Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager at Data Diversity. We'd like to thank you for joining today's Automated Data Governance 101, a guide to proactively addressing your privacy, security, and compliance needs sponsored today by Amuda. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. If you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the bottom middle of your screen for that feature. For questions, we will be collecting them by the Q&A section in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag Data Diversity. As always, we will send a follow-up email within two business days containing links to the slides, the recording of this session, and any additional information requested throughout the webinar. Now let me introduce our speakers for today, Andrew and Matt. Andrew is a Chief Privacy Officer, Legal Engineer at Amuda. He is an internationally recognized expert on data privacy and the intersection of law and AI leads Amuda's legal engineering team comprised of lawyers with deep enterprise in data privacy, security, and data science focused on automating compliance and oversight activities within Amuda's software platform. Before joining Amuda, Andrew was a special advisor for policy to the head of the FBI Cyber Division where he was the lead author on the FBI's After Action Report about the 2014 Sony data breach. Andrew also served as Chief Compliance and Privacy Officer for the Cyber Division overseeing privacy and compliance policies for sensitive data across the FBI's 56 field offices. Matt is the Director of Global Solution Architecture at Amuda. He has over 15 years experience in architecture and engineering in large-scale enterprise data center infrastructure. Matt came to Amuda from Hewlett Packard Enterprise where he was Chief Technologist working with enterprises on their hybrid cloud initiatives. We're excited to have both of them here and with that I will turn the floor over to Andrew and Matt to get today's webinar started. Hello and welcome. All right, this is Andrew Burt talking. Thank you so much, Shannon, and thanks to folks who dialed in. We're excited to be here today. So I'm going to kick things off with a deep dive into automated data governance, what it is, why we think it's so important, really kind of the one introduction to the concept, and then I will kick things over to Matt in a little and he's going to provide a deep dive into some concrete use cases. So to start with is the question of what exactly is data governance? So this is a term that we see bandied about with really a wide frequency and it's very generally just kind of overused. It means in some cases, I think, everything to everyone. And therefore in practice, really nothing to most people. And so to start off with, I wanted to talk about what we view as the three central components of data governance practice. And so to start with is the notion of privacy. We're going to dive in a little bit deeper, privacy and the others. But I think a good kind of working definition for privacy is basically privacy is about stopping people from using our own data to make insights that we don't want them to make about us. So that's the consumer perspective. From an enterprise perspective, it's all about protecting the data that organizations are entrusted with. And so the harm really is about a loss of control. It's about a loss of control for what data can be used to do what. The second key component of data governance is security. This is most frequently thought of as kind of just protecting information from an adversary. We're going to dive into a little bit more detail about what information security means in practice. And then compliance, which I think can be thought of, is just kind of like a minimum threshold for are you meeting the exact requirements of laws and policies. And so this is data governance in a nutshell. We're going to, of course, dive a little bit more deeply into these three components. But I think it's worth starting with this question of why is data governance so difficult? Beyond simply the fact that it means so many different things, so many different people. And I think at root, it's so difficult because each of those three different key components, security, privacy, and compliance, each have their own really, really difficult problems. So I want to walk through kind of the core central problems that make protecting each of these components so difficult in practice. So starting with privacy, what exactly is the root cause of privacy being so hard to protect in practice? And I think the answer there is this fact that there is just too much data. We'll get into this, but we generate as individuals and we collect as organizations a mind-bogglingly large volume of data every day that number increases. And as a result, it can be very hard to protect data and the insights that data can be used to generate. So on this problem, I wanted to start with a story about Judd and Leslie. So this is Judd Apatow and Leslie Mann, the famous director and actor. Also happened to be a married couple. So this picture was taken June 21st of 2013 near Judd and Leslie R, a taxicab in New York City, living very happy lives. And I think this was snapped by a member of the Paparazzi. So around this time, a few months later, a New York City researcher named Chris Wong is bruising Twitter, and this is March of the following year, and he sees a post from the Taxi and Limousine Commission. It's basically just a chart of traffic patterns in the city. And out of curiosity, he makes a freedom of information request, asking this public entity to release records for 12 months of data, for all the data in 2013. And as a result, he received 50 gigabytes of data. On the right here, you can see the email that he received from the actual Taxi and Limousine Commission. And this is a sample of what was actually released. They released 12 months of data containing taxi pickup, information drop-offs, location signs, et cetera. And at the bottom here, they actually tried to protect this a little bit more. Some of the actual columns were hashed. And so this seems pretty harmless, right? It's not particularly, definitely not malicious. It doesn't seem as if they're doing anything that could harm anyone. The truth is this caused a huge headache for Judd and Leslie through this information. It was folks in the media were able to figure out how much they tipped. And they were able to do this by geotagging this picture and querying the medallion at the time. So we were able to figure out what their actual fare was, what the tip amount was. And so I think the key, one of the reasons why I'm starting off with this, one of the key takeaways is that this is an example of a link attack. So on its own here on the right, that New York City taxi data doesn't seem to be particularly sensitive. And on the left here, that photo of Judd and Leslie happily driving in a New York City taxi cab, that also doesn't seem to be particularly sensitive. But when we combine the two, we can derive insights that are not entirely predictable from either data set alone. And so as we start to generate more and more data, link attacks become an increasing problem. Of course, it's worth saying that this wasn't just a problem for Judd and Leslie. It turns out that a number of different other celebrities were exposed in this, and some of them it actually did create a PR headache somewhere alleged not to have tipped at all. Here's one example, Courtney Kardashian and Scott Disick. This incident also got Bradley Cooper as well, alleged that he doesn't tip when he takes taxi cabs as a result. And the same thing for Olivia Munn. So, well, this does seem kind of fun and light. It did create some headaches and some problems for folks. And of course, this is again just symptomatic of this broader problem, which is as we generate more and more data, it's very hard to understand what that data can be used to do and what about that data is particularly sensitive. So this comes from a study at MIT. There was a credit card transaction database of over a million different users, excuse me. And that credit card transaction database was thought to be anonymized. And it turns out that researchers were able to prove that with just a few data points outside of that data set, they were able to de-anonymize the data. So this is really all about the increasing prevalence and the increasing ease of link attacks to reveal potentially sensitive information. So as we go forward, as link attacks become easier to conduct and there's more data that we can actually use for these types of attacks, we have to kind of just accept that privacy as we know it is incredibly difficult, if not in some cases, possible to uphold. Things need to change for us to adequately protect the privacy of the data that we generate and the privacy of the data that we collect. And so here at IMUDA, we are very firm believers in purpose restrictions on data. The key component of increasingly large number of regulations on data, which we'll talk about shortly. But the key kind of concept is that placing, limiting how data can be collected is just not enough. We need to assume that more and more data about us is going to be released. And as a result, controlling how that data is used is really, really central to how we can actually protect it. And it also, this trend in what it really means to keep data private and secure and protected. Excuse me. It also leads to this question of how do we actually balance data privacy with utility? This is a wonderful quote from a legal scholar, Paul Ohm. The data can either be useful or perfectly anonymous, but never both. So the key concept here is that as we are seeking to protect data, we need to realize that it is a trade-off at the core of all data protection efforts. Privacy and with security, as we'll see shortly, what we're really doing is we're making a trade-off between privacy on the one hand, utility on the other. And we can't have a perfect amount. But in fact, this chart here on the right, if you can think of accuracy and utility as being quite similar, and so what we're really saying here is that the way to make sure that individuals' privacy is protected is not a science, it's an art. And it's an art about making a trade-off between privacy on the one hand, utility on the other. So that's why privacy, I think, is so difficult in practice. I think the sheer volume of data is a key component of the privacy problem. And so on to security. So the security problem, why is security so hard to implement? I would say that the key root of the difficulties there are just this overwhelming amount of complexity. And so to start with, let's just get a baseline definition of information security. It's traditionally defined as a, it's called the CIA triad. So we have confidentiality, which means only the right people can view the right data. Integrity is only in the right form. And availability is at the right time. So these three together comprise the traditional conception of, you can also think about attacks on each of these three as different attack vectors. So if you want to, excuse me, I'm getting over a cold here, I'm going to have to pause a few times to drink water. Thanks for bearing with me. So you can think of each of these three as ways to attack the security of data. So you might attack its availability, might attack its integrity, say altering or tampering data that someone might want to access or confidentiality, making it so that right, the wrong people should say are viewing that data. And so when we think about today's IT landscape, it is just really deeply defined and characterized by an increasing complexity. So here on the left, we just have some talks about all the data we generate. So 2.5 quintillion bytes of data are generated each day. By some estimates, 90% of the data in the world was generated in the last two years. And there are an estimated 50 billion devices that are going to be connected to the Internet by next year, which is over six per person on the planet. And so just thinking about how to protect all that data and how to frankly even just understand it is incredibly laborious and it's incredibly complex environment. So that's just the data that we are generating as individuals and then collecting as organizations on the one. Now when we think about the software we use, it is similarly defined by a huge amount of complexity. So we have just the statistics here on the right. I think there's a wonderful chart from NIST documenting the known vulnerabilities and software systems over the last couple of years. And it is very clearly increasing over time. And similarly, the complexity of the software systems that organizations are using and tapping into also appears to be increasing. And as we begin to adopt more and more AI and machine learning based tools and techniques, all of these trends are really becoming exacerbated. And so all of this adds up to basically complexity as I think at the root of the modern IT landscape. This is actually a quote to paraphrase the cybersecurity expert Bruce Schneier that complexity is the enemy of security. So it's very, very difficult to think about protecting an environment that is just defined by lots and lots of different moving components that are incredibly, incredibly hard to track. I mean, in fact, and we talked about this trade off in the world of privacy and that trade off really translates to the world of security. And so this is another wonderful quote that comes from cybersecurity pioneer Willis Ware. And he said basically the only computer that's completely secure is a computer that no one can use. And so again, on the one hand, you have full utility and on the other hand, you have security. And when you're trying to achieve both what you're really trying to do, you're trying to balance again an art, not a science, you're trying to figure out the right way to make a trade off. So OK, that was the privacy problem, the security problem now on. So what is it that's at the root of this problem? I would say, and this is a little bit unintuitive, but the key challenge here is simply the fact that there's not enough time. Now, I'll get to exactly why I think time is the problem, but we can just start off with this basic fact that the number and the complexity of regulations on data is really increasing drastically over time. I like to say that if you just kind of close your eyes and throw a dart at a map of the world, chances are that dart is going to hit a state, a jurisdiction, a region where the number of policies and laws and regulations on data is increasing. And so just as a sample, we looked at the U.S. alone just to get a sense in 2019, just at the state level, not thinking about federal privacy laws. And over 150 privacy laws have been proposed in 25 states throughout this year and over 250 laws on information security have been proposed in 45 states. And of course, all of these different laws have huge, huge costs. And so estimates say that all of this could add up to some very high numbers for organizations. So a few examples of some of the major regulations that are really driving the data security and privacy landscape is GDPR, which most folks are familiar with. It came into force in May of 2018. Really, I think as the first wave of this new era of really robust stringent privacy laws, security laws on data, the fines of up to 4% of global revenue. We have the CCPA, the California Consumer Privacy Act, which was passed just about a month after the GDPR came into effect. It'll come into effect next year. There's a lot of developments going on. The CCPA is a little bit of a moving target, but I think it's fair to say that it is the most stringent privacy law in the United States. And I would say just as a personal prediction that I expected to kind of spur into action some national federal laws I would expect in the next year or two. And then outside of Europe and North America, there's China's cyber security law, which was enacted a few years ago. And again, just plays to this broader trend of it is increasingly difficult and there are more and more penalties and the compliance burden is growing on using data when it comes to protecting that privacy and that data security. And of course, non-compliance have very serious consequences. Here are some headlines related to security breaches. Many of them are tied to some of the laws I referenced on the previous slide. We have Equifax, Marriott, British Airways, among others. Again, I think the key point is just that as governments are enacting more and more regulations on data, the fines for mishandling data are increasing. And I would expect them to increase over time. So back to time, why is it that time is really the main challenge here? The main challenge is because it involves a huge amount of analytical work to try to understand and integrate all of these different complex laws and all of these different jurisdictions. So if you look at this map here on the right, this comes from the law firm DLA Piper, the global law firm, and it's what they call their heat map of laws on data from heavy, robust to moderate and limited. And so when you look at a map like that, if you are a global organization or an organization that's just operating in multiple jurisdictions, it can be incredibly difficult and incredibly time consuming to try to figure out all the different rules that attach to the data you want to use and to integrate them. And so these processes to try to understand what's going on and to try to implement all these different rules are not simple and they are not fast. And so we just simply do not have enough time to try to integrate and comply with all these rules in traditional methods, which we'll get into. So when we think about how organizations are actually addressing all of these problems, the privacy problem, security problem, the compliance problem, what are they actually doing? How are they doing? What is kind of the center of the current approach? And the answer there is that organizations are all overwhelmingly, in our view, approaching this challenge passively. By passive approach, basically what we mean is ad hoc, it's really defined by kind of one-off and reactive ways of watching new laws and provisions on data come in and then see if they're seeking to implement that. So when you think about what it is that makes data governance passive in the traditional approach, here are some kind of five signs. So time-consuming meetings, long policy memos, we had a muta call this the meetings and memos approach where in order to make a decision or to implement any particular regulation on data and involves gathering lots and lots of different stakeholders over long, sometimes extremely and excruciatingly long periods of time. And then the output is usually a very, very complicated policy memo, very difficult for most folks, especially technical folks to understand. Other traditional signs of passive data governance are custom permissions, varying policies for database, we call this the snowflake approach, where policies as applied to specific databases are kind of custom created. And they are just very far from standardized and then creating new copies of data to satisfy compliance, privacy concerns. So that looks like something like a group wants to use a particular set of data for a project that data will then be copied and something like, you know, customer ID number might be master or hidden for that group. So these are all signs that data governance is being implemented in a passive way. Another good test is just to ask yourself, how long does it take between when your organization collects data, when that data can be accessed and used, is it day, days, weeks, or months? I think for virtually every organization we come into contact with, the answer is almost always the too long, it takes too long between when data is collected and when it's able to be used. But there are many organizations we've dealt with where the answer is in the many, many months, sometimes approaching years. And of course, the longer it takes from when the data is collected to when it can be used, the less valuable and the less reflective of the present, that data actually is. So how can we move away from a passive approach to data governance? What can we actually do to try to address some of these, those three problems I talked about, I think really define data governance landscape? The answer is automated data governance. It is the subject of this webinar. It's something that we get immuted, take extremely seriously, and we spend huge amounts of time figuring out the best way to do this, and I'm just going to walk you through really what this is and the best way to implement. So to start with, what exactly is it? So we have here a definition that automated data governance is the process of proactively applying rules on data to ensure compliance and drive analytics. I think more broadly a good way of understanding automated data governance is to understand data governance as really being driven by two competing objectives. So on the one hand, there's an objective of controlling data, controlling privacy, security, and the compliance aspects of that data. On the other hand, there is the objective of the actual data analytics program, which is to use that data. And so these two objectives, governance and turning data into insights, which is where the return on investment comes from these data Linux programs, these are competing. And so it means that at the core of data governance is this tension created by the problems we just discussed, and automated data governance is a really, really good, in our view, the most effective way of resolving some of this tension. And really realigning governance programs and data analytics programs so that they're all effectively positioned to achieve the same goal. So automated data governance has five pillars, a walkthrough, each of those pillars. So it starts with any tool, any data, so any tool should be able to be used with organizations data. Any data should be able to use with those tools. No copies are made of that data. Any level of expertise can interact in this compliance and governance environment, and all policies are stored one place. So a walkthrough of each of these in more detail and provide some more context and why they're so important. So to start with pillar number one is any tool. So we think about creating a long term effective data governance program. We look at traditional and passive approaches to data governance where there's something we'll call vendor or tool lock it where data governance program will come in and they'll say can only use these tools and if you use something outside of these tools, you're not going to be compliant. It turns out that over the long term, that's actually a recipe for non-compliance. The data science and data analytics environment is very far from static. So it means that it's developing very quickly and data scientists will discover and want to use new tools, some tools today that we might not necessarily even know about and tools tomorrow that haven't yet been created. So locking any data science program or data analytics program into specific tools for compliance reasons is a really, really, really big problem and it's something we see quite frequently. So we need to get out of that. We need to make sure that the data governance environment is tool agnostic. So any tool that an analytics program might want to plug in is supported. So that's the first pillar. The second pillar is any data. So for the same reasons that we don't want to lock in data analytics programs to only using a handful of different tools, we don't want to lock them into using any specific types of data. Data comes in all sizes, types, different storage technologies and we need to make sure that our data governance program is agnostic to all of that so that, again, we're not locking in data science and analytics programs and that we're ensuring that flexibility and adaptability are actually built in for the way that these programs access and use the data. So pillar three is no copies. So I mentioned earlier how copying data might work for one specific project might result in hashed customer ID numbers. But the problem is that over the long term and copying data for compliance reasons in the short term does seem like a good solution. But it is incredibly passive. It's incredibly reactive and it doesn't scale. One of the reasons it doesn't scale is because when you take that approach and we've seen it many different times. Over time what you have is a massive amount of copies of data that are just kind of floating around the organization are very, very, very difficult to control or simply even to understand. And so this makes updating policies or keeping track of how policies are being implemented very difficult when you have all these different copies floating around. So it's incredibly important when we're talking about moving away from this passive approach, it's incredibly important to make sure that everyone is looking at the same live data and that we're not creating new copies that are just kind of accreting over time. I should say when we talked about realigning data governance objectives with data analytics objectives, this I think is a really good example because no copies actually means better data. It means more current data. It means data that is going to tend to be more valuable for these programs. So pillar number four is any level of expertise. So frequently, I mean just in the conversation we've been having today, we've touched on all sorts of different topics and it requires good data governance really requires a huge mix of different levels and different types of expertise. And so when we have these environments, many passive and traditional data governance environments are incredibly segmented and they can be incredibly hard to understand. So the technical folks don't really understand the policy or where it's coming from and the policy and the privacy and the compliance folks don't understand what's happening technically. So when we think about actually scaling and realigning these competing objectives and scaling data governance in automated fashion, what we need to make sure we're doing is enabling any level of expertise, technical, legal clients to be able to interact with each other and really collaborate to make sure that this environment is able to ensure that data is protected and to do so over the long term. So the last pillar is one policy in one place. So we talked about policies at Snowflakes under the traditional passive approach to data governance and we simply just can't have a sustainable efficient data governance program if policies are stored all over the place. If there's not no single sheet of music, if there's no one single source of truth or what policies are being implemented where and so as we move to a way of automating data governance, we need to make sure that all policies are represented one place. And so that makes it easy to understand what's actually happening in the current moment and it also makes it very easy to update and change policies over time. And this is clearly, you know, we talked about how complex and how hard and time consuming it is to integrate different policies that are coming from all the different laws on data. And I think there's one thing that's for certain is that this environment is extremely dynamic and it's going to change. And so we need to be thinking about how we can set ourselves up to update these policies as they change over time. So if you've been listening to this presentation so far and asking yourself, you know, I'm thinking automated data governance sounds good. It sounds like something that might be able to help my organization. And the next question is, what can you actually do to get closer to being able to implement a version of automated data governance? So we really have five questions for you. And I'll walk through each of these five questions. And what they're geared to do is get you thinking about basically the things you need in place to be able to effectively implement automated data governance. And if you can answer these five questions, you will be, I think, extremely close to being ready to actually automate data governance processes and your data governance environment. So to start with, and this is I think really just kind of like the threshold question. This is perhaps the most important is just ask yourself what process governs how an analyst receives new data in your organization. So if you can understand that process from where and when new data is actually collected by your organization to when that data is able to actually be accessed by an analyst. So if you can understand all the different midway points, sometimes choke points between them, I think you'll be in a very good place to understand what the current environment looks like. And then to start thinking about efficiencies. So question number two is understanding where your policies actually come from and what rules you actually care most about adhering to. It looks different according to different organizations, according to their geographic footprint and what their priorities are, but understanding where all these policies are coming from is really key. And it's also key so that you can make sure they're accurate and up to date as regulations change. Third question is understanding where your data is and who's responsible for it. So what different groups or units are tasked with collecting, managing, performing the ETL on data. There are all sorts of different ways that data is gathered and piped across organizations and understanding that landscape. Very similar to question one, but understanding that is really key. Fourth question is understanding how data is used, cataloged and tagged. And so we actually at Emuta think about use, cataloging and tagging each as different basically metadata fields. And if you can understand that, if you can understand the purposes your data is used for, you can understand the tags like sensitive, what you consider to be personally identifiable. If you can understand all of that, you're in a very good position to write some very simple policies that can take advantage of all that metadata and again can scale and scale over time. And then lastly, looking at what types of technology you currently use to share data faster and control data more effectively. So understanding basically what types of technologies you're currently implementing that might help make the data governance landscape your organization. So if you have even just a basic conception of the answers to each of these five questions, I think it will be a long way towards implementing the approach we discussed today. And so on that note, we actually have a white paper focused on automated data governance bears the same title as this webinar, you can download that at the link here. I will leave this up for another second or two and then otherwise I'm going to turn it over to Matt vote and of course both of us will be here during the Q&A session. Answer any questions that Matt over to you. Thank you. Yeah, I appreciate it. Great. And I've been participating in some of the chats that have been going on. Really appreciate everyone's participation in some of these conversations and as I walk through some of these examples of what customers are doing to automate their data governance. I'll probably tie in some of the questions, some of the comments and ideas and thoughts that we had there too. So let's let's move forward. What essentially I wanted to do was to walk through a few examples of of customers and interactions that I have of what people are doing to address a lot of these challenges Andrew spoke about. Let's let's move forward one. So, Cognola is an interesting company. They're a medical startup company out in the West Coast. And what they do is build and provide analytics for early diagnostics and personalized therapies. Specifically for for children. And obviously their data set is highly governed, highly regulated. It is not just healthcare data, but it's healthcare data on minors. And so there are some very well understood regulations that govern their data. But as Andrew spoke about things like CCPA continue to come up and even data they collect on constituents outside California. They continue to evolve. And so, even though they have some well understood regulations, there's a lot of variability and policies that need to be applied to their data. And so they took kind of these of the pillars that Andrew spoke about the two that they really keyed it on where this concept of any tool. They were using tools of choice they were using predominantly Jupiter notebooks and just seal access to run analytics against their data, but they moved actually to be using data bricks and Tableau. And so they needed the flexibility to support their existing tool set, as well as move to new tool sets to consume data build models etc. And as well as this concept of no copies of data. They needed a way to give different constituents secure access to the same data without making an extra copy of it, which is what they were doing in order to develop these models. They had to make a de identified copy of the data set. They had to go through HIPAA controls to satisfy what's called safe harbor. And they were ending up with essentially a month long process to de identify data. And there was a new policy that had to be implemented. They again had to rerun this static process to make a de identified copy of data that then they could hand to their developers to develop these models. And so what they put in place was such a centralized platform that allowed their developers live access to the data as it came in. And then customized dynamically de identified access to that data with whatever tool they were coming from so they could switch between tools, get the same access to data as they would have had to that static copy of data set. But again, it was dynamically removing a PHI in HIPAA sensitive data for them to comply with safe harbor. They were actually restored in Aurora. They actually were using RDS instances. They switched data platforms halfway through their project. But the impact of this concept of any data as well satisfied their requirement of both moving data systems and moving tooling, but not losing any visibility to data. And so we'll move on to to the next which is a actually an automotive company. And they're based out of Europe. They're a large multinational auto corporation. And of course, as you could probably imagine, GDPR is a paramount concern to them. Their predominant use case was actually looking at telemetry coming off of their automobiles. And they were essentially, if we tie back to what Andrew talked about, the tension of ROI versus governance, being able to get value out of my data versus give a privacy compliance and security is they were limited to the number of telemetry points to collect out of the vehicles, because there was no way to govern the usage of that data once it came in. And so they used a combination of things again for this automation of application of policies to data, as well as some advanced techniques, something called differential privacy, which is a mathematical guarantee of privacy of every record in a data set. And because of these advanced privacy techniques available to them, as well as this concept of purpose based controls, which I'll kind of get back to in a second to tie into a conversation we had in the chat. Those two things together allowed them to essentially 10 fold increase the telemetry points they could collect off of a car, which allows them to get more data off the car while complying with GDPR. And now they can do some advanced analytics, specifically something like this re identification while doing analytics on vehicles, again, completely anonymized data, going into their analytics. And now they identify a car that's an anomaly, something's going to happen. You know, a sensor is going wonky, which I think was the technical term, they needed to be able to identify the owner of that vehicle. But obviously the analyst doesn't have access to the data by data sets, so they needed a workflow to be able to re identify data sets post analytics. And again, kind of this central platform of that all being one copy of data with customized views based on people's roles and permissions and attributes, allow them to automate that process. The next operation we'll talk about is LMI. So they are a government contractor, and essentially they are in the business of solving problems with analytics with government data. They work with most defense organizations within the US military, and they are working with, take all five pillars of what we think an automated data governance organization should entail is they were really going hard after all, all five. And so they have data from everywhere, not just within their purview, even data just being sent to them or data they can connect to external to their four walls of their data center. But then being able to provide that dynamic view of the identified data to that data where it sits again not copying the data, and getting access to that from any tool again, being able to say to one organization that it doesn't even own the data, they can access the outside those walls, but still maintain governance and privacy and security controls at the policy level to enforce those. Now, the kind of any expertise pillar, they have folks who are authoring policies that don't know SQL, they don't know code, they don't know Python, and they need to be able to author policies that are enforceable across all connected data and have those policies be understood and interpretable by people who on the consumption side the bi and data analysts, they need to be able to understand the policies that are being applied to data they're consuming, which again to kind of tie back into a conversation we had in the chat about where is where does the bi and analyst community fall in responsibility, more in kind of this governance world. And then obviously they need a central policy location, any one place to author policies to govern and control all their data across these six authoritative systems, and consistently apply policies to data, no matter where it's coming from in a matter of the tool it is going to whether that data is on premises, or in the cloud. One policy one place from an enforcement perspective, one of the key concepts to not lose there is this consistency of application, or we want to tell a system from a policy or governance perspective look we need to mask these kinds of data. I need to go back to pillars one and two that needs to be regardless of the tool regardless of the data that policy needs to be applied. And certainly, I want to talk about a, there's a bank, and it's a international bank, and they had to, they essentially their use cases they built a big data lake. They were really proud with this kind of use cases they were going to bring everybody together, all the analysts all the data scientists all the data builders. But the problem is nobody could use the data lake because they could not put data in, because they couldn't effectively govern data coming out. So frankly, they did not have a data lake. And so, what they were able to do is provide the self service access to data, begin with with no, no custom tooling. All the data in a single place with customized views of no extra copies of data. But some of the biggest aspects to them of automating this governance was again this any level of expertise. They wanted the central governance and legal team to be able to author policies in the platform to have them be enforced in the data in real time, and have both the data owners and the people consuming data understand the policies that are being applied to their data. As those policies are actually enforced in real time. So it's not just about policy authoring and policy management. It's about policy enforcement as well. And so they're, they're taking this across 8000 different data sources, abstracting these policies out of the data and out of the tool to for the very first time grant access to data to people and organizations that previously did not have access to data. In many cases both HR marketing, where they were wanting access to data, but there was no good way to govern that usage of data. And the big thing they put in place. And this is a concept in the chat, we talked about is just because I can use data doesn't mean I should use data. So how do we control. So there's a couple things there's how do we enforce. How do we control and how do we audit the should of data access GDPR tries to address this with this concept of appropriate data usage. I mean, you can only use data for the reason it was given to you. Andrew spoke about it at the beginning CCPA has language about this HIPAA has language about this. It's kind of easy enough to put into a database that now I collected this data for this reason, and therefore people who are accessing this data should know they should just know because it's part of our policy that you're only accessing data for a specific reason say marketing. But that's well and good, but how do you actually enforce that. And so you really want to be looking at platforms that can enforce purpose or sometimes it's called context as part of analysis, or as part of someone's job or someone's day to day activities and then they as an analyst from working in project a for marketing group, they are in the morning and I'm working in the fraud group in the afternoon on the same person, but obviously my context has changed. And so how do we capture context as part of the analytical process. I think again that's kind of the marriage of where does where do bi developers and analysts fall in the process of governance. What is my context, what is the context of the analyst or analytic and being able to capture that. And so what this bank did is they essentially, they're putting a rule on all data saying these are the caught five to 10 different purposes or reasons you can use data. You have to declare you're at least working on one of these purposes before you have access to the data. So it's available for these 10 purposes, but you have to at least tell us when you're accessing data for this reason. And then be captured again from on the very end of the spectrum of compliance is auditing. And so we need to be able to then capture or audit and then report on the purpose of data usage. And so those are four examples just for use cases I wanted to walk through of organizations that they have successfully implemented an automated data governance approach. And some of the key pillars for them again, it's not that everybody did one to five certainly they focused on different pieces of that that were important for their processes. But one of these five were certainly a component of it. So from there, I think we will kick it back to either Andrew or Shannon. Matthew, thank you so much. Andrew, thank you so much. These have been great presentations from both of you. We've got a lot of questions coming in and just to answer the most commonly asked question. Just a reminder, I will send the follow up email by end of date, Thursday. With links to the slides links to the recording and anything else requested throughout here. So diving in here. And if you have questions, feel free to submit in the bottom right hand corner in the Q&A section. So who defines the data policy state of governance team or the privacy team? And how do we measure compliance with the data policies? Yes, so I'm happy to take that. This is Andrew, Matt, you can jump in and correct me. I think I'm going to speak. So I think honestly, that's a wonderful question. I think part of the reason why it's such a good question is because the answer seems to change from my perspective on an organization by organization basis. If you look at exactly what it is that privacy groups are responsible for, that tends to vary pretty wildly over different organizations in the same in terms of the governance. So I assume that whoever is asking that question within the organization, the boundaries between what constitutes governance and what constitutes privacy are relatively clear. But that's not something that we're really seeing in the market. So there's just a lot of different overlap, a lot of different variation that we're seeing. I think the one point I would make in response to that is and one of the things I think we see that different organizations really struggle with is again involving all different types of expertise. And so one of the things we frequently see is so for this example question, there might be an organization where the privacy ends up being the most legally focused folks. And they're the ones that are the most familiar with the memos and meetings approach or the complex legal ease that gives rise to all these different policies on data. And the governance team might be on the more technical end. And so it's very common for us to see this kind of split. On the one hand, the folks thinking about the laws, on the other hand, they're the folks thinking about the technology. And so I think the key there is to make sure they're all talking to each other and that they're all effectively collaborating because if they can't collaborate, if there's that division, you're going to end up with lots of lots of different bottlenecks and barriers to progress.  You know, what do you think about metadata management? I think it's crucial, especially if you're looking at automating this process. Because if you look at all the disparate data systems that are coming in, even across, across schemas and data systems, nothing's going to be called the same when you label it. Think of the column names. And so it's really important to have a robust metadata system in place where you can effectively categorize or tag data. Think of like social security number, a phone number, a name. To give it multiple things, you could say this is a name, even if the column name doesn't match up. This is a credit card number where the column name might be CC. You couldn't tell by the column name, but you can tell by the data. And so you need a place to be able to have kind of commonality of metadata and then how that translates into automating governance is you need to be able to write rules against that commonality. You need to be able to define globally across all connected data to say this is how I treat names. This is how I treat PII. This is how I treat specific entities of PII credit card numbers, addresses, etc. Dealing at that metadata layer, rather than at the physical layer. Yeah, and I would just I think that's exactly right now I would just add that a really key component of that is thinking about metadata as being hierarchical. And so if you structure that correctly, you can have kind of like a top level. So you have parent tags and then child tags and what you can do if you can have very, very granular metadata attached to all the raw data. And with the parent tags, you can use that to create just a few very simple, very easily understandable rules. And that's not something that we found is really key in terms of effectively having the policies mapped to the data, but also making sure that the policies themselves don't kind of spiral out of control and become too complex. Yeah, because of what you get with that is the ability to write their author and enforce a policy that says, this is globally how I deal with PII. Everybody has access to PII except for these people, except for this purpose. Right. And so you get that by having robust metadata management. See this question came in when, as you were giving your examples, your example on linkages was very interesting. How does one consider all possible associations to control for disclosure of PII? That might that be that might be for me. That was a question focused just on link attacks more broadly. Yeah, I may have mixed that up, but not a problem at all. So I mean, I think so. I think the first step. I mean, that's a great question. I think the first step is understanding. Excuse me. Sorry for the coughing today. I think the first step is understanding that it's impossible. Again, we talk about privacy and security protection being an art, not a science. If you're going to use the data, you are going to create some sort of risk. And so I think the first kind of starting point is just to understand that it's impossible so we don't want to have unreasonable expectations. And then I think once we kind of have grounded ourselves in the realities of risk management here, I think it becomes a little bit easier to say, OK, what is it that someone wants to use the data for? Does it justify any potential risks that we think might be incurred from it? And then also kind of just what can we do as a prudent matter to reduce those risks? So a lot of different regulations encourage what's known as minimization basically means that you should be limiting the amount of data that people get as a default. So you should never be giving someone more data than they actually need. And so there are a variety of different ways to put minimization into practice. You can only use statistical samples of larger data sets. You can ask or hash particularly sensitive values. There are a whole host of techniques you can actually implement. But I think more broadly it's really just about thinking about for one specific purpose, for one specific task, what data is really necessary and not going beyond that. So thank you for the question. It's a wonderful question. Yeah, a lot of great questions coming in. And if you do have questions feel free to submit them in the bottom right hand corner. We've got time for a couple more here. So one of the five pillars is to have no copies. How do you feel about using replicated databases to limit users permission accessibility to the raw or sensitive data as well as removing server traffic on the original database? Yeah, I think from the kind of infrastructure architecture side of my brain, I wouldn't necessarily consider a replicated database, a copy of data. Often I think of replication for availability, disaster recovery, etc. And so I think kind of the security rules still kind of come into play often in that arena. But I would, I also often think of kind of transactional systems. I think like the one that this question is trying to get after from a load perspective, having some relief is to create like a replicated a data set. And from my experience is that's the predominant, predominant load of data is for analytics. And so having a basic a method of data for analytics, we don't consider that a copy of data for governance. I think what we're really trying to hone in on is reducing the need to make a copy of data, basically a snowflake copy of data for a specific role of governance. So like HR and the credit fraud group shouldn't get two different copies of data just because they need a different view of it. Take this replicated data set as your quote unquote analytic data set to point people to to reduce load on the production database and point your governance tool, your privacy tool to that database and provide those customized views live against that data set. So I think that kind of fits squarely in the use case of no copies of data for the purposes of governance. How does it security come into the picture. We deal with security a lot. And I think they play a central role. And what we're seeing across organizations and I'd love to kind of hear this from folks on on the webinar maybe in the chat they could chime in too. But more and more, we are seeing the, the quote unquote problem of both privacy and compliance. Start to fall into the bucket of security in the, the CISO organization. The governance has completely landed there, often still stays in the CDO, the chief data office, but who owns the risk was a thought I had a we saw above that often is now starting to land in in the CISO office. So the security folks need to be a central part of any, we think, wholehearted and holistic governance approach. It's got to be kind of a good marriage between data offices, analytic offices, security offices, and then obviously the people who need it. So we think they have a central role, long with that answer to say, they're very crucial. Indeed. So, you know, I'm, I'm wondering if we can slip in just one more and less in a minute here, you know, what is the necessary first step I should take care of out of the three domains privacy, security or compliance. That is a very, very good question. And I'm not sure. I mean, so I think so you can tell me if this is a cop on answer, but I think at the end of the day they are all the same. They're all about understanding and better controlling your data. So I think they're really all about control. So if the question is, you know, I'm looking at this landscape where have so much I need to comply with. Where do I start? I think I think that the good way to start is just to understand to go back to those five different questions we listed. And just to look and understand what's the process for how my organization collects and gives access to data and to make sure that that data is thoroughly controlled at each at each step. So really it's all I think the end goal for all of them. Yeah, I think my answer is going to be similar. I think they all actually informed the other. The requirements of privacy can come out of security and regulations for compliance. You fall underneath and vice versa. I think that's actually a through a play. I think you can't completely address completely address security. If you're not also addressing privacy and compliance. Well, that does bring us to the top of the hour. Andrew and Matt, thank you so much for this fantastic presentation and thanks to all of our attendees for being so engaged in everything we do and all the great questions. And again, reminder, I will send a follow up email to all registrants by end of day Thursday with links to the slides and links to the recording of the session. And again, just thank you everybody. Thanks to Muta for sponsoring and hope everybody has a great day. Again, Andrew and Matt, thank you so much. Thanks for having us.


Hey everybody, Matt here for Layton sports car trip in one box of 2022 tops chrome baseball hobby for Matt K. Good luck, Matt Jack Flaherty refractor and Matt Manning. There's rookie Otto John Hesley Chanager Carlson Winker There's Jose Serie rookie refractor Pretty close game here with the Vikings and Saints. There's Vlad Jake Burger and prism refractor rookie Brandon Marsh Tris Mookie There's Albert Pujols refractor Jackson Reitz Again, not very many boxes in queue only three after this Shoei Otani 87 box. I'll get a rip for you. You will not be waiting long boba. Shet. There's Nelson Cruz skill jaren Duran boba shet prism Come on. Let's see a big hit here. It's Jed Lowry prism Xander refractor That is rookie Otto Chas McCormick flying Chas Aranado Let's see a nice rookie parallel in here then. There's a wander rookie 87 Strasburg Purple refractor Ronald Acuna jr. It's 250 nice one Matt late Three boxes in queue after this grab your boxes. I can be here as late as three as long as I have the Orders to keep me here It's Kyle Schwab or prism and at three I have to get out of the way so they can set up for sunday night breaks Cronamore Jose Ramirez Ashby judge Lyle vado packs you go so Tani Rosarana Aaron judge refractor and last pack Saia gray And that'll do it for your box Matt. Appreciate the purchase. We'll get these right out to you


Gang, let's go to our last movie. Let's go to our last movie. So we had one war movie, light war movie. We had three funny movies, and then we got the banshees of Inishirin. Whoa! How many of you were able to watch the whole thing without your nervous system going into overdrive, right? Elder God Tom Cruise was talking rap dancing lessons at the time, was taking rap lessons. Really? Elder God Chicho, I'm giving you the finger.  This is the movie that I I suggested it in one, right? Because I wanted to see it. Because these two actors, let me read their names. Colin Farrell and this movie came out, The Banshees of Inishirin, 2022, directed by Martin McDonough. And he did, he's done a few movies with these guys. One of them was in Bruges, which is a fantastic movie. And this is Colin Farrell, Brendan Gleason, Kerry Condon. All three of them, I believe. No, Kerry Condon, I believe is UK. Another actor, which was absolutely brilliant in this, was Barry Keegan. Okay, was Barry Keegan. This movie gang, holy...  No, no, it's not a prequel to in Bruges. It's not a prequel. This is set in 1923, the movie. Okay, by the way gang, this is spoiler-glure. You haven't seen this movie.  Okay, in your life, this is it. Right? If you like those kinds of movies, do not listen anymore. Do not watch this anymore. Come back to this or whatever. Go watch this movie. Okay, Cheryl, I ran out of time and didn't even get to start it. Oh, Cheryl, I saved it until last night because I knew this was the movie that I wanted to watch before we did the live stream. Cheryl, oh no, this movie is so complicated, so complicated, so brilliant, so brilliant, so brilliant. Cheryl, I plan on watching it tonight, but don't mind spoilers this time. Cheryl, stop watching. Okay, I was on the road a ton this month. Cheryl, get out. I'm gonna kick you. I'm gonna ban you. Starts April 1st, 2023. Starts the day before April 1st, 2023. Right? January, February, March. January, February, March, April. So starts March 30, was it? January, February, March, March. It starts on March 31st and kicks into April, right? Ali, I haven't seen this film, but it reminds me of, oh, The Lighthouse with Willem Dafoe. Brilliant movie. Brilliant movie. We watched that as well. We reviewed that as well. And that was phenomenal. That was heavy, dark, deep. This is disturbing. That one was not as disturbing. That one was Shakespearean. To a certain degree, this was Shakespearean as well, but this was disturbing, right? Cheryl says, dare you do it. The dancing visual, that shopkeeper obsessed with gossip was vile, vile. Every character, even the shortest amount of time they were on screen with just the minimum amount of words they had portrayed their persona brilliantly, right? To me, this movie is a 10 out of 10. It is a masterpiece. It is an absolute masterpiece. Oh, God, no, it starts April 1st. It does start April 1st. Okay, okay. Cheryl, okay. If it's truly better for me to not have any spoilers, I'll bail. Cheryl, bail. I know if I wanted to watch this, if I was going to watch this, I wouldn't want to hear anything about this. Really? Anything about this? Oh, God, Pat thinks it's an April Fool's joke. Yeah, yeah. But I thought the day before, because he crosses, he pulls the calendar, and the reason I know this is because I was trying to figure out, because there was, by the way, gang, what was a civil war that was going on in 1923 across the Isles? Was that Northern Ireland, Ireland battle going on? Is that what was going on? Because that was going on. I was trying to figure out what period this was supposed to be set in. I realized there was no electricity, really. And then they brought out a calendar in 1923. Cheryl, okay, friends, I'm out. Have a wonderful week, everyone, and thanks for the heads up. You're welcome, Cheryl. And gang, don't forget, Cheryl's taking off next Sunday. We're doing our yearly auction. Okay, so a yearly auction next Sunday. Cheryl, I hope you enjoyed the movie. Let us know what you think about it on Gilded. Okay, Pat, dancing vigil. The sister was the only intelligent one on the island. She saw the light on God out there. She saw the light and didn't lose her mind. She got out before she lost her mind. Right? Oh, God, everyone on the island are people who are social misfits unable to survive in mainland Ireland. Cheryl, I'll post my reaction on G tonight. Awesome. Ali, thank you very much for the support on Sensor Tube, Sensor Tube. What do you call it? Special chats? Forget what this is called. I love your reviews. Very knowledgeable on many subjects. My pleasure, Ali. I did some, some, some stuff, some stuff. And thank you very much sort of for the support brother. Thank you very much for the support. And if you don't want heavy, man, if you haven't watched this, and if this is type of movie you want, Ali, you, you like, if you like the lighthouse, you will love this. Okay, so you might not want to watch this, brother. What we're talking about. Okay. Irish Civil War. Yeah, Irish Civil War. Okay, okay. So is that the one that separated Northern Ireland and Ireland? I don't have my timeframe set on that, Allah, God, just before the treaty that separated the Irish into two groups. Okay. Okay. I know a lot about this period. Yeah, I bet you do. Yeah, yeah, I figured you would. It was in the far off distance. And one of the most brilliant parts was a column Farrell says, I wish you luck because he didn't know what side was what. He just said, I wish you luck in your, in your war or something like this. I forget the exact quote. And then the cop saying he's gonna go and watch an execution, and he doesn't know if it's the Protestant killing the Catholic or the Catholic about about to execute a Protestant, he was just gonna be, he's just gonna get paid to do this, right? floor, floor, sir, floor, sir, cherd, floor, floor, sir, floor, sir, cherd, uncensored. Good afternoon to you as well. Thank you very much for popping in. I can't pronounce your name. Oh, a super chats Ali is called super chats on sensitive. Thank you very much for the super chats. All the God, the friendship is a metaphor of the future of Ireland. You think so you took it to there. I didn't take it in that direction. I didn't take it in that direction. I took it to a level of people coming. There's so much going on. But one of the things was a person being able to do the calculus to see his more mortality, right, his lifespan, and realizing that he wants to contribute something contribute something period, and do more in his life than just be, right? sort of looking for immortality. There's so much immortality. Maybe if you want to take it artistic expression, if you want to take it, one person saying that they, you know, people deciding of what is important in life, what do you want to be is the most important thing in life to be a kind person to be a good person to be a wise person to be a well read person to be a social person to be a creative person to be a person that provides a safe space for people to interact. There is so much to it. There's so much to it. You could take every five minutes, every 10 minutes, every dialogue, and just break it down, right? Flowers child flowers child, of course, I couldn't believe I couldn't read how to read it salutations flowers child welcome to our live stream on censor to lark bar. I love watching stuff on the Irish Civil War. Yeah, Ireland has such a complicated history.  Oh, the God the police officer is the British government in human form, abusing the Irish under his control. Yeah, I could I made that connection, authority, authority abusing anyone, right?  Just if brilliant, brilliant, all the God, colon, colon woke up and realized that he had wasted his life and decided to attempt new things. But his real motivation was to help his friend. You think so? Oh, maybe. And he did in the end. But there was definitely insanity there. Where, right? But he's he cared. He cared so much, so much for him, first friend, like one of the most powerful scenes when the cop was beat up, Colin Farrell, and the cop comes up to Oh, man, I got to get the names. I should I should mention that. Okay. Column, Colin Farrell column. So the column character is standing there watching the cop beat up on pad. Right. And the cop, when he's done beating him up, he walks by this character here, right? And says, see you later in the pub for the beer, you owe me. And this character over here looks at the cop and says, I don't owe you a beer. Right?  And he goes over and helps this guy up. And there's a relate. There's something going on there that he's not allowed. He's not allowed to talk. Put some on his horse and carriage, and rides them to the fork.  Rise up goes to the road, and there's a fork going in different directions. This guy crying, this guy just going and like, amazing. That five minutes, literally that five minutes puts to shame 99% of the movies that are coming out out of Hollywood, any studio right now.  Right. All the God, we won't call it quits. We'll call it the starts in the bar. Pats him on the face. I think that's the scene you're talking about, right? So brilliant. So after this movie, I told my partner last night, we need to get a little mini, mini donkey, mini meal. Right. So brilliant. So brilliant.  And I took a little break. And I didn't want to go back to the movie, even though I was loving it, it was intense. It was brilliant. One of the best movies I've ever seen. I didn't want to go back to the movie, because it was becoming insanely dark. Right. But I had to go to it because we had to do our homework. And I'll mention this gang. Again, I'll give you a spoiler warning. Right. First time chat wrote maxing 3500 hello chicho long time listener first time chatter. I finally cashed a live stream also work. Thank you very much, brother or sister. And thank you for popping into our live streams. I'm going to give you guys a spoiler regarding this. Okay, to the end, I'm not going to say what happens in the end. But I can I can tell you this. Okay. Turn it off. If you want to see this without the ending was absolutely made me fall in love with it even more. The ending was perfect.  One of the most perfect endings to any movie I have ever seen in any genre. It was perfect. This movie is perfect. Okay. Perfect. Okay. All of God, the donkey and the kid, Dominique were metaphor for the victims of the war to come.  That kid deserves so much respect. What's his name? Barry Kogan and gang. You know, I don't know if this is true or not, but only the Irish could have made this perfect. The actors and all three male actors were Irish. And Carrie is I think she was born I think she's English. Oh, I looked this up before, but I think she's English. So it was just brilliant. Just absolutely brilliant. I think the only male Irish actors could have really given that much to this, right? Given that much to this dancing visual. Only time I laugh was during the confessional scenes. I laugh more than once. I laughed a few times. But it was a dark laughter to a certain degree and it got darker and darker.  Absolutely brilliant. Where the guy turns to him says, do you have dreams?  And that's implied, right? That's implied. That's in the background. You know it, right? The authority there, right? So deep, so deep. Oh, God, there was some village colors, color ads as well. You see a lot of yellow in scenes for hope and optimism. Oh, I didn't pick up on that. Despair 2077 salutations Good evening, all the God and red for death and anxiety. Yeah, yeah. Yeah, some of the buildings were yellow. That's right. The color on them, right? Yeah, yeah, yeah. At some point, I'll have to watch this again. But not any time soon. Not any time soon. Not any time soon. For sure. So heavy. So heavy. I woke up this morning thinking about it. Oh, God mentioned that he couldn't sleep after watching it. It was brilliant. I would love to see someone do a full blown analysis of everything in this movie. Right? Oh, God, the priest was aggressive to call them as the church were against the IRA and wouldn't take their confessions.  Right? I have no, like, I have no idea how the Irish can still go to church, the Catholic Church. I really don't know. Like Shanae no Connor, when she tore up the picture of the Pope, and a lot of hate came her way from the from the Irish. And that was a time where all these revelations about the child abuse and stuff that the Catholic Church committed weren't known, right? Or weren't revealed, let's say it that way. Rope, Rope Maxing. Colin Farrell is a great actor. I'm so happy that he finally take break from blockbuster movies and shows his talent, good taste of screen 100%. I think he's one of the greatest actors of our time, both of both of these guys, two of the greatest actors of our time. So lucky to to be seeing watching movies from the really and Colin Farrell. Wow, man, James Dean. James Dean ish, like, Colin Farrell. Brilliant. Brilliant. I'm glad he's lived longer than James Dean. Okay. What an actor. Colin Farrell was amazing in Batman. Rope, and I'm kind of jealous of his thick hair. Oh, the God, an Irish girlfriend got a lot from her about Catholics over friendliness. Yeah, I bet. I bet. And she must have been a firecracker. Emotion, roller coaster ride, Elder God, was it? Let's not forget Daredevil. One of the greatest villain portrayals ever, Bullseye, right?  Him made it worthwhile watching. Right? Yeah, the old woman was death, basically. The old woman, as soon as I saw her, reminded me of what's that black and white movie where death is playing chess with the character really old movie 1930s movie. What's that movie called? I've seen it as brilliant. Elder God, hit us up. What's that movie called? Anybody else? Ali, him and Kaleon, Kaleon Murphy. And have you seen open? No, I haven't seen Oppenheimer yet. I haven't seen Oppenheimer yet. I plan on watching it hack and torp. Ingmar Bergman directed it, right? I believe so. Yeah, yeah, yeah. Death is called death. It's not called death. Torp on censored to Elder God. When she's watching them at the end of the movie, I was saying that more death is coming. Yeah. And one of the most brilliant parts was Colin Farrell. When he's walking up to his house, right?  And he says, don't put any, that's when you know he has become a man of the times, right? His intelligence level has gone up. He's, he's no longer the seventh seal. Thank you very much, dancing vigil, the movie of thinking about seventh seal. Yeah, yeah, brilliant movie, right? But that's the, that's the part where Colin Farrell, you know that he's not desperately trying to be the innocent, likable, nice person that his parents were. He turns to the old woman says, shut up woman, don't put any thoughts in my mind that aren't there, right? He's aware of evil, right? He's now aware of evil. Brilliant, brilliant, brilliant. Nicholas Simpson, how many streams do you have open? All the gods everywhere. Yeah, the seventh seal, the seventh seal, 1957, I thought it was older than 1957. No, it's got to be older than that, isn't it? The poster or one part of the movie, huge chunk of the movie is this old man playing, I think it's old man, it's not a long time ago, I've seen it twice, but a long time ago, playing chess with death, right? Is it in a burger is a Russian movie? I don't know, I forget. Pat was so happy at the start. By the end, he is alone and bitter. He is alone and wise to the world, I think.  Bill and Ted, all the gods hilarious. That's like some adventures. Was he bitter? I think he was like, for example, when the young kid when they're drinking, and Colin Farrell tells him the young kid that he lied to his students, that his father was was dead, and the young kid goes, he's so innocent.   But when Colin Farrell tells him when he's done that, right?  Like you're not a, he turns to him and says, you're not a kind person. You're worse than them. You're worse than them. Right? His, Colin Farrell's delusion of who he was just shattered, right? Showed how selfish he was. Right? Oh, my God, I love the constant use of people looking in from the outside. It's a metaphor of him looking at a life he was losing. Dominique committed suicide. Dominique committed suicide. The young kid, there's no way he could anyone of her caliber would ever love him. He knew he was, he was desperately seeking for someone outside of this, this village, right? Outside of this village. And when she said, when he asked her, could you ever love someone like me? And she says, she shook her head. No, that was it for him. That was it. So, so much, so much, so brilliant. So brilliant. Really love the movie. Really love the movie.   Me gang, what do you guys give it? I give it 10 out of 10. Easy. Easy. 10 out of 10. Easy. 10 out of 10. Right? Oh, my God, he was doomed to drink in the pub till he died. Yeah. And continue to be beat by his dad. Right? Continue to be beat by his dad, made fun of by society. Right? Dancing visual says, seven out of ten. Elder God, nine out of ten. Nine out of ten, elder God. I give it a 10 out of 10.  When something makes me feel this sad, like this heavy, I have no choice but to give it 10 out of 10. Only because I saw some historical inaccuracies. Ah, I bet, I bet there would have been. Right? But, but what was one of the historical inaccuracies? What would a historical inaccuracy be? I can't think of any right off the top of my head. Clothing. Oh, clothing. I don't know the clothing from that era. It suited it for me. Language. I can see language. If the Irish accent wasn't as harsh as it probably was back then. Right? Because I understood 98% of the conversation. Right? So, I could see that. Andy Walker. Hey, Cheecho on SensorTube. I'm saying hi from my post operation room for having two hernia repairs. Ouch. Hope you're well. Andy Walker. In-girl hernia on SensorTube. In-girl hernia. I got a recommendation for you if it's in-girl hernia or any operation. Go once you're, yeah, get well soon, Ali says. Gang, anybody. If you ever get an operation, okay. Once you're in the healing process, once, you know, the the wound, the opening is healed and stuff like this, go see a massage therapist and get them umbilical and umbilical and get them to break apart the scar tissue. You want to get rid of the scar tissue. So, go see specialists to help you get rid of scar tissue. Okay. That's my recommendation. Dancing visual.   Allah God. The both, the sister, got on. Ah, okay. But I love the scene when they paint the uh, what were they painting? What is that? What's that symbol? I don't know. What were they painting? They were painting, oh, the mailbox. Yeah, yeah, yeah, yeah. They were painting the mailbox green and she comes in the postbox, yeah, and puts the envelope inside. Yeah, yeah, yeah. Those, that shop owner gross. Wow, gross, gross, gross. Walker, thank you Ali. Thank you for the advice to show my pleasure and take your time. Make sure you don't injure yourself again, right? Make sure you don't injure yourself again. Yang, good movies. We'll do again. Let's say within criminal offense to open mail, criminal offense to open mail. Indeed, she opened that up. My question was, she kept on asking, was it a job offer? Was it a job offer? Is it because she really didn't understand that it was a job offer or it wasn't a direct job offer? Right? Andy Walker, I'm here to try and get some rest with your relaxing tone. Also, you caught us, this is a heavy, heavy movie we're talking about, right? Dancing with a thanks for the good laugh on a Monday morning. My pleasure, my pleasure, Sunday afternoon where I am. Gang, let's call it a stream. Let's call it a stream. And we'll probably do a movie stream, let's say in a couple of months. So we're in November, December, let's say January 2024. We'll do our stream again, right? I'll look out the people on the island were institutionalized. It was like an insane asylum. Literally craziness. The sanest person there was the barkeep, right? And the sister and the sister. 10 30 p.m. for you. You're UK, UK. Yeah, UK or Ireland or Scotland or Wales or UK, I guess. Gang, let me take this poster down. Watch it if you haven't seen it. If you can bear it. Gang, thank you very much for being here. I hope you enjoyed the stream. Thank you for the discussion, discussions. Thank you for the recommendations. Thank you for the references. Right? Thank you for the references. They couldn't leave if they wanted to. Only the sister could leave wearing optimism yellow. Oh, I didn't realize that. I didn't realize that. I did not see that. I did not see that. Walker is laughing at the clown and censored too. Allerious. Peace out. Peace out. Gang, if you want to follow this work, I'm on Patreon. Substack, subscribe star. You can definitely follow the work there. We do have a gilded server. You're definitely welcome to join us at our community and share information. All the God for your information. Her clothing color changed throughout the movie. Oh, I didn't even notice that. That was red she was wearing at some point. I gotta watch it again at some point. I can't bear to watch it right now, Gang. And Gang, do not forget. Do not forget. Free Assange. Free Assange. Free Assange. June Assange publisher and journalist that is being crucified for trying to bring transparency and accountability of capital's power to humanity. Something that we desperately desperately need in our societies. For more information, see wikilees.org, defend.wikilees.org, or countless resources available online. Yo, you and me. Hi, yo. Arrived late at the end, it seems. Yeah. Yeah, indeed you. Yo, you and me. Watch the village. I got so much so much about this movie. Gang, we are live streaming on Twitch, on SensorTube, and on Rumble. For those of you watching this live, thank you very much. Thank you, Allie, for the super chat on SensorTube. Thank you guys for the discussion. Again, thank you for being here. We are recording this on a lapel mic, and I will be uploading the audio of the live stream to soundcloud.com forward slash chicho as a podcast. And this podcast should be available in your favorite podcasting platform. We're on Twitter, we're on Mines, we're on VK, we're on Gap, we're on Getter, we're on Substack Notes, and we'll see where else we end up. And gang, salut to the mods for being here, having our backs and helping us build a beautiful little community we can share information and talk freely. Salut to everyone. And don't forget, next week we're gonna do our Twitch auction. Apologies if there's no points on Rumble and SensorTube, but on Twitch, if you built up your points there, we're gonna be auctioning stuff up, and sort of appreciation, yearly appreciation for everyone that's been supporting this work and joining our community. Oh my god, Pat has a yellow house with a red door. Yeah, yeah, yeah, fingers. And when the guy's playing violin, when he was doing the the, when he's just gone, and he has the students in the bar, and the blood just dripping. Crazy, crazy, crazy. Good night, chat. 1.30 a.m. for you, rope of mixing. I'm on Substack Slayer, Darth. I'm on Substack. Yeah, indeed, indeed, indeed. Oh my god, no pumps in bar as well. They did exist though. They did exist in 2023. 1923, I didn't know that. Glasses, glasses, glasses. Then no fingers dance. 1923, yes, oh wow. Didn't know that. Maybe it didn't exist on the island. Maybe it didn't exist on the island, right? Gang, I hope you have a fantastic week. If you can make it, 1890 actually came in. Pumps, really, super cool. Gotta have bubbly beer. If you can make it, next Sunday, gang. Twitch auction, annual celebration. Hope you can make it. Bye everyone.