and a new series called The Club Car Special. It brings you a radio dramatization of the march of events and city life section of the Hearst Sunday newspaper throughout the country. In this section, you'll find the cartoons and writings of America's most popular humorists, Will Rogers, Arthur Bugsbear, O. O. McIntyre, George A. and many others. Let's climb on board The Club Car Special, settle down in the big easy chair and watch the world go by with a great big laugh. Music The first thing we see as we turn to the march of events, city life section of our Hearst newspaper is a snappy cartoon. Yes, it's a picture of your municipal fire company. The chief seems to be mad as hops. Anyway, he's doing a lot of hopping up and down.  Music Losos, what is this? A fire department or an old lady's home? Are you guarding into the public safety or are you just a lot of stuffed church? Answer me that. Well... I don't care how many fires you put out. What do you talk? We saved the 14 lives of this week, chief.  We rescued all their elephants when the circus burned down, didn't we? I don't care if you saved all the elephants in Africa. Well, what's the matter, chief? If you're not cared about putting out a fire and a saving the lives of a rescued elephant, so what do you care about, eh? I'll tell you what I care about. It's those newsreel fellas. Newsreel fellas? Yes, newsreel fellas. They've beaten us to three fires since last Tuesday. Laughter Millions of followers everywhere. During the late but not lamented depression, everybody had to forsake some of the things they previously enjoyed. But the people of the nation did not surprise themselves of their McIntyre. Man, that's loyalty for you. Think of it, millions of readers demanding their usual contact with this writer no matter what else was happening to the rest of the world. Well, no one follows McIntyre more closely than does Billy Murray. And nobody likes to talk about McIntyre more than Billy Murray. So, Billy, how about it? Right with you, Harry. Well, everybody knows how McIntyre likes dogs. Here is a quotation that Odd printed in his column a few Sundays back. It's a tribute written by Jerome K. Jerome, and it says, A dog never makes it his business to inquire whether you're in the right or in the wrong.  Never asks whether you're rich or poor, silly or wise, sinner or saint. You are his pal. That is enough for him.  If need be, foolish, brainless, soulless dog. McIntyre says that whenever he feels his enthusiasm for dogs is getting out of bounds, he always turns to this tribute and reads it again. In another paragraph, McIntyre tells us that Ken Hubbard was more right than we appreciated in some of the ruralistic philosophies he used to write. Here's one from way back in 1918, when Hubbard stated, the best way to double one's money is to fold and put it back in the pocketbook. The other day, McIntyre described one of Park Avenue's swankiest most-to-date drug stores. He said its windows have a plushy magnificence and they have on display perfume at $125 a bottle and toilet powder at $3 a shaker. Well, I suppose they charge a dollar a dozen for aspirin tablets, silly, eh? I don't know about that, Harry, but they do make the clerks wear dinner jackets after sundown. Then McIntyre turns serious and unfolds one or two not very well-known facts. He says that Queen Victoria wanted no black mourning and was buried in white, believing death would reunite her with her beloved Albert. McIntyre says also that he believes 98% of all breach of promise suits are really nothing but blackmail. But here's a good one. McIntyre states there's nothing so comic as a pedestrian trying to avert a funny posture when he falls on the icy sidewalk. Ahead of him the other morning, a man went into one of those Wilma Honeyside skitters and wound up with one knee on the ice and with his arms extended heavenward. And what do you think a passing taxi driver yelled? Mary! In his column the other day, Will Rogers got off on the subject of wars and how to prevent him. With typical directness, he turned right to human nature for a solution. Will figures that behind every war there's a man and behind every man there's a woman. He paints a picture of a prime minister returning home from a hard day in the cabinet. No sooner has the weary prime minister hung up his hat than the little woman pounces upon him. I was going to call you up but the Chancellor of the Exchequer has put through a new ruling. We have to pay for all personal calls during business hours now. Pay for your own telephone calls.  What kind of a government are you working for anyway? And are you a prime minister? What is this country coming to? I wouldn't stand for it if I were you. Well, these days a job is a job. You're too easygoing. That's what ails you. If I were in your place, I'd start looking for another position. I hear Dipsomania is looking for a dictator and they're willing to pay practically anything for the right man. I heard that story too but I couldn't get a job with Dipsomania. I'd like to know why you couldn't. Well, for one thing, I just declared war on them this morning. You did what? I declared war on Dipsomania this morning. Why have all the means, selfish, inconsiderate things to do?  But you don't understand, dear. Now please let me pay your- I don't understand, don't I? I understand, Penties. You just want an excuse to go off on a lot of trips and leave me stuck here at home. Well, I won't have it, do you hear? You'll call this war off or I'll know the reason why. Oh, listen, sweetheart, think of the position that would put me in.  You've gotten away with Penties since we've been married. But you're not going to get away with this? Now, wait a minute, honey. Don't call me, honey. I'm going right upstairs and back on this. Wait, wait, don't do that. You try and stop me. I'll show you. You can't declare war to suit yourself. Angel, listen. I'll call the whole thing off. Honest, I will. Cross my heart. I'll call the war off the first thing in the morning. My horrible club called Fiddle. Nix up, Georgie! He wrote about a little group of his Indiana neighbors who had gotten together and fell to discussing the banks and bankers and how many of the local banks had to close up shop last November. Well, finally one old fellow named Luke Simkin got warmed up to his subject and analyzed the situation. Something like this. things was going last fall I about come to the conclusion the only way to keep banks going in small towns around here was to elect directors who hadn't any friends or relatives the average country banker didn't seem many more qualified to protect depositors and use his head in loan and money than he did to be an aviator I remember not so long ago I attended a big stag party down south it was just at the time when a lot of them banks was popping off right in their depositors faces there were a lot of us Hoosiers present and one fellow full of sentiment or maybe it was sediment he got up and proposed we sing that good old Hoosier lullaby on the banks of the wall bash another member of the party a fellow who'd been pretty badly hooked got on his feet and said that I move we emit the singing of that song I happen to know there ain't no bank left along the wall bash author bug's bear is recognized as one of the world's wittiest writers his first comments on science politics society or any other subject in the daily headlines are recognized as classics of modern humor today we're going to learn more about what a man bug's bear is famous fighter and hero of over 1100 battles all of them crooked but no matter according to bear what a man is fighting turtle neck newbins today at Madison Square Garden it's just about time for the bout to begin a porter will you cut that music on the radio and see if you can pick up the water man newbins fight at Madison Square Garden it's all over but the shot there's a bill for the second week wait a minute hold everything I'm going to crawl through the crowd and he would have thought about I never saw anything like this in all my life turtle neck came out for the second round wearing handcuffs right here rep well what's the idea said in your point of the ring wearing handcuffs there's another one of your frame ups I don't know nothing about it rep ask him oh him turtleneck hey you turtleneck what's this guy trying to pull who slipped the handcuffs on you say mr. referee how you talk I wear them all the time they're only slave bracelets the club car special has come to the end of another trip on the air and while the journey has been briefed you will find a longer session of fun and nonsense by these same writers and others in the march of events and city life section of the Hearst Sunday newspapers think of it long articles each chuck full of written by the world's leading humorous no other newspaper presents such a list of names oh oh McIntyre will Rogers Bugsbear George a Damon Runyon milk gross Sam Hellman and others there are a lot provoking cartoons to keep up with the best humor of the day just as millions of readers do each Sunday the club car special a program built upon the articles of these foremost writers of comedy will be ready to arrive at your home next week at this same time over the same station be sure to meet it and enjoy another 15 minutes of original comedy


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Hello, and welcome. My name is Shannon Kemp, and I'm the Chief Digital Officer for Data Diversity. We want to thank you for joining the latest in the monthly webinar series, Data Architecture Strategies with Donna Burbank. Today, Donna will discuss business intelligence and data analytics and architected approach sponsored today by KatanaGraph. 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. For questions, we'll be collecting them by the Q&A panel, or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag DA strategies. And if you'd like to chat with us or with each other, we certainly encourage you to do so. And just to note, the chat defaults are sent to just the panelists, but you may absolutely change that to network with everyone. And to open the chat or the Q&A panels, you'll find those icons in the bottom of your screen to enable those features. And as always, we will send a follow-up email within two business days, containing links to the slides and recording of the session and additional information requested throughout the webinar. Now let me turn it over to Abhi for a brief word from our sponsor, KatanaGraph. Abhi, hello and welcome. Oh, thank you very much, Shannon. I hope you are able to see my screen. Looks fine. Yeah. All right, wonderful. Hello, everybody. Welcome to the webinar today. I'm Abhi, Abhi Sheikh Mehta from KatanaGraph. I had the field engineering team. And I'll take a few minutes of yours to give you an introduction about KatanaGraph before I pass it on to Donna to talk about the purpose for which we are here today. About KatanaGraph, we are a graph intelligence platform. We are a technology built for high performance, scale out graph processing and analytics. Our company is a COVID child. We were set up as a business unit in March 2020. It may give you an impression that it's a very new company, but in reality, our co-founders Keisha Pingali and Chris Rothback have been professors at UT Austin where our engine was developed over a decade of research by various different DARPA funded projects. So our technology is very well proven out. And as soon as this business entity was formed, the top notch investors from the world, as you can see on your screen, came back and gave us a pretty big series A round. And that has led to many commercial engagement on the front where we are dealing with many Fortune 100 companies in financial services and healthcare side. If you want to know more about us, please visit our website. That's KatanaGraph.com. So here is a glimpse into our leadership team. It's a very well combined, very balanced view of technology, PhDs and the business acumen. And the industries what we serve are a pretty wide variety. We are seeing a lot of traction all across the industry of healthcare, life sciences, financial services, from very generic to very specific use cases of fraud detection, anti-money laundering, from capital markets areas to talking about wealth management. So various different variety of spaces where you're dealing with big data, interconnected data, KatanaGraph technology comes into play because it's a distributed craft processing platform. And why people are trusting Katana a lot these days is because where our technology has been proven out on billions of records or trillions of records and terabytes of data sets. We have unmatched performance of which is 10 to 100 times faster than some proven technologies and it's cloud agnostic you can run it on any cloud vendor and whatever your poison is we kind of serve it. And it has scaled up to 256 machine. So, all in all, I will take one more slide for you guys before I pass it on back to you Donna, what we do in the graph compute domain is we provide you all the basic capabilities back by open cypher programming in the language of graph database for curing capability, link depth analysis, multi hop shortest distance shortest path. You know, giving you a huge advantage over the RDBMS technology in terms of ad hoc analytics. And of course we have our biggest value comes in graph analytics and mining space where graph algorithms which be backed by our on demand partitioning processes and heterogeneous scalability can run many complicated graph processing algorithms in parallel. Along with that we are also seeing a lot of traction with the latest technology trends out there around graph neural networks. So if you have any such interest feel free to reach out to us, and I will pass it on to Donna to discuss more about intelligence and data analytics and see what kind of role graph can play and fit you know fit in there. Thank you very much. Thank you so much for kicking us off and thank you to coat Katana graph for sponsoring and helping to make these webinars happen. And if you have questions for Avi or about Katana graph you may submit your questions in the Q&A panel as he'll be joining us in the Q&A portion of the webinar at the end. Let me introduce the speaker of the monthly series, Donna Burbank. Donna is a recognized industry expert in information management with over 20 years of experience, helping organizations enrich their business opportunities through data and information. She currently is the managing director of global data strategy limited where she assists organizations around the globe and driving value from their data. And with that, let me give the floor to Donna to get her presentation started hello and welcome. Hello, I'm Donna Shannon. Always a pleasure to do these each month and always nice to see some familiar names in the chat. We have a very loyal following at day diversity. So, awesome group of people. So, yeah, without further ado, as I just sort of alluded to this isn't a monthly series so it's awesome new state names on the list, which is great. This is the first time joining either this series or a diversity. All of the previous recordings and that's always the first question people ask is that will this be recorded will we get the slides. Yes, yes. And then all, and I think and correct me from wrong Shannon all of the materials from data verse you're kept in perpetuity from not only this year or this series but other series on the past. Please take advantage of those and then also would love you to join us in some of the upcoming ones. Next month is a topic near and dear to my heart metadata management. And you'll see the other list of topics for the rest of the year so, but the reason we are talking today is bi analytics architecture and a little bit of intro there from the sponsor which is great. Because more and more organizations really are, I know it's right and we keep hearing that buzzword, but becoming data driven, and a big part of data driven is bi analytics, you know, some of the graph use cases that were mentioned in the intro. That's great. But where my soapbox comes is that only works well when you have a strong architecture behind it so analytics are great. Visualization tools and some of the great new vis you know the people can do is wonderful, but where, you know, I sort of nerd out in my realm is that doesn't work well and the numbers won't be right unless you have that strong or perform, unless you have that strong architecture behind it so we'll kind of go into that in this presentation. So, because we're data driven we always like to start with stats and data. So, each year. We have a little bit of strategy and the diversity kind of work together on a trends paper trends and data management, which I find super interesting because some themes sort of say stay consistent some change year over year some grow over time. The one that tends to say consistent over time is that reporting in analytics are really key drivers to a lot of what we're doing in data management so you can see that one in the middle of reporting analytics is a key driver. The fact that data is seen as a strategic asset is one of those in the category that we see that growing over and over year over year so probably no one in this call is surprised at that I think most of us in this call probably agree with that but I think the rest of the industry the the quote the business that we tend to say was he is open and really wanted to do more with data which then drives the need for analytics. What I was heartened to see, and I hope this one grows over time because even though it's bigger than 50% it wasn't well over 50% or I would have said that but that people specifically said that they see improved collaboration through a defined data architecture and really well we want to get reporting analytics and being data driven that's all about collaboration and getting the numbers right and all of that so glad that folks called that out specifically, but would love to see that kind of grow over time. Also from the survey that hopefully folks will find interesting is you know this is when we asked what are the main goals and drivers for this was data management more broadly but just think data what are you trying to do is data. And really, again reporting analytics is at the top continues at the top and probably will keep growing that said there's a lot of other use cases for data operational data and etc etc but topic of this presentation and top of mind for many, many folks across the industry is and continues to be reporting and analytics and I see those as separate which could be a whole other discussion but they are kind of distinct things. If anyone joined our webinar last month, we kind of use this example last month was on data literacy and kind of the need for data literacy and when you read a dashboard. A big drive of data literacy is well firstly are you even using a dashboard, are we a data driven culture that doesn't go on to gut feel, you know on the sales or I really want to understand my customer and how trends over time are going not the answer of I know my customers I've been doing this for 20 years, which may be true, but we can also learn from the data so you know that's a big part of are we even using dashboards and the next question is, you know, are these numbers right and do we have the right data quality behind it and we have the right governance support. So, kind of a little mnemonic we had in the last session was this allow kind of asking you know what what about the data, the dashboards pretty the visit actually this one isn't pretty. But the visualization might be nice. But what about the data. So, kind of want to go across that theme, you know last month we sort of hinted at this, this session, which will delve into that last bullet at the bottom which is the architecture so and anything in data management has crossover. Often it's hard to get the quality right or the governance right if you don't have that right, right, correct architecture, but also just things like performance or, you know, are we using the right tool for the right job and are we thinking of the architecture so you know, we, I do this for living on a company called global data strategy and a lot of the anecdotes from these presentations going to come from real life. And a good trend that I am seeing is that more and more companies are becoming data, becoming data driven you know I've been doing this for longer than I want to admit and you know in the old days, folks have stayed with data management. And all you have worked and many of us have worked in government or financial services, you know, some of the folks that have been doing data management the longest, what I find fun about my job is that you know I've worked with small museums and nonprofits and you know, companies that you typically wouldn't maybe have been as data driven in the past but now everyone is. So that's positive. I do see though, not every, where a lot of folks can sort of go in and it's fun to build the visualizations and the dashboards and the tools have come a long way. It's a little harder to understand that architecture behind it and what's the best solution out there and it does not help that there are a lot of solutions out there and to tease the vendors a little bit. But you know a lot of folks have a vested interest in saying well our solution is the best and the speaker did not say that today. But you know there's a lot of, you know, I don't say it goes as far as misinformation but you know a lot of vested information in our solution is best right. So that is an exciting time to be in data management. Because there's a lot of options and a lot of choices but with choices also becomes the little bit of stress and risk right there's a cacophony of options I would say out there, in terms of what you can choose. You know, is it the good old fashioned data warehouse, and I will kind of give the punchline already yes that still exists and is still very valid it is not old fashioned. I've just tried and true and tested and there's a difference right is it the only tool in the toolkit absolutely not right. What about the data lake is that I'm old enough that that was a hot trend and I was a fat and now you know it was an old school thing that is a data swamp right or now do we have a data lake in our house, or a data hub, maybe data warehouse and data lake and data lake goes we just do data hub what's the data hub isn't that is that the same thing as you know, oops, sorry, an MDM hub, a data mesh. That's the new thing we do data mesh no wait data fabric data clothing data is not the same as data virtualization. I think no we just need a data catalog to put all our data in one place. You know, we don't have a data catalog I think you mean metadata catalog well no maybe it's a data marketplace, or let's put it all in a knowledge graph that sounds great or is it relational non relational stars give us equal. You can just, you know, that might be how your brain is feeling trying to keep up with things and I do what I do for like, even I've been doing this for years and years and years.  And I always say with that, and many of us. As a, I see a note that I might be fading and coming back is the sound okay. You're walking away from the mic a little bit, but I am not. Yeah. Okay, well keep going. Um, so I always go back to first principles and I think you know universities and schools that are teaching this that's the best way to think this what is the right tool for the right job. So, this isn't the only way to look at things but it is a helpful point is data lamp is that a light bulb moment love it. What are people using in the real world doesn't mean that people are always right, but it does give a good indicator of what amongst all the things out there what is has been tried and true. And I think we've been doing this survey for gosh I don't even remember so it's probably 10 years now data warehousing and business intelligence generally are and continue to be at the top of things that people are integrating and this isn't necessarily all around reporting there's other areas, you know that are all overlapping and that could be a whole conversation of what's the difference between governance and architecture and modeling etc but you know there are other other things to explore but nothing is wrong with building a data warehouse and bi and a lot of the the organizations that are doing some of the more, you know, modern or cutting edge, you know data science AI graph patterns that were mentioned. Also do that in conjunction with a data warehouse doesn't mean one is better than the other and you'll hear me continue to rant about that. They're just different use cases and different tools in the toolbox. I found this sort of interesting as part of that survey, you know are you using a data lake, not as many as you would think but many who are using it in conjunction with the data warehouse which makes sense right. I'm trying to understand total revenue by region by sales rep. That's a great use case for a data warehouse and I probably want those numbers to be right. You know if I'm trying to do some exploratory use cases and do some social media analytics or you know real time streaming of product sensor data that's a great use for a data lake could those be combined for some really great insights, of course. So, great way to think of that. When one is building an architecture and I highly recommend you do when write it down even if it's just a whiteboard of you know how do these things fit together.  This is a big fictional but based on several real world patterns, you know what what is kind of broadly called those enterprise systems record right things like master and reference data you're good old fashioned data warehouse. And a data mark was the difference between those that could be a whole other session right, but you know the fact that you're doing data science and advanced analytics maybe or some data discovery off the lightly modeled data or some exploratory data is only augmented by things like master and reference data right I would think if I were a data scientist. It would be better to have clean data to be working with, you know, plenty of surveys out there that, you know, unfortunately, a large part of the data scientist day is spent cleaning data, rather than, you know, actually doing the analytics on it so again that box in the green only helps everyone but that's a whole different way of modeling and managing and governing the data than maybe some more exploratory analytics. So at the top of that, whether it's, you know, standard bi reports or visualizations or self service bi really, you know, can be several patterns behind it one of which maybe a warehouse governance should go across all of those layers, maybe maybe it's more lightly governed on the left. Maybe not maybe maybe you have some really, you know, sensitive health data coming from some of the stuff in the lake doesn't mean just because it's in the lake it's sort of open open season. So security and your privacy and your PII is is still very important. One of the stories I like to tell in the person and company is nameless but we were building a model like this and we were talking about the PII aspect and you know personally there's a lot of valuable information that should be obviously secured in this case it was sort of health information as well as credit card information. And we're going through this model and one of the younger gentlemen on the team raises hand these things so I shouldn't be putting the credit card data out in the, you know, sandbox in the cloud for the exploration. The boss basically said send Jeep we're going to. Oh, I guess I didn't really name was today. Person X will talk after work.  So, you know, the fact that the security and privacy governance to go across all the layers regardless of the technology. So if you've been on my my sessions you've seen this slide before. This is kind of our framework that we use it global data strategy but it does sort of speak to the fact that, you know, bi analytics you'll see they're kind of right there in the center. But there's a lot of things around that that that make that same. So whether it's governance or even having your overall strategy of where where analytics fits in. You know, self service, is it all enterprise is it, etc, etc, what's the quality of that and of course the architecture behind that is a big piece of it. So I did want to kind of get into that idea of the design aspect of data architecture so some of the things we talked about before or maybe, you know, platform or hardware or styles. I think some of it is the business kind of layer of your bi in your analytics and that's often kind of what's this sort of overlooked or I won't say forgotten but maybe not focused on as much. Again, if you've joined these you fold disclosure I'm a big fan of data modeling at all levels I think it, you know, really is a proactive way to understand things. And yes I have a kind of inventory of data modeling cartoons out there. Here's one and maybe it's not funny, if you haven't done this and maybe it's just not funny, but you know we've all probably been here hey we've built the applications we're done with testing we've got this great new marketing application just wondering what do we mean by a customer. And maybe that's not funny until you've gone through it but there's a lot of different areas of customer what is even a customer, you know, and I have worked for been a member of large corporations that have made very embarrassing mistakes. We work with one where they set out kind of renewal notices to people who are prospects. You know, so when you talk to a sales person I'm going to go talk to a customer. Generally, often they mean a non customer someone they're trying to sell to but kind of colloquially, we use the word customer. In this case they literally went to the kind of, you know, pre sales database and use that because the database literally was called customer, not a great way to kind of do your data governance. You know, a lot of different flavors of mistakes about that or even, you know, is it a lapsed customer does that person on maintenance today. It's a customer of different account types is it a premium customer etc etc so anyone in data governance or data management data architecture knows that that should be a big part of that and often and I don't I see less of this but maybe again we have a filtered audience because we work with the data models, but there has been kind of in the past let's just not do the model that's so we unwieldy and it's going to take so long it's just a lot faster to just start building it will kind of do modeling later. But I guess I'm my mom used to always say if you don't have time to do it right you have time to do it again. And you will answer that question of what is a customer or what is a patient or what is a student or etc was a citizen. It's just, do you want to do it when you're fixing it or do you want to do it up front. And, you know, one of the comments from Gail you know every CRM system has that idea of a customer a contact a prospect. And, you know, I ran it over Twitter the other day, I was actually reviewing you know I model and it literally had a party related to a thing. And I said, yeah, that's very reusable. But that tells me nothing about this company one of the things I love about data modeling is you can tell almost everything about a company by kind of a one page data model. And yes, maybe behind an ERP system or CRM system there, there is a party table or an account table, but how that's filtered what flags you use what account types really make a customer. Literally just on a call right before this going through that what account types in in one of these big CRMs, and how do people use it and how is it entered, you know, as a big part of that so to me this is that business level modeling that absolutely cannot be skipped. But there's levels of data models there's that that business level whether it's up at the enterprise subject areas. The subject areas or down to kind of that business level with the conceptual model of that sort of both of those are sort of where is what is the customer. What what is a product working with the company right now with full seriousness and no irony of that says, we don't know what a product is in our company, we're successful, we're a multi billion dollar company we're making a lot of money. So if my management asked me to tell me revenue by product we can't do that we don't know what our products are. And again that's your core of the business that modeling that reveals a lot of interesting things across the data and across the business processes as well. Once you get down to the logical, that's still a business centric view, but you are, and that could be a whole debate and webinar of how physical is a logical. Does it need a relational database behind it, or is it kind of business rule centric. Again, that could be a full webinar, but it really should be at the business level of using business terms, using business logic, and getting those business rules around the data, and both of those are really related to understanding the business so that that is a sort of a definite when you're trying to build those reports why does this matter. We'll talk about this more in the presentation give please give me a report showing products by customer by region what's a product with the customer and then I will now want to say what are total sales. You know how do we calculate total sale all of that needs to really be defined. And then of course the physical model and I will get into this, because I've talked a lot at the business level but again years and years of this physical stuff is as hard and tech is super fun, but most of the problems we run into that we need to fix our that business level it's a business rule that wasn't described yes this performance issues and things like that from having a badly designed database but generally is the business rules that are going to cause your big problems. So getting that right up front, super super important beginning with the end in mind was one of the comments there. So, but yeah, but the physical model and again not everything needs to be in a relational database right and we have a lot more options a graph model right that's, you know, was talked about in the beginning. There's key value pairs etc but again, and I pick on the vendors pick on people human nature right there's always that well we have, we have no sequel now we don't need sequel that's just silly, you know we need both. They both have their place and just you know, don't fall into that trap but do think very carefully about that. There isn't only one option for your physical data. So the customer had a long day already say another customer where that whole idea of they had customer defined they were very far along, but then what are the different physical instantiations of customer in those different use cases yes they had graph and yes they had relational third normal form, and they also had star schema and they also had operational data and they also were starting to build MDM, and all of them were correct. How do you get that architecture together so I have really strong feeling that they will be successful because they are asking there's some hard questions in there where's the overlap but they're asking them. And at the get go, before they get too much further so good to do that. So, as I kind of mentioned there's physical different physical models for different use cases and a lot of tools in the toolkit don't just use the hammer, not everything is a nail. So there's another data modeling cartoon you thought you were done. And maybe that's not funny either but getting into third normal form. Okay, so if anyone doesn't know what that is that that's a great way to increase your data quality and ensure consistency or thinking of acid transactions, these are great when you do have an operational database I want to make sure that my records match up and I'm using referential integrity, great use for your relational database and that's one of the reasons they are still valuable and it's still well used. I would say when you use them use them correctly I mean the number of databases I look at when we come in and the don't have keys or referential integrity to find it and sometimes that's a design choice, but often it's I think folks just don't have those fundamentals of what a relational database was meant for and how to use that wisely. I'm like and I'm guilty of this using an Excel spreadsheet for just, you know, a list of my, my friends or their phone number there that's not really, you know, it's a financial modeling tool, and I'm using it for something else. And then I complain that the formatting doesn't work well with Excel and I can't, you know, in dead things and like because you're using a tool for not what is what it's not meant to be. I thought we all do it. Star schema and you know is that, is that still old fashioned we need to have in a webinar earlier this year, you know he's the star scheme of your dad. No, I mean there's, there's, there's part of the reason one does the star scheme is for performance are the systems built nowadays and the hardware and, and systems a lot faster that you don't need to necessarily put it in the star to get performance yes, but is there just a human friendly, logical way of organizing data that you have a fact like total sales by region by product by campaign by. Yes. And so that ability to slice and dice and have a nice semantic layer is still still in use and still super super helpful. There's other options no sequel. You know, I do want that faster truth on building a website and I want you know high vape data ball ball volumes I do want to have a more flexible pattern for change. Great, great choice for that. And that's just the beginning is hierarchies is XML this graph this good old fashioned cobalt copy books that we still run into, and we can laugh at that. But they work. We think of that why are we still using the mainframe. And I think it and you can't use the mainframe that's old fat. I'm not, I'm not proposing necessarily to start with a new mainframe as your new application with all the options out now. But when people sort of roll their eyes and say this thing was built in the 60s or whatever. And I think it's still running and still working. So we can't necessarily pull through those designs. But again, there's a lot more options, you know, S3 three buckets would I say do that for your enterprise data warehouse know, but is it a great way to store information at low cost, yes, data vault, you know, great way to store data kind of as an initial kind of storage, etc, etc. Lots of different options. And my rant that actually is not over. I lied to said rant over there in the lower right will continue ranting that no modeling technique is inherently better than another it's sort of like saying a screwdriver is better than a hammer. Both are good they just have different use cases it's only a problem when that old fast and saying when you have a hammer everything looks like a nail and don't do that. There you go there's the webinar don't do that. So, the question I just asked is the poor old good old fashioned star schema dead. I already gave the answer is not dead.  Not dead yet. Anyway, maybe I've had too much coffee today. It still is a very user friendly and performant way to do that classic slicing and dicing so folks aren't familiar with the star schema. It sort of can theoretically feel like it's being shaped like a star. That's the name. So at the center are sort of your facts or your measures that those I always like to think of it. What are you reporting on, you know, I'm reporting on sales transaction or patient visits or things you're basically counting. So that's what you're reporting on.   I'm surprised that some of these we talked already that single view of customer that single view of product are your master data efforts and so the more you clean that up. The whole topic of this webinar is an architect approach to reporting analytics. I don't fight them after the webinar. There's any report, you know, data scientist bi analytics person that doesn't want good nice clean master data that they can work with do they want to build it maybe not that's a whole discipline. But if someone said you want to clean de duplicated, augmented with with industry perfect data of all your customers. Yeah, send along. And so that's this idea of certainly old fashioned it's even more relevant with these new technologies. I want to build a graph database and see all my connections between customers. If I know the right customer, it's not going to work so well we actually had a really advanced financial services customer who is doing you know graph patterns and a lot of advanced analytics on their on their customers and had a great team doing that. The problem was, when they tried to integrate that with their customer list. They didn't have good master data so they didn't know if Joe Smith was a multi billion dollar and high net worth customer or Joe Smith was the Joe Smith who was the bankruptcy, you know, so people don't always wait and wait for it. You don't always have to wait for it. I mean there's a, there is a case for, you know, let's do just enough for now let's do some graph patterns on the on the data to get some exploration as we build master data you can't realistically stop the whole company, trying to sign that to management you know we need to stop operations for the next six months while we get our master data right you know that's you're literally you know changing the wings and a moving plane, which makes master is sometimes hard. But yeah, it does have to be done the more you can do that upfront, then you don't have to do as much cleanup. So yes, it is still a good, a good tool to use it's just not the only tool in the toolbox, and another good old fashioned well used tool that business users love I, I sort of stopped using this a bit my own practice full disclosure and brought it back to the bus matrix, which is a nice business centric tool as you're defining your bi and your analytics. And I walked into a meeting. I get a lot of characters from my clients and one of them is a, he was a senior financial analyst and we walk in the room he goes data people, bus matrix, bus matrix, and he was sort of getting annoying and sort of a funny way. So we started using the bus matrix and then we sort of add that in almost every, every project now because it is just so helpful, you know, again, what are we reporting by in this different flavors of bus matrices. One of the ones we, what are reporting by how do we define, and this could go into your glossary, or your data catalog or your metadata catalog or your data lamp. You know, we're reporting by total sales and wholesale revenue and what's the difference, etc. So that's what we're reporting by, and then you order reporting on, you know, I want to know revenue by location by sales by product and maybe and maybe another report just was location by product for the wholesale stuff. So a great way to map out a nice easy to you. Generally, we have a little prettier one than this but nice intuitive way for business people to understand, and a great way for a roadmap right because this this very simple grid, I just picked on spreadsheets, but this is fine as spreadsheet there's probably other tools out there. You know, it's a it's a glossary exercise and how do we meet by total sales revenue it's it's a governance exercise it's a master in reference data exercise it's a data warehouse exercise right this one simple graph can really be a roadmap for moving forward of how you how you build things out. So, big fan of the good old bus matrix bring it back. But there are many design patterns sort of behind the scenes so again I consider something like a bus matrix requirements doc, which sort of leads itself to the star schema but you know you might have come very different things we want website clicks by minute across the website probably wouldn't use a star schema set so if the stuff across the left is, you know, website clicks and, and across the right is by second, you know maybe this doesn't lead itself to a star schema warehouse maybe, but there are other tools out again, if you haven't heard me say it 16 times this webinar. There's a lot of options no no one size fits all. So even within the tried and true warehouse, still arguments over, you know, is it in men is it Kimball is it relational is it star schema. It's a nice case for both right a nice use case for the kind of relational model is getting that, you know, the quality and the single view of things. Like, you know, single view of customer and things like that when I didn't want to report on it and either an enterprise warehouse or a mart. The star schema is great for slicing and dicing. So, you can both go back right a lot of tools data vault is kind of growing in prep popularity especially in Europe. It is a flexible way to store the data. As you're discovering it. Is it as easy for reporting maybe not. So again, there's good tools for each, you know that the column data, a kind of flipping flipping the columns and the rows for performance is another great tool in the toolbox that might be a great answer to the website traffic that you're looking at, or just flatten everything out right I why do we need everything in third normal form as a data modeler. My answer is you don't. Right, maybe you do and again these can all work together maybe you do have in third normal form to rationalize it get your master data, and then flatten it for the data science team because I would love a nice clean list of all my customer data flattened out so I can do analytics on it. Right. A graph, a great way to discover those connections and patterns fraud detection patterns between customers. And I, these can also be iterative. So we have one customer who's their main goal is customer master data and it will take them years it's a massive company. So the graph first in the fact that graph isn't necessarily the tool that's going to cleanse it and master it, but they're discovering those patterns that then can be fed into their rules engine which is their true master data management. So it was kind of a good, you know, both their tools in the toolkit they will continue to use graph as they build out back to that you know can we wait and get all the data perfect before we do anything unfortunately you can't. They're kind of doing both in parallel. They're smart enough to not be necessarily pushing the, they can make some educated guesses on single view of customer for graph for some analytics for marketing. They're not yet pushing that back to your source system, you know I don't want my, my bill being wrong or my invoice being wrong as a customer because you made an educated guess I want you to know it's me, you know, think of healthcare even even more so right so some interesting use cases and use them or evaluate them all. Right, and we all get stuck in our ruts and I would say you know if any of these are new to you so much information out there on the web on diversity on, you know, on good old YouTube. I'm not going to end your week on a data management YouTube videos, but there's a lot of great and the vendors themselves having knocked them in the beginning. I've learned a lot from the vendors a lot of them have a lot of great information about their kind of tool patterns. In a typical organization, there are many use cases many data models. And this is just really even just a subset of some of the options but you know as we think of patterns and that's how I think if we think of that that that I'm trying to use my words of the report or the analytics, you know visualization in the very beginning of the presentation that might be a starting point maybe your bus matrix is a starting point maybe it's a, you know, user stories or design thinking workshop or something with your users really understand what you need, and then kind of work backwards into, you know, what do we need to support it. Again, if you start I always think left to right is kind of a, where housing kind of person, but if you're your operational systems are on the left. Again, I might at the bottom, have an operational system that is my accounting system may be on a relational database or my CRM system. Why are they on a relational database because those are really good for that application I want my bank to know that my, I have the right number of accounts and I have the right amount of money in my bank account. I actually have a financial institution that just gave me a set of stocks that I didn't purchase I'm still wondering what to do with that. So, in one sense I'm glad that I have a whole new set of stocks in my portfolio, but longer term, I'm a little bit nervous working with that financial institution what sort of governance they have, you know, as soon as their relational database but who's governing that how did I just end up with stocks in my portfolio. I shouldn't have said that they don't come find me. So, you know, that's one of the great mistakes of a web application. Again, maybe that's more of a no sequel or you know there's so many different applications. I probably wouldn't put my everything on my web and a relational database. And then how do you kind of move that maybe analytics and reporting it and not necessarily the ETL or, you know, ELT or how you do it but that data exchange. You know, JSON XML there's a lot of different patterns of how you even format the data for you either exchange to another in-house system or outside the organization a lot of industry organizations have kind of XML standards or JSON standards and things like that. And then how do we store for analytics and reporting. A lot of use cases if you notice at the bottom though, I am a fan of having some sort of master data or hierarchies or data quality, kind of as that hub that can feed some of those maybe your source is kind of a source of truth for some of that data. But when you're thinking of operational you really want to think of what are those master data. And because and we already saw in the examples almost everything we're reporting touches. The reason it's called master data and reference data is because it's used everywhere. It's that master source and it's referenced by a lot of different areas. So, you know, kind of starting with that is going to have a lot of benefit because then where am I if I want to do a star schema. Those dimensions can come from there. I want to make sure my operational system. You know, my stock trading company knows that I'm Donna Burbank that owns AT&T and Donna Burbank somewhere. We lost some stocks because they're there in my portfolio still flabbergasted by that one. So getting that right is only augmentative is that a word that only only can add to these storage areas for analytics to be important. It doesn't have to be just one right. And again, the negatives of data warehousing, which I'm a fan of and it works and it still will work. So what is, you know, is everything a monolithic warehouse, maybe it's yes and right we have the financial reporting we have the corporate warehouse, but maybe I want to do, you know, a graph storage for somewhere else or, you know, data vault for particular use case it doesn't all have to be the same. And then even on the consumption patterns, you know the good old cube, which is your kind of star scheme approach your bus metrics type approach already mentioned kind of flattening it out. You know, kind of time sequence data sets for certain use cases graph databases for different patterns etc. Again a lot of different choices and I can't say that any of these are inherently good or bad, but mapping it out. Again, it's been some good chat often data quality problems are either caused by things nothing to do with how it was stored or gave the definitions could be could be how it's stored in separate places, etc. But just mapping it out making sure you're doing it intentionally can console a lot of these problems. We had one whiteboarding session with a customer the great sense of humor, and then we were mapping out, you know, both the process and the data and you start to map out your data we've all seen that you know the lines and the lines you know systems going back into and going out around she was wow when you map up what we're doing we look ridiculous don't we. And again, I don't think anyone decided to have these these lines going all over the place but that's the problem. No one really did decide so you know even if it's a quick whiteboarding session on a back of an envelope, still better than not doing it at all. So, really, really thinking this there was only a positive. So, I do want to leave it open for for questions because I know there generally is a lot and you guys are never a shy crowd. So just quickly in summary, you know, analysts reporting growing and growing and in importance that strongly architecture, which is not one monolithic choice it's a bunch of kind of dairy say a fabric or mesh of different objects. And there's a lot of different choices that you can do just focus on those core fundamentals, I am as guilty as anyone else. There's a new cool technology and I want to use it, and I'll play around with it and that's fine to play around with it. But just make sure the use case you're using it for your enterprise is the right one, and just choose that wisely. So before we open it up for the, the chat in the q amp a just a blatant plug for the next month session which is on metadata management. Another blatant plug we do this for a living so if you need help think of us at global data strategy or website is there. And without further ado, I will send it over to you Shannon to open it up for q amp a. Donna, thank you so much for this another great presentation and thanks to our attendees for being so engaged in everything we do here so if you have questions feel free to submit them in the q amp a portion of your screen for either Donna or Abbey. And just to answer the most commonly asked questions just a reminder I will send a follow up email to all registrants by end of day Monday for this webinar with links to the slides recording and anything else requested throughout. So, is logical like data views. I'm sorry I don't understand that question what was that. It's logical like data views logical. Yeah. Yeah, yes and no. So, when I was saying when I kind of had that data model slide. Often logical is sort of views as kind of a shorthand for the business view so in that sense, yes it can be a view on top of your data but logical and it was more, you know, can customer have a product how do customers and products relate together. I think of you, and then one of my buzz words I think I had up there was kind of almost more your more semantic layer. Right, which is kind of driven by that logical view so if my logical view is this is how customers and products and locations fit together. So you might generate some business views that can kind of help drive that semantic layer which which may drive some views so that's kind of your, you have your. So yes and no you have your physical layer, and then you have a layer on top of it that may be instantiated through a view. The view itself is at the physical layer, but kind of that logic or the business logic is at the more logical layer if that makes sense. I mean feel free to jump in on any of these questions or you want to. If you'd like to add. And are there a good set of question standards for evaluating what is the best solution. That's a great question. Not anything I can show off off hand. But, I mean, some would be, you know, the question I have to start with one of the questions you're trying to answer.  I did the warehouse it could be I want, you know, so one of it is the type of question that's asking asked the other one can be kind of the when you know I always go back well there any question the who what where why when. So the what might be I want, you know, sales by customer, the when might be I need it, you know, by the second stock prices so I can make informed decisions well that might be maybe a data streaming, you know, and so kind of think of it or maybe some of the best and user stories, you know, as a user, I want to, you know, understand my financial things I need to report to the street well that's probably a data warehouse as a user I want to know instantaneously, when there's a cab available at the or an Uber available at the airport. Well that's probably something much more real time or you know that type of thing so kind of a lot of tools in the toolkits but kind of probably going back to the who what where when and why and some of the what might even be what am I trying to get if it's in a relational database and I'm summarizing it again that might be a great warehouse. If it's sensor data from our machines, and I need again real time or something. That's always, no matter what question is asked I like to go back to the who what why when, and that's kind of a probably as simple as to get there helped. So, um, there's, so what is a typical startup time for beginning to use no sequel options looking for to looking to augment relational databases but enterprise raising concerns about time to value. Oh, that's a tough one. I mean, often what what, you know, is probably a good way to do it this kind of exploration phase and then trying to scope into some sort of quick win. And you know that from your company and what what does fast look like and that that's something again a question I always ask it and you, you may just have this inherently in you because you work for the company, but I as a consultant you know what is quick and some folks will say oh a year, you know some folks say a couple weeks, and so kind of defining in that nerdy data model that always clarifies what do you mean by, and so what do you mean by quick. And then, you know, maybe a set of a proof of concept sandbox being very careful that sandbox doesn't become production. So one way we can do is this is this technology even suited for purpose so start with that to answer those questions. And then one way to kind of, you know, make sure you can get faster time to market just scoping some sort of pilot that's small enough that people can have some value, you know by limiting use cases or limiting volume or something so it's kind of a hard question to answer because it's different for every company but I would say try to get something out in a few months that's at least a piece you start to show people, and then build for it rather than build over time and again in an architected way. Make sure your sandbox doesn't come production but try to just limit the scope so you can so some value and even see if it is the right fit, you know face exploratory. It's a big that once at the end of the exploration you're like nope, you're in. That's not going to work.  I mean anything you want to add to that. No I think that was a pretty self sufficient. I love it. I'm a request here for a topic. We will be putting together topics for 2023 season here soon but exception on differences between data mesh like lake house fabric etc would be helpful, especially for real type life examples can be shown. Most articles are so confusing everything looks all the same. Anything you want to comment on that Donna. Well, maybe it's a plug we just did. What was it, you tell me. Shannon it was a revisit data architecture online. We had a, we had a keynote panel just on that topic. And so maybe we can send that in the link. I don't know if that's available to everybody. But yeah that's a good we are going to be putting together topics for next year so I both love and hate the buzzwords but they are out there so maybe some clarity on what we will be featured in this year's data architecture online as well. Pretty consistently throughout the throughout the day. And also, so does data like provide value if you don't have a quote unquote big data storage need data scientists or analysts wanting to pump in external data and do ad hoc analysis. I think so. In fact, a lot of folks the data lake is almost your just your landing area you're going to question landing area the warehouse. And it could just it could be text based data it could be non textual data it's it's it's a, you know that that need for I don't know everything I want to model before I even start. I want to loot you know that the beauty of a warehouse is I have it planned and organized ahead of time but you don't necessarily know all those answers so kind of having at low cost way to, you know, but it's like your closet you don't have too much in there becomes garbage but Yeah, that idea of kind of a data warehouse. I mean I'm sorry data lake architecture or, you know, so especially the cloud systems kind of have that idea of just that that that storage. A pattern is, I would say yes you don't need to even have big quote, big data to use a data lake architecture pattern. One of your diagrams pick when where you land in from where do you land information architecture. Where do I land information architecture. There's another word I could have added to that. I would say I mean data architecture and information architecture co related I guess information would be more broad, you could have documents in there you could have so I think probably my my diagrams would you could say their information probably aren't as broad because I don't don't have things like documents I didn't have everything in there. I also would say information architecture should definitely look at the semantic layer. I see this information is more holistic than the data which I tend to think is more of like data and database or data and streaming and things like that. So it looks like everyone's been. Oh, we got more questions come in. But couldn't information architecture not influence master data management. Could it not there was a double negative there that confused me. It could. It could. I guess my definition of information is this broader than data so it could influence master data management, or it could, if you're just say master information is just documents you may not have master data but you would certainly have referenced data. And that is that it's kind of having your reference data is your hierarchies kind of your, your tags for documents and things like that so I think they should still be related. I also just, you know, sometimes we talk of circles with information versus data versus knowledge versus. But yeah, again mapping it out. I could be neutral what you call it but yeah I would say data is a subset of the information. That's all the questions we have coming in I'll give everyone a quick moment. I was going to say that'll be a first. I know. So, which technical certification do you consider, if any. Oh, there's a lot out there where there's the, you know, the data. I think it's the Dama CDMP, which is kind of your certified data management professional I like that because that's your, your sort of broad TMI has some great certifications, especially around data warehousing. I also again, we both love and hate the vendors. I do think a lot of the vendor certifications, you know, are you cloud certified I guess I can say some of the names are big enough, you know, AWS and Google and Azure. I think and we do a lot of hiring. The perfect architect in this world can do both right I know the fundamentals or even just university courses and relational algebra and some of those theories. If you know that and I and I know what master data if I here's my perfect candidate. I know what relational algebra is I also know, you know, data science fundamentals and graph patterns and all of that so I know like the academic principles. I know enough of CDMP that I know the difference between data government data that master data and reference data and I know the dimensions of data and I can open up something like, you know, Azure and understand all of the, the architectural patterns and be able to know when to use a key value versus document versus, again, I'm talking about a unicorn. But I think some sort of mix of those of the theory and some of the tools because you know the tools are hot and they have their own. And I would almost question someone that you know said they're an architect but doesn't have any certificate like doesn't have never used one of the platforms like you got to roll up your sleeves and I've done it right so hopefully that's a good good mix and again a lot of it is out there and a lot of it is very low cost or some of them are even free so Hopefully that helps. Very helpful. Thank you and perfect timing because that's going to bring us right to the top of the hour here. Thanks to everyone again for being so engaged in everything we do love the chat that's been going on as always. And just a reminder again I will send a follow up email to all registrants by end of day Monday with links to the slides and links to the recording. Thank you to Katana graph for sponsoring today's webinar thank you Abby for joining us a pleasure and hope you all have a great day. Thanks everyone. Thanks everyone. Bye bye.


Can I explain to you why I'm vegan? It's terrible. Vegan, he's like, why aren't I vegan? Well, like, why am I for the animals, bro? Obviously, because I'm not a lion, obviously, because carrots don't feel pain, obviously, you know what I'm saying? I'm going to convince me now. I know you. We'll talk, see what happens. All right. Hi, my friend, are you Jewish? Yes. Oh, bless you, mate. And where are you from? From Washington, DC. Washington. Would you mind? Yeah. Just a little bit like that, so that we can hear you. All right, so I'm asking people why they aren't vegan, and are you going to tell me why you're not vegan? So I just really like the taste of meat, and I always feel that even though not eating meat would reduce the cost, I feel like already in the super markets, there's all this meat, and if no one eats it, it's just going to go to waste. OK, but why is it there to begin with? Because the demand is high for meat. OK, so what can you do to change that? I think you can, like, supermarkets can gather numbers of people who do actually shop there and actually buy meat so they can cut down the amount that they're ordering. Hopefully that'll cut down the amount that animals are killing. You don't think there's a direct relationship between the demand and the supplier?  There's obviously, like, they only supply it because people demand it, yeah? Yeah. Otherwise, they would take it off the shelf and put something else there, yeah? Yeah. That's how our businesses do usually work. It's not selling, they don't sell it. But I feel like so many people are, like, don't know why. Like, they don't know why being vegan is good, so, like... Can I explain to you why I'm vegan? Yeah. OK, so you know how you're having a conscious experience right now? If something bad was that happened to you or something good was going to happen to you, you would experience those things as either good or bad. Yeah. You have your own subjective experience of reality. So to be imprisoned and slayed or tortured or harmed is bad for you, which is why we have human rights protecting people from bad things happening, yeah? Yeah. Animals actually share that trait with us. Well, the ones that we eat definitely do. Not all animals, like oysters and things like that. But pigs and cows, you don't eat pigs, obviously. Cows, lambs, chickens, dairy cows, egg-laying hens, fish. They all share slightly, you know, there's gradient here, but they share a conscious experience. What happens to them also matters to them. So why do we have rights for humans and not rights for these animals who also have a conscious experience? So my argument is if we don't need to harm, kill, and eat these animals to thrive or to survive or for any reason, other than, like you said, taste, then why are we doing it to them for? So I might as well just boycott the harm to animals that I can and be vegan. But I feel like. What do you think of that first? Before you come back with your answer. I think it makes sense, because I like what you're saying, because we're all here on this earth, and we all contribute. OK. Definitely animals contribute sometimes more than we do. Like bees, we're not for bees. Like our plants wouldn't be, you know? OK. And I think that it's important, but I also think that survival of the fittest, like humans came to this world and took over. Like if we were in here, like if the dinosaurs wanted to eat us all, then they would control everything. Like I feel like the way we advanced, like we became more powerful than these animals that were eating. And like it's like the food chain for animals. Like we're on the top. OK. I'll get you. OK. So you're making like a might makes right argument. Do you know what that is? Oh, really? It's like because we're more mighty, powerful, more intelligent, we basically have just cause to do this to the vulnerable beings. Like pigs, well, I'm going to say pigs, because most people watching do eat pigs, pigs, cows, chickens, fish. We can basically control them. We can breed them. We can manipulate them when we can slaughter them. And your argument is it's because it's survival of the fittest. And we can do it. Therefore it's justified kind of thing. That's might makes right. I guess. But I'm seeing like we can have a society where lions just go eat humans. And then they'd be like, where is human rights? If all animals are human rights. Exactly. Yeah. And that would be wrong as a human. Do you think it's wrong from the perspective of a chicken? What do you mean? I actually think the chicken feels like it's justified. What happens to them? Probably not. Yeah, yeah, yeah. I'm saying if they could express to us what they're feeling about this, because they probably just don't know what's going on. Chickens are actually killed at like 40 days old. So they're just little babies, but they're in overgrown bodies. So they don't really get to grasp what's happening to them. But they know they're suffering. They feel terrible. And then they go to a slaughterhouse. Coastal slaughterhouse or whatever, they still get their throat cut and having their experience dropped from them. I have like a more at ease eating Coastal food just because like in the realm of hurting animals, because it doesn't hurt them.  But I also have a question for you. What do you think about eggs and milk? That doesn't hurt the animal. Well, I think slashing someone's head off hurts the animal. One, I think that harms the animal and also murders the animal. And my argument is if we don't need to do that, why would we? Why would we try to find some humane religious way of cutting animals' heads off if we don't need to? Why are we always trying to find this good way of doing something horrible, which is to mass murder? And what was your? I was asking, what about your viewpoint like eating eggs and milk? Yeah, and eggs and milk. So my argument would still hold, even if they weren't caused like needless suffering beforehand, because I'm an animal rights activist, I believe they should have rights protecting them. Now in the dairy and egg industries, you're talking about two industries that contain more suffering than the meat industries in many cases. Yeah, because those animals are exploited for a product that comes out of them. They're kept alive through prolonged suffering. So like the dairy cows, how do they produce milk dinner? Why do they produce milk? For us? No, actually they don't. Why does your mother?  So in order for like a human woman to begin producing milk, what needs to happen? Yes, that's good. Exactly. Oh, so they force them, I see. And they do it in a very crude way. Do you want me to explain the way to you? How old are you? 15. 15. May I explain these two parents? No, they're my grandparents. I'm going to explain the way, I think 15 is old enough to know what happens in the dairy industry, considering you drink out of there.  Leave it up to your imagination how they do that. Very crude. And then they take that semen and inject it into the lemal. But what they do first is they put her in a rack so she can't move.  And then they get the pipette of semen and they guide it in whilst holding the cervix through the anus. So disturbing? Yeah, definitely disturbing. Now they do that because it's a more effective way of impregnation.  Once she becomes pregnant, she has a nine month pregnancy, she carries her calf for nine months in her stomach. Immediately after birth the calf is taken because the calf will drink the milk that's meant for the humans. So we can steal it. If the calf is a male, they don't use them in the dairy industry, they kill them. They'll, you know, maybe two weeks later, maybe four weeks later, maybe for veal, maybe for beef, but they will always kill the males. The females will go off to confinement and they'll be grown and used in the same way. She'll go for about three, four, five cycles of pregnancy. Kids being taken, lameness, they get mastitis because they're producing so much milk. Then they go to slaughterhouse with burgers. Now these cows in the dairy industry, they don't necessarily go to get Kosher slaughtered. So you're putting into demand an industry that doesn't even do something that is in alignment with your religious beliefs anyway, because they just go to any slaughterhouse and get minced up into burgers. That's dairy. Now eggs, the males don't be used either. Upon hatching the males get gassed or they go to a macerator, which is a big blender. In America they'll go to a big blender. They wake up out the egg and they go into a big blender. The females get taken, they get grazed and they'll be used for their eggs for about 18 months. So they're pushing an egg out a day, 300 a year or something like that, which puts strain on their body.  That's free range as well. We waltz around into these farms and do investigations and you can see enough footage from there.  Pet food or processed, in America processed chicken products. So those industries are the meat industry essentially? I mean, I think it's like, it's definitely very gruesome and like it definitely changes my way of like... Is it justified? I think that. We'll talk about for an egg here and for breast milk, which when did you wing? I don't know. Probably about, what, two years old you stopped breastfeeding? Probably. Still breastfeeding to this day? I think that's different because like, it's like when you think about it just with one chicken and one cow, like whatever, it's easier to think about that. But then the way you're explaining it with like how it works within a cycle is like definitely crazy to hear. Well, how many people do we have on earth? A lot. And everyone wants milk and eggs and fish and everyone wants beef, everyone wants lamb, everyone wants chickens. So now our days, it's factory farms to meet demand. I'm still against the backyard throat slashing but now we have factory farms which entail a lot of suffering and cruelty as well. I'll tell you today, I've pretty much eaten all vegan. I haven't had any meat or fish or something like that today. Maybe I'll try it for a day and spread the word if I like it. Yeah. So wait, wait. You're not vegan because of, first it was cost, right? No, it was taste. It was taste. Okay, let's talk about taste because do you think that because you derive like sensory pleasure from an action that justifies what happens to the animals in order to get that taste pleasure? Like it's kind of a tough question to answer because like in the moment you're not thinking about that. Of course. Of course you don't. But now you are. Yeah. Now you're thinking. So what you could weigh up, okay, the animals experience a knife in the throat, a life in a factory farm, they lose their life, they lose their children, they suffer a lot of the time, most of the time, just so you can have a five minute meal.  Five, 10 minutes. Do you remember what sandwich you ate last Wednesday? No. So you've forgotten about it? Yeah. You've forgotten about that sandwich and that animal went through that for it just for that quick taste. You're right. It's terrible. Yeah, well it is. It is a, well, I think about this is why I'm vegan but you can actually make, you can actually take actions in your day to day life.  You're talking about a small taste difference because we've got vegan cheese, vegan meats, we've got amazing vegan food and pasta and pizza and ice cream and products. But pizza is a vegan. Yeah, but you can get vegan pizza, I'm saying. Is it as good? Well, let's just say it's, let's just say it's 10% not as good. Do you think that 10% justifies what happens to the animals in order to produce it? You know what I mean? And we're talking about a small taste difference, marginal and I don't miss anything now. Now that I've been vegan for it. How long have you been vegan for? Nearly 10 years. It'll be 10 years in November the 1st this year. Feels good to be vegan. It's awesome. Yeah. I don't have to go, but it's definitely a very eye-opening conversation. Bless you my friend. What's your name again? Lior. Lior. Lior? Yeah. Nice to meet you Lior. Take care. Hope you can convince other people. Do we convince you? Or do we create some awareness? I can't just do a different app. You can look up some footage online. You can watch a film called Dominion. Dominion and you'll see how animals are treated in each industry. Where is this postage? YouTube. Oh, I'll see it. TikTok. Bless you friend. Take care. See yous later. Yeah, that sandwich last week meant nothing to him, but it meant everything to the animals. Someone had a conversation like that with me when I was 15. I'd be like, what? What? I gotta look into this. See you in videos. Cheers brother. Apologize to him. Bye. You should be vegan. This is your sign actually. This is your sign. He said he apologizes because he's not vegan, but he watches all my videos and I'm right. Yeah, don't apologize to me, mate. I'm not the one getting chopped up.


If I make the statement, golf is hard, I think it's fair to say we should all agree in the comments section below. Will that, yes Andy, we agree with you. The thing is, I also believe that we as golfers make it as hard as we possibly can. I'm going to show you what I mean by that. Ac mae'r 7 iron, mae'n cefnwys 1,20 gram o'r sgaf i'r cyfnwys. Y rhefyd, mae'n cyfnwys yn y clwb, mae'n cael ei ddweud o'r cyfnwys i'r cyfnwys i'r cyfnwys, yn y fwynghau, ac mae'n ddweud o'r ddweud o'r cyfnwys i'r cyfnwys i'r cyfnwys i'r cyfnwys i'r cyfnwys. But I'm starting to question that a little. This is a 7 iron with a 40 gram light graphite shaft in it. Huge difference in weight, but what actually happens in terms of the difference in performance. That's the question I'm raising in today's video, I've got some real interesting answers. Okay, so not only is there a huge difference in terms of the weight of the shaft in terms of the flex of the shaft. iaf, dwi'n ddweud 3.25 oes o oeddech chi ddweud o'r ddech chi, o holl�beth o'r hawa i ddweud o'r hollwch. Mae'n 36.25 o o hollwch yn y 7 iron o'r hwnnw, o'r graffaitei o'r hollwch yn y 37 o o oeddech chi o'r cyffredin a'r hollwch y CBS Tor Steel. Da i'r cwestiwn i'n gwneud o'r gweithio, ar y cwm ni'n gwneud i chi'n gwneud o'r rhan o'r rhan o'r rhan o'r llwyll o hyffredin yn y rhan o'r hollwch. Fy fwy fyddo, rwy'n gweithio bod ymlaen o'r cyfrifio sy'n cyfrifio ar y sgwm, ac yn ymddangos i, rwy'n gweithio bod maen nhw'n gweithio'n cael llwyllog 40 g o'r llwyllog sy'n cael llwyllog sy'n cael llwyllog sy'n cael llwyllog ffyrddol, i fy nghylch i'r 120 g o'r llwyllog. Yn mynd i'n gallu bod yn eu cyfeilio'r llwyllog sy'n cyfrifio'r llwyllog. Yn cael llwyllog, rwy'n gweithio'n gweld eich cyfrifio'r llwyllog yma, 120 gram o'r llaw, ond mae'n gweithio'r llaw o'r llaw i'w ddweud y llwyddoedd a'r llwyddoedd o'r gweithio'r gweithio'r gweithio. A'r hyn o'r llaw o'r llwyddoedd o'r llwyddoedd o'r llwyddoedd o'r llwyddoedd o'r llwyddoedd o'r llwyddoedd, yn ymgyrchol, yn ymgyrch. Felly, rwy'n gwneud y 7-9 o'r 8485 o'r hwn, o'r 120 gram o'r llwyddoedd, rwy'n byth os mae ar freedomau llaw o dweud o beth, ar hal y rien ti yn bant yn cyrraedd. Felly mae'n tirelu, bydd y gwaith neu dweud e Österreich fel fel fyr第 o wybodaeth newyddol yn cael gwaffi am hetewdd hynny, ach chi'n f稱io'r chaf, ond roedd e'n gwaith hon Tyryr ac Byth yn ei ffrwng y cyntafog, ein patterns dim genbl dechel winners oly. Felly rwy'n hybl atebstaethod arna, y gallwn y gallwn ni'n dweud hwnnw i ddweud y 40 grwm'n chath, ond gan un o'r llwydd yn gallu'r ddechrau, yma gan y gweithio'r llwydd, ac mae'n gwiswch yn ei bod yn eistedd yn ymddangos ei gofgol. Mae'r llaw ddweud yma, mae'n ddweud i ddweud yma. I'm all of a sudden just for three-quarters of an inch, I'm a lot close to the ball than I would expect to be with a seven-iron. And what happens is subconsciously I swing the club a lot lighter. What that also then does is my tempo and rhythm just slows down, my breathing slows down, everything becomes at a much slower pace. And what I've said to Hannah off camera is I can't believe that like I said subconsciously everything just becomes so much more rhythmical. The timing becomes better, the strike becomes better. But what happens in terms of performance? What does this tell us? Does performance become better? I feel like I only else swing in this at the minute. So let me start by telling you that this shaft is in a ladies golf club but sent to us for some other testing that we're about to do later on today. It is in the new tailor-made clear range which is a superb set of ladies golf clubs. And like I said there's a real interesting video to follow. But I picked up this club to have a go myself earlier this morning. I was impressed with the way things happened. Like I said tempo changed. I seem to be hitting down the middle. I seem to be swinging very easy indeed and performance seemed good. We hooked it up to GC3. The performance numbers suggested it was doing pretty good as well. We then go and get the other seven-iron from another range with a 120g shaft in. And all of a sudden questions are being raised yet again. How much harder is it to swing with that 120g shaft than it is to swing with this 40g shaft? And I, and maybe you, making a huge mistake by ignoring this kind of thing. We're going to take it out on the golf course next and I just want to see in reality what happens. Can I hit the ball as consistently as I have done inside? And then I'll reveal that dry ball data and a big surprise for you. Right, so out here on the course of Cardin Park I want to find one thing and that's can I take the form, the performance that I got out of these golf clubs or the shaft at least from inside out here on the golf course in reality. Because if I can, then there's a real question mark over what shaft I would always use compared to a 40g light shaft that is also three-quarters of an inch shorter than my normal. Now I normally leave dry ball data until the very end, but I think it's an opportune moment to throw it up now. We've come at you to lay these two because we're 146 from the flag, which is perfect for this seven iron. In fact, dry ball data suggested that there is not a great deal to split these in two in terms of how they perform distance wise because ultimately one hit the ball a little bit higher in terms of the launch conditions. It also spun a little bit more, which is this caleia iron and the other, like I said, was lower spinning, lower launching and a little bit longer. But the consistency from the caleia was the bit that impressed me and it's that ability to swing the club with a little less effort, a little more concentration on timing and rhythm and tempo that produced far more consistent results than I would ever expected with a 40g light shaft in hand. I'm amazed at how steady these were. So much so, we're also filming another video of a full bag of these things from putter through to driver with 40g shafts in. That's also extremely interesting. I think it should just stop very briefly and talk about the actual product that I'm using. That's this tailor-made caleia range and like I said, we're going to play a round or nine holes at least with a full set. It's essentially a game improvement iron, sort of thick top line and a wider sole. It's a really good performing game improvement style iron. It does the job extremely well. The loft on this head is 32 degrees, so much more like the sort of traditional lofts that we would be associated with 7 iron width. And like I said, overall just performs incredibly well. I just want to go back to that previous hole. I ended up at it in a second shot because it was at 146 on the rangefinder and that was on the limit in terms of the dry ball data we'd collected. So I kind of like just forced that first one a little bit down the left and it went left and long as well. So there's clearly distance in these if you want to force the issue. But that is totally against the grain of what I would be trying to do in terms of playing golf with this kind of setup. The second shot I hit was again going back to that concentration that I've waffled on about, which is concentrating on tempo and timing. The ball finished pin high. The ball didn't deviate in terms of left or right, which is a straight shot. And I think that's what you've also got to sort of tell yourself consistently to make sure you understand what it is you're trying to do with this shaft in hand. You certainly can't or shouldn't be want to try and go after shots. And it teaches you a great deal of, well it teaches you a lesson really about concentrating, like I said, on that timing. Well I think that's, I don't need to see any more. That's another iron straight down the middle of the fairway. No deviation in terms of ball flight. So what have I learned from today's video? Well I think that I hope you've learned as well is that never dismiss anything. This is a ladies club essentially with a 40 gram light flex shaft in it. It shouldn't be in my hands but it is. And it's performed as good as I've ever tested out here on the golf course. And that's largely because I think that I've found the centre of the clubface more often than not. And I've achieved that by the fact that tempo timing rhythm has been key to my swing. And also maybe that shorter shaft has meant there's greater control and therefore I'm finding the centre of that clubface a little bit more. I don't know what the video started off being. I literally hid these things for perhaps a bit of a laugh early on in the this morning and realised perhaps I'm not taking these as serious as I should have done. So if you're a man out there that's considering looking at a new set of irons, once a bit of help with launch, wants to concentrate on just a nice slow smooth swing, then all I can advise is when you next go for your custom fit, ask can you try something down the lighter end of the spectrum and perhaps a graphite shaft. And just see how your club performs in your hands with that. I think a great deal of assistance with that lighter shaft. It certainly hasn't affected my performance one bit today. In fact, perhaps just the opposite. Right, as ever, let me know your own experiences. Have you found the same or have you found different? Guide your fellow golfers by putting some constructive feedback down below. And I'm pretty much done for the day. A gorgeous day here at Cardin Park. Thank you for watching and I'll see you all soon.