back to another Exo's Heroes video so it's been a while since I've done an account review and if you're asking I am still playing but I'm still salty because I don't have Misty but I'm still playing the game again I'm a sucker for being loyal so as for this account review there's been a lot of you know changes to my account a lot of improvements actually let's start off with my roster so manage heroes okay so as you all know I have been you know mostly wasted red for those of you who haven't seen my you know my last account review I have had I've started building stories so if you can see here I cannot believe that Maggie is actually my top in terms of combat power but again well it really depends on your build for her but close behind for wasted red I don't want to elaborate more on my wasted red team because I built it I built it I finished I already finished the the blue fate core which is blue fate core Baraka I already finished the signature force most of my heroes if you can see here are built already so let's just have a review for those who kind of forgot what I have or had or have currently so as you can see here this is actually my wasted red team so you have Maggie you have Redley of course you have you have sorry Maggie Redley Annie you have Jean Jean actually recently I have fully unleash potential already so again more more firepower for my wasted red team you have Garf so far those are it that I have built in terms of PVP and I focused on then I actually stopped then I transferred to another what do you call this another nation which is Astorys Republic let's see my development in Astorys Republic but wasted red is still my main team Astorys is actually for either tag or arc verse so that is why I built Astorys again I have Jinai here I built Adams even Jin is actually built already um I got his last skin I'm actually using the gold fate core but I'm using his skin and I was fortunate to get his weapon as well so that is a development for me then I have here shell which I will be um fusioning five once once I get her fate core and in the log in for 30 days then currently the project that I'm doing for Astorys is going to be Lager I'm trying to create a team because revive is actually a good combo with I think with other Jin the the black fate core Jin so I'm trying to build an Astorys team that probably I can use in the future black fate core Jin so Lager is coming into play unfortunately I didn't want to pull for for what do you call this for Kaia because I was actually selecting whether I would go with Kaia for blue fate core or I would go with Yao currently I'm still I think up to plus two with Yao which we'll take a look at later so I'm actually trying to build Yao so plus two I actually had to pity that banner you know I only got one so very bad luck for that banner I didn't really had you know I'm time to film that summon or pull but again as you can see here for Yao she's at two so this is actually what I'm building now and I'm nearing nearing actually finishing Astorys I just need one more copy of Jinnai actually two copies of Jinnai one for the fate core I already have the fate core the I already have the fate core but not the fated hero I need a fated two copies of the fated hero one for signature force and one for her fusion so Jinnai for me is at let's take a look at her stat so attack is at nine eight which is actually ridiculous but I intend to as I am able to level up my signature force one especially my blue fate core and also I think I have to get copies of blue fate core era for chaos units this will go above 9000 I'm sure this is actually very important that is why I really like the synergy of Astorys because with Jinnai as long as Jinnai is has a tremendous attack then you won't have a problem so right now I'm using these guys one two three four five and more or less I'm developing blue fate core and signature force the third nation that I am currently hoping or I'm currently developing right or I'm planning to develop right after Astorys is going to be Greenland so for Greenland I already have the core I'm actually I'm actually unleashed potential I already have unleashed the potential of Rera Bathory I would be next then Talia Greenland is a mainstay guys I'm sorry but again for for PVP for for any mode of PVP they are ridiculous they can be splashed in any you know any nation so that is why I've decided to go with them as my third nation so again I'm not expanding towards the new nations except probably for Bran if Dorca is coming around I don't know where Dorca is don't ask me so there's always the question where is Dorca um that's probably my fourth nation but I'm taking my time because I don't see Dorca in sight yet so I'm going to build um I'm going to build um Greenland as my third nation currently guys I'm I have been free to play already for roughly two months or three months I have been solely relying on on Zess on my dailies and air squadron and weeklies so more or less it has been good um I I have been able to save for the crucial banners so far so far it's been good so again so these are my uh Greenland is actually my third nation so again for the projects we'll go back to go back to Legger so she's what I'm building right now and so far let's let's let's go to um let's I'll show you my PVP team so I haven't been going up I haven't been spending Zess actually for for PVP that is why I'm still a challenger I at this point I don't want I it for me it's not it's not at this point I I have most of the heroes already so Zess is going to be for the difference of let's say for example um from challenger one to master three that's a difference of um sorry not that one 140 Zess so for me to push that and just to you know just to attain the Zess would not be would not be very you know productive for me except that there are other you know there are other rewards here that are also good but at this point I don't really want to spend Zess so I'm actually content to be in challenger one um so far unless they make changes to the rewards probably gonna have to go up and try to try to go back to I haven't been you know I haven't been to grand for a while it's been a really a while so um but I have been you know I have been cruising in in PVP for a while and I forgot to show you guys my team so I have been using either this this one or not this one this one so I've been using Jin for a while um Jin has been fantastic uh even in even in auto so I'll show you guys I usually use Maggie for auto uh for Manuel I usually use Annie so again I'll show you one match for Jin um Jinai is gonna be here and this team is actually very very good so let's try to have one match for you guys I'm gonna do this manually so that this will be faster so again for for Jin Jin probably and Adams in one historic public team good I just have to figure out who I'm going to take out whether I'm going to to take out Maggie so the first would be Radley so I see I think everyone is in beach attire oh this guy forgot his name um what's his name again I forgot uh Glenn yeah Glenn Glenn I remember Glenn I remember again so probably I have this on manual so that we could have this faster so historic team um once you merge your wasted red with your stories it kind of speeds things up so the what do you call this the hitting power of your wasted red coupled with stories especially with Jinai having that high attack it's it's going to be it's going to be ridiculous so again um as you can see here most of them are charmed and this is going to be more or less a quick one the shields here are just you know crazy um Misty the matchup with Misty is actually manageable um but again Misty will be a force to reckon with in the future but for now she's still manageable it's really hard to get copies over most don't even have one including me again so really having a hard time accepting that still salty very salty I haven't gotten a copy of Misty so as you can see a dismantle of linomba here so challenger one um this this actually is a fun team that that I'm using with Jin but if I'm going to climb up I'm not sure if I'm going to use a stories I have to go back to my default uh let's see let's go with Xeon really hate the shield or the barrier so mana bind silence everything is on him so let's hopefully this this doesn't take long it's already eight turns and you guys I know are excited to see my signature force so let's uh let's be fast guys fast silence you again so I think this is going to boil down to the lightning strikes again with the pesky shield mana bind they're not going anywhere so again with the stories you're going to have more you know attack power the enemy is going to be softer and also you're going to have more control over your enemies on the other side only actually the shield is the only one that is keeping them alive or the barrier so as of now I'm just trying to you know just trying to weather this so again I don't see the value of climbing anymore um it's I waste a lot of zest just climbing just to get more zest so that's it it's actually a counterproductive I don't I don't really uh go after the standings anymore I used to you know I used to go after grand every now and then but again the only thing that I'm after the most is if I could go grand I can but at this point it's already a Sunday I don't think I'll have the opportunity to go to grand anymore so it's going to be a painful grind and you know and I'll be you know spending a lot of zest if I do that so I'd rather you know have a deficit in terms of 140 zest because more or less I'll be spending more of that um if I go up it's an only difference of 140 let's say I gave 140 then I spend probably 160 or more so that's probably 240 I don't want to do that guys it's it's really not you know in terms of this a business it's it's not really profitable at this point so I'm happy with challenger one probably in the next few weeks if I am able to have the what do you call this if I have the timing in playing um Zeben Stunier or PVP um earlier then probably recharge and then have another five fights probably or another probably have 10 to 15 fights just for the usual recharge that would be good but paying for zest is going to be it's going to be counterproductive at this point where are they getting their shields Zeon is already out I really don't understand why oh there we go they're out and it took us around six minutes just to finish this off okay so we're done with PVP so I'll take you guys to signature force and my blue FC build let's go first to my blue FC um let's go here blue fate core as you can see here um I think this is at plus two this is at I think plus two as well this one this is only one copy I only pulled one so two consecutive blue uh fate core pulls with pity only so that's very bad so again this is I think at the plus three already I'm not sure but I'm sure that um my buffs over recent red is actually full so this is ready for fusion fusion five and this is at fusion two so again attack has to be increased uh beyond plus six this is already good um I have to improve as well on this one so attack is only at 3.8 percent for blue fate core for my signature force guys let's take a look at my signature force so as you can see most of my most of my signature force is ready good except for vagabond and saint west so for dorca or brun brun signature force is actually not that bad I'm up to um the third level already so I have been busy with uh with brun for vagabond recently I've gotten copies of um this is not it that is a schmid for schmid obviously I'm done for vagabond I forgot so that's schmid then you have for saint west I I mean carry recently I got a copy so uh mostly mostly saint west is at the lowest that I have so vagabond is full brun is actually up to level three um north one frosty is actually at level two 2.2 then you have here wasted red of course is actually full as well so I'm just waiting for another level to open up um linombe is actually catching up I recently got a lot of copies of fc rachel so um they're at uh 2.3 if you can see here greenland so greenland again is also good at 2.2 but if you can see here historius I'm only missing one more just to complete the signature for so I'm not concentrating on the new nations right now because um I don't see any value in you know in building them especially that I need copies of the generals for their signature link I'm focusing more on the old nations then probably after after greenland and brun then probably I could find another you know new nation with signature link to uh tinker with the best possibility that I'm looking at for me to tinker with is going to be shadow bay with luna luna is my best bet that I'm going to build but for now for now I still have to you know to leave her be I'm building up my other nations and building them towards green okay guys so this has been a kind of long video I'm sorry but thank you for sticking around and also please subscribe don't forget if you haven't subscribed yet click that bell icon and also put a like to this video guys take care stay safe this is the warden and I'm out of here


Recently, a soon-to-be freshman at Pikeville High School was accepted to attend a leadership conference in Washington, D.C. Jayden White was nominated by one of his teachers to attend the Junior National Young Leaders Conference at the end of June. During the trip, White will be learning valuable leadership skills and making connections that will help him later in life. I'm going for the Junior Leadership Program that I was selected for. At the beginning, I was a little bit nervous. I had to read up on all the schedules and learn a lot of things, but eventually, I started easing into it. Now I think I'm fully prepared for what I assume to be an amazing time. I think that the way that the program is set up, that it can make room for more extra activities and that the workshops could help me find jobs or be able to be more experienced and get more opportunities such as this. Cancer is a very scary word, but an outstanding treatment facility was here at home. I was led to the Lawson Cancer Center. As a nurse practitioner at the Lawson Cancer Center, we are champions for our patients. I am so happy for Robert. The treatment worked. The support from White's teachers, friends and family has been overwhelming as the trip comes closer.  Right now, his aspirations are absolutely amazing. He's got a great future ahead of him, and just to be able to participate, to be nominated, it really gets to show him what we get to see in him every single day because it's hard to see that within yourself and especially at the young age of 14 and being a teenager. That's a difficult time in general. So it's always good to have that support and to kind of have that confirmation that he gets to go where he needs to be and he deserves this amazing opportunity. And we are so thankful for Pipeville High School for his teachers to be able to give him that opportunity. Jaden is just so incredibly talented. Just anything that Jaden gets involved in, he gives 110 percent. His determination is amazing and he is so smart and no matter what, he's going to excel in everything he does. Reporting from Outantop News, I'm Nick Colum.


Unfortunately, there was a time, once upon a time, if you said neurology or stroke, people would shrug their shoulders and say there's not much to do here. And we've come a long way since those times and the present and the future is very exciting, especially with regards to stroke care. We have amazing imaging that we could do on patients to look at their brain, to look at their blood vessels, whether it's CAT scan, MRI imaging, and even ultrasonography to better understand our stroke patient's anatomy and treat them properly. So currently, one of the things that is exciting for us is we have these portable, carotid ultrasound machines where we can, typically, when we ultrasound patients' vessels, we're looking for, is there any stenosis in the vessels? But now we can look at the morphology of the patient's plaque and determine what would be the optimal treatment for these patients. We strive to be on the cutting edge of providing our stroke patients treatment. So we're always pushing to, you know, how can we expand the windows for our stroke treatments? Because currently, when we give clot-busting medications, for example, we have a limited time window where we can offer patients this. So we're looking into how can we expand this time window safely and do it in, you know, select patients because you can't always do the same thing across the board with everybody. Everyone is so unique. Tinectoplace is a clot-busting medication that we use in acute stroke patients, whoever is eligible to receive it. So for many years, we were using Alteplace, which was the only FDA-approved clot-busting medication. In 2019, the American Stroke Association published guidelines, and in those guidelines, we were reviewing them, and they had a section where they recommend, one of the recommendations was to consider using IV Tinectoplace in select patients with acute ischemic stroke. From there, you know, I started to get people together to see what was our ability to not only bring it into our hospital and our system, but what did we have to do to make sure that we could give it safely, which patients we would give it to, from when the idea hit us to when it actually went into effect. You know, it took about a year because in the middle of the whole thing, COVID took over, but we managed to pull everything together so that we could give it to our patients in a safe way. We still use both, and we have a protocol for who we give which agent to, and the beauty of this is we can study this for ourselves, which is exciting because rather than just jumping onto a bandwagon and doing something that may be in vogue, we're actually studying this ourselves and seeing which situation is, you know, which drug tends to be more superior for which patient type and which strokes of type.


I just wanted to wish everybody best of luck. The remainder of the semester, we will get through this together. We'll be stronger on the other side. With challenges comes growth. Andrea Nichols from Starkville, Mississippi. Here's what you need to be successful online. TP. I'm not talking about toilet paper. Time and place.  It'll help you stay consistent. I'm a distance learner with the Doctor of Nursing Practice Program. And for those of you who are having to study from home, stay on track, study just like you're going to class. Roger lectures at two times speed because it'll save you a lot of time. One super important thing to do while taking online classes is to print out your course schedule and just mark it off as you go. If moving to online classes has you nervous, Bama by distance has allowed me to work on my master's while being an active duty army officer. So let's grow together. Roll tide. God bless you guys. You've got this. If I can do it, so can you. Good luck and roll tide. Never forget Alabama is where legends are made.


What's up everyone? It's your girl Kenzie here from Leighton Sports Cards And I'm ripping a personal here for Zach S first time buyer from Tik Tok Thanks again Zach for hopping in we've got one 2022 leaf autograph Jersey multi-sport edition box. All right Here we go Zach. Good luck. Um Jeff the queue is about an hour and a half right now I'd say about an hour and a half Here we go Again tick-tock you would like to hop in queue our link is in our bio. Let us know if you have any questions Here we go Zach. Let's see We've got No way Christiano run. Oh my gosh. Look at that. I got to take this one out I gotta take this one out of the oh my goodness Whoo first time buyer mojo, baby. Look at that. Oh My goodness Ronaldo Beckett Coa right there. You got your matching jersey or your match.  It looks like right there You kind of see it. We send the boxes with these so you'll have the box, but that is just This might be an authentic Jersey Fly Emirates on that's sick. That's so nasty made in China. No, it's a it's a custom jersey, but it looks almost like it's a Authentic Jersey. Wow.  I gotta take a picture of this one. Wow Congrats on that Zach. Thanks again for hopping in and we'll be getting this right out to you


Today we have another lovely package that has come from Studio and this is a perfume that I've heard so much about and I decided that I have to try it. It arrived yesterday. It's the Rihanna Fleur set. Rebel Fleur Love Always, Rebel Fleur and The Nude and this is the miniature set. Lovely box, so really excited about this. So I've heard good things about the Rihanna perfumes. I'm not a big fan of her music personally. I mean I like that umbrella song but that's probably as far as it goes. So I thought I'd try them and I decided to get this set to get an idea of what the range is. This is a very nice box. It's got a mirrored finish. Let's get them out. This one's the nude. That's very cute. This is the Rebel Fleur. That's the original one and this is the Rebel Fleur Love Always. I'm going to start off with the nude one. I like the cap. The cap is really cute. The bottle, it's got a good design. I have to say it's very clean and fresh. Just gonna spray it on some paper. Oh wow I'm getting a lot of freshness. A bit of musk to it as well. It's very subtle. Definitely a little bit sweet. Not too bad actually. I like it. We're gonna try Rebel Fleur. This is the Love Always. Yeah I'm not getting anything straight off of this. Not like what I did with the nude but let's just spray. Oh wow this is darker. A little bit more powdery. This is definitely deeper. More heavy I want to say. A little bit of sweetness. A little bit of sweetness. Not too bad. Not too bad. I feel like I'd wear the... I'd use this if I felt like being more serious. Like if I was wearing a serious outfit. It's something bright and floral. So for this I immediately knew what sort of outfits I'd be wearing with this one. Okay let's try the original one. I wish it's really lovely. I like the black and the gold thing. So one of the things that I've absolutely loved about being nude to fragrance is the bottles. I love the bottle designs and it's always so sad when I see a bottle that I like but then I try the perfume and I don't like the perfume. Yeah. Let's see. Let's try you out. Oh this is lovely. It's like fruits and florals and this is just taking me right back to the middle of summer when the plum trees are just heavy laden with ripe plums. Oh I love this. This is a happy fragrance. Am I getting a bit of vanilla? Possibly. I love it. I think out of the three definitely the Rebel Fleur is my favorite. The nude is probably my second favorite and this one is the third one. I will report back at some point when I've tried these on my skin. Can't try them on my skin right now because this morning it's my cleaning day and I decided to put on Viva La Juicy Gold Couture which I love. I love it too especially to wear it around during a day where I'm going to be very sweaty because the smell is so strong that I keep on getting the beautiful wafts of fruity floraliness. Thank you so much for watching. Please like and subscribe and join in my fragrance journey as somebody who's new to fragrances and just loving, loving the journey.


Hello and welcome. My name is Shannon. I'm the executive editor of Data Diversity. We would like to thank you for joining this month's installment of the monthly DEMA international webinar series. This webinar series is designed to give our Enterprise Data World Conference Attorneys education year round and to inform you about all the great things that DEMA international has got going on. We are excited about the upcoming Enterprise Data World 2016 conference to be held in San Diego, California, April 17 through the 22nd. We've already had a lot of inquiries about that, so I'll be sure to save the date. It was sold out last year. Today's webinar is being presented by a longtime DEMA member and speaker, John Hendricks. Today he will be discussing taking information governance to the next level. Just a couple of points to get us started. Due to a large number of people that attend these sessions, you will be muted during webinar. For questions, we'll be collecting them by the Q&A in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share our highlights or questions by Twitter using hashtag DEMA. As always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now, let me introduce today's speaker, John Hendricks. John is a highly rated consultant speaker and author who has been active in the field of information management or relational database management since 1986. John's experiences combined with information, architecture, and management expertise have enabled him to help many organizations to optimize the business value of their information asset. He has a vision for innovation and ability to translate visions into strategy. He is the director of the Belgium and Luxembourg chapters of DEMA, data management association, and runs the Belgian information governance council. He has published articles in many leading industry journals and has been elected to the IDUG speakers hall of fame based on numerous best speaker awards. John is chair of the president's council at DEMA international as well. With that, I will take it over to John to get us started. Hello and welcome. Hello. Morning, evening, afternoon, wherever time is when you're in. Thanks for joining us for this session. What we're going to do today is really talk about what it means to create an information center organization. And I try to sort of say, well, let's catch it in a hashtag, because apparently that's something that you have to do today. And to summarize it, I came up with a hashtag, no Hadoop, where no Hadoop, as you probably can imagine, does not mean that we do not want Hadoop, but that we really need to think about what it means to be information-centric, and that means that it's not only going to be Hadoop. So looking at the context to really see what is going on today and one of the words that I see used and probably abused very often is the fact that people are talking about the fact that we're living in a disruptive age. Because things are disruptive. We have a disruptive business model. Data becomes disruptive. But if you look a little bit at this from a broader historical perspective, we notice that actually most of the things that we're so excited about today, they have been invented many, many years ago. So just as a refresher, I'm sort of looking at saying, hey, what are we going to do today with things such as machine learning? Well, in 1959, Arthur Samuel defined machine learning. And he said, yeah, but wait a minute, there's a whole new paradigm in terms of storing data. We have column stores, but I benchmarked against model 204 in the 80s, so that's not novel. Memory computing, SAP HANA, that's something quite new. Well, not exactly because IBM PPF is there since 1979. And data mining was defined in 1999. And even in the 1960s, statisticians were already talking about data fishing or data dredging. So the point is not that we've come up with a whole bunch of new things. But then the question is why are we so excited about this problem? One of the things that has fundamentally changed is the cost of value equation. And actually a lot of the things that we're doing today, we can really say, well, it's thanks to more and more law that we now have reached the tipping point where the cost of computing allows us to do things that we were not able to do before because the value was simply not in proportion to the amount of data. So the point is that because it has become cheaper, we can start storing things and we can start processing things in a way that we didn't do it before. And it also allows us to change some of the problem settings. And one of the things that is an important thing to understand is, for example, the fact that we are now starting to automate processes where issues such as acidity is not really a problem. And what acidity basically means is the fact that I can run a transaction and re-run the transaction and have consistent output. For many cases, if we are now just looking at statistical relevance or proposal of a negative offer or trying to understand whether that person is actually the person in this picture, then this whole aspect of acidity doesn't play anymore. So by dropping some of those techniques, we can start using quite different techniques than we used to do in the past, which gives us more capabilities of processing things. Computing is pretty much everywhere. So it is networking, connectivity and things like that. So that means that the methods are not novel, but the price model is novel and also the way that we can connect things and access things pretty effectively change the environment. The result of that is also that we see what I would call an explosion of informal events. The fact that we have this internet of everything or the internet of me or whatever, most of the data growth is actually not in the more traditional things. So if I look, for example, at an organization and I can say, how many additional invoices do you have, the exponential growth is not in that area. But the exponential growth is more into a space like, and I think the best example is when I look at my holiday pictures, when I went with my wife on our honeymoon, we spent three weeks in the United States and we, believe it or not, were able to take pictures for the entire three-week travel with three rolls of 24 images. So we were very, very fortunate in which pictures that we were taking. Today, if you are going out for dinner with friends and you spend 30 minutes together, you probably have taken more pictures or selfies or whatever than we have taken over that three-week period. So the growth of the data is not into these formally recognized things, but really into the area of smart metering, Fitbits, RFID tagging, and they have all one thing in common, that that kind of data is potentially less formally described. That's one problem. And the second problem is not only that it's less formally described, but also that the actual value that you can derive from it requires quite different processing than just looking at an invoice or just looking at the sales order. So it's not so apparent to actually say, hey, if I have for myself for the last three days, what is the actual value in doing that? So that means that we need to do something differently and do something potentially more complex to actually get insight out of that. So that growth that we can pay now because it's not too expensive to do that. The question is where are we going to store that information and how can we actually make sense of that? What I would call marketing bus around data lakes or pools or puddles, whatever, where people are saying, yeah, but if you store everything, the fact that we have the data that is available, it is much smarter to just store everything and handle it. And by the way, while we're at it, why don't we try to replace all our problems and solve all our problems by applying Hadoop. So my point is that, don't get me wrong, that Hadoop is not something bad, but we should really start testing and saying what is the added value of, for example, processing something in Hadoop. And this comes back to my first remark considering acidity. If you're saying that, hey, I'm running my banking transactions, I really want to know how much I have in stock or what's the balance of a specific bank account, then that problem is not potentially a problem that you need to handle with massive parallel computing or machine learning that the bank account is plus or minus and that's pretty much what it needs to be done. So the point is not that we're saying, yeah, but you should get rid of a data lake or a data pool, that's not the issue, but really saying what would be the added value of handling something in a way that we can actually say it's massively parallel scalable and I can do all kinds of things, yeah, but what is the added value of having that? We're handling this in a much fundamentalistic way, where they're basically saying put everything in the data lake and then all your problems are solved, well, not necessarily because the value proposition of a Hadoop cluster is not necessarily there when you start looking at full tolerance, high availability, et cetera, et cetera. So the point is the lake is not the issue, but the problem is what problem are you dealing with and you pretty much know that people are not looking at the entire picture when you look at an architectural drawing, which you often see today when people are drawing a lake, where then they push aspects such as operational systems or master data sitting on the outside of it, basically sitting on the side of the lake and if you look at something like that then you know that this is not the issue. And the actual answer that I'm saying here is that a true architecture and the best design is actually considering the fact that you have a hybrid environment where we can apply the best technology solving the best problem. And as I go back to what is disruptive today is not the fact that we can do novel things, but the fact that you can combine the novel things with the more traditional way of solving the problem. So that means it's a little bit more complicated, it requires a hybrid architecture, but it's not only going to be a dude that is going to solve our problems. Which brings us to the second point, that we're basically saying, yeah, but what about statistical relevance and why is it so that our data lake is not going to be able to solve all the problems. So we're basically saying, what is the problems that we're still having and shouldn't we be looking at the small data. Now, I come up with a new business model because if you go back to my explosion of data being saying, yeah, let's look at the data that is coming from a Fitbit. If you look at the data coming out of a Fitbit, what does the Fitbit really tell you? The Fitbit only tells you that the device was at a specific location, at a specific point of time.  So what I come up with is a new business model because I actually had a dog walking company before. I had no time to actually walk their dogs, they could give them to somebody and I was doing that and I find today more and more people that buy a Fitbit but they don't have the time to run and exercise the Fitbit. So if I would combine the two business models and say, you know what, I'm going to stick the Fitbit to the dogs and let the dogs run around in the park, it exactly shows you what goes on that the dog is moving. Which brings us really to that weakest point or the weakest link of actually saying if I capture all of this transactional data, all this device data and I throw it into the data lake, what is that really telling me? It is just telling me that a sensor has been at a specific point in time. It tells me that the device has been at a specific point in time but it doesn't really have the demographic context of that. The point is throwing all that captured data into the lake, it is going to be pointless and we will not be able to draw any valid conclusions if I don't have that surrounding environment that is going to tell me that Fitbit was bought by that specific person and that person has this income scale and that person has a household of such a context for the children and that is not the kind of thing that you can get out with statistical relevance. That also means that the aspect of active quality governance and active data governance in terms of defining what things are and defining what the quality objectives are, that still needs to be combined. It is not with simple volume and statistical relevance that is consistent with taking actions and drawing the right conclusions. That is another thing that I said is sort of friction happening today between the people that are more, you could call it old school, you could call it traditional but that are basically saying I nurture the quality of the data and I want to take the effort of maintaining the quality. There is the other school which is more the statistical school that is not consistent with my quality objectives, what I'm going to do and there's a very nice statistical term for that, I'm going to consider it to be an outlier and I'm going to discard the outlier because it's not going to contribute to my decision-taking model. Both are true but if you really want to say I want to make sure that I truly take the right decision, then taking out an outlier is actually an operational system. Imagine that you have a product master and you would like to understand what the allergens are of a specific product and whether people are potentially going to have an allergic reaction by eating that, then I don't think you probably want to say what we're going to do is we're going to use statistical relevance and we're going to make the sample set large enough so that I'm 100% sure that the product doesn't contain any allergens. Everybody that's concerned with the health of people will probably say no, you can't just use statistical relevance to have the bill of material or indicate that the product does not contain any allergens so that this operationally is something that you can't simply do. The point is that just having enough data is not going to be a problem of understanding what it is and how it works. That is going to be maintained and that's going to define the ultimate value that you will be able to drive from the fact that you've stored everything into your data lake and that the data is then going to be able to draw to the right conclusions. With the rise of the heart of to obtain value data or what some people call big data, what we see is if you actually have to have equal importance or spend equal importance to the small data or the reference data or if you want to talk about business intelligence terminology then you really should think about the fact that your conformed dimensions are actually going to allow you to make sense of that. The point is not having the right quality of the smaller data that is explaining the transactions is going to really keep you from drawing the right conclusions and the data that is sitting within your data lake. So that means that you really have to fundamentally address that mindset of your organization, say what do I care about? Do I care about statistical relevance and then you can throw in cognitive computing whatever to sort the things out. The point is that proper data quality is not going to be solved by that statistical relevance, you really have to have the right persons and the right purpose. Which brings us to this ultimate question on who should need this context? Who should actually bring this mindset into your organization to say that data needs to be nurtured and it's not just having the volume which brings us to the question what do we really need? Do we need to achieve data officer, achieve digital officer, achieve information officer and looking back at that saying this whole idea of chief information officer is something that we have actually done many, many years ago because the CIO is a role that has been in an organization for quite some time. But in reality what you notice is that the role of the CIO has slowly converged in many cases to more a chief technology officer where IT has pretty much taken up the role of delivering technology services rather than actually putting information at the center of what needs to be done and in fact it already goes a step further today because what we notice is that a lot of the chief technology officers are in many cases becoming COOs and then you could wrongly assume that means chief operating officer but in many cases the CIO today is actually becoming the chief outsourcing officer which means that this differentiating value of technology as such is decreasing. But the fact that the information is as such a business enabler that requires attention, that requires ways of transforming an organization potentially from a process-driven organization to an information-driven organization. It's not something that's going to happen overnight which means that essentially what we need to do is we really need to make sure that that at the C level the focus on proper data management is being maintained. So what I'm typically saying here is that you really need to look at what I would call a chief importance officer which basically is how can I get a roadmap in place, how can I get a budget transition into what was most cases more a process focus and then we did a little bit of lean right now into the point where we're saying how can we truly become data-driven. So the point is why and how are you going to be data-driven and looking at the industry you will notice that that focus could be quite different depending on what kind of an organization that you're working. So looking in a highly regulated environment then the importance of information is not necessarily the creation of additional value but merely to be able for example to just keep your operating life. So just looking at insurance or banking or anything that has to do with life sciences. We see that there is a very big push for compliance as a driver. So in that case it is quite likely that the top of mind for your board and the top of mind for your CEO is not potentially to create a lot of new value but is at least the ability to stay compliant and be consistent with the fact that your markets authorization is still going to be there. So in that case as the drive is essentially compliance what we see is that this information focus is often driven by the risk officer or by the CFO or by the compliance officer so that our roles and responsibilities and activities are actually consistent with what we need to be compliant. Now if you're more into an R&D kind of an organization then the fact that you have the information truly becomes a part of the business model. Now we'll talk about that a little bit later but we see a lot of companies where we're actually saying that they have to be fully integrated in the business model so that they have to be fully integrated into selling mobility or selling the ability to understand what is the quickest way to get from point A to point B. And just looking at Google today what they are for example in the Netherlands what they've launched is they've launched a fully integrated view on traffic coming in a new business model so in that case it's not pretty much a risk angle but it's more of a value or innovation angle that can be taken. But the point is that coming from more of a process view into a data-centric view it's a multi-year transition that needs to be done which means that we'll have to have somebody that can make sure that it stays at the horizon and the scope of the board which means that we're going to use or we're going to need this chief importance officer or data officer or whatever that you want to be putting in place. Another element that we see today happening in the market is the fact that there is more and more drive for having the data being opened. We're basically saying next to the fact that there is net neutrality it's also the fact that the data is going to be exposed so that we'll get the benefits out of that and understand what is going on. And a very good example is what I just recently worked with a retailer where they were trying to understand the effectiveness of their sales campaigns and they were just looking at that they're all sales data and they realized that we're doing great we're actually having our own brand being better positioned versus the competitive branches. And what they noticed is that by bringing in some of the external and open data sets, they could dramatically improve their decisions taking by looking at the data that is being opened and a lot of the new business models are actually coming from the fact that we can do smart things and interesting things with the data that is available and the data that is being opened. So there is a huge potential of that. However there is a quite there is a potential risk there because if you look at some of the things that the semantic web can do, what we notice is that the information governance piece of that is sometimes lacking. And a good example is what I saw in the newspaper a few months ago in Belgium where the head of the newspaper says 300 nuclear incidents happened this year in Belgium. And sort of say well, that's a big number. And then when you start digging into that data set, you notice that what is the actual definition of a nuclear incident? So the data is available and the data is open, but the data is not always the data points are not always sufficiently governed and described that we can draw the right conclusions in a very safe way. So that is a bit of a risk of this kind of an appointment. A risk is what we saw which happens with the data that is being published with the London bike. So when you rent a bike in London, those data points, which bike is rented at which point is actually data that is available. Now somebody was smart enough to trace the individual bicycles and by the nature of people being lazy you could pretty much derive the fact that people do not go from one bike station to another, they typically go to the closest bike station. And when you get off a bike, your destination is probably within the proximity of that bike station. So by looking at the actual bike movements, you could pretty much always go down to the individual saying at nine o'clock in the morning this person lives in this area, goes to work and works in that kind of company. So they still have a sort of a breach of privacy because you could understand this person is going to be out of his house from that point to that point on. So we have to be a little bit careful by making the stuff too much open so that we can actually potentially run a risk A by drawing the raw conclusions or B by exposing things that we should not necessarily be exposed. Which brings us to the next point of saying, yeah, we have all the data, we can process it. But the question is, should you process it? And what we see today is that especially with all the legislation that is happening in the European community, there is going to be a much stronger push, a much stronger drive of having data privacy really as a core value. So today we see companies that are actually using the fact that they are respecting your privacy as a selling proposition. If you just look at the way that Apple is approaching Google, Apple is basically saying, listen, you have to pay to store your images with us, but we won't be invading on your privacy. So the privacy is actually sort of the new green or the new eco saying, well, if you stay with us, then we will respect your privacy, we will not invade on you as an individual and we will treat you in a way that is being ethical. So that's one of the key issues today now is that with all the data being available, the question is, should you as an organization do this? Now you can take two angles. You can take the angle which was published recently in the Harvard Business Review, where we're basically saying, well, disruptive is by pushing the envelope, disruptive is by pushing the envelope. So we actually argue that if you really want to start working with your customers in a respectful way, then that's not something you do. You do not invade the privacy because people, when they have an option, they will actually say, well, I'm not going to stay with this company, I'm going to take my business elsewhere. That's actually an interesting case that happened quite recently, and it's an exception that the bank was going to monetize the data of the customers. And there was a lot of pushback from customers saying hey, I'm not going to pay you to run my banking transactions and then you get business value out of me. So that's sort of a very important balance that you need to be keeping saying, are we going to use this information even if we can, should we? And commission is likely to be quite severe. And in fact, the Netherlands and Belgium have taken the lead in that. And from the first of January on, when you have a breach of data privacy, there are fines of up to 800,000 euros, which is quite significant amounts. And this is not only applying to European companies, but it's applying to anybody that the data processing or with a European subject, so any company will be exposed to that. So ethical and should you is suddenly becoming a very, very important question. Especially if you start looking at information as being a business model. And this is where we're basically saying where the disruption comes from. And a lot of data management projects today are actually significantly missing the point. Especially when we start looking in the area of master data management. Where people are saying, oh, I want to handle the quality. I want to process the quality of product information or I want to do customer information. But they don't really think about what it really means to have data being a differentiator. And data being a differentiator is not necessarily doing better product management, but it's about selling more. It's about better using digital intelligence. And then digital intelligence basically means that I have the ability to take the next best offer. To do a smart attain. For example, having an only channel approach in retail or turning people from a show groomer to somebody that will actually buy. That's a big, very big distinction there. Or something like, for example, what are we doing with drop baskets. There's some very interesting market research that has shown that if somebody drops a basket and you can follow it up with a proper next best offer that, for example, conversion rates are up to 30%. And this is the point where a lot of the exercises that people are doing are saying, yeah, we're only doing product master data. We're not doing, for example, price configuration or we're not doing something with drop baskets. The point where it can actually become disruptive is the fact that we are not probably using this information. And especially looking a little bit at this context of disruption, what disruption really says, it does not say something about your competitor. Disruption is a level playing field. Because if you look at this environment, the fact that the data is available and the processing power is available, I mean, anybody can do that. And I've seen many cases where we see this larger existing corporations complaining that they're being attacked by a small competitor that is acting in a disruptive way. Now, if you truly believe in this idea of a data-centric and data-driven organization, then in most cases, the larger corporations with a little bit more of legacy, a little bit more of background, what they have is a lot more data points. So for example, if you look at e-commerce, the customer profiling and understanding whether a customer is a type personality or a B type. So basically, are they more sensitive to pricing or are they more sensitive to brands? That is something as an existing retailer, you have a much better insight in the transactional history than a new kid on the block. But the other thing is because that larger retailer is not using the data points that they have and to turn that into an insight and turn that into a better way of doing business. And we see, as I said, a lot of shifts from people selling electricity to people selling what is the most effective way of consuming or people moving from selling cars into selling mobility solutions. So going from a more physical world where data is actually becoming the business value. Same thing with Nike where Nike is now installing more and more sensors into their apparel. Why? Because the data that we can capture from that is becoming the business model rather than selling the shirt or selling the shoes. That is idea of data being the asset is in many cases leading to a complex which is called hoarding and what hoarding basically means is that you hang on to the data and just as recently as this afternoon I was sitting in a meeting with somebody where they said, you know what, let's just capture the data and we'll figure out what to do with it later. Now that is a completely wrong assumption because if you say I'm going to store the data anyway and I'll figure out what to do with it later, what happens is that the value of data is only there if you actually have a function to do something with it and if you don't have a function to do something with it, then the fact that you hang on to the data, it's actually becoming a liability and that's really something to think about to say what is the validity of having it and if I capture the data without truly understanding what I want to do with it, to what I have enough context to actually draw the right conclusions afterwards. So that's really the situation where just having more does not necessarily mean doing better and those are things that we should actually just distinguish and this idea of capture the data and we'll apply the schema to it later as its limitations because in many cases you don't really understand what it was which caused the right conclusions. So I have a very simple rule. If somebody wants to hang on to data, then the real question is if you want to keep it, do you have the ability to maintain it and do you have the ability to have the way to reflect the reality within the data point that you have and often the answer there is that we do not have the ability to make sense of it at this point. We don't know what to do with it. Well, let's not capture it at this point and then we'll see what we can do at a later stage. Which basically means that if you do not have the proper metadata, then you really don't have the ability to draw the right conclusions and to actually use this thing in a most effective way. And a lot of the regulation that is coming up today is really more focusing on the traceability and the lineage where we're talking about the data definition. If I do a price proposition, if I have a conclusion that I have regarding to a customer, I need to be able to back it up and say where does this thing come from. And that goes way beyond just having a common definition. Because you could say yeah, but this is something that we could potentially solve by having a proper glossary. A proper glossary without truly having the right definition, but I cannot guarantee I do not know that I'm drawing the right conclusions. So the point is that that metadata is really something that is quite fundamental because it's only going to allow me to be compliant, for example, with BCBS or with any data privacy regulation, it will require me to truly understand where did the data come from, what are the sources and how when I take a conclusion, when where does this really come from. And that's an area where I see a lot of companies still being quite weak today and just being able to comply with regulation is going to be an issue if you do not do proper metadata management. So documenting it, understanding what is going on is quite fundamental. Now one of the challenges there is that there is no very strong industry standard that will actually facilitate this. Because our design repositories are very often not truly linked to the actual IT assets so that in some cases we sort of have to come up with something happened in the middle but we don't really know what it was. So there's sort of still a leap of faith involved when I start looking at numbers and I really want to justify that this five is actually a five because that's still going to be quite limited. So looking at all of this thing, what we notice is that we're pretty much living in a perfect data storm. Why? Because we're in the data age because the cost of execution both hybrid or cloud or storage or network processing is going down. So thanks to war's law, it's costing us less and less money to manage the environment and the availability of data through censoring, through capturing is dramatically increasing and our analytical capabilities, the algorithms to actually understand a whole bunch of data porn saying this is going up or is going down or I can draw the right conclusion the availability of the algorithms is considerably growing. At a way that we can actually do this at a reasonable price and of course the visualization techniques and the interfaces that we can have in terms of self-service are also available. So that means that the fact that we have the data, we capture it, we store it and we process it gives us really a new environment that we didn't have before. But, and this is where it becomes the big but, we really have to make sure that we govern it properly, that we architect it properly and that we have the right policies to apply to that. And up to now, most of the data processing designs have been quite linear in process and this is where we sort of have the tale of the two chickens, right? So how do I get on the other side? We see sort of basically two worlds evolving in parallel. We have one which is the more traditional relational world where we basically say I need to define the schema, I will normalize it, I will try to understand what we're doing and then I get started. So this is one way of doing it and this is a way for many, many years where we basically say we need to govern the semantics and then I can make a safe decision. The new kids on the block, the not only sequence block is actually taking a different angle and some of them pushes quite far where they basically say the entire world just exists of a bunch of events and I can bring to the data the function and the schema as I process it. So basically the data as such, the data functions are quite complex neutral. So what they do is they bring the schema and the function to the data and then anybody can sort of take the data set and try to make sense of it. But those two worlds are not necessarily contradicting each other and they can actually quite effectively live together. So what we need to do there is to truly understand what is the governance challenge that we have and the governance challenge that we have on the right. So when you look at this thing, the relational world really is all about defining it as form of semantics. So that says I have to have governance, I have to have the proper definitions and once I have the definitions then I get going that's one model. But if we start looking at this no-sql world and the late binding or the schema-less processing, is we could say, well, I will define it as I go along. But that's quite tricky and I always compare this kind of a model by giving a bunch of hand grenades to a monkey. And they play with it and they draw conclusions and everybody is happy but at some point this data I will find the schema when I start processing it will realize that if they pull out the pin it does something quite differently. So this processing or late binding is no reason for not doing governance of the data as such. But the difference is that the focal point is not by managing the semantics, the focal point is on managing the container as such. Because if you look at it and you probably have already heard this where I have a data scientist and what the data scientist needs, it needs a data lab and what do we put in the context, that container still needs to be managed. So for example, if you look at the way of extracting data from Twitter or from Facebook or any other data source, very often there are quite strict rules on how long you can keep that information and what data can be processed. So if somebody, a data scientist is taking a data set and taking a whole bunch of Twitter feeds what they need to do is you know what that container is and say, oh, wait a minute, this is potentially containing personal data. That means that I have to get rid of it or I have to understand where it came from and how it needs to be managed. So the point is that there is a very strong data governance element in there that says I need to understand enough metadata of the container so that I can apply my policy in terms of context. So what we're saying is that this schema less or late binding world is no excuse for not actually applying proper data governance but the focus point is essentially in the first stage on managing the container rather than trying to come up with all the semantics and trying to define what things are. So at one stage if I really want to start industrializing and drawing that it makes sense to take this late binding or apply the schema to the function is to bring that into this world of I'm gathering the data, I'm serving it, I'm using it, I'm getting rid of it and I'm maintaining it which is a more traditional world governance semantics and this discovery phase where we then say let's define it as we go, let's refine it and revalidate it where we do the hypothesis of industrializing it then you have to bring it into the more semantically managed environment. So the point is that instead of having a linear process and having information that is sustainable because I describe it in my run-up what we're basically saying here is that really through sustainable information management means that in some cases my exploratory work and I will discover what the data that it will come first but if you then want to sustain it then industrialize it you have to bring it into an environment that you actually describe the schema and understand what things really are. So that means that this whole idea of sustainable information has two layers in it. It has a layer of managing the container and having enough metadata on the container so that I can do this in a safe way and I can approve the set subsequently if you want to industrialize it then you have to take it to the next stage where you really say what are the semantics of this field, what it means, what is the traceability, what is the lineage and that's a non-linear process. Which is also starting to look at what it really means to do proper sustainable HOV data and I'm sort of trying starting on a crusade to replace these worlds of big data with something that makes more sense because it's not necessarily only about the size, it's about the fact that in some cases it is harder to obtain the value out of that. Hence the HOV elements of processing the data. And what you notice is that we need really a three part model where we're saying the data scientist and what is the data scientist that is the person that is able to define and validate the validity of the data set and that the person that truly understands the algorithm in many cases today what we see is that these people are actually spending too much time on trying to source the data or to transform the data. So what we're saying is that we really have to understand what the data manipulation data engineering side is and put that data engineering side next to the data scientist so that they can both work on the data store and get the value out of the data store and then the business expert can actually work together and then see how we can industrialize that and start looking at potentially what could be a next best action. So basically saying a truly data driven organization is an organization where we're saying well what I can do is I can look at the different data points that I have and this is another area where we're saying what does it mean to be information centric what it means to be information centric is that we're basically saying we are moving from our guts so that this is a gut feeding that is probably the right thing to do we should be moving from our gut feeding into a quantifiable environment. So one of the things that for example our chief data officer or chief information or chief important officer should do is to say for every point where I am taking a decision in my organization do I have the data to back up that decision do I have the data points do I capture the events that are actually making sense and a very good example in that context that I had quite recently was a company that was investing a lot of money into this area of digital excellence and they bought a data capturing tool that was sitting on their website so any web interaction had a tagging layer that actually could understand what the customer was doing on the website you could argue oh great we measure we understand what is going on but what we noticed is that the reality by not properly managing the data that was being captured the only thing they were doing is they were filling the data lake with huge quantities of information but those huge quantities of information were actually pointless because they were not linked back to the understanding part so the measuring is one piece but then understanding what you actually measure and understanding what insights you can get out of that is the morning more data this is good for bragging and having a discussion at the bar oh I capture so many gigabytes, gigabytes per day and if you want to win that game then you should go to CERN because in terms of capturing they are pretty much the champions of that but the point is how can we understand what is going on and how can that actually lead to action which brings us to that next stage where it is not only about Hadoop it is equally about complex event processing and for example having this zero latency or very low latency acting on the fact that we have a specific insight so that's really when it becomes really useful not the fact that we can measure not the fact that we can understand but the fact that you are from an information point of view going to have the right understanding where you can take that next best action based on the insight based on the right context based on the right quality of the right data quality level so that you can draw that conclusion for the right purpose so if we put that puzzle together and look at it in a little bit of a broader context what we could really say is that our bigger picture is at the center the fact that you have sustainable information management there is these two layers where you haven't formally described everything and you are just managing the container which you could look at it as a challenge for data governance and this is where you can then set up your data lab or sandbox or lake or whatever to capture the information but if you really want to do this in a sustainable way and start running your process in a safe context you have to start doing proper information governance with the right ownership and for the right reasons and on the right side we really have to start looking at saying yeah but what I want to do is I want to differentiate from a strategic point of view I really want to have better insight, better actions and the drivers for that which is what we basically see at the bottom is to say why are we doing this for risk avoidance reasons and that's basically the space where the your CDO should actually look at saying okay what is the data that we have do I have my data in a way that it's managed properly because you don't want to be ending up the next target or whatever company that runs into trouble because you have a data breach and you haven't applied to PCI rules to that so that's just part of the describing of the container and the running of the container. So my point that I was trying to make is to say if you really want to have this information synthetic organization that what we need to do is to look at the operational small data making sure that the right data is being put into the right context and on the other side making sure that you have the right data points draw the right conclusions based on the right level of quality that is available and in most cases this broader picture is something that is missing because a lot of people from their organizational point of view are a little bit like multi-Pytons 100 meters for orientations challenge people where you have the data scientist who is going off in one direction you have the people running the MDM projects going off in the other direction then people that are on another tangent and these things they really do not come together and the way to make it come together is by looking at these roles and executing these roles and this model in a consistent way so that you are doing it for the right reason for the right outcome. That pretty much puts me through the end of my slide so that is the point I would say that we can sort of opening it up for discussion questions thank you so much that was a fantastic presentation. Go ahead and submit your questions if you have any questions and the Q and A in the bottom right hand corner of your screen and one of the most common questions that we get as a question about the slides we will be sending out a follow-up email by the end of Thursday for this presentation with links to the slides and links to the recording of this session and this is very, oh, here we go. Well, actually, if there are no questions, can we talk to everyone? I wonder if everyone has their coffee today. It's very unusual for our crowd. We do have questions. Everyone has a type of question? All right. Well, Yon, thank you so much. Anything you want to say about what is going on with Dama to everyone? Yeah, well, as we already said that we've just launched the central membership. So for people that are not in an area where they have a chapter directly available to them, we now have a quite good way to interact with us and to join and get some of the benefits that you would normally have by being a chapter member. So I would encourage people to have a look on our website and see what we have available so that's one quite interesting announcement that we just recently made a few weeks ago, so that's certainly one thing to look at. That's a very exciting announcement and hope everyone has a chance to go to Dama.org and check it out. Yes, so I guess that's it. Everyone's very quiet today. Everyone's ready for their summer, the rest of their summer holiday. Thank you again for the presentation today. It was just fantastic and thanks to everyone for attending. I hope to see you at EDW in April 2016. Absolutely. Thank you. My day. Thank you. Bye.


Things are much better for women than they used to be when in many fields they were just hardly getting women at all and entry was impossible in many fields. But now we have, you know, proportions of women as executives, as higher level political officials, elected officials, but there's still problems. The question is, what are the solutions that has to do with how we understand the problem? There is a powerful metaphor that's not just in any one country, it's throughout the Western world, which is the glass ceiling, that women's problems is that there's a ceiling up there that's rather impenetrable and that we can't go past it. I think that metaphor is particularly unhelpful because it rather simplifies the problems to a challenge or a solid barrier even at a very high level when actually it's much more complicated and the challenges that women face are not just at some advanced level there at other phases too. Actually, women encounter challenges in their careers that are difficult and different from those that men face at all levels, particularly when women are young, many women choose to have a child or children and to marry and given that responsibilities for caring and domestic work are not usually equally shared, of course that can slow down women's careers and that happens young. So there's the idea that challenges that are at all levels and women are falling out at all career stages. There's also the issue of the invisibility of a glass ceiling. You don't see it, that's part of the metaphor and then women hit their heads on it. It really rather describes a kind of lack of intelligence whereas women who've been in careers for decades, they kind of know how things work and it isn't as if challenges of that sort are unanticipated. You've seen other women have problems, etc. So it's rather unflattering in that sense.  Whereas women have gotten to high positions. I think New Zealand has had two prime ministers who were women, for example. Some women are CEOs in the United States of Fortune 500 companies. It's not many, but hey, the CEO of General Motors is a woman. So women are not inevitably blocked as the metaphor might suggest, but some of them find the way around it. The labyrinth, that's the one I'm promoting. Now a labyrinth has complexity. There's a goal in the center which can be seen as women's career goal, a particular woman's goal. She might want to be CEO. And so you enter a labyrinth and you encounter some challenges, it's good to be alert and to think about them. It's like a puzzle. So it would reward intelligence and analytical qualities. Also in the labyrinth, you might take a wrong term, you're not lost, right? You may have been in some labyrinths.  And the secret really in a labyrinth, you can make mistakes and recoveries, persistence. That is true in careers in a way, in a sense. We all have reverses and challenges. It's days that don't go well. But rather than say, oh, I can't do that, the idea is persistence and thought and analysis. And that there are multiple routes sometimes. So I think it's a more appropriate metaphor that's more hopeful. It doesn't say that everything is equal. In my writing, I say that women don't exactly have a labyrinth, they have a road with a few bumps in it, you know. But it isn't the complexity of the issues that women face. In many countries, dropping out can be a career killer. It shouldn't be. But it slows things way down and often it's impossible for women to get a professional man-dial job again. So that's one part of the data. You're much better off if you can arrange your life to at least stay in part time and stay in touch, stay connected, don't get isolated in the domestic role if you care to have a long-term career that is at a high level. But there's also advice around behavior. And what we find repeatedly in research is that when women are tough and strong, which is take on the masculine repertoire of behaviors, they get what we call backlash. That who's she to do that or to tell me or to order me about or whatever. So there's a lot of backlash demonstrations. Even for taking the floor and talking a lot, it's like, who's she to run on like that? Men run on like that and we think it's okay. So there's the whole backlash component that women need to negotiate. My advice is always push the envelope but watch how people are reacting.  But that would be perceived as weak and would be a hindrance to rising. So negotiating, we call that the double bind where there are expectations about a woman as a leader, a potential leader to show strong leader-like characteristics and then as a woman to be nice and kind and socially sensitive. So negotiating that is really challenging. And if women aren't aware of those hazards, then they may tend to personalize the reaction. Well, I'm just not a good manager. People aren't liking me, but not to see the gender dynamics. And so in general, research suggests that women in particular do better in a kind of a middle ground where they do take charge, of course, when they have a leadership role. But that they blend that with a fair amount of warmth and consideration, which are wonderful human qualities. So if they move very far out of that middle ground, they can damage their chances. And that is just a gender dynamic. Women don't have to be in that middle ground. We like them if they are. But they don't need to be. They can be tough and tough, ruthless types and do well, and people admire them as heroic leaders. So there are all these pitfalls in terms of everyday behavior in the workplace and studying the gender aspects of it can be very helpful. But yet, in the actual concrete situation, the woman has to figure it out. There's no one solution like smile lots. Sometimes that's not going to work, or say how is the kids or something relational. It would depend then. It takes a lot of creativity, I think, and is a bit of a burden, actually, to be thinking about this as well as thinking about your job. Well, if I could give a single piece of advice to women who are interested in career, in having some impact, in giving back, as many women say, to their communities, in powerful positions, it would be the persistence argument that women need to stay in and strive for these careers despite the fact that they will sometimes get reactions that are quite unfair or they may be misjudged because of stereotyping. So those things happen, but in the labyrinth, of course, you get to turn around and take another route, and if you stay in, you've got a good chance of getting to something that more or less fits your goal. So that's the staying in and remaining hopeful.


So anyway, can I start? Yes please. Yes, okay. So we'll find out that this is the first time I'm presenting with Kido. Okay, good job. I'll do the same. What? I'll say it, right? Because I don't have the time to do proper selects, you know. Because she's from Nami last year. Thanks. Anyway, what I'm going to talk about is about design principles with typography. So it's going to be quite simple from where I started. There are four principles from where I'm referring to. This is the book where it's Robin William's book on design for non-designers. So this was one of the books where I started reading, where I started learning design theory as a whole. Then no one come try it. Yeah, I'm looking forward to it. Well, you have to know I just took a keynote thing and then Jeremy started. And then it looks okay here. Okay, great. All right. So anyway, it's from that book called Design for Non-Designers. It says there are four principles in total. Repetition, contrast, alignment, and proximity. So what I'm going to think of design and these four principles, right? We usually think about design and big, great, great stuff. But it actually relates a lot to typography and the anal microspaces a lot. So what I'm going to do today is to go through the four principles. Then we'll go through the four principles and then we're going to see how it applies to typography on a slightly macro or micro level. So sometimes it's repetition. Anything I've repeated? So repetition reads familiarity. This is why there are some typography principles. If you're familiar with them, it's called vertical rhythm. And sometimes we use this method of model scale. So basically, type faces all with one-sided color, height, line, and shapes can be repeated. And they should be repeated as many times as possible to create that sense of familiarity. So just to illustrate, if we just see one circle in the middle of nowhere, you start thinking why is this circle here? What does this do? Then if you see more circles, you get more familiarized with that circle and you think of the lesser. But sometimes you see one whole jingling of circles, you think it's just a background. That's not that much. That's how familiarity works. The more you see something, the more you get familiar, the more you can focus on anything else. So repetition and typography is used in four places. Model scale, vertical rhythm, fun sizes, and typefaces. So model scale, if you're unfamiliar with it, I'm just throwing terms out there. So it's just like a typography scale. And each variation and size, I should probably... Because you're blocking the word. Okay. So... Yeah, no idea why. But 320 by 240. No idea. I think it's... I hope. My mum would love this presentation. I can read the words on the screen. So basically for model scale, if you take a look at this and this and this and this, it's just a ratio of 1.5. So... There's an option for that too. You can choose a ratio here. But basically what model scale is is that this number... The relationship between these two numbers is that 1 is 1.5 times the other. And then we teach and we teach. So that repetition brings into model scale and that's how... If you use model scale, you kind of have somewhat of a rhythmic font size, which is why it kind of looks better. You can justify that you should either use the square root of 2, because you actually look at the area. And know the size. Yeah, but that's not... There's a lot of things you can think about. Go pressure is not the only ratio. You can use a ton of ratios. You can choose the line line and play around with the repetition anyway. So the main focus here... Oh my... Oh, where's my slides? I forgot I did it in keynote. And I hit the contrast here. Okay, anyway, for vertical rhythm it's just basically you are going to repeat the white space in your website with a specific amount. So that white space is repeated. It can be half of that, it can be twice of that, so you vary their repetition. Consistent font sizes, well, we all know this is kind of one of the best practices. You set maybe h123456 and we use that h126 throughout. You can introduce 27 font sizes into your website. And probably the best example would be to max use two typefaces. Because the more typefaces you have, you just break that repetition. So this is how repetition is used in type production. And next contrast. So there are three main ways to contrast. You can contrast with color, size and shape. So the first way is to contrast with the color, with size or with shape. The basic idea is that when you're using contrast, if something is contrasting, you'll catch your attention immediately. So whenever you contrast, you want to insert huge contrasts, but not very small contrasts because it kind of gets UV out of the way. So if you take a look at this, it's a little bit odd, but this is slightly lighter than the rest. Or if it's just one slight little bit off in alignment, then it catches you very, very easily, and you can't really move on to the rest of the image. So it's just annoying. Yeah, that's the way. And you can use multiple methods to add contrast. So like this, we change the color. If we change the shape, it's even more contrasting. You can see this are much faster than you see in the circle. In the first place, it's the best way to control designers. You can show designers with type-based clothing at home. Except you better put it somewhere off-center because it takes a bit longer to cast, and it'll be more and more accessible to attention. Best way to control designers. From the circle to the left. Yeah, just make it a little to the left by one pixel. Okay, I think I had that. Can you see it? That's a cool space. Does contrast apply to slimes as well? Yeah, but I'm just too lazy for this. Don't know why I did it quickly. So anyway, for contrasting type of clothing, there are three main things that we want to look out for. The first one is, I mean, where we use it. The first one is monitor scale, because if you insert a large number of ratios between each step in the monitor scale, then you generally have that contrast immediately. So don't try to use 12 and 14 pixels, but add more variations. So for colors, you want better contrast than this. Definitely. And for tight faces, if you want to pair tight faces together, make sure they contrast each other, like a sensory and a serrated, and not too sensory. So this is kind of how you use contrast and type of clothing. These are just examples. I'm sure they are much more than I can think of right now. The third one is, the third principle is alignment. Alignment is pretty easy to explain, because if you just take a look at this image, it speaks everything about alignment. So in the lab, it's very, very neat. Alignment on the right is just messy. So notes about alignment is that alignment reinforces familiarity that is provided by repetition. Because if you take a look at this thing again, it's repeated anyway, right? But you feel better with this one, because it's more orderly. And we kind of need order in our mind. It depends. That's the same for it. If you are a business, then you see the problem now. Yeah. I think some websites need a little bit of work. Yeah, definitely, definitely. But not like that. If you, for example, would repeat the same or similar text and perfectly align it, then you confuse it. Actually, it works well in here, because it's actually in so many variables the same. It's the same amount of circles in the same color, but all the same size. And it's just one dimension is off, which is the position. Right? And in that sense, it actually works. It actually works, yes. But I'm just comparing between the two of us.  I know. I shall let him throw. So alignment also affects contrast. Like this is the one I showed you earlier. That's the throw. People are getting more mad. I think for non-class screens, I at least see that this screen is not perfectly flat, which means that you need two pixels, three pixels to notice, because unhappiness of the screen already doesn't have the effect. Yes. And you probably need a smaller computer so you can have a look at my screen a little later. Can we talk about ancient Greek architecture yet? Okay. It's not because it's fast. That's right. Okay, anyway. For alignment and typography, we generally take about either vertical alignment or horizontal alignment. Vertical alignment is what we usually talk about, and that is the left-right central justify thing. In general, justify to the left and don't ever justify text on the web, because justifying text on the web leads to things like this. See that space in between things. So it's harder to read. I think to be more correct, justification makes sense only if the text is long enough. Now, justification makes sense only if it doesn't disrupt the reading experience as much. Why does it not disrupt the reading experience? Unfortunately, it doesn't disrupt if it's long enough. Okay, I'm going to interject about this. Because traditionally for traditional printing, for letterpress, I'm not sure how many people are actually familiar with the actual printing process, but how people actually set type is they would set the type by hand. So there are several types. I think linotype machines were really popular for newspapers in about probably all the way up to probably the 70s or 80s before they started moving to photo, so-called cold type. So what they would do is they would actually cast the individual letters and they would set these letters by hand and then place them to send them in for letterpress printing. And what they could do, because you could actually control the space between each letter and each word for every line, and when people tried to recreate this on the web, they actually tried to write an algorithm that would do that, but I think what they realised was that it was too expensive to compute or something. So there were two algorithms. The one that we have now that is not really very well, that's why it says don't use it, because that's the so-called lazy algorithm. There was a smarter algorithm, but I think they weighed the pros and cons in terms of performance and they canned the other one. So that's why we are left with this not-so-good justification algorithm. But I think when they came up with Justify, they did have a better algorithm, but it just didn't make it. The thing is that if lines are long enough, then you don't see the difference. You do see, because of the statistics, if there are enough words, the spaces will be inserted between words. So whatever the difference, we will not see it. The good algorithms, they also change the current life. That's the thing. If the line is long enough, it doesn't matter. And still, if it is short, then you see the detail. Yes, you do make a point. Yes, you state the columns that are only about 20 characters wide, but you can get away with the justification. I've read a paper on how a human mind interprets characters in words, and justification is more complex to wrangle than not, because even when it's solved, the change of the coding from one line to another in the spacing takes people processing. So you still have to read into this. This is when the difference is presented more. If the line is long enough, then you will have very minuscule differences between words, between lines, and you will not perceive them. Statistically, you make sense, but there's another point about the ideal line length, and then you realise that that's where we get stuck, because the ideal line length is not long enough for justification to look good, with the current algorithm that we have. Strangely speaking, if you're thinking about justification on the web, you don't want to use it for a longer line length because of the way we read, but if you are concerned about space, and if you're concerned about how the site actually looks, so justification is very good for creating a grid-like column layout, like newspaper. So for that, instead of longer line length, you actually have shorter lines, and that is actually slightly better, but we don't really want to do it on the web as well. Okay, next. So this is one thing that most people don't take off, and this is about horizontally unlining multiple columns of text, because it's just freaking hard. So if you take a look at the top one, where's my mouse? I can't even see my mouse. Okay, so anyway, like these two are not unlined, but this part is unlined, for example. And you don't really see multiple columns on the web because it's just hard to pull off, because there are tons of things you want to think about. Even if you read newspapers, they don't even make the lines unlined properly. But if it doesn't line properly, it's just a better reading experience, because it's just ordered. So it's just like that. But it's hard on the web because of the way line types are handled. I don't want to go into that because it's another big complication article. The last one, proximity. Proximity is basically the distance between two things, and it can be used to help organise things. So for example, if you take a look at these two pictures. Oh! Yeah. This is a balanced answer, especially with this. What day is this? We'll be late. Too late? Oh, I'm still at SF. Roll eyes. Okay, so like, you can see the two orange circles are closer than the two blue circles. Test watcher. So it can be used for organisation. Let's say you have this messy block of squares and triangles. You can organise them in this way, and that creates some order. But if you get some spacing between, then you kind of know that the squares are on one side and the triangles are on one side, and they are about to fight. Okay. So proximity and typography is basically just managing the white space. The clearest example of that is, you know, some people try to put a piece too, and then they put the same amount of white space to the top and bottom of that. But this example can be improved a lot.  And with this, the H2 feels more like it's part of that second paragraph. Which is what content dividers are there for. So, for example, the white space of proximity is a list. So the white space between the list items should be slightly smaller. And the white space between the list and the paragraph items should be equal. So it just breaks things up slightly. So it's easier to do. Yeah, we're done. Fun times. Okay. So if I can come by for justification. Yes, please. Yes. For many people, it may be an aesthetic effect whether it's justified on both sides. Yeah, and whether it's properly stretched if necessary. Especially if you wrap the images next to the text. This jacked border will be unpleasant. Definitely, the jagged border will be unpleasant. So you want to think about how you put that images. So, slow write images are not always the best. For that example. I think the tricky part is that this, whatever you mentioned was very, I won't say easy, but it was very achievable when we hand set a grid. The thing is, I feel what we haven't gone to at least right now, but things we change in the future is that, like I mentioned the algorithms, there is a specification for hyphenation. Not very well done yet, but I know that people working on the series are trying to improve this. Because basically whatever you mentioned makes sense and has proven to work in print. It's just we haven't been able to get the technology to replicate what we've been able to do in print. But I believe that will change in a few years, right? Because the spec is being worked on and as computing power and browser code gets written better, I think that is an issue that is very, very soft to work on. And we may be seeing such, like maybe a few years later, there won't be the, you know, to use justification because it has improved a lot. So I think things actually, you're not sure, but I think things are improving. It's just that one other question with what justification is that you, there are so many languages that we are writing with. For most justification libraries, they are out there. They only justify English. I think that's in text. All right. Then you mentioned... No, it's not only in text, just English. If the line is longer than that? I mean, if the line is long enough, you probably don't need that justification in the first place. Because you will still have a jacked edge. But you will not see the difference between just... Like once you have like enough words on the screen on a website that you would say or enough words to make this simple justification... It's a block of text that people don't even know the meaning of it. Yeah. So it's just that the case... It's a long text. It's always the difference. Every time we talk about, you know, what you're talking about, the person can use only an average, if you're looking at the least viable consumer that you want to have in the area. I'm talking about the visual effect or the effect of the text. So it protects properly and justifies it. It is a different impact. And it is used in advertisement to first the idea of something. Yeah. So I would not just call it utopia. Yeah. If you have time to get embarrassed at the presentation, like the underwear that's not yeah, yeah it's like most of the jersey vacation I've been in it's in print yeah, in print okay, so this is my new segment also because I'm hyping CXS to print and um