Hello and welcome, my name is Shannon Kemp and I'm the Chief Digital Manager of DataVercity. We'd like to thank you for joining the latest installment of the DataVercity Webinar series, Data Insights and Analytics, brought to you in partnership with First San Francisco Partners. Today, Kelly O'Neill and John Lathley will discuss trends in data analytics from database to analyst. Just a couple of points to get us started. Due to the large number of people that attend these sessions, he will be muted during the webinar. For questions, we will be collecting them by the Q&A in the bottom right-hand corner of your screen or if you like to tweet. We encourage you to share highlights or questions via Twitter using hashtag DIAnalytics. 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 to you our speakers for today. Kelly O'Neill. Kelly is the founder and CEO of First San Francisco Partners. Having worked with the software and systems providers key to the formulation of enterprise information management, Kelly has played important roles in many of the groundbreaking initiatives that confirm the value of EIM to the enterprise. Recognizing an unmet need for clear guidance and advice on intricacies of implementing EIM solutions, she founded First San Francisco Partners in early 2007. John is a business technology thought leader and recognized authority in all aspects of enterprise information management with 30 years' experience in planning, project management, improving IT organizations, and successful implementation of information systems. John frequently writes and speaks on a variety of technology and EIM topics. His information management experience is balanced between strategic technology planning, project management, and practical application of technology to business problems. And with that, I will turn it over to John and Kelly to get today's webinar started. Hello and welcome. Hello. Good morning. And hello, everybody. I am here. I am here. Sorry. I was wrestling with my mute button. All right. Let's get, yeah, those mute buttons, they're tricky. Well, welcome back everyone to our December data insight analytics webinar. Oh my gosh, Shannon and John, I cannot believe that we've gone through 11, almost 12 of these now for the year. So super exciting. And we're excited to continue the webinar series in partnership with Dataversity next year. So although this is the last one for 2017, we will launch back quickly in January and we welcome your continued active participation throughout 2018. So it's been a wonderful year, but today we're talking about trends. And of course, this is a common area to explore at this time of year. I've seen lots of articles going around in terms of what's hot, what's not, what's trending up, what's trending down. And as this year closes out and the new one starts, we've thought about a way that we can bring together what we've presented in some previous webinars and also just talk about what people should be considering as they start to move into 2018 as well. And we're pulling together ideas that we have seen both through our client work, what we're seeing and learning through industry exposure at key conferences. There was another conference this week down in Florida and then ideas from our peers across the industry as well. So lots of different sources of the information that we've pulled together today. The way we're going to explore this is we've focused on trends like people, technology, data and process. And so what we're going to do is we're going to walk through each of these areas and we welcome your comments throughout the webinar. And then of course, we always take questions at the end and we will do our best to have time available at the end as well. And like most of our webinars, we do want to start with a polling question. So Shannon, why don't you go ahead and open the poll? What we're trying to learn is how do you stay current on trends and industry news? So do you search the internet? Do you subscribe to different channels and or publications? Clearly some of you dial into webinars and attend conferences. Do you learn from your colleagues? Do your companies have interdepartmental training? Maybe they have data and analytics training as part of core curriculums at your organization. Maybe it's some combination of the above. So all right, we've got just a few more seconds left. Hopefully everyone can participate. This is an easy question and helps us to understand how we can continue to serve you and be an educational source for you as well. Okay, I think we're about closed out here. All right. Do we have the answers, Shannon? The poll has ended. The suspense is terrific. I know. Did you not see it? I just pushed the poll results out. Did you? I cannot see them. Am I looking at the wrong spot? Now I see it. Thank you. Okay, so 13% of you chose search the internet. You know, it's funny because a couple of years ago we did a research paper also in partnership with Dataversity. And at the time it was the topics were business intelligence and data science. And at that time a majority of people were learning via just searching the internet. So it's interesting to see how this has changed. Subscribing to channels or publications, attending webinars and conferences. Well, this is a pretty big category, second only to all of the above. So I do think that the webinars and conferences seem to be supporting this topic area a bit more. It seems like we can tell by the poll. All right. Well, let's just jump into our next trend or our first trend, excuse me. We decided to start with people because really without the people then all of the other trends around technology, data and process aren't fulfilled and aren't sustainable. So, John, why don't you kick us off then with your thoughts around what's trending and how our audience can ride the trend. So especially as they think about their resourcing plans for the new year, what are some of the trends and obstacles that they might be seeing and how would they want to plan accordingly? Well, I'd be delighted and thank you. So, you know, trend discussions are always fun because, you know, as Kelly said around this time of the year, really, really nice to speculate on what's happening, what's going to happen. And we're doing budgets and everything is like that. But we're going to make sure we're a little prescriptive today too. So we'll talk about the what and then we're going to talk about some of the reactions you might want to give to those. We put people up first deliberately. It's not something you typically see in a trend. The trends tend to be technology, business movements and things. But our number one topic still for our work and our number one issues even around the big data and analytics with our work is boiling down to people. So, of course, culture. Everyone talks about, oh, well, yeah, culture. Okay, and that has been with that label, the number one obstacle, and we'll continue to be so as organizations start to figure out where their analytics functions sit, what big data really means to them, where where discoverants live, continue to debate some things that are new to them. The next part of that is training and with big data and analytics, what we hear in conjunction with those words is being data driven. Well, you know, what does that mean? Organizations aren't being very clear with their expectations because they probably don't know right off the bat what that actually means. So we're going to find this to be, this is going to be a real tough one for 2018 for leadership who have been told from some either by the market or by their board or by whatever leadership to be data driven. That course boils down and into the organizational impact in terms of people's roles and people's titles. There's a lot going on in that area. There's many, many forms of analysts. There's that that people are going to have to figure out and we're even starting to hear the terms citizen data analysts, which is just any old person can be an analyst now. All of those, all of us that are listening have been probably through at least one or two iterations of technology change. And we, this is very, very common. Who does what? And what's the title? Real key there though is the CDO, CDO, Chief Digital, Chief Data, both roles needing to be clarified. We've seen some problems and we see other organizations that are going to continue to struggle with, with what are the roles of these top level positions. And lastly, it's going to be super, super critical to have that racy or rascally mentally in your head. And, and already for our pipeline for next year and from the conference I was just at in Florida last week, we're seeing a lot of questions about, well, who's accountable? Who has to carry this forward? And you're going to have to have a people plan. So along the line of prescriptive, let's just go. Kelly, if you don't have anything to add to that, we can move on to the next one. I just want to talk about how to react to that. Yeah, you know, I thought that you bring up the citizen concept and there's really been a lot of, a lot more conversation around the citizen roles, the citizen data scientists, the citizen data analysts like you indicated. We've even seen roles like a citizen data engineer, a citizen data steward. And the idea is that the, this is a broader number of people within the organization able to perform data and analytics roles without that specialized training. So I think that that kind of rise of the citizen role impacts each of these categories. So how does that impact your culture? How do we maintain and create a data driven culture? And is the citizen role a response to that data driven culture? Or is this sort of distributed and federated world going to impact all of these different categories? So I think, you know, the foundation of this people plan and ensuring that there's clarity of role across a distributed and federated organization is really important. And I did want to just clarify if anyone didn't ask the question around what a racy versus a rasky. We've also seen ACs. So racy is responsible, accountable, consulted and informed and it's a way of identifying who does what within a process or an activity. The rasky is adding the clarity around supporting roles and who supports a function. Some organizations consolidate this rather than adding an S they remove the R and call it an AC so accountable, consulted and informed. Anyway, just to make sure everyone's clear on what those different categories are. Okay, so now let's move on to how do we ride this trend. John sauce. Sure. Well, this is the fun one. We put it first, the organization chart. When, when we do our work, we get to the operating framework because we don't like to call that an organization unless you actually really working on the organization. But once you create how your capabilities will be deployed or what capabilities will be deployed. The org chart always comes up and this is not going to change at all all into 18 and probably in the 19th. And the real key here is if you're going to be data driven and really serious about it, depending on your industry and your culture, you might see more than one need to do some reorganizations. So please just be open minded to that. There is, you know, we've roles are going to continue to evolve in conjunction with that you will have new job descriptions. So, if you are connected with human resources or, you know, get them ready. If you're not start to groom a relationship with human resources, if you're becoming data driven, or analytics and big data are playing a more prominent role in your organization because they're there are when you start to add attached to the racy and the rasky charts. You're adding new account abilities and new account abilities means different performance measures, etc, etc. You can see how that daisy chains into a human capital issue. The collaboration and cooperation between it and business we all know that that's always there. It's, it's, it's becoming more prominent and that means work to improve collaboration and working together. All right, so collaboration is a little different than cooperation cooperation is kind of working together collaboration is working together but having an agreement really that if if one party doesn't agree you still have a way to go forward. And, and that's something to kind of get yourself ready to deal with that's a mindset to have rolling into 2018. And of course, the sustaining plan or the organization change management or whatever. When you go to being data driven. When you start to rely more on data as a monetization either as a revenue source or an expense manager, and you're bringing data into much more prominent. You're making changes and changes. Just to recap really quick we've said this many times before on our series here. Three things happen when change happens and two of those things are not what you want to happen. You don't want me to end up where you started and you don't want to end up taking an entirely different path and giving up on on the cause. You want to move forward to a data driven world. So, if you don't have a sustaining plan for your big data program. Or you have kind of a life training thing and and there is no thought about how do we embed this into our culture to be thinking about data on a day to day basis in addition to processes and stuff. It's time to get that ready to go. You will need that in 2018. These just a quick story and I think Kelly will probably have some more to share here after. We have seen several times in the past year and are actually working with a few now on into next year where the chief data officer is really a chief analytics officer. And, you know, in one case it works okay because the organization had no expectations in another case. The expectation in was the chief data officer was going to manage the entire data asset, but they only concentrated on analytics and everyone was kind of confused there for a while. So, I'm spending some time on the people stuff is going to be really, really important Kelly back to you for another story or for your additional comments. Yeah, so I think that the this concept of the chief data officer which now is across probably a majority of organizations have the concept of a chief data officer or a top data job. Now with the advent of the chief analytics officer and then as was mentioned in the previous slide, a chief digital officer. There is a potential for confusion and for some battling for seniority amongst those three different roles and which one is subsumed under the other role. So I think that that's important to understand in this revisiting of the org charts around what works and what is going to be most sustainable. I think the other thing that when we're thinking about org charts and we're also thinking about IT and business collaboration, it seems that it's collaboration cooperation, but also some of these roles that are traditionally business or traditionally it seem to be starting to merging or starting to merge or blending. John are you seeing that too. Yeah, I'm seeing a muddying of the waters between what business traditionally has done and it is traditionally done. From a prescriptive standpoint, it's it's kind of connected with this collaboration thing in some organizations business has taken over what would have been traditionally data things because they're tired of waiting or impatient and there there is some friction. Okay. Between the two and others it has more or less of stepped back and said we are going to be the wires the fires and the plumbers and the movers of things like that. And you do what you need to with the data. There are pros and cons to both of them. And both of those approaches and again, spending some formal time on how you're going to connect it to is really, really important and I think from an encouraging note here. I was listening to a speaker this week who who has had a great style and everything was presented in a positive note. And I kind of got inspired by some of that so in a positive note. We should understand that those lines between it and business are blurring and by the way they've never been separate businesses within your business. They're just different departments. And everyone's paycheck says the same thing up in the corner of the document. So, so it pays to get everyone on the same team and take conscious efforts to do that. We've been saying that for years, but in 2018 it's going to become really visibly important to do that. Yeah, I would agree. And I think it is more so now than ever and people being comfortable with technical role within the business, a business role within technology, etc. And it's also I feel interesting that the educational institutions are starting to support this view as well in the sense that the analytics programs are not necessarily within the computer science department. So we've got some affiliations with some schools and one of the schools were affiliated with their analytics program is underneath their marketing line of study. Another school has their analytics program under their, you know, continuing liberal arts and professional studies, not so not necessarily business, but not technology or computer science either. So I find that that whole blending of capability is really interesting, although our organizational structures still tend to be hierarchical and departmentally driven. So anyway, food for thought on that one or charging for sure they sure will be. Yes. Okay. Onward and upward here onward. So let's talk about technology. So lots of folks on this line are really interested in the new technical trends and that sort of thing. This is an area that keeps us all on our toes because there seem to be emerging technologies and vendors. And consistently throughout the year and talking about what's coming up and what's going down is always very time dependent because give us another month and it might change. Anyway, John, what are you seeing out there. Well, this one is so cool. I think we actually have two pages of trends. Yes. So database technology that's this one I put, you know, I put this one first week. We take this around. We said, yeah, let's do that first. Things like graph databases are going to require you to really open your mind. You know, Hadoop was a bit of a mind bent for folks, but everyone kind of got their head around it now here comes graph. And now you're starting to see a real strong likelihood that that if you haven't gotten your head around it, you're going to have more than one type of database technology, not just different vendors with the same type but different architectures. You're going to have those. So you're going to have that. And you know, the cool thing about graph is that graph is going to be probably the go to ways of controlling your vocabularies, your semantics, your navigation around data lakes and things like that. And we have some prescriptive stuff coming up here and how to deal with that blockchain. What did I see this morning, Bitcoin 19 no 12,000 something like that is just ridiculous. The technology I'm not talking about Bitcoin today, the technology behind that is going to continue to expand in its application. So you need to be exploring that this blockchain will become a part of your architecture in the future. Now, there's not a lot of examples now. So this is one of those things. This is a fun trend. We can go study something and maybe not be held accountable for quite yet. Okay. But, but I had talks with at various times with some of our peers like Len Silverstone over this week, a Malcolm kind of dropped in several other folks. And we all ended up coming to just blockchain do this does blockchain do that how does it do that. Why are they mining for blockchain. All these questions are running around, and everyone has a quasi accurate answer, but nobody has it now done but this technology is powerful. It has a lot of different things that can go not much not unlike relational math, when you have cod and date started to to come out with the relational aspects of things at the beginning. Nobody knew what some of you folks aren't at all. To beginning, nobody knew what to do with a relational database. It was a truly foreign concept. Now, you know, now it's, it's, it's pretty standard. Moving on the predictive analytics as an enterprise capability will grow predictive analytics have been there for a long, long time organizations have always been doing those this type of work.  So that's going to just continue to embed itself even in companies you wouldn't have thought about it. The Internet of things, lots and lots of data from lots and lots of places means handling and stuff really, really fast. And so of course your technology is going to have to be able to drink from a fire hose, such as it is. Kelly anything on this page or what can just flip on. You know, I think one of the other things about the Internet of things is, yes, the need for speed and companies were putting everything on the Internet, you know, whether it's the nest, you know, technology, whether it's your car, what have you. But one of the things that this has also created is an awareness and acknowledgement of the need for greater security, because in the rest to put so many devices on the Internet, many security gaps were not properly closed. And so it's the Internet of things I think is, you know, it might change in terms of where it's placed on our trend chart for next year. In fact, I've read some articles that in which people are saying that the Internet of things is falling off the rising trend. And is now in the not hot category based on some of these security risks associated with it. So, anyway, I think that's very interesting. Okay, a next page of technology. Yeah, yeah. AI and machine learning. If you want to sound smarter to talk to the party now, say, right, or machine learning. It's coming mainstream, but everyone, you know, you can get to in a handful of people, someone thinks, no, we can't do this. It's kind of, and we don't want to do that. And for those of you that are not science fiction inclined, Skynet is the evil machines taking over the world from the Terminator movies. Yeah, anyway, it shows what I do in my spare time. Anyway, but, you know, there's really good stuff on the other end are people saying now it's impossible, can't, you know, can't happen. This is just sophisticated algorithms. It's not a futuristic map. Things like that. The point here is that it's not going to go away. And, and you've got to start to understand it's for example the difference between AI machine learning machine learning is a subset of AI are not two different things. There's lots of AI you can do a lot of AI type things without doing machine learning. All right. I mean that is, and you can, or you can focus on machine learning. There's all kinds of different flavors going on out there. Two of our two trends down as cognitive that's probably could be in there's a great area between those. You can even consider that there. But anyway, more consideration is the data lake. Data lake is now part of your architectural toolkit. All right, so the trend next year isn't oh people will use data lakes that's gone and data lakes are here. All right, but they're going to evolve much the way the data warehouse evolved.  We have data lakes. Now we have these regions or zones within the data lakes. All right, you're already seeing that we had that is data warehouses and we had data warehouse to auto and corporate information factories, you know, all those kinds of things. All this is going to happen. The tools to manage this are going to happen. And you probably in 18, even if you've already done data lake and have something up and running. Things are going to be happening so fast that I would say maybe by second or by third or fourth quarter. If you've got a day like it's been up and running for a couple of years, you're going to start to consider a generation to of your data lake. Things are happening that fast out there. Cognitive learning will enter the vocabulary of enterprise architects. So in other words, are we willing to have algorithms. Look at things and recommend new things to us and things like pattern recognition and facial recognition and things like that. Are we willing to take that dive into pushing some control off to the algorithm. And you're going to see, yeah, yeah, we're going to do that, you know, we're going to do that. And of course, you know, as a data governance person or analytics person, whatever, what's that mean, what can we do with it. You've got to understand that stuff. Overall technologies in general, we've experienced Kelly and I have joked about this.  But, and you feel like you need a scorecard to do this, but these are all various shades of things that are were added on to the old core Hadobe through the Adobe stuff to do different things. Well, you're going to start to see things get more general and things become more less purpose built and more general, more mainstream, much like our modern DBMS. Prior to the do technologies were, and you're starting going to start to see more yet and inevitably with all the vendors entering here you're going to see consolidation and things going and things merging and all those kinds of things to so there's a lot. That's like, well, two pages for technology. That's the only thing today we've had two pages on, right? Absolutely. Yeah, I mean, Hadoop used to be the answer to every big data question, right? And, you know, there were so many add-ons required, it became really confusing costly to manage. You know, I think this concept of, you know, both the combination of the database management tools evolving and technology swinging back to being more general is those providers of solutions, you know, technology solutions or combined technologies that are coming into a solution will really start to gain traction as companies are wanting to simplify, you know, their cost for managing their infrastructure. I think some of these are very related for sure. Yeah, so then, you know, what are we seeing around writing some of these trends? John, how do folks take advantage of it? Well, the first one, and I'm doing this now, working with a good friend here in St. Louis, a good friend of our firm who's into this and he is showing us stuff with graphs that's pretty, pretty incredible. I think the key knowledge map for your organization is going to be vital. So, if you don't know about graph technology, you've got to get yourself educated on it. I would say of all the things we've talked about, this was a mandate, if I were a CIO or a CDO, I would be spinning up capability in this area as an R&D basis before anything else, honestly. It's just that, you know, it does really cool things and it's going to be a vital element of metadata tools. You've got vendors out there that are starting to literally gut their tool set and take out the relational engine and the relational metadata models and insert graph engines and graph aware metadata models. That's how important that this is going to be. So you've got to get onto it. The next thing in terms of all the speed and everything, I mean, look, machines are going to run faster and faster and faster, but it's going to have all this volume. And I use the phrase drinking out of a fire hose and I use that with great deliberation. Everything's going to want to have to run faster and machines are going to be able to do it, but you still got to squeeze all these, all this data down into something that's meaningful to use. Edge computing is going to be significant. And if you're not sure what that is, it's going out to where, on the Internet of Things, going out to where you're getting the data collected and doing some data management all the way up in what you would call the data hinterland before it even gets to the core or lake or anything like that. So you're distributing your big data processing. And then, of course, the last thing I think we like to get, well, we have, what's not the last thing because we have two pages of technology. That's right. But the last thing on this page is the blockchain. That is one of those rare things, Kelly. I think that we have a solution looking for a problem, you know. And there's somebody in your organization who's really smart is going to look at that, understand it, say we can use it here, whether it's for compliance, whether it's for security, whether it's for privacy, whether it's for managing Internet of Things data. There are a lot of folks kicking around a lot of uses of this. So please get smarter on that. Absolutely.  They got all everything they were supposed to get spark and all the tools to go with this. And then they discovered that, you know, be careful what you ask for my grandmother used to tell me that be careful what you ask for you might get it.  No idea what to do with all of that. It couldn't even process it then. Then the other thing on the other side, a positive thing we had a client that was just started to dabble with things and had a sandbox and use some fairly primitive tools, but did use a Hadoop and a data lake type construct and started to run some AI type algorithms and found some profound high return reshaping of its business processes. And again, they, what they did was kind of what we're encouraging you to do here is spend some time thinking about what kind of solutions you can bring to bear on on your problems, but you're going to have to get smarter on them. Yeah, you know, just to piggyback on top of the first bullet point, I heard a presentation recently from a fellow named Tim Baker at Thompson Reuters about how they were leveraging graph databases and knowledge graphs to better understand their content repositories. So exactly what you're talking about here in terms of graph database technology to give us a much better view of metadata and give it to us in a way that's much more consumable and quickly consumable so that we truly understand what are the data elements, what are the relationships to other data elements, where are they in our organization, how are they consumed and a lot of information that's best represented via a graph. So anyway, just piggybacking off on that. So we do have a second page of trends. So let's jump to that second page of trends and we can talk a bit more about the AI and machine learning on this one as well. Oh, and I did forget to mention our friend here at St. Louis John Singer is his becoming a he has many. By the way, John has some stuff on on data diversity. Kind hosts, there you go. Tracking and monitoring about the data lake. Again, I'm not much of a sports fan but when I do sit and watch sports actually like to watch American baseball and which a lot of people things like watching paint dry. But I find it a good, a good afternoon to relax. And the reason I'm using a baseball analogy is in the 1980s, the coach of the St. Louis baseball Cardinals was Whitey Herzog. And he played a very active dynamic game and move people out and move the pitcher to right field and the right field to the pitcher and just and and it became a part of baseball lore that you didn't you had to run a scorecard or a tally sheet to know who was playing where when you watch this man coach a baseball team. And it reminds me a lot of data lake and big data technology. You have got to stay awake. If you go out for hot dog and come back, everybody's moved around the field. All right. You know, so you've got to be really on that. The other thing is kind of tied to the story I just told there is be serious about your late late latency requirements, you know, latency being the time from when you want to do something. Data is available to when you really want to use it and do something with it. There's everyone saying, yes, we're going to be real time. No idea really what that entails. All right. You know, do your business processes require to be that real time. Do you have the controls in place that will allow you to be real time.  Right. You don't you know that. So you might want to say, let's hold off on being aggressive on low latency stuff. Let's hold off on some other things as a way to do this. And then the last one was what does AI and machine learning really mean to you and define what you can do with it. And again, if you don't have a strategy or not so much a strategy, Kelly, I guess almost a position paper, right? Or a white paper on how to deal with these technologies, AI, big data, cognitive learning, machine learning, edge computing, you need to sit and think about this. This is one of these that we can very, very often. Two things happen. And if there's a third Kelly, just chime in. But right now two things happen. One, you go ahead and get stuff because you have a vague notion that you're going to do it. And then when you buy all the stuff and start to do it, you realize that that notion was too vague, and you are not at all ready to exploit that technology. The second thing is you don't consciously think about it. You end up kicking the can down the road unintentionally. And so when you do need the technology, you are now a laggard. Everyone else in the neighborhood has it. And you're scrambling to get it in. And of course the price has gone up because the demand's gone up and you can't find enough people because everyone's hired all the people you could have hired. So with all of these, you've got to get smarter and get yourself prepared. And considering these things proactively, even if the answer is to wait, or the answer is to accelerate, you've got to talk about this stuff now. Absolutely. Yes, absolutely. I think that there's, we're seeing some practical applications of artificial intelligence via robotic process automation or RPA in which companies are automating some really time consuming processes. That need a level of smarts that can be done via artificial intelligence, but at the same time are repetitive enough that it is easily tested and implementable without a massive infrastructure. And then I think the tools vendors legitimately are trying to make this easier for people. And if we're looking at some of the applications of things like AI, machine learning, cognitive learning, Microsoft's coming out with these cognitive toolkits. You know, TensorFlow, which is an open source neural networks and deep neural networks capability, you know, et cetera. So the vendors are also helping people make it easier to test and see, is this something that we want to do, or is this something that we're going to table for later. So anyway, I know that we're talking quite a bit. We've got two more topics to cover. So let's go ahead and jump into really the implications of not just the people and the technology, but the actual data. What are we seeing on the actual data? This one is fun because everyone wants to monetize our data in some way. So many things are point to a trend that more and more attempts are going to be made to actually make money or save considerable money off your data. Our friend Doug Laney at Gartner just wrote a book on infonomics and he talks about monetizing data and that's what the book's about. And it is sold more than any other book in our area of thought than you can possibly imagine. It's, you know, you're going to have more data sources.  And we'll talk about how to deal with that. Then we have the regulations. Those are not going to go away. We're not going to see a decrease in regulations. You're going to see an increase regardless of what political wins are blowing whichever way because of the pervasiveness of personal data flying around. You're going to see more and more and more and more. And it's going to require more rigorous understanding of your data landscape. We're starting to use the word smart data instead of big data. And that means make your big data actionable. We'll be smarter.  This goes back to some of the things we said in some of our first events this year. Make what you have actionable. You're going to see more and more demand on the sandboxes and the data scientists. Thank you. That was really cool. That was a nice report. We got a couple of good nuggets now. What can I have that I can use day in and day out? And then the internet thing, of course, more and more data sources, more and more data volumes. Some of it's probably silly. I don't want my refrigerator to talk to the internet, but it's going to. And you're going to have to figure a way to deal with that. Absolutely. I would agree. And I think if we, from a data sources perspective, there are so many more specialized data sources now. You can buy a highly specific data source to solve any sort or many types of questions that you have within your organization. And I think based on the volume of new entrants into the data services market, the big players are starting to feel some pressure. I had a client say to me yesterday, look, these guys are really putting pressure on us and they're becoming much more demanding and they're expecting us to manage their data and essentially put together processes that enable them to audit us more easily and charge us more money. They're becoming aggressive. So I do think that as we see more companies monetizing their data, selling their data, that this is going to continue to happen until we hit saturation point. And then, of course, it'll go back the other direction. All right. So what do we want to do about it? Well, if you're going to monetize data and yes, either selling it or using another because another depth is monetizing data is directly using it to manage and lower costs. Okay. So whatever when you're going to do you are in essence making data a product. So product management practices really, really important. That means data quality. You're never going to avoid that. Quit trying to just going to be there. That means having a product manager for your new data source. We've got several clients that have found data they can monetize and are selling it into their market and into their customer base. And they're finding, okay, yeah, they're interested in it, but you know what, you got to have a support line. You have to have a release strategy. You have to have a good pricing card. You know, you've got to have salespeople that understand what they're doing.  You need to have really good governance and really clear policies over your buying of the data as Kelly was just inferring to the data vendors are starting to get a bit belligerent about some things. I don't think that will last a long time, like Kelly does, but I, there's going to be a pair of times as a we've got this data and it doesn't matter what you think, you know, take it or leave it. So, if you've got cloud storage agreements, moving on in the next year or they're coming up for renewal, time to take a hard look at them. If you're going to have regulation, you need to know your data landscape. Now this ties back to our tech trend of graph databases. You have got to have metadata. You have got to have a data landscape, which is where is it, where is it sitting, how much is it, who put it there and who looked at it. You have got to have that. Without that, you are, you risk, this is a bit bold, Kelly, and you know, dampen me down if I have to, but there's some organizations without this risk being coming non-competitive in their markets. And then be smart data.  Use the words if that's what the boss wants to hear, but really be smart. All right. What is your data strategy? What is your data portfolio to look like? And what is your data architecture? And make sure it matches what your data strategy is, which reflects, of course, what your business strategy. Too many CIOs, my story for this one is pretty broad. Too many CIOs are saying we're going to go in this direction with data because they have this fuzzy image in their head of doing really cool things with the data. It's got to be tighter than that. It's got to be a lot tighter than that. I'll kick that back to you, Kelly, for some additional thoughts. Yeah, absolutely. And, you know, when we're thinking about reviewing things like cloud storage agreement, you know, that also comes into play with some of the regulatory environments in the sense that the general data protection regulation does apply to, you know, where you're storing your data and you need to understand what those storage providers are promising you around the data. And so I think thinking about data not just as a repository, but we need to be looking at contracts. We need to be looking at agreements. We need to be looking at what we have, what we are authorized to use that data for, what our vendors are doing for us within that agreement. And so that's a new process I think that's happening more regularly now within organizations. Speaking of process, let's go to our last and final section where we talk about that kind of supporting practice or structure for all things that we do to make it sustainable. So, John, what are some of the trends that you're seeing from a process perspective? Well, digital transformation, that's almost buzzwordy, but it's going to keep happening in in 18 being data driven, monetizing data, moving more into mobile expressions of your organization, pushing interactions deeper and deeper into mobile type things. Understanding what's going on at there via social media, all of these types of things becoming less of an, it's not that they're going to grow, we know that it's going to become less of an option because just everyone else is doing that. Of course that points to other types of innovation. Now, a lot of innovation has been the one off data scientist kind of yahoo moment, right? But you're going to start to see because this is what really keeps organizations moving forward. It's not just the innovative one bright point of life, but it's that ongoing efficiency and sustainability of new product or a new program or a new service. That's really, really cool. So start to look for innovation being a little bit more on that sustainable efficiency thing. Data monetization needs new processes because you're going to have new data products and data-centric programs, which are going to have different goals and objectives. So all of that's going to drive new processes. Just accept it, folks. You can't do business as usual with data the way you've done business as usual. You've got to change some things. And lastly, good old data governance. Day without data governance is like a day without sunshine. You, it is a requirement. Please just, if there's any executives listening to this, just accept it. Quit asking people to do ROIs. All right, really, I'm sorry. I'm getting a little bit on a soapbox here, but it is a requirement. It's no different than, you know, you're doing all this. You're investing all this money. You're going to try to get all this out of that data. You want to be data-driven.  You want no accountability to find. You want no rules. You know, try doing that with building a new factory that uses a few chemicals and a complex supply chain. See what you can get away with that same mindset. Day to day governance is required, folks.  But having the rules of engagement with your data is really, really important. Not going away. All right. Anything else, Kelly, then we'll just start to wrap up. No, I think that this ties – this really ties it all together. And all of these different processes are linked together to make sure that you are becoming data-driven. And I love this concept of being data-driven. It's becoming so much more pervasive in our culture and in our vocabulary. In fact, yesterday I was listening to a presentation of a kind of a business colleague of mine that runs less than $200 million alarm business here in the Bay Area. And he had a slide talking about data-driven. And I was like, oh, my gosh, I love it. And so this doesn't have anything to do with the size of your company or anything like that. The idea is that using what you have available to you from a data perspective will make you more competitive and more efficient. It will help you innovate. It will help you monetize. And then, of course, governance is really making sure that you're understanding it. So I do feel like all these process trends are really tied together. So how do we take advantage of them then? Let's go on to the wrapping up some of these slides. Yeah, because it's the holidays. I have to go shopping. No. Put data-driven into your annual planning. If those words are being kicked around, the way to get serious is say, you know, and everyone's doing it at this time of year anyway, pretty much, right, is what's it mean? How does that instantiate itself in our annual financial objectives or market objectives or strategies or initiatives we're planning, what we're budgeting for, what we're resourcing for? Put someone who really knows data into the planning process. Too many of us still react to budgets and annual plans. So the data governance people sit there, the door opens, out comes the big stack. Here's our annual plan and I'll tell us how governance is going to deal with it. That's actually kind of backwards. The governance people should be behind those doors telling you that, wow, that's not a really good idea to do. It's to fund two separate projects that deal with customer data. Why don't you make it one separate project, right? Be flexible with product procurement. Everyone out there probably pretty much talked to me, has a procurement department that can procure a certain way and you're going to get challenged on that. If it takes you 12 months to get a product bought and in place, it'll be obsolete by the time you're done. Kind of pass that off the line. You're going to have some flexibility with these products. Lastly, for a data person to say this, Kelly, I hope you understand it. For me to say this is almost painful, but start thinking about process design. I know when you're monetizing data, data governance, standing up all these new capabilities means new workflows and new workflows equals new process. And that means think about processes. Those of you that have thought, well, I never have to go back to Six Sigma or ISO 9000 or Lean, guess what? You've got to start to think about that stuff. And then lastly, governance has to be adapted to be a bit more agile. So one criticism we've noticed here that's valid and which is going to have to change next year and you're going to hear more and more of this out of the market is, you know, a quicker time to solution in the data governance area as well, just simply because things are changing so fast. So if you are thinking of taking three years to stand up a data governance program and nine months before anything visible is from a data governance is going to happen, you better rethink those plans. You're going to have to get a bit more iterative and start to carve it up into smaller pieces there and I'll turn that back over to Kelly on that one. Yeah, absolutely. And I think that, you know, best the way to take advantage of this is to really integrate the data requirements with the process requirements so that you're leveraging your technology and that you have this people plan that supports all of it. And all of this needs to be supporting a dynamic environment. Your businesses are dynamic. The industry is dynamic. And so thinking about taking a long time to start governing data or to start ensuring there's data understanding means that you're not going to be supporting the needs of your enterprise because even some of those traditionally stayed types of industries are becoming are seeing the need to be competitive around data, manufacturing steel companies, etc. So with that, let's just summarize our key takeaways. And if we've got any questions will probably still have a couple of minutes left. Yeah, we've got we've got one or two that have come in and I've already started to form some things on those. So take away that you do. Are you just going to do the TTA. I'll do that. Yeah, yeah. Absolutely. And you can monitor questions. So people issues are really still the main challenge in the sense that if we are going to create a data driven culture, then we need to make sure that we are helping our people understand what it means to be data driven. We're providing the correct training, we're understanding how their role fits into how they create consume data. So the people issues around this are still really the biggest challenge and the biggest opportunity. The diagram on the right hand side is a great way to think about this in the sense that the severity of the challenges versus our ability to rise to the challenge. It's hard to rise to people challenges sometimes. There's personalities, there's relationships, there's commitment. So it's both the biggest challenge and the biggest opportunity. Technology, this, you know, as usual, the technology industry is extraordinarily dynamic and changing, which means that you will then need to be able to respond accordingly and adapt your infrastructure, your architecture, etc. In a way that isn't chaotic, but is able to be flexible and where you can respond quickly to those new demands in the dynamic environment. And it's, you know, we talked a lot about business and IT collaboration, but it's kind of internal versus external as well in the sense that you're seeing collaboration also amongst industry and that sort of thing. And so internal processes, external processes, products that we're using internally that we're sharing amongst industry peers that will continue to evolve. And then of course the data that comes out of those shared processes and shared systems are now becoming monetized. So that's going to continue to evolve. And this drives towards recognizing your true requirements for speed, latency, security and governance around data is really going to drive the business outcomes that you're looking for questions. Yeah. Okay. I've got it. I've got them lined up here. We've got a handful. We're going to be able to get to just some of them. I'll take the first one here. Could you define graph database technology in a couple of sentences? Well, the official definition of the graph database is a database using graph structures for semantic queries. Now, first of all, that's a cheeseburger definition. And second of all, it doesn't really help anybody because graph databases are kind of a whole new way of thinking about data if you're embedded in the relational world. So real short, graph databases are a particular type of structure and database mathematics that will allow n number of dimensions on a data element. Relational is a row and a column. Graph database is almost an infinite number of dimensions that are called tuples. Okay. Graph databases came out of the semantic web world. The aforementioned guy, John Singer, has a lot of good articles on graph on data versity. I would encourage you to go there. If you want to get a product sniff, there's an open source product called Neo4j. You can download kind of a play version of that and have some fun with it. And that'll be our quick answer to that. Next one, Kelly, I'll let you take a shot at it. But you and I both could just have this stuff. This is a great one. We get this one all the time. The governance initiatives are often considered exercises in fatigue management. I like that. If data is supposed to always be owned by the business but after time they stop attending committee meetings, jeez, does that happen? Great. And we are right at the top of the hour. So if we can just wrap it up really quick with that very quick answer. All right, Kelly, 30 seconds for Kelly. Yes, I would say focus on what the business needs and requires from their data to increase their revenue, reduce their costs and reduce their risk because they will not get fatigued if they're seeing values from it. And that helps to answer the next question, which is recommendations related to data governance, find the business purpose and execute according to what's going to improve your business outcomes. Awesome. Sorry, guys, we are right at the top of the hour. So we've been on a roll. We were on a roll. We get to talk about analytics driven culture next month. I'll get the questions over to you so we can get that in the follow-up email, which will go out by end of day Monday for this webinar. And I hope you can join us next year. As Kelly and John just mentioned, the next one gets keys to creating analytics driven culture. It's a great presentation and thanks to our attendees for being so engaged in asking such great questions. Again, I'll get those over to John and Kelly to get in the follow-up email. And I hope everyone has a great day. Thank you so much. Thank you all. Happy holidays.


Give yourself a hand everyone. Give yourself a pat on the back. We just saved the internet It didn't take any amount of effort, but we did it, okay? Other week or so I did these two videos this one here a demonstration of modern web bloat and the war against web bloat continues Okay, that's the sequel. This is now the sequel of the sequel This is the this is the end of the trilogy because we have actually fixed everything So in these videos I complain about the fact that whenever you look for a recipe online You have all of these millions of informational sites that are all garbage because they have ads and trackers and JavaScript and all the You know things moving around. Oh, we see that you have disabled ads. Could you just say can you enable those? Oh, well, do you want to take our cookies blah blah blah all that nonsense And I told people list listen it would be a very easy thing for someone to just make a simple recipe site You could even have it managed with git Okay, and you can have people submit recipes for free and a bunch of people started doing those things started making these web Websites, but I figure I should probably be the one since I'm talking about it You creates the site where everyone can you know be on the same page. Okay, so that's what I did based Dot cooking base dot cooking that is the website apparently dot cooking is a top-level domain nowadays This is what it looks like right now. This is subject to change, but look at this a nice informational site In fact, we have I don't know. We probably have around a hundred recipes maybe more now I don't know looks like a lot. You'll see these nice tags for different things. I'll explain how this works in a bit There are no ads. There's no anything I mean the only sense in which this site is monetized is that I've started a bit Bitcoin donation thing if you want to donate to the sites fund But right now. I'm just running this on my own VPS I might get its own VPS in a little bit if it if it gets big enough, but anyway, let's look at let's look at a recipe Let's go to beef goulash. Okay, this is one of the ones with pictures I actually most of them right now don't have pictures the requirements I'm saying right now is that you have to actually take a picture of the thing that you make yourself Okay, you can't just get some stupid You know the search on Google for like a public domain picture that might be a picture of a slightly different recipe So every page looks like this just they have a section for ingredients a section for instructions That's it and at the very end the people who submit the articles this guy in this case You can put you know links to your website or a donation page or something like that and you'll see again They are tagged with all this stuff. Let's click on the beef tag Let's see what else is there if we oh, I have beef. I want to make something I can make something from this list, okay? And of course oh actually on the main page as well, of course I think I mentioned there's a tag list there as well. So Very nice and simple responsive CSS. This is all you need it should work on mobile and everything for example if our screen is a Little smaller actually at the beginning you'll see if you have a really wide screen There are three column to lists just to have everything on one page I'm thinking about how to change this, you know have a catalog structure or something like that But I'll talk about that in a second But if you reduce the size for example, if you have a very narrow screen, it'll just be one list so everything fits It is also if you're one of those like weirdos and perverts who uses a light theme on your browser This will display as a light site. You don't need JavaScript for that. I don't know what anyway So yeah, that's basically it and so what you can do is you can go to the github site. This is how it works If you want to submit a recipe Let's go to source. Let's actually look was the one we just looked at beef goulash or something like that So when you submit a recipe, you just submit it as a markdown file Okay, it doesn't actually show this source of this. Let's look at the source of this. So this is all it looks like, okay? Ingredients instructions blah blah blah Just mark down. This is what people submit and then it is compiled by the website into a static site Or by a static site generator now Originally when I made this thing I was using SSG 5. That's a static site generator I have not done a video on it I think Wolfgang did a video on it if you want to look that up SSG 5 and that's what I was using originally But someone opened a very nice pull request. Oh, and by the way, well, this thing also gives you RSS and stuff like that And he suggested I moved to something that I guess him and his friend made called blog it and this is very nice It is a make file based static site generator It can deal with tags. It can create an RSS feed and an atom feed automatically So people can subscribe to updates and stuff like that. Actually, I think I pulled up. Yeah, I pulled up before So this is what you can get Let's see. Let me actually go back to the website You can get the RSS feed link or the atom feed link put them in your RSS Reader and this is what it'll look like you'll get updates. Oh, there's a new recipe on shrimp and grits Okay, here's what it looks like. I don't even have to log on to the site the RSS feed automatically contains the entire page Links with everything. So if you just want to look it is just very convenient Okay, very nice stuff.  Okay, I will say that in that Literally you guys know I do not use a phone. Okay, that is not my thing if I have my phone on me It's probably for like an emergency or something weird But the other day I literally because this site is so usable. I think there was something I was getting Ingredients for in the store It is so nice to actually use this site because it loads as quickly as you know a local thing on your I mean It's not a big bloated site. It works on your phone I can look at my ingredient ingredients list when I'm in the store or something like that It is very convenient. It actually works. Okay, that's all I want. It doesn't have to be pretty I might make this index page a little more pretty this index page is actually automatically generated by the static site generator It actually it goes through so when people submit recipes They add tags to the bottom. I don't think they show up while they show up as this right so they show up as tags The static site generator automatically generates a tag list with all this kind of stuff a tag cloud However, you want to put it all of that stuff is is automatic. So this is what the internet is supposed to look like Okay, this is not a difficult thing. This is not a don't interpret this as a flex. Okay. Yes Is this site the best recipe site on the internet? Maybe but that's not because this is a good site It's because all the other ones are terrible. Okay, it doesn't take that much to run a website This I just put online. I probably only put like maybe three or four recipes on this thing Just to start it out as a proof of concept and people just logged on to GitHub to add stuff themselves And so if they want to add something All you know, they can just put their own links or whatever that that's the only incentive they need to contribute to the site Now you can go here right now You could submit recipes if you want to revise recipes if you feel like something's missing An image you can submit a small webp image there. I usually don't want them over a hundred fifty k or something like that Most of them are like that. I mean, let's see the beef goulash one-head one This is probably, you know, it's definitely less than 200k So just very simple if you make one of these recipes and take a picture of it send it in if it looks nice Or if you want to make any changes to these recipes, you can just go on the github make any kind of changes you want Nice and simple to see this is how the internet supposed to be this is how it's supposed to be kids And it's not difficult and it's not costing me a million thousand dollars Whatever Bitcoin donations, which have been, you know, mostly nominal I'm sure that they are covering my costs even if I have to, you know, rent my own VPS extra VPS to do this because it's not expensive It can be done based on cooking. Okay, let this be an example not just for recipe sites But what for any kind of informational site on the internet? Okay, this is how you do it You don't have to do do don't run up with some JavaScript nonsense to me Okay, we don't do that soy dev stuff. We don't do none of that stuff when a simple HTML page will suffice Thank you for watching


The United Nations Special Envoy for Haiti, Helen Lalime, says Prime Minister Claude Joseph will remain the leader of the Caribbean nation until an election is held. She urged all parties to set aside the differences following the assassination of President Jovenel Moise. Helen Lalime says stakeholders need to set aside the differences and chant a common way forward and overcome this difficult moment in a peaceful manner. Prime Minister Claude is the Prime Minister of Haiti and he is such, per Article 149 of the current constitution, the 1987 constitution, which states that in the event of the death of a president, the current government assumes executive powers and that the Prime Minister presides over this group, gives them guidance and they take Haiti to elections and a new elected president of Haiti. There certainly are tensions, there are certainly people on all sides of this issue taking having different interpretations of Article 149, that's why it's important that dialogue happen. Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Let's explore how to find the Z critical value using the Art of Stat web apps So from the website if you scroll down you will find the list of distributions and On there you will find the normal distribution. That's the one we're going to use From here you have three options and we're going to choose the third one find a percentile or quantile Which is basically finding the boundary that's going to cut off a given probability or a given percentage So on here, we're going to leave it at zero and one for the mean and standard deviation since we're looking for a z-score The type of percentile we're going to choose is two-tailed and this is going to allow us to capture the central probability and What do we put in for the central probability? That's going to be our confidence level So if we want to be 95% confident, we're going to leave it at 95 and here we see we get our Bounders 1.96 and negative 1.96 and if we just take the positive version of this, this is our critical value


Hey welcome to Wing Chun Dummy Techniques. We're going to show you three techniques for beginners that you can start practicing on the dummy or anything that you can put together that resembles the dummy. And then I'll show you the applications for them right after that. The first one is going to be Huen Sao. Huen Sao is like this. You're going to start like this. And then here. So with one hand it's going to be like this. You want to stick to the arm as much as possible. Fingers pointing out. And then rotate the inverse.  We're pivoting. Pivot. Pivot. Pivot. Pivot. Pivot. Like that. 25 degrees. Okay you combine both of them and we'll be like this. Now when you're Huen Sao, it's not going this way. The energy is actually going into the dummy. So that when you're Huen, it's actually a punch. When you're Huen, energy is actually in this way. It's not going this way. So you always have forward energy going into the dummy. So that's Huen Sao. You can practice that at home and I'm going to show you the application right now. Hey welcome back. So we've got my group buddy, one instructor at Combata Wing Chun. And he's going to help me demonstrate the Huen Sao. So Huen Sao is very useful in sparring or any kind of situation where he has his hand. What you do is you just feed him a couple of these so that you get the energy coming to your hand. Then you use one of the Huen Saos. So I'm feeding him and they don't like me to hit him so they're going to counter it with this. So you do a couple and the third one do a Huen and then boom he lands right into my punch. So even the first one. So we go one, two, three, boom you're in. Make sure you move in. So one, two, three, move in. So that's the first way of doing Huen Sao. Slow motion. One, two, three. I meet the energy. I feed it out. Now I go past here and then I go for the punch with the same hand because it's closer than this one. So that's the first Huen Sao application. Hey here's the second part application for Huen Sao. Now remember this is not really a street fight. This is more than sparring. So a lot of things a little bit different. Now a lot of people are saying oh how do you know he's going to do that and you know you just you can't predict what a person is doing. Well that's fine because what I'm doing is I'm making him do something. Right so I'm making him do it. Okay so I'm making him because if it doesn't block this this is going to reside already. Okay so I force him to block it a couple times make his brain used to doing that motion and the third time then boom he's giving me that energy and I'm doing this. Now you're saying okay here's the next part right so here's the next part. One, two, three, boom, boom. Okay now you're saying how do you know he's going to do that? Well let's say he blocks this one right then I go this one you block this one boom boom. You know what I'm saying how do you know he's going to block? Well hopefully he doesn't block. If he doesn't block you don't need to know all these things but we're going to train it so that you're automatically do all these things even though he blocks. Okay so like I said fights you can't protect it but you're training so that you can react automatically without thinking in the fight. Okay so once again one, two, three, boom. Okay so once again you can go with this one you can hune and punch so hune out with the right and punch with the left or you can hune with the right and punch with the right like that. Let me slow it down for you. So once again we're doing one, two, I feel the energy I hune boom and then the second one he blocks this one I hune this one out boom and then finish it off. Okay so those are some techniques a lot of times this will just get them right away. Oh let's see there you go. Okay after a couple finger jumps to the eye for sure you'll be blocking it you'll be doing something giving some kind of obstruction. Okay so a lot of times that will just finish a fight already or even the smart match okay but sorry about that. But if he blocks it and boom you use that right and then this will sometimes just finish it right there but if it doesn't finish it you got this right and he blocks it then boom and that will finish it if that doesn't finish it you got these ones. Okay so this is just a drill it's just a technique and this is how we practice it so that you can follow up and do things automatically to counter whatever it gives you. Hey here's the second Wing Chun dummy technique for beginners that you can start practicing at home very useful technique it's the pa and low jump so you start here step a little bit to the left pa step in with the low jump and go back to center step a little bit to the right pa step in low jump okay so like this like that all right now what you can do is substitute this low jump with a punch okay so you can substitute with a punch well jump doesn't matter some people can do it even at high jump so you can go up high jump up so those are a few things and there's a fourth one where you can go pop and then put your foot here and then kind of go backwards with your foot so one two three one two three one two so it comes up it's kind of hard to do in this dummy because the leg it's not it doesn't come out as much but I'll show you how to use that notification in the next section all right here's the pop and low jump application okay so pop and low jump very useful first well you do your pop one two and then third one okay low jump run the chest and notice how I put my foot here and when I grip your chest we do it back one motion with this this ourselves will do a little bit of trip and we combine it with the low jump run the chest here if he's facing this way then perfect I got a nice grip but unless he turns this way oh no I'm missing something that's okay right now I can go and use this part of my arm like that okay so either way it's going to be right so one two and then if I don't get anything he turns a little bit he turns this way and I just use I just use this part of my arm instead of the low jump and the same thing you can also use it like this way just like this pop and jump this case you can go this way same thing so either way just use the use the dummy technique right so it's going to be this way right a pop this way so either way it's cool here's the third Wing Chun dummy technique that you can use and you start practicing even for beginners so standing as in a regular position we do a bong self wrist in between then we step over here a little bit and I'm going to do a bong self this one this one goes underneath and pull and just kind of kick your foot here like that all right so go back to the center do bong self this one goes underneath it has a lap this one goes right here pull and kick at the same time so do a little bit faster we boom boom boom boom now what some other people do is variation is this sweeping it can stop like that so there's two ways to do it you can lift it up a little bit and stop some people say oh that's not proper technique but you're going to use it like that I use like that like that okay lift it a little bit and stop down sometimes you can even just grind it down the front like that grind it down grind it down so those are some variations now let me show you how to do the application hey so now I'm going to show you the grappling hand application let me show you just what I did there so it's good for a punch you go in a regular pack just like the second technique right another one comes I'm going to grab like this see one hand on top one hand on the bottom and then you can just do a sweep pull them down face on the shin just right there or even higher in the knee like that or you can do the stomp just be careful and then twist it up like that why I'm going to show you the third way I did it which is actually grind it from the front shin and pull on the instep which is quite painful if you do it hard and it's because you have like work boots or something like that grind it in front of the shin and down the instep well now I like to do these both best it's just a knee just be careful so you want to want the knee to rotate out like that right that and then run it back in the neck to finish it off right so that's a simple one and you can practice that just be careful with the stomping just do it slowly and make sure you don't hurt anybody with your stomps hey here's a variation of that application okay so before I went like this and then I did the cross right his legs on this side his arm is this side so this time I did a bong style the wrong way so I went this way and this same side the right leg right hand so I'm going to pull here pull it down to my hip and then I'm going to step on here okay it's nice that's again just be careful don't go boom okay but ideally it's like that okay grab 10 pull like that that's the energy you want to use you can easily twist knees and yeah it's kind of dangerous so just be careful when you train so use that wisely and train hard see you soon hey thanks for watching this three techniques of wooden dummies for beginners so if you have a dummy year if you have something like a dummy you can start doing those techniques apparently at home I hope that helps you out so if you like this lesson make sure you check out some of my other lessons I have on this channel and make sure you subscribe to this channel to get more Wing Chun dummy lessons just like this one more self defense fitness and all sorts of really cool stuff thanks Herman for helping me out and we'll see you soon


Hey guys John wise here with come again and Today I am going to go over a review of the Funko Pop television Elvira mistress of the dark number 3 75 Let's get to it. Okay guys. So here we have Elvira In her first Funko Pop form Funko just released a new Halloween themed where she's dressed as a Mummy I Don't have my hands on that one yet, but I do have my hands on this Particular one here, which is the first one. So we're gonna go ahead and go over it For those not in Elvira is known as the mistress of the dark She is a horror host who's been on TV since and movies since the 80s middle 80s and She's known for many things puns and really big comedic puns So go ahead and take a look at the box art here first. So I'll pull this a little closer Good all right, so here We have the package we have the Image of the Funko on the side we have the Funko inside in this big window here Of course, we have the logo Elvira master of the dark along with the number 375 We have the name and we know this is a vinyl figure and not a bobble head So if we turn it over to the side here, we'll get another headshot and Side shot along with the name Over here we get a full body view Of what the Funko Pop looks like Nice little art drop backdrop in the background there, and of course we have the mistress of the dark herself a photo of Elvira along with her Awesome hairdo signature dress I'm gonna come out and say the obvious she's hilarious I've met had a chance to meet her in person. I wish I owned this At the time I met her excited definitely would have had her signed it Just you know kind of Elvira mistress of the dark or something like that. That would have been really cool but you know Didn't have it at the time and that was a few years ago. I think it was about five years ago, but at this point So really cool box, but let's go ahead and open her up and see how it looks on you Yeah All right guys and there she is Elvira out of the box So let's go ahead take a look here Like your typical vinyl figures her head does move very very slightly Her hair kind of gets in the way It's still pretty cool the detail on this the paint job is amazing on this We have the painted lips here. We have our signature little dark hair We got some eye shadowing going on there, but it's the other details that we really want to pay attention to Like the dagger That's her signature dagger right here It's really cool. We have her ring now the ring is significant For those who it's really hard to get a good light on here for those who haven't seen the movie Elvira misses the dark the ring is a magical ring that gives her the wearer powers like witch powers so The we do have her signature Stockings there her shoes Turned around her long flowing hair, which is always huge Nice and flat here at the bottom so there was no problem standing her whatsoever You can tell she stands Just fine no problem All right, well there we have our Elvira mistress of the dark Funko Pop To my knowledge these are still available. You can get them online. I happen to find mine at a Toys R Us So good hunting everybody and thanks for tuning in to come again Please make sure to subscribe like and comment below. Happy Halloween Like don't want to open this up.  I'm not opening up another one


You said that we didn't play the defense. We didn't get no turnovers. We got to play better.  We have to get the ball to our offense. We're good for the position, whatever, take the, get our offense, you know, going. And like I said, we just got to play better on defense. We got to create turnovers. We have to try to get the ball out, the running backs hands or whoever it may be. But we got to start playing better than opposing defense who are going to win games. It was getting the ball off.  You know, when we did, when he did hold the ball, everybody was on the same page.  So, I mean, we all got to be on the same page. We all, like I said, we all got to be better. You guys? I mean, they were trying to run the ball against us. As you see, I think I saw at like 47 rushing yards coming to the fourth quarter. I mean, we knew they were going to try to be bullets. And we weren't going to get bullied. And they bullied ball. They're trying to knock you off the ball and try to run the ball. And I think we were standing up to it. We were stopping the run. You know, of course, they got the rush to shut down at the end or in the fourth quarter.  So, I mean, that's what it was. We were stopping they run and just getting out of the field, making the kid field goals. You saw that, didn't you? I think we, like I said, we just got to play better as a whole team. You know, it's not just defense and not just offense and a special team, too, as well. So we all have to be kind of on the same page. And I mean, we're going to get everybody healthy. We're going to get everybody going, like coach said, physically and immensely, coming off this bye week. And we've got two weeks prepared. Get ready for back at home after the bye week. So I mean, we're going to take this bye week. You know, hopefully we could get a lot of guys back. But you know, make no, we're a good team.   But I have a lot of faith in my guys. I have a lot of faith in this team.  How big is that part of them?   Like all goal was to play better than the Patriot defense. And it wasn't there today. So I think just that mindset coming back off the bye week, that we have to play better as a defense. We have to, you know, continue to, when they get down to make them for kick field goals and stuff like that. So I think coming off this bye week going to be big, not just for, like you said, our body, but mentally as well. Which one? Yeah, that's, that's, that's bad.  So I know for a fact me holding myself to a high standard, I wasn't running to the ball because I did, you know, assume he was out of bounds. So I think we all have to not assume that they running back. I mean, the ball carries out of bound. We all have to get to the ball.  He stayed in bounds.  But yeah, we all have to be better than get to the ball. Of course, man. I think that was our message this week, you know, especially trying to outplay their defense, you know, you against another good team and a well coached team. You can't, you got to be mistake free.   You can't come in and not stopping to run. You know, of course, we got to take care of the ball wherever it may be. But that's not my job. Let's talk about that. My job is, how can we keep getting better on defense, making them kick field goal when they get in the red zone or whatever it may be.   But I think just from my aspect, I think I just got to come in and see how can I improve. I think that's the main thing with me, you know, going to our coach T offers, sitting down and watching extra film before everybody get away, you know, later in the week. But, you know, of course, you want to get away from the game just a little bit, not too much, I should say, but you want to get away and just kind of get back to the normal, you know, because I think that's the best thing about them by weeks, you know, you could kind of take off from football for that week, but you also still have to keep your mind sharp because you're getting ready for another opponent coming in right after the bye week. So I think that's the big thing.