to solve now. And the third thing is technical skill. I told you, I don't think in our space like in cyber security, it's hard to overlook the technical aspect of it, both in terms of like understanding the problem and the solution.  I think that being able to understand the technique, you know, the technical aspects of the solution and the problem are key. Definitely. And well, this next question isn't the easiest one to answer because job titles in product management are pretty meaningless. Let's face it. Like you can be a VP of product at one company is doing something completely different to a VP of product at another company, but maybe to help us sort of demystify things a little bit, what's a typical day to day or a week to week like for you as a VP of product? First, we are very, it's funny to mention it. We are a very flat organization. We barely have teams with the, with the R and D and product teams, product team, because exactly for what you said, we actually work in what we call squads like we have two developers, one PM and you know, just go get it. Like you don't need anybody else. And I think this independence is important. So just thought it's the right place to say it about titles, et cetera. It's really, it doesn't matter. But to your question, I think that essentially for being VP of product today, it's really, I think about it as managing two coins that we have for the team and for the company. One is how much value do we bring to customer? Like, are we doing a good service? Are we bringing value to all the customers?  Are we doing it in a way that allows us to continue on in a fast way, right? So we are not agreeing technical debt. We are not agreeing too many like features that are not there yet. So making it in a very solid and consistent way that allows us to continue and run fast. I think that my typical day today, I think that the first talking to customers I actually have a lot of emphasis at this also with the team. So we actually do spend at least a third of our time. I know it's a lot, but at least a third of our time on customer calls across the entire life cycle. So across like them first demos, POVs, and like proof of concepts, proof of values also in terms of delivering. So a post-sale engagement and in general, like working very closely to customers to develop this really deep customer empathy. I think this is key and I keep doing it as like as much as I can. Second, I think I'm trying to read and understand the market to really asking the why question. So what's happening on the red side? What's happening on the blue side? What are the processes of driving like organizations to secure their space today? What are the challenges they face today? What are the most like, you know, latest and greatest on the technology aspect that we should account for? What happens with cloud technology with competitors? So all of these are things that we do. We start building a team. So being able to build a product management team that is independent, that can focus on various topics that is diverse, versatile in knowledge, in expertise and this is key, of course. And the fourth thing that is like, I think the reality check all of the time is identifying and continuously like unblocking, like sort of points in the organization. It can be anywhere, but I always say like, there is nothing like product love to untie a problem, right? So when APM looks like gives attention to a problem and now we use all of the tooling that we have from engineering for all of the resources like engineering data, all of these in order to basically solve a problem, like we can fix anything. So making sure that we identify the areas that are not smooth and unblocking them. So these are like the four things that my day-to-day looks like. Pick your choice. It's so interesting how I asked that question to all of our guests and I never get the same answer twice. I mean, there's some core things that tie product people together at their levels that are sort of universal, like caring about the customers, building teams when you get to a leadership level, but every role has its own certain flavor or certain choice. So I always love getting different answers to that question. So you mentioned team building in there. What are some of the things that you look for specifically when you're building out your team of product people? Are there any particular skills or traits or things that you particularly like to see that add to your culture? Yeah, so I like, most I like the curiosity, the natural curiosity of APM to want to learn, to want to understand better. I think this is like a driving factor that I'm looking and excites me about APMs overall. I do like the, I do think it's super important to have communication, very solid communication when describing a problem. And also the way, by the way just the way I look at it, I like to ask questions and by the response, I like to assess like what's the structure in which the response was given, right? Was it a structured response? Was it a super logical one? Was it like framing it? In the end, I think that the way I would love working with PMs is that they can describe to me their framework, how they are thinking about the word so I can maybe give my opinion, maybe I can adapt, right? So the ability to explain very clearly, how do you think about the word? How do, what do we learn? What should we learn to be, to do better products to build, to solve tough problems? I think this is key for them. And in the end, it's teamwork. I think that PMs in the end, it's all about teaming up with customers, with engineers, with marketing, with sellers. Like it has to be about the team. So that would be something that of course, I mean, part of the culture, it's really important for us to see. Absolutely. And another thing I love about getting answers to that particular question is all the things that product leaders love in their teams. It's all something that's very achievable for everyone no matter where they come from. No one has ever answered that question with, well, you need a CS degree from Harvard or Yale. No one has ever answered with specifics. It's always things that are very universal to product managers. So fantastic answer there. We're coming down to our last couple of minutes, but I think we've got time to squeeze in some more fun questions. Product people are always learning, lifelong learners. What are some of the things that you're learning about at the moment or something that you want to make time for learning in the future? Wow, about learning. So I think that today, I think that I'm seeing lots of challenges around. I call it enterprise usability. Okay. And it's interesting because enterprises, so we're operating in a B2B, of course, actually in the higher end of enterprises as well. It's interesting to see that enterprises have all of the technology they could afford, but the value they can get out of it is not always that it's max, right? And they can have various tools, but they don't work together. They don't integrate. So actually with, when I build with, one of the challenges that I really try to solve deep is how can we get it to an enterprise and they can simply use it across. And when you realize that there are many new, like many funk features that are not glorified in any way, but this is what makes it enterprise ready. I think this is like, for me, I'm learning more and more about these, let's call it the small things that make enterprise software consumed easily. So it's around functionality and integration and permissions and logging and all of these. And you could really see how great products fail because they couldn't cater for the enterprise space that early. Also, I have to say that in terms of cyber, you cannot stop learning and let alone, I know it's a bit happening like recent, but you're seeing what happened between the Ukrainian and Russian conflict and the role of cyber in the discussion. And you can look at the articles like cyber is like such a dimension, it's happening. It's happening and you can't, it's happening fast. So keep learning like what is this, how the threat actors use cyber and how can we defend against it? I think that in the end, and this is also when you look at the word economic form and they mentioned like, what are the terrifying things we should worry about? So of course there is climate change, but second, it's cybersecurity. These are things that can devastate the words. We know it because everything is connected to internet. Everything is from critical infrastructure to anti-airplane missiles that protect different countries. It's crazy and understanding this I think is key and I'm passionate about it. I'm reading about it every morning. That's the benefit of being in Tel Aviv. Basically, I wake up, there is nobody else that is awake, so I have all a few hours to read all the news, all the research, so I get up to date and then start my day. That's how I feel being on the European product school team. Everyone's asleep for a few hours, so we have like ourselves. As much as we love our American colleagues, it's really nice. So anyway, we've got time for just one more question, which you will either love or hate. If you could sum up the feeling of working in cybersecurity at the moment in just a couple of words, what would they be? It's a tough question. Actually, I would say it needs to be like it simply works, right? I think that basically when we think of cyber, I just want to make like when I think about it for me with, it simply works for customers. And I think this is what we're missing a lot. And when I hear from customers, for instance, I love it when they say, oh, it was just easy. I just used it. It seems simple. That's like after so many years of challenging products that you crank everything just to make it work. When you hear from a customer that it just simply works. Yeah, I get it. Yeah, I think this is like where we need to go to with cyber. It just simply works. Fantastic. What a positive note to end on there. Yunan, thank you so much for joining me. It's been an absolute pleasure chatting with you. Thank you very, very much for having me and it was a pleasure. And thank you everyone for joining us. We will see you in the next fireside chat. Bye.


The third tier of government, local governments, as perceived, are not under any other tier of government, as believed by most Nigerians. And being the first handshake of democracy to the electorate are probably the most important tier of government available within a democracy. The success of a democracy is also based on the local government, all marred by poor participation because of political illiteracy of the electorate who, of course, come with their voter apathy and poor participation. Let's not forget the kidnap of local governments finances by the state governments, who with another side of their cheeks are clamoring for restructuring while strangling the autonomy of the local government regardless of the executive order turned of 2020 by the president that guarantees the autonomy of these tiers legislative and judiciary. Let's note, not less than 13% of total registered voters participate in this election across every state. And no matter how young to run or too young to be in position, according to some politicians, the local government is of course not considered worthy of the quality of representation by credible candidates. So what are the jurisdictions according to the constitution on the most undermined tier of government in democracy? Its functions are simple. So you have the collection of levies, rates, issuance of license within its boundary, the provision of basic primary education, the provision of primary health care, even though that has been kidnapped by the federal and the state at the same time. And it's also to participate as local government in advising the state on economic planning. These are key to basic development and a lot of myths exist around the local government and here are a few. Local governments are duly elected and cannot be sucked by a governor. Any time a governor attempts to do this, he has infringed on the constitution of the federal republic of Nigeria. Local governments are financially handicapped from poor autonomy, especially by constituency projects from the national assembly, meaning the national assembly, instead of paying attention to its legislative role, directly jeopardizes the functions of the local government. Local government can also be the basis of the more agitated position which is state police because, of course, structure accounts from the ground up and not from the top down. So key will be LG security in the buildup to state police. The general downplay of the local government has provided for poor dividends of democracy to the most populous black nation which is Nigeria. There is more to the quagmire we are in as a country, but we have concentrated on other parts that are further from the people we can hold accountable. The local government is the guy you can hold accountable who lives just next door. Thank you. You know, I actually was ignorant of the fact, I think I learned that from you the first time, that this thing called constituency projects which are members of the national assembly get fat bonuses from the federal account to deliver is actually a function of the local government. That's a crying shame. I mean, how does that, I mean, really it's a crying shame because you want to ask yourself like why would someone whose duty in streamlined want to take on the responsibility of someone else? So what is the local government doing if people in the legislature are not delivering constituency projects to people in the local government? Maybe we have very poorly educated people in the local government because until now, my impression of, you know, when you think about local government representatives, you really think of the people who, those people who don't really have much to say or to offer. It's not even something that I love, we will think about. If I went there thinking of politics and running, they don't even think about local government chairmanship in their minds is something that a tokenism that somebody just gives to another person in that area. Maybe the most popular. The most popular area boy, yes. But you know, you know. Which is wrong from what you're saying. Let me give you an example. I'm something that was popular that happened in Lagos. Let me borrow from that. So let's look at the lucky situation. You know, before you hold the governor accountable, you're supposed to hold the chairman of Ethiosav because the bridge is in Ethiosav. Correct. Yes. Exactly. Local governments. And you know what's even most shocking is, okay, the federal releases its finances directly to local governments. But states have come up with, you know, a very smart thing. And Lagos, I would be honest, actually does better than most states. It regards local government autonomy. So they've formed something which they call the Ministry of Intergovernmental Affairs and then all the money for local government goes there. They love not chooses how it goes, where it goes and what is done with it. Sola, would you want to say something? That's it. You know, it's so painful for me to see, to witness in my lifetime the degradation of local governments. Because I grew up at the time when the local government chairman was a champion. And councilors were champions.  I'll tell you today, Lagos, for instance, almost all the local government chairman don't live in the area. Oh, certainly. The system has been hijacked. And there's a reason why that has been so. Because don't forget that in the old days, the local government chairman was so powerful. So imagine being the local government chairman in my area, for instance, and you are the person that everybody. So if somebody wants to go for government, I want to win the koshofe. It had to be your friend. You have to be somebody that you have to endorse. So what the system, what they've done with the system is to basically wipe out the entire local government structure because the politicians at that level were very strong. They were very strong. They were very strong. But the system, why part of the entire local government structure? Because the politicians at that level were very strong. And basically, create a government where it's only passed from the states and then trickles down back what is not normal. I think that if that changes, Nigeria will change that policy. Yeah. I agree. But you know, one of the things that I think I find most shocking about local government is that they were more functional in a military regime than in a democracy. So we all remember local government, LEA schools, we remember local hospital authorities, we could remember all that, all gone, donating cars to police. Now the local government cannot act in a democracy where they are even supposed to function. So of course every dream, every great dream begins with a dreamer. Always remember you have within you the strength, the patience, and the passion to reach for the stars and change the world. Harriet Tubman. While we continue to push for the best options for Nigeria and Nigerians, we urge you not to relent in playing your role no matter how small. The Advocacy continues on our social media platforms on Facebook plus TV Africa. Hashtag The Advocate NG and Instagram at plus TV Africa. Hashtag The Advocate NG. To cut off with previous broadcasts, go to plustvafrica.com forward slash The Advocate NG. Don't forget to subscribe for our YouTube channel plus TV Africa. Join us next week, same time on the station, and keep advocating for a better society. Thank you.