Live from San Francisco, it's theCUBE, covering Informatica World 2016. Brought to you by Informatica. Now, here are your hosts, John Furrier and Peter Burris. Okay, welcome back everyone. We are here live in San Francisco, California for Informatica 2016. Exclusive coverage from SiliconANGLE Media as theCUBE. It's our flagship program. We go out to the events and extract the signals and noise. I'm John Furrier, my co-host Peter Burris. Our next guest is Nick Milman, Managing Director for Big Data and Insight Analytics at Accenture. You cover Europe, Africa, Latin America for Accenture Digital. Welcome to theCUBE. Thank you.  So I got to ask you, obviously Accenture has relationship with Informatica among all of the partners. You guys serve as a steward to the customer, their customer and your client base. Big Data certainly has been well past the hype stage into the reality of integrating in cloud, apps. What are the big trends that you're seeing right now with your clients?  Tier one integrator, you're on a global basis. What is the biggest thing that you're talking about with customers? There's a number of things, obviously, we're talking about with our customers. Some really significant trends, some of which I talked about in the keynote here at Informatica World this morning. One is the whole shift towards intelligent automation and we're certainly seeing a number of solutions with artificial intelligence at their core and those solutions require significant amount of data and then clearly provide their own kind of data and analytics opportunities to actually measure what the machines are doing and fine tune and make the algorithms more effective over time as well. So there's certainly a big trend around artificial intelligence and machine learning. How about Accenture's practice you guys have? You guys have very comprehensive, it's almost like your own cloud company. I know a few of your data science we've interviewed in the past.  I'm the exact number, but a lot. Is that part of the go-to market for you guys to help customers by getting down the dirty in the trenches with data science and part of the delivery? Yes, our approach is to provide the end-to-end service that our clients might need in the data analytics space. So we help with everything from the upfront strategy what might be the use cases in this industry or this function that drive value from big data through to data management, data governance, the architectural thinking, and then the implementation and also the advanced analytics and providing the data science capability for our clients. If they don't have that capability themselves they can use Accenture's data science capability. So I've got to ask you a question. I've got some feedback from your keynote you mentioned it. You were explaining how data-driven digital health helps cardio patients. Really with this emphasis of platform economy shifts and shifting the business model. Notable tweets from your keynote. Can you explain and be some color around what you meant by that tweet? Yes, what we've been doing with the digital health platform is actually looking at the data that we can capture through the patient's experience through hospital, both the kind of pathway that they take through the hospital process from admission, how long they spend in each stage of the process and looking for whether there's any ways to make that an efficient and more effective experience for the patient. Also looking at the care management. So how has the diagnosis done? Who's done what in terms of the medical procedures with the individual patient? And then looking at the whole kind of patient engagement part of the equation as well. So how has the patient been communicated to when they were first diagnosed, when they were brought in? And then the ongoing communication as they're discharged, all with a view to improving the patient's experience and eliminate or reducing the number of readmissions. Yes, so hold on before it gets on to a question. But is that holistically? Is that more historically a real time? Or is it just the journey of the patient historically when they come in real time? This is using the historic data in order to predict and make the right decisions in near real time. So actually using it in a decision making sense. So it's all the data. So it's all the data you can get. Yes. That is an application that's predicated on the idea that people are going to change their behavior so that over an extended period of time they'll adopt new behaviors because the data provides visibility into the correlation between a behavior and the outcome. In your practice at Accenture, how are businesses transitioning from episodic uses of big data to operational or let's call it more holistic, comprehensive, persistent uses of big data? Are you starting to see your clients starting to move from periodic success to broad changes in how they do business? We are. I think it depends a lot on the specific use case that we're thinking about. Certainly as the use of data and analytics becomes more pervasive, then actually it's becoming more automated into business processes. So it's a regular part of doing the business process. So let's say, for example, you're looking for fraudulent transactions in an insurance context. As long as you have the algorithms and they're well tuned and they're finding the right cases to investigate, then that's kind of a regular repeatable success that you're going to get every time someone makes a fraudulent claim. In other cases, of course, I think probably what you might be referring to is sometimes the management decision-making in organizations, sometimes it's data-driven, sometimes it's more gut-field-driven, and that's part of our overall approach from an analytics perspective is looking not just at the kind of technology that you implement, but also how you change the culture and the operating model of the organization to make it more data-driven. You have visibility into how Accenture operates with clients in Europe, but also Africa and Latin America. How are different regions approaching common problems differently as a consequence of big data? Yeah, I get to see what clients are doing across different parts of Europe, Africa, and Latin America. There are probably more similarities than differences in terms of the kinds of industry issues that our clients are facing and in terms of the kinds of approaches that they might be using with cloud-based platforms, machine learning techniques, advanced analytics, and then wrapping that into kind of a comprehensive data management kind of solution. There's more similarities than differences, I would say. Okay, we have machine learning and AI. We love the buzz. We used to cover in Google I.O. as well as Sapphire Now, which is the SAP conference last week, up this week, you used to send your folks on there. You're seeing the digital transformation on the enterprise side as clear as day. That's pretty obvious. That has to get worked on. People are working on it. Then you spin over to Google I.O. at Shoreline. You have machine learning, augmented reality, certainly virtual reality, Facebook's talking about that, and AI, the center of the action. How do you guys see that rendering itself with the customer environment? You mentioned some of the healthcare stuff, but is this now on the forefront, on the radar of your clients, AI and bots and automation at the workload level? Or is that still kind of come later? I think there are some real examples out there of organizations who have really done the automated intelligence. So I know, for example, that Siemens is running a lights out factory in one of its locations in Germany, where much of the actual physical activity in the factory and the supply chain planning around that is being done by the machines. And in a consumer context, we've recently developed a prototype that I talked about in my keynote this morning, a robotic mortgage advisor, who can actually make sure that financial institutions, when they're, for example, recommending mortgage products to their consumer clients, are following the legislation and regulations about providing the right impartial advice to the consumer before they take out the mortgage. How are you doing this? Is this basically text-based analysis of the communications between the mortgage institution and the client? It works essentially as a robotic agent that uses natural language processing and machine learning to ask the appropriate questions and then interpret your responses and then ask the appropriate follow-up questions in line with the kind of legislation and regulations. Got it. What's the landscape of the tier one system integrators? And can you share what you guys do differently? Because it seems now that in the old days, go back in the early days, 25 years ago during when an SAP started at their 25th anniversary, you had delivery, long delivery cycles, the big six accounting firms, now Accenture wasn't even around then, now Accenture, but deliveries changed, mentioned data sciences. And each one now, each integrator, this is an integrator, whatever they call these days, PS, whatever, has interesting differentiation. You are now becoming software companies and technology companies, not just delivery. Talk about how you guys differentiate, not necessarily comparing which other guys, Accenture specifically, and how you engage with clients. Yes, I mean, I can talk to what we see in the market in terms of what our clients want. I think there's probably broadly two sorts of client request or client demand around analytics. The first is for clients who've got a specific job to be done, like help me identify the fraudulent transactions in insurance context or help me identify what my maintenance schedule should be for my offshore liquefied natural gas asset because I want to make sure that I'm following the kind of the best maintenance routine to prevent outages, et cetera. So they've got a specific function in mind that they want to apply data and analytics to the problem. And then what we're looking to offer is a very industry focused, if you like, almost product type of solution where we're coming with a proven approach and using that to solve the specific business problem. And in order to facilitate that, we've actually created something called the Accenture Insights platform, which is a pre-configured platform of some of the leading technologies which we can offer to our clients on a cloud basis, i.e. as a service and scale up, scale down. So you walk in, solve a problem, productize it and share it with your other customers. If it's generally available, I mean, I'd say generally available. Some customers might have different data, but you can look at the patterns and get these recipes or use cases. Is that what I, you know? Yes, the idea is, the idea of course is to use the Accenture Insights platform, make it very focused on specific industry issues that we can then provide a rapid solution using a cloud-based approach. The second sort of area of client demand that I think we get in the data analytics spaces is a kind of broader help us transform to be more data-driven and insight-powered where there isn't a kind of one-size-fits-all approach, but it's a matter of looking at the strategy and the vision for the organization, working out where data analytics fits in that, and then having a roadmap and an architectural approach and looking as much at the organization and operating model as it is about the technology and the processes. How about the silos?  I know Accenture has a lot of different practices. They have a cloud, an Azure practice or a practice over here. All kinds of different practices. You are involved in a practice. When a customer has a crossover between this, how does that work internally for you guys? Single-pointed contact stage with the customer? Do you manage that internally? How do you guys think about this in terms of, you know, being agile yourselves? You know how to tell what customers ought to be agile. Yeah, I think we generally do a good job of that. I mean, our dominant access around that is the client themselves. So we have teams that focus specifically on our main client accounts and then they help to orchestrate the rest of Accenture to bring the right solution to the client problem. And of course, big complex problems require different parts of Accenture, but we're quite used to kind of pulling those together and creating the right team for the client. What's the big barriers for customers, as they say? Okay, digital transformation. I got to get on this track. I've taken some baby steps. Now I'm on the beginning phases of that journey. What is the biggest barrier to that? Is it organizational? Is it actually data architecture? Is it lack of certain technologies? Is it the unknown processes? What's the core thing that you keep on bumping into if it's a repeatable pattern? Yes, every organization is obviously slightly different, but there's probably at least a couple of things which can come up as pretty common challenges. One is, as you mentioned, the organizational aspects. So working out in a kind of enterprise transformation, who's actually going to own it? Is it something which is going to be run out of the CIO function, something out of one of the business functions? Is there going to be a center of excellence to kind of look after the initial implementation but then become the kind of place to go for the data analytics after the transformation? I mean, these tricky organizational issues need working through and there needs to be a clear operating model. And the second common challenge is in the type of large scale clients, often with global presence that Accenture works with, then they typically have quite complex architecture in terms of the source systems that we're going to be relying on for the data analytics. So then looking at the data architecture and actually coming up with the right data supply chain to allow the data to flow in and out of the right parts of the enterprise and put the right kind of governance wrapper around that is also a kind of key challenge. That suggests that the services that you provide are also going to be provided through a lot of different parts of Accenture as well. Are you, do you anticipate that at some point in time that Accenture as a supplier of insight and know how to build big data-oriented systems will increasingly be expressed through other parts, the application parts of Accenture? Or is it something that customers are going to engage directly for an extended period of time? Sorry, I'm not sure I really followed the question. Well, as these technologies and these approaches get embedded into different classes of systems, presumably Accenture as it solves those customers or customers' problems around those classes of systems will embed big data techniques directly into their delivery structure. Are you going, do you anticipate that you will be a service provider inside Accenture? Just as you said, the organizational challenges within a business are myriad. How do you anticipate that Accenture will view big data as it was going to be a set of capabilities or going to be something that's embedded in other solutions? Yes, I understand, okay, fine. And I think that we have some good examples of I think what you're describing where we are, for example, doing BPO business process operations for some of our clients and we embed what we would recommend for client organizations in how we run the BPO for those organizations. So we're making it insight-driven operations. So yes, it makes sense to provide the kind of service to our clients using the kind of the techniques and the trends that we see in the rest of the market. So on the big picture, customers, what would you say is their priorities right now when they move to the cloud, with respect to data? Data first, if you will, and they have all this stuff going on, legacy. I mean enterprise, a true enterprise. What do you see in terms of their priorities as they look at the cloud? What's your takeaway? What's your thoughts on what customers should be thinking about? And what anecdotal things you've seen or you can share? Yes, there's no doubt quite a significant shift towards doing cloud-based data and analytics. I think when organizations first start to look at it, then the first question they typically ask themselves is the security question. And then we also then get into things in terms of the latency, how it's going to be populated, how it's going to integrate with existing systems in the organization. But in most cases, there are solutions to that. And we've architected, for example, the solution in a number of places where we're using data that's coming from the client's internal systems going into the cloud and then can also be backwards integrated into existing solutions they might have for business intelligence and analytics. What advice do you give clients when they say, I want to go to the cloud, I got to use Amazon, Amazon's, I see the results of $10 billion of which Wikibon predicted first. But that kind of came out of nowhere but they're getting into the enterprise space, right? And a lot of people look at the cloud like Amazon and certainly Informatica's got to play there because they're kind of agnostic. That might be a knee-jerk reaction for some customers. How do you guys advise them and say, whoa, not so fast or, I mean, what is the take? What's your take on that? Because now you're also international so you have a global perspective. So what is that conversation like? What happens on those whiteboards or in those conferences when you say, whoa, you're a global company, settle down. What does that happen? Well, you won't be surprised to know that Accenture takes a fairly agnostic view on this. Or Azure Cloud in general, non-prem, non-prem. We're favoring one cloud product over another. In terms of does it make sense to move to the cloud? Then I guess that just depends on the client's situation and we look at each case on its merits. So it's very hard to give a general answer in terms of what we would do. But then if we think moving to the cloud is the right answer, then of course we would look at what cloud variants are out there, what might best suit their circumstances and the kind of commercial basis on which they can do it. And it differs by country, I imagine, right? The different policies and governance. Does that take a big play into it? It's part of it, of course, and in different geographies, very complicated topic. We could spend the next 20 minutes on probably, but... Germany versus Ireland, right? Exactly. Different countries have different data sovereignty rules. But of course the mainstream cloud providers are kind of cognizant of that and coming up with ways of addressing it. So it's something that has to be born in mind as designing the solution. So Nick, I got to ask you the question here at Informatical World 2016. We're live here in San Francisco. What is the biggest thing that you see here that jumps out at you from a trend standpoint or notable news or technology or product? Yeah, what's really been noticeable from my perspective has just been the whole kind of vibe around the event. I think that in my view, when I've been working in data analytics for more than 20 years, there's never been a more exciting time to be working in data analytics with these new technology trends that we've talked a little bit about today with the fact that it's now pretty much on most C-level agendas to actually extract the value out of data analytics for an organization. This is a really exciting time to be working in the space and I think that's come across to me as I've been in the sessions and walking around the event, just the kind of the enthusiasm and the excitement is really, really strong. So we had one of the guests on early and I said, you know, I'd like to draw a line down the middle of the street and one side's the old way and then the new way. What pattern or winning formula do you see that's emerging as kind of a best use case example of what the winning formula is on this new way? Everyone's trying some stuff. You see Hadoop out there, now it's more like big data, data lake, you know, can make it more integrated. You see a lot of things happening on this new way. Old way is kind of stagnant, slow. We kind of know what that is but how do you tell the difference between who's the old way and who's the new way and what's the winning pattern that's emerging that you could point to that's emerging for the people that are doing it the new way? So I think it's probably a big question but the easy part to answer I think is how do you tell who's doing it right? I think you know that an organization has really cracked it around data analytics if it's almost become pervasive. It's almost part of the normal everyday process. So if for example, your Thames water and you're using information and data coming off the sensors around your water treatment plants and actually using that in near real time to decide how best to optimize your operation and be able to respond in near real time when critical things happen like a leak or a weather incident, et cetera. And it's almost embedded in the way you run the business and it's very easy to kind of pick those examples and say well actually if it's embedded in the way the business is running, then you've got it right. So the progress bar almost can be pegged by how well people are actually thinking about their business outcomes and applying some sort of analytics framework and the further they go along, the more in use it is. Or is that kind of how you're thinking about it? Yes, I agree with that. I mean, I think the more that there's to be human intervention or human interpretation the results of course that's required in some use cases but actually really get it embedded and pervasive in the business process and then you're going to maximize the value out of analytics. Nick, final question to end the segment. I'm going to put you in the spot. Share with the folks who are watching something about the big data analytics space that you've been involved in. You said it had 20-year history. That's happening now that they might not know about. That's really worth sharing. Like, hey, you know, could be a friend, could be a colleague, could be a practitioner. What thing could you highlight? What data point or example can you highlight or technology that they should know about that they might not know about? That's worth pointing out. I think the one area that I'm certainly quite excited about and it's very early stages but is the kind of convergence of some of the digital technologies that are out there and we're probably only starting to kind of scratch the surfaces a little bit but if you look at digital technologies like virtual reality and augmented reality, I'm really excited about how analytics can become part of that immersive environment and how in the future I think we might have new ways of looking at information and interpreting it. It won't be kind of getting your tablet out or your phone out and looking at the data. You'll actually be more immersed in the environment and we've started to look at some of those kind of ideas and that's certainly something that I'm really excited about. That's actually changing the user experience, not the consumption of the data. It's actually directly impacting the users. Yes, exactly. Nick, thanks so much for sharing the insight and the data on theCUBE, really appreciate it. Nick Milman with Accenture, sharing his insights here at Informatica World 2016. I'm John Furrier with Peter Burris. You're watching theCUBE. Hi, this is Christi.


Thing you will miss out on if you don't teach English abroad, change of routine. If a typical 9-5 doesn't satisfy you anymore, why not quit and move abroad to teach? Many EFL teachers move away because they are tired of the same old routine, they want some action. Whether you want to teach English in Europe, Asia, the Middle East or Latin America, say goodbye to your old routine and make the move. Many teachers enjoy a lot of freedom when working abroad, and aren't bound to a 9-5 workday. This is especially true for digital nomads teaching English online.


Hello, I'm Mike McKee, the Bishop of the North Texas Conference of the United Methodist Church. I want to share with you today some good news about a decision that I have made regarding the leadership of the North Texas Conference. These decisions will take effect on July 1. At annual conference this year, I will be appointing three new district superintendents. Those three new district superintendents will be Dr. Vic Casad, who is currently serving at Stonebridge United Methodist Church of McKinney, and he will begin on July 1 as the East District Superintendent. The Reverend Cammie Gaston, who is currently serving as the director of the Wesley Foundation in Denton, will become the superintendent of the Metro District. And at that same time, Dr. Ron Henderson, who is currently serving as the pastor of Custer Road United Methodist Church, will assume the responsibilities for leading the North Central District of the United Methodist Church. I am delighted that these three persons are joining Dr. Marvin Geyer in serving the churches, the laypersons, the clergy, and the mission field of the North Texas Conference. And I wanted you to know about my intentions and what I will be doing in terms of our leadership of the annual conference. I continue to hold you daily in my prayers about your work and ministry, about your servanthood as either a clergy person or a layperson. And of course, I want to invite you to continue to pray for me and for the cabinet as we begin the task of making appointments to serve the mission field, the people that our Christ is calling us to meet. Thank you for who you are and what you do.


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Get an outside temperature display on the instrument cluster there, leather-wrapped steering wheel, information center controls on the right as well as for collision warning and then Bluetooth on the left and audio controls. This one gives you AM, FM and serious XM capabilities on the radio. This is also where your backup camera shows up. You do get a CD player on there as well.  Leather-wrapped dash here, looks really nice, passenger side format and seat are in excellent condition as well, no rips or tears on that. This one does have power sunroof. Get map lights up there and we'll take a quick look at the back seats and then check out under the hood. Back seats are just as nice as the front seats, no rips or tears back here.  All weather floor mats roll back here, like I said these seats do fold down. They go down almost completely flat which is really nice if you have to haul a bunch of extra stuff. They go up like so. Looks like they also recline a little bit so you can see how nice they recline. And you do get child safety locks on the back doors too. We'll take a quick look under the hood and I would personally like to thank you for checking out the video today and hopefully from this HD video you will have been able to tell just how clean this RAV4 is all the way around. Inside out under the hood we have the 2.5 liter dual overhead cam for cylinder motor 176 horsepower engine that is very clean. Runs very smooth. This vehicle has been fully safety inspected by our service shop, has a fresh oil and filter change. All the fluids have been checked and topped off. The vehicle has been gone through mechanically 100%. It has four brand new tires, brand new, looks like brand new brakes all the way around. And we'll do one more final walk around here. And to see more pictures of this Toyota RAV4 or one of our other 450 new and used cars, trucks, SUVs, minivans, Wranglers. You name it we got it go to our website www.summitauto.com. Full pictures and descriptions of every single vehicle from two locations all at summitauto.com. And if you'd like to check out more HD videos you can go to youtube.com slash summit auto. Remember to like subscribe and share on this video and all the videos that you see there. In fact in a second you will see a link to subscribe to our YouTube channel on your left. A link to more Toyota RAV4 vehicles like this one on your right if you have not been to our website on the bottom link to this vehicle on our website click those check us out and we really look forward to helping you with this super clean 2017 Toyota RAV4 XLE and magnetic gray metallic. Thanks again.


Because you're like, no one would make a product that would fail you. Yeah, I kind of like kept it to myself. I didn't tell anyone. I was like, what is, what's wrong with me? I'm just a really smelly person. Right. Is it me? Is it my diet? I started to talk about it and talking to friends, talking to colleagues. Even, you know, look, I remember Kim Kardashian was on the Kardashians talking about how aluminum-free deodorants don't work. So I realized talking to friends and peers, that this was a problem for other people as well. There was this, you know, idea that it's aluminum-free deodorants.


The Provincial First Nations Métis and Inuit Professional Learning Project focuses on four goals, learning to be, learning to know, learning to do, and learning to relate. Learning to be asks the question, how does what we do relate to who we are? As educators, how do we role model being engaged thinkers and ethical citizens with an entrepreneurial spirit in the context of First Nations, Métis and Inuit education? Learning to know asks the question, how does knowing relate to understanding? How does knowing about historical events and current contextual realities relate to how we understand our students differently and our communities differently? Learning to do, learning to do asks the question, how does empowering differ from delegating? How can we empower educators with resources and tools to support their curricular programming? Finally, learning to relate. Learning to relate asks the question, how does where we learn influence how we learn? How do students see themselves represented in their educational communities?


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I have lost over half of my income per month over the last couple of months or so. Now, YouTube is paying the YouTube advertisers are now paying out a lot less because advertisers are not willing to invest that much into advertising on YouTube or other social media platforms or even commercials or TV shows. That is, of course, because we have been seeing the United States economy and the downfall in these big large companies do not have the same budgets they had last year in 2021 that they have this year in 2022 to even invest inside the stock market. So yes, I did lose over half of my income from YouTube and from other sponsorship ads because they're not able to pay that much. And a lot of you guys are wondering how am I able to survive? Well, in this video, I'm going to give you three tips on how I lost half of my income and I'm still able to live well off inside of every session. So tip number one is don't be house poor.  So here's the rule of thumb, the 28% rule of thumb. 28% is a rule generally guideline that says that you should try to spend no more than 28% of your monthly gross income on houses expenses to determine what your monthly home ownership budget should be under this rule. Simply multiply your monthly income by 28%. So for example, if you brought in $50,000 every single year, you divide that by 12. That means you're bringing in before taxes $4,166 every single month. And if you multiply that by 28%, that means you should not be spending more than $1,166 on your monthly houses expenses, guys. So let's say you live in a household where you have two sources of income, your husband or your wife, multiply that by two. Now you can spend up to $2,333 on your home expenses before it reaches over the 28% rule of thumb.    That is the big thing, guys. Do not be house poor at any time. Yes, there have been months where I made more money than I expected. There's been months where I made so much money to where it was like, hey, I could upgrade my house. Hey, I could upgrade my car, but no, I stay on my same living expenses that I've been on for the last couple of years, guys. So when I did have that extra income, I could put that aside, I could invest it and all of my money was not going to a new house that I bought or a new car or a new car I was going to have to pay my lease on. No, I stay at the same living expenses no matter what my income was just so that I don't become house poor in the future, guys. So the way that the market is, there's no telling what's going to happen. So even though I lost half of my income from YouTube, I'm still not house poor because I did not upgrade my living lifestyle just because I had one good year of success on YouTube. I want to have at least 3, 4, 5, maybe 6 years of success on YouTube of consistent growth before I even think about upgrading my house income just so that I don't be house poor. So that's tip number one, guys. Don't be house poor.  I don't know why I keep forgetting that one, but yeah, guys. Don't be house poor. That's tip number one. All right. So that's going to lead us to tip number two. If you're not house poor, that means you have a little extra money on the side. Tip number two in order to feel safe and comfortable inside of a recession is to have a good amount of money put away in your savings.  Don't invest any of it or I'm not saying, hey, all your money should be investments and none in savings. You should always have some money in your savings and rule of thumb for your savings is to have at least three to six months where if you was to not make another dime at all for the next three months that you have another money put away in your savings that will cover your living expenses for that month. So the rule of thumb is three to six months. I have another money in my savings to where if I can make another dime for the next 12 months, I would be fine.  Now, I know that sounds crazy, but I live in Texas to people outside of Texas. I'm pretty sure that number is much higher. But for me, let's just say it's $2,500 a month. So if I if something was to happen and I needed a savings account for three months, I need to put around away around $7,500 and for the next six months around $15,000 inside of my savings account. And that could take me anywhere from a year or two years to get $7,000 to $15,000 inside of my savings account. But once you have enough money saved up in your savings account to where you don't have to worry about anything for the next three to six months, just in case anything happened in a recession. So let's say if jobs have a cutback, if jobs don't pay out their bonuses like they used to pay out their bonuses or just anything happened during the recession that you will be comfortable for the next three to six months in order to not make a dime and still be comfortable. And once you have your savings account filled up, then you can start invest inside the stock market, your business or yourself or your financial freedom guys. So that's another way to feel real off inside of a recession by having money stashed away inside of your savings account. I got the third way is how I started this channel and that's from dividend investing. Now dividend investing is investing into different companies inside the stock market that pays out dividends. Dividends is by far the easiest way to earn passive income. I mean you literally do nothing at all in this company seeing your check every single month or every single quarter. Now dividend companies that I love investing into during the recession are dividend kings. That means they are companies like Coca-Cola, Johnson and Johnson, Procter and Gamble, PepsiCo. These companies right here has been increasing their dividend payments for the last 50 years is in the end is inside of the S&P 500. That means no matter how many recessions they've been through, no matter how many pandemics they've been through, they was able to increase their dividend payments for the last 50 years. That means these are some recession proof stocks to where if you invest inside these companies for every share that you own, they're going to mail you out a check or they're going to directly deposit into your brokerage account every single quarter or every single month no matter what happens. So this is a good way of passive income and I built me up a pretty decent sized dividend portfolio that is able to pay my bills to my house without me having to lift my finger at all. So good solid companies like Coca-Cola, Pepsi, Johnson and Johnson, Procter and Gamble, they are able to pay my bills by me doing absolutely nothing at all just from investing to dividends and I know that I'm going to have consistent income from these dividend-paying companies because they have not cut their dividends for the last 50 years guys that's longer than I've even been alive. So if you guys want to know what dividend companies I'm investing into, I'm going to leave a link down in the comment section that's to my Patreon. The Patreon to take it to my Discord. Inside the Discord I post every single time I buy and sell a stock. Plus my swing trades, option trades and we have pro-day traders in there that are also going to start doing chart lessons inside there. So if you guys want to be a part of a great community of traders, great community of investors and like-minded people just like you that are investing inside that financial freedom and you want to be a part of that community all in one place, go ahead and click the link down in the comment section and that'll take you to my Discord. All right, so now that's going to take us to way number four and it kind of bounces off of way number one, don't be house poor, but option number four is don't be state poor. So if you can't afford to live inside of your state, sorry to say it might be a tough move but you might have to get up and move out. So let's just say for example that you're living in a $600,000, $700,000 house in LA, $700,000 house in Seattle, $700,000 out in LA would get you maybe the smallest little square house that you've ever seen might have a front yard, might not have a backyard, might be sharing that do place with somebody else, but $700,000 would get you next to nothing inside of LA and even with the prices going up in the state of Texas on housing, you could literally share your house in LA, Seattle, Washington, you could sell them in these expensive states, sell your $700,000 house, move out to Texas and buy 300 or $400,000 house and that house will be the size of this whole block. You will buy the whole block for $400,000 in Texas.   You're going to have upstairs, downstairs, pooling your backyard, all for $400,000. So yeah, with $400,000 it could get you a lot of snow.  Now you got extra $350,000 in straight cash that you can use to jumpstart your life that you could either invest by another rental property, by a couple Airbnb's out in Texas. So if you did make that move, you could buy a bigger house in a different state, start your new life, have hundreds of thousands of dollars on the side to start any other kind of business company, investments that you want to do on your own that you could get started in the next couple of months, guys. So if you are living in Texas and it's still very expensive for you, then you guys already know my next answer. Move to Cabo, Mexico, living out there.  I love going to Mexico. Every time I get the opportunity though, I could go out there and buy Corona for 65 cents compared to $2.99 cents at the gas station out here in Texas. So yes, if you are state poor in Texas, then consider moving to another country where you would have beautiful sights, beautiful oceans, and a beautiful breeze. So these are the top four things that I do in order to live successful inside a recession, even though I lost half of my income. I'm not house poor. I have my savings already set away just in case anything ever happens. I invest inside dividend-paying companies and I live in a pretty cheap state to where I could have a pretty big house for next to nothing. So if you guys are already doing some of these things, let me know which strategy are you using down in the comment section. I can't wait to read what you guys are writing down in the comment section. If you guys are using any of these strategies at all, I can't wait to see what you wrote. But yeah, guys, that's the end of my video. Don't forget to get to free stops from Weeble. Don't forget to get to free stops from Moomoo. It's free money, guys. Do not miss out on that opportunity. Go ahead, subscribe to this channel, hit the thumbs up button that helps out this channel more than you can even imagine. But other than that, guys, I'm Zeke. Bring you to Dream Green Show. I'm out. Peace.


You're about to hear a romantic drama, romance on the Sunday from Love Story magazine, featuring the love story girl in the role of Nadine Grayson. The scene is a small restaurant on Broadway in New York. Alone at the table is Nadine Grayson, feeling rather sorry for herself because although she's worked in the city for six months, she knows no one in New York. But now a tall, good-looking man in English tweeds approaches her table. I beg your pardon, Miss, but could you manage to go to Bermuda with me tonight? It's terribly important. I... I never saw you before in my life. Well, that's a pretty silly reason for refusing a job. Job? Of course it's a job. Didn't I explain that? I'm offering you $500 in addition to your expenses. Oh, $500? Well, I'm afraid things aren't very clear. Why don't you sit down and start from the beginning? All right, all right, but I loathe them. Beginnings, I mean. By the way, who are you? Oh, yes, of course. I forgot that. I beg your pardon. Here's my card. Oh, Norman P. Warren, publicity agent. I hope you know what a publicity agent does. Well, I suppose you get people their names in the paper. Yes, that's the idea. Now, here's the setup. I've been in business myself for three months, and my first client, in fact, my only client was the Windy Day cold cream company. I persuaded them to run a beauty contest, the girl with a perfect complexion. The winner of the contest was to get a trip to Bermuda. At the last minute, the cold cream people got cold feet and wanted to back out. But I was so sold on the idea that I offered to put up all the expenses myself. Then, if they were satisfied with the publicity, I'd get a contract. Go on. Well, we picked the winner. I've got every newspaper in New York down on the Dr. Snapper. She boards the Bermuda boat tonight, and then, bang, no girl. What happened? Mumps. What? Mumps. I tell you, mumps certainly work wonders with a beauty contest winner. Oh, please don't laugh. Here I sit, the boat sails in two hours, and ruin stares me in the face, unless, of course, you'll go in her place. Will you? Oh, I'd love to only. Will I have any clothes? Think nothing of it. There's a complete wardrobe waiting on the ship. You wear about a size 16, don't you? Yeah. Perfect. You keep the stuff, of course, when you get back. That's extra, besides the $500. Well, what do you say? Do we go? You're awfully impulsive, aren't you? Well, good Lord, who wouldn't be impulsive in a spot like this? How about it? Well, of course, I lose my job. Is it much of a job? No. Well, then, who cares? All right. I'll go. But how did you say the boat sails? Smoke. What's the matter? You're absolutely marvelous in that dress. Oh, I like it. Say, I knew you were pretty, but, good Lord, I never dreamed of anything like this. Thanks. Is your estate room all right? Oh, it's perfect. Everything's perfect.  Well, we'd better be getting down to dinner. There'll probably be a couple of cameras waiting for you at the door of the dining room. Oh. Then there's a dance afterwards, and you'll have to meet everybody on the boat. Don't be nervous now. I won't be when you're with me. Good girl. I'll be right beside you. And I have a premonition that I'm always going to be right around when you need me. Come on, let's go. I should say your first evening was a howling success. Oh, Norman, it was marvelous. You certainly did all right on the dance floor. You had more stags cutting in per minute than any other gal on the boat. I didn't seem to have a look in. Well, I guess I'd better go down. Don't go down just yet. Can't we make the night last just a little longer? Will I ought to? Oh, come on out on deck and scare up some moonlight. There must be bushels of it lying around, free for the ascii. Come on. Well, just for a little fire. That's well. Oh, there are a couple of chairs over there by the lifeboat. Oh, it's so beautiful.  You better let me wrap this rug around just a little chilly. Comfortable? All right, now, tell me all about yourself. Oh, well, there isn't much to tell. My family live in a little town in Maryland and I thought I'd come to New York and see if I could earn my living there. Seems as if there'd be more opportunity. But somehow New York's a pretty big place. I've been terribly lonely for the last three months. Oh, poor kid. Oh, but this makes up for it. It's so new and wonderful. Oh, this is nothing. We've just started. Wait till we get to Bermuda. Oh, even if there weren't any more. Just tonight has been more to me than... Gosh, I love it. Oh, Norman, please. Oh, just one kiss before you go. Please, darling, I know I haven't any right to ask, but I do want it so terribly. I... I... Until tomorrow and tomorrow and tomorrow. Good morning, Miss Grayson. Do you suppose I could persuade a queen of beauty to take ten turns around the deck with me? Why, I... The name is Harvey, Brooks Harvey, and I was one of the many you danced with last night in case you've forgotten. Well, I hadn't forgotten. I was... Well, I expected to see Mr. Warren this morning, but... Norman?  Gloria Gay? Oh, the movie actress? Yes. Didn't she know she was on board? No. Oh, that's right. You didn't know. You didn't see her show up at the dance last night. Oh, does Noah... Uh, I mean, does Mr. Warren know her? Noah? He was engaged to her once. Engaged? Sure, but she... She got sidetracked by a millionaire from Pittsburgh. However, from the looks of things, I say she's got her hook made of her Norman again. Oh. Well, that's the way, huh, kid? Listen, I hope you didn't take anything Norman might have said too seriously. Publicity men always make love to any girl they're featuring. Creates the right atmosphere or something. Take my word for it, and if Gloria wants Norman, she'll get him. She knocks him dead at 90 paces. I'm going down to my cabin. Pull yourself together, and I'll... Here they come. I can't. Just a minute. Nadine, I want you to meet a very old friend of mine, Gloria Gay. How do you do, Nadine? Isn't it amazing I never knew Gloria was sailing on this boat? Gloria, what's the matter with your publicity, man? He's no good, darling. No one's been any good since you left me. Well, it serves you right for fighting with me. However, I'll give you one more chance at my valuable services. Oh, William Norman, that's grand.  All right, darling, you're elected. Oh, by the way, Nadine, Gloria's consented to let you pose with her for Sunday's road-os. That's a break for us, because my new client here is always front-page news. Well, haven't you anything to say? Seems to me... I'm afraid you'll have to excuse Ms. Grayson for a while. She promised to play deck tennis with me. Perhaps you'd like to get a picture of that. Once in a while, I make the front page myself. Oh, here's my statement. Good night, Nadine. Thank you for a swell evening. Thank you for sticking by me. Don't thank me for that. It wasn't any hardship. Good Lord, you're beautiful. Well, good night. I saw them. Norman and that woman up in the top deck. Making love to her in the same spot where he and I... I was right over... Oh, how dare he! I wish I were dead. Who's that talking? Oh, it's a couple of men on the deck outside my window. Well, what do you think Warren will do when he finds his publicity scheme has been queered? Oh, my God. What can he do? He just wore the papers. The Gloria Gay was making a personal appearance and they'd be ready for her.  Oh, my God. Pretty smart of your company to spike Warren's scheme. Well, I suppose you couldn't let a competitor get away with it. Oh, it's a frame up. They frame Norman. I've got to tell him right away. Yes, who is it? It's me, Nadine. Nadine, what's the matter? Norman, I heard two men talking. Some competing company is arranged to have Gloria steal all your show. What? Good Lord, that old dish is unfair. Norman, why don't you ask Gloria not to do it? Well, perhaps something can be arranged. Don't worry. I think Gloria will be reasonable. But suppose she isn't. Then I'll be broke. Broken out of a job. Not nice, but it won't be the first time.  That would be enough, wouldn't it, for you to start all over again?  You're out of a job yourself, and still you want to give the money back to me. Not that I'd touch it, but... Well, the fact that you wanted me to have it. Well, the scheme just can't flop after that. Gloria's got to listen to reason. Nadine, it's all right. Gloria's not coming. Everything's swell. Come on, we've got to get off the boat. Oh, Norman, I knew you'd do it. You walk over to that man holding that basket of flowers. Right. And don't forget to smile. And look at the camera every minute. Do you like it? Tell them the idea that a beauty queen on the spot is better than a movie queen some other place. I'll try. Nice, Nadine. That's our boat over there. I know. From the terrace here, it looks like a bracelet of light against the black of the sky. It does, sort of. Norman, do you know what really happened to Gloria this morning? She was taking a shower, and the stewardess locked her trunk with the keys inside. When she came out, she didn't have a stitch to put on except the black negligee, and she couldn't very well get off the boat now. It took hours to get a locksmith in. Norman Warren, you did it. What? You did too. I can tell by the funny way your eyes are twinkly. Me? Locked Gloria's trunk. All right, all right. You brought the stewardess to do it, then. That's right. It's much too grand to waste time talking about Gloria. You know, Nadine, the moment you offered me that $500, I decided we were going to succeed if I had to murder the fair Gloria to get her out of the way. And it worked. I got a cable from the windy day, people, an hour ago, and I'm going to get my contract and a fat bonus besides. Oh, Norman, I'm so glad about you getting the contract. I need... Darling, we've only a minute before the grand march, and you're due to be photographed as Bermuda's prettiest guest, but in between publicity stunts is at where I'm... I'm taking time out to tell you that I want you to marry me the moment we're back in New York. Oh. That is, if you feel you know me well enough, it isn't too soon. No, it's never too soon to get married. Not when you love somebody as much as I love you. Nadine. You have been listening to a romance featuring the Love Story Girl and presented with the permission of Street and Smith, publishers of Love Story Magazine. Listen for the Love Story Girl in a new romance next week.


Talk about revolutions, talking about demonstrations. Iran, we've been talking a lot about Iran in the last few weeks. Iranian demonstrations, as far as I can tell, continue. They have not abated. The regime is struggling to figure out what to do about them. They are, interestingly enough, resisting going all out mass murder. You know, it was 13, 14 years ago when there were demonstrations. They killed 1,500 people in a very short period of time and crushed them. They have not killed, as far as we can tell, anywhere near that number. As of now, we suggest that there must be some resistance within the regime to going all out military force. So it is interesting that there was kind of a stalemate over there. The regime has not brought out the tanks into the streets. They're not mowing down people yet. They're all mowing down selectively, but they're not doing it systematically. And the demonstrations are just continuing. It is also interesting that the Iranian soccer team and the World Cup feels brazen enough to actually not sing the national anthem in the first game. The second game, they were seen singing it kind of reluctantly and softly. I think they were threatened if they didn't. It is also interesting. And here's a thumbs up to the American Soccer Association, or whoever's responsible for this. But the United States has started in advertising. It's in the same bracket as Iran. And it's actually going to play Iran either today or tomorrow. I can't remember. Anyway, it's in the same bracket. So the U.S. soccer is constantly advertising the game, U.S. Iran, that's coming up. When showing Iran in the bracket or in advertising the game, they have taken the Islamist symbol from the Iranian flag. They've excluded it. So the flag is the flag just of the colors without the Islamist symbol in the middle, which is basically an attack on the Islamist regime in Iran. Now, who would have thought that the American Soccer Association would be that courageous and that brave? I mean, that is truly stunning and truly amazing of the Americans.  As I said, these little moral support, actions of moral support make a difference, make a difference. And so they just took out the symbol. Hopefully the United States will crush Iran in the game. Again, symbolically in the name of the demonstrators. But moral support, the Biden administration, I don't know, sanctioned three individuals from different cities in Iran where they have been particularly oppressive in terms of the protests. I don't understand these selective sanctions. I mean, sanction everybody. It just don't, you know, make it clear. Why are we allowing them to come for the UN and to UN meetings? Why are we these Iranian monsters? They should be banned from all flights to the United States. I mean, the United States could take much stronger measures than it has, but no administration has ever done that. But yeah, let's continue supporting the Iranian women. Let's continue supporting them. Interestingly enough, the Qataris who, as I think I told you, are basically allies of Iran. The Qataris are banning where the United States has a huge presence, military presence, but the Qataris have banned demonstrations against the Iranian regime. So people with shirts, women, freedom, something, you know, people with shirts are forced to take them off if they come with banners or any kind of, any kind of signs or anything like that. They're confiscated. So the Qataris are doing everything they can to make these games nonoffensive to their ally, the Iranians. There's also been a call by many supporters of Iran to kick the U.S. out of the World Cup. The idea is that a country cannot disparage another country during the World Cup, that can't disparage politics or whatever. And this idea of showing the Iranian flag without the Islamic symbol is viewed as disparaging. And therefore, on Twitter, you can go on Twitter and you can find all these people calling for kicking the U.S. out of the World Cup. I've not seen any attempt by FIFA to do that. So, but kudos to the Americans for doing something like that, even though it's a small little step, it matters. These things matter. So, yes, we continue to support Iran and continue to be excited about the demonstrations there. I continue to be hopeful about their success. Again, I think it's a long shot because they need somebody from the inside to help make the change. But you never know what will spark. More violence, some people getting killed, hanging of some of these protesters. Something could spark these things to just explode and lead to a real revolution.  Thank you very much for your attention on any one of those channels. Also, if you'd like to see the Iran Book Show grow, please consider sharing our content and of course, subscribe. Press that little bell button right down there on YouTube so that you get an announcement when we go live. And for those of you who are already subscribers and those of you who are already supporters of the show, thank you. I very much appreciate it.


БЫТЬЯ ГЛОВА ПЕРВЫЯ This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information on auto-volunteer, please visit LibriVox.org. БИБЛИЯ СИРЕЧ КНИГИ СВЕЩТЕННОГО ПИСАНИЯ ВЕТХАГО И НОВА ГУЗАВЕТА КНИГИ ВЕТХАГО ГУЗАВЕТА ПЕРВЕ КНИГИ МУЙСЕЮВЫЙ БЫТЬЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ ГЛОВА ПЕРВЫЯ DVER ZHEKOVCHEK SOTVORISHE OTSTRANAY OBITALIŠTA DVOKROVNA I TREKROVNA SOTVORIŠI VNEM AZ ZHESE NA VEDU POTOP VODO NA ZEMLU POKOBITI VSYAKO PLOČ VNEJ ZHES DUH ZHIZNI POD NEBESEM YELI KASUTI NA ZEMLI SKUNCHAJU CYA I POSTABLU ZAVET MOJ STOBOJU VNIDI ZHEKOVCHEK TAY I SEINOVET VUY I ZHENAT VUYA I ZHENAY SEINOVT VUYH STOBOJU And I POSTABLU ZAVET MOJ STOBOJU VNIDI ZHEKOVCHEK TAY I SEINOVET VUY I ZHENAT VUYA IZHENAY SEINOVT VUYH STOBOJU YOD VSECH SKOTOFF JUR This recording is in a public domain, by Tiaglova7, read for LibriVox.org. 1. The Book of Moses, chapter 7. 2. The Book of Moses, chapter 7. 3. The Book of Moses, chapter 7. 4. The Book of Moses, chapter 7. 6. The Book of Moses, chapter 7. 7. The Book of Moses, chapter 7. 7. The Book of Moses, chapter 7. 7. The Book of Moses, chapter 7. 8. The Book of Moses, chapter 7. 9. The Book of Moses, chapter 7. 9. The Book of Moses, chapter 7. 10. The Book of Moses, chapter 7. 11. The Book of Moses, chapter 7. 12. The Book of Moses, chapter 7. 13. The Book of Moses, chapter 7. 14. The Book of Moses, chapter 7. This recording is in a public domain, by Tiaglova9, read for LibriVox.org. 1. The Book of Moses, chapter 7. 2. The Book of Moses, chapter 7. 3. The Book of Moses, chapter 7. 4. The Book of Moses, chapter 7. 5. The Book of Moses, chapter 7. 6. The Book of Moses, chapter 7. 7. The Book of Moses, chapter 7. 9. The Book of Moses, chapter 7. 10. The Book of Moses, chapter 7. 11. The Book of Moses, chapter 7. 12. The Book of Moses, chapter 7. 13. 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You can see mine, and I will make this active. Here we go. Okay, so I'm gonna talk a little bit about quality assurance at Nevcan. So this is the Nevada Climate Ecohydrology, excuse me, Assessment Network. And I'm gonna just hopefully get through it pretty quick, talk a little bit about the network and how we are keeping long-term observations running, hopefully in a uniform manner. So a quick overview of the network. We have 12 core sites that involve 10-meter towers with a number of sensors. We use Campbell Scientific Data Logger equipment, and we operate on a three-second scan interval for most of the data loggers. So we're integrating over a very high-time frequency there, and we run one minute, 10-minute and 60-minute tables that are compatible with different regional meteorological data standards that we share data out to. In some cases, we have over 50 variables on a site, depending on what we're trying to measure, and we're connected in real time. We basically ping the data loggers every five minutes and bring the data back. Should the network go down, we have over a year of local storage for all of the tables on board the data loggers. We also have cameras that are point-tilt zoom that every hour in the daylight, they rotate around, take a picture of 20 different scenes, including the cardinal directions, instruments, and items of interest on the sites. We also are connected to the National Phenology camera network that the Harvard Forest, Harvard University, University of New Hampshire group has been maintaining, and we, I think we have four of those cameras out on the network right now, and every 30 minutes, we upload both Nair IR and visible spectrum images to that network. We're measuring things of interest besides weather, like tree sap flow and diameter, snow water equivalent, and this sorts of thing. By now, we have, since 2011, we have over three billion sensor observations in the database, and over four million camera images, not counting the phenology cameras. So we're starting to get to the point where we have quite a bit of data going on. The question that we had to answer as we built this was really how do we construct a useful and quality, a long-term up here, intermountain west of North America, this region called the Great Basin, which is kind of a high cold desert, down in the south where it transitions into the Mojave Desert near Las Vegas, it becomes a hot desert. Still gets cold in the winter, but it gets very hot in the summertime if you've ever been to Vegas. Our sites cover quite a range of elevation from down below 1,000 meters in the south, up to 3,300 meters near Tree Line, up in the North Transect. So we're really looking at mountain gradients and changes over vegetation zones in mountain gradients. So under these conditions, building and maintaining a network is difficult, and in theory would require a lot of man hours to do. So how do you reduce the expense of maintaining this kind of a network when there's this issue of sustainability and at least in the United States, getting the National Science Foundation and the federal government in kind of a central mode to sustain these kind of long-term networks is proven difficult even within things like the LTER program. I would say every single LTER data manager would argue that they're significantly underfunded for their mission. So when planning and deploying these kinds of sensor systems, uh-oh, looks like Microsoft PowerPoint's trying to stop on me.  Sorry, folks. Maybe I have too many applications open, but all right, let's see what happens. Okay, hopefully we can see this. We'll go through to here. All right, please don't stop PowerPoint. So when we're planning these kinds of system deployments, so whether you've gotten a grant to do this or you're pursuing a grant to do this or you want to expand your network, before you jump off the dock and land in the water, there are kind of a few items that you want to spend a lot of time up front in and that's engineering the systems, making sure that you have the right people on board that can handle the variety of systems and the kind of the long-term maintenance issues. And I think really hard about that sustainability. So when I talk about sensor networks to people, I really get to these three key issues, which is what's your science question? What are you trying to acquire the data for? Make sure that you have the right sensors for the job as well as the siting considerations and where you're locating them. Think about what your level of acceptable reliability is. Are you okay with it being down for six months, for 12 months, for two minutes? And design the support systems to that. So the support systems, things like the structure, the protection from wildlife or from electrical disturbances, things like redundancy need to be concerned, you need to be concerned with. Power supply, of course, is one of the big issues. And then there's this idea of connectivity and telemetry, which is really the force multiplier when it comes to both accelerating the science that you're doing, getting the data back sooner and getting to analysis sooner and evaluating issues with the data sooner, as well as checking into problems via that connectivity and deciding what you need to bring to the field the next time you go out. So having real-time connectivity to sensor networks really reduces the turnaround time to deal with problems and it increases your overall data quality. That management, how to keep it running. So what are the tools that you're gonna implement in your data workflow, both for acquisition, as well as sort of near real-time visualization, processing and archiving? What's your access plan? How often will you be able to get out to these sites depending on where they're located? And then of course, what are the quality control and quality assurance tools that you use to both check the data and to continually enter metadata so that if there are issues you can go back in or if you're sharing the data people can evaluate how that observation came to be. So a few of the planning acquisition items that I like to go over. I encourage people to review kind of the state of the art. So get involved in a community of people that have been doing this. So for instance, the LTER group that forms the cluster that we're in, a lot of those folks have been doing this for a long time. So they're familiar with kind of what the state of the art is. You can see people like Paul just presented on pushing the boundaries of new technology and how to make this stuff more accessible, more interchangeable, easier to deploy, more uniform. Choosing an unbiased deployment of course is very important. So if you wanna measure kind of air temperature associated with the free atmosphere, put it up on a mountain ridge, don't put it down in a mountain valley, things like that that sometimes we take for granted. And then of course, scale to what you can sustain. Make sure that if it's a seven hour drive away from your home institution that you know how often you're gonna be able to go out there, what seasons you can access these sites in, and then what you're willing to pay to do over time in terms of both field time for a technician and gasoline for the car or whatever it is to get them out there as well as replacement parts. So the most common failure points that we've seen of course have to do with the power supply and then structural failure, animal or human disturbance or other environmental disturbances. And then I group misapplication in with the failure points because quite often we're installing sensor networks to measure something specific for kind of a specific science question or application. So whether that's storm water flows in Michigan like what Matt Bartos does or whether that's air temperature associated with let's say fire weather out where I live. If someone wants to leverage some of those sensor data to do something else, for instance, maybe someone wants to look at air temperature across agricultural fields out in the valley below this tower. Well, the temperatures up at my tower aren't gonna necessarily tell them very much about sort of the ecological window of temperatures going on down in that valley. And so misapplication of sensor network data is a common and real thing across the sciences and we need to be aware of that. And one of the solutions of course is really good metadata and making sure that when we go to use data that aren't from our particular observation network that we talk to the people who made those observations and find out kind of what their purpose was and what the assumptions were. When we talk about planning support systems, think about building the structure for the worst case scenario. So is that three meters of snow? Is it one meter of snow? These sorts of things. How are you gonna power the system? Are you going to have things like solar power or small wind generators? Do you have line power available? What are the weak points in those systems and then how do you adjust for that? What kind of communication can you use? So we're gonna do burst satellite transmission. Are we going to do a graduate student with a combat flash card? Or are we going to do our own wireless network? We're going to connect to cellular communications. There are all these different options and it's always best to have two to three options on the table if you wanna run a long term network. Tracking system health is very important. So do you track all of the battery voltages, perhaps the battery temperatures, temperatures inside the enclosures, relative humidity inside of the enclosures. Being able to see those parameters on a day to day basis really lets you evaluate whether or not your support systems are actually doing what they're supposed to do and you could predict whether or not you're going to have a failure sometimes based on the behavior of the support system. Then of course, where did the data reside? Your support system for the data themselves is that a central database back in a lab is that a computer under a desk is it something in the cloud that maybe has a bit more resiliency to it? All of these sorts of questions need to be addressed from planning. One example, so something practical for the field people in the audience, how do you make a power redundancy for one of these remote systems? Well, in this case I have two parallel redundant PV systems but you could substitute wind energy or you could substitute DC conversion from a hard line into one of these systems as well and you get the same result. Basically two DC systems that have their own batteries or own charge controllers and their own power sources that then are combined passively. In this case, if you're an electrical diagram geek you see that we have diodes here. So there are these passive combiners that you can purchase that use diodes to combine the voltages so you don't have moving parts. The side of the system that has the highest voltage at the time is where the current will flow through. It's pretty straightforward. All of that can go into your sort of master load to voltage disconnect and out to the production equipment. If you have a system like this you just built some redundancy because you can lose a good portion or any one of these components along one of the parallel systems and you still have system functionality when you can only get out to a site for a month or two out of the year if you're at high elevation for example or it's just expensive to travel there. Spending just a little bit more money up front and having more reliable data kind of worth it for me and it's saved my, there's one example of how you can combine these. That's, this has saved my bacon several times having this kind of design. Low power protection is very important so batteries in the field die if you discharge them and then get them very cold. So you want to avoid discharging them in the first place. So one way to do that is to use low voltage disconnects and that's a type of basically inline sensor that will detect whether or not your battery voltage has dropped to a threshold that in some cases you can set in some depending on the product, their preset and it will shut off the current to your production equipment once you get to a certain threshold. You can use several of these to tear different items that have different priorities on your system. So cameras and sap flow sensors and radios aren't as important as let's say the meteorological data and the data logger associated with that. So I can tear my low voltage disconnect down so that the very last thing to turn off if I've lost my charging source is the data logger itself. That's another example of maintaining kind of a high level of quality on your network. Okay, networking all the things for better science is important. I'm going to accelerate it here because we are so far behind and we only have a few minutes left. And so I'm going to go through these next slides quickly. I apologize for having to rush but sometimes that's how it goes. So when we network all the things we really do network all of the things. And that's everything from the power systems to the experimental sensors to the production data loggers themselves and everything in between. Okay, researcher data management. So this is something that's very important because researchers don't usually think about data management. My paycheck as a scientist doesn't necessarily depend on having the best data management as long as I can publish then maybe I won't perish. That's changing now as we all know. And so researcher data management is something that we're all playing catch up on hopefully everybody on this call and in your organization is very concerned about. So a typical workflow that we've developed inside of the cluster for instance looks like this. So you have sensors at a field site. You have communications layers. You have downloaded files. You have various checks before you bring that into a level zero or raw database. Then you can go through various quality control processes that have management both for automated and for manual. And then you have a production say level one database where you've got a delayed release that has quality control flags associated with it. Those are data that you would make available out to maybe the general public with a caveat and that sort of thing. And then of course you have kind of a quality control database that you also want to be maintaining at the same time so that people can access the checks that have been made and what sort of flags have been thrown and what those mean. And also the quality assurance management interface. So how you collect things like installation information associated with instruments and systems on the site. So this is one application that one of our students that's inside the cluster is developing. This is a mobile application that connects directly to my particular systems database and I can enter information, take photos, add documents to all of the deployments on my site and those are tied directly to the data streams and the database. So I'm pretty much going to wrap it up here. The true cost of sensor deployments actually has a lot to do with things like lost data having to constantly repair, losing momentum, you're losing science discovery and all that sort of thing when you have failures. So you try to minimize those costs and try to upfront invest more in not just your sensor system but that workflow and the expertise and the kind of the software systems that you wanna use for tracking the data and the quality at the same time. If you invest upfront then you're likely to have quality data that costs the same or less over the long term. Researchers have a lot of concerns on their mind and usually they just wanna buy the equipment spend the money and then it's sort of plug and play. They get their first publication after that they don't care about it or maybe they do and they just didn't think that there are these management issues. So these are all things that we try to make the researchers in our lives more aware of as they design their particular systems. So we'll go to the questions and we'll take I think we'll have the rest of the conversation at this point, Ming, I'll turn it back over to you. And this is just our last cluster poster from I think the winter 2017-2018 meeting kind of gives an overview of what's been going on in the cluster. So thank you very much and we appreciate the time and attention.