Live from the Fairmont Hotel in San Jose, California, it's theCUBE at Big Data SV 2015. Welcome back everyone. You are watching theCUBE, our flagship program. We go out to the events and extract the signal from the noise. We are live in Silicon Valley for the Big Data SV. It's our second event in Silicon Valley fourth overall. We're now running Big Data SV here in Silicon Valley and Big Data NYC in New York City to really capture the geography differences of what's happening in the Big Data business, how is it evolving, and the implications for business and innovation. I'm John Furrier with Silicon Angle. My co-host here is Jeff Kelly, Chief Data Analyst with Wikibon.org. And our next guest is Dr. Satyam Priyadarshi, Chief Data Scientist at Halliburton. Welcome to theCUBE. Thank you. Big Data, I'll say the science is at the root of it. Data science is not a new field. Data processing has been around since mainframes and data has been always a big part of computing. Now more than ever, data is proliferated in all kinds of forms. Certainly real time in the moment and systems of record, systems of engagement. However you want to call it, data is everywhere. And you have a great background in data science and your company certainly doing a lot of stuff there. So first tell us about your role at Halliburton as a Chief Data Scientist. What does that mean? What are some of the things you work on? I'm sure you can reveal all the secret projects you're working on. In general, what is the main focus for your role? So Halliburton is a oil and gas service company. So we actually play in what is called the upstream oil and gas. Which means exploration, drilling and production. The oil and gas industry is much big. So technical terms people talk about upstream, midstream, downstream. And downstream is where we all come in when we buy the gas. And we find this fall into the midstream. So Halliburton is in the upstream business. And it is a very, what you call scientifically driven company. So in order to do exploration, you do what you call the seismic waves and you convert them into some meaningful analysis. And that is all driven through first principles and empirical models. And because you want to predict where you will go and drill, right? So if you think of it, we do collect quite a significant amount of data like the oil and gas industry has said. But they have been doing mostly scientific aspects of it. But as we know, the data is growing for everyone, not only oil and gas, but for every industry. And data driven models, what I call them, augments all these first principle models, right? First principles are when you understand some phenomena is happening because of certain event or attributes. You want to actually augment that with all the data that you have in principle, right? So oil and gas is a big data company because there's a lot on the line, right? So Jeff has covered this with the Internet of Things. That's a big application. So you're in the middle of the Internet of Things. How deep are you in? I mean, obviously it's a lot going on.  Is there certain practices in place? Are the systems all out there? Where in the journey of that are you? So I don't think anybody can claim that they are mature in this Internet of Things, right? This is still a evolving field because especially if you're talking about sensors, they are evolving day-to-day. And the amount of data that is being collected at different phases of this oil and gas is going to grow. So we can't say that we are mature. We can't say that we are in the elementary state. But so we are somewhere in the middle. Almost all oil and gas industry would be like that. And you've got probes up. But you've got action going on. Absolutely. You've got some stuff happening. Absolutely. So Internet of Things is, again, it's a big word. Whatever we are talking about, which phase we are talking about. So I think there's a lot more to learn in this space. We'll talk a little bit about the evolution of the oil and gas industry from the way things maybe were done in the past in terms of exploration and deciding where you're going to put the drill on the ground and those kind of questions from maybe a less data-driven approach to some of the things that are happening now. Because obviously it's changing a lot, as you said, it's still evolving. But kind of walk us through how it's evolved. I mean, this is an old industry, obviously. It's been around for a long time. We're not talking about the little web startups that are just kind of cropping up. This is an age-old problem, really. How has it evolved? And how are we kind of taking those next steps? If I can add something to Jeff's comment, the value piece, because in your industry, there's a lot of value. So data can add a lot of value quickly. So I would just bolt on that real quick to that. So if you think of it like at a very high level, right? So you do the seismic studies, which is basically you have sound waves created by the sensors. And then the sound wave is collected in some digital form then converted into visual forms to understand what is lying under the earth, right? What clay, water, whatever you want to. The geological studies, geosciences studies take place. And so we employ a lot of geophysicists and geologists to explain to us what is there. Based on that, they figure out or they predict saying this is where likelihood of oil is going to be. And based on that, then they do the land surveys and all the legal governance, right? It's a highly regulated industry, right? So once that has been figured out, then they plan to do what is called exploration drilling. And then they try to figure out if yes, whether how big the size of the reservoir is likely to be. And once that is done, then they go into the production phase and then the completion phase, which is like trying to put the drill well and the oil comes out and all those chemical treatments and things like that. So it's a highly complex process if you think of it. But data is collected in all these phases. So data at every step is important for us, right? It's been collected, it has been analyzed from a first principle model, somewhere you want real-time analytics, somewhere there is no real-time part of it. But again, if you think in big data terms, if you look at the volume velocity problems, right? So when you talk about velocity by default, when you say big data and velocity, people assume that the data has to be real-time. That's not the case, right? The data can come in real-time, you may not have to analyze in real-time. Data may not come in real-time, but your predictive models will have to be done in real-time. So it's a two-by-two metrics, as I say, right? Real-time collection, real-time analysis, right? So which phase of this oil and gas life cycle you are in, it depends on that. It's not one thing that fits across the life cycle. Seismic, for example, is another case, right? Data is collected and then it has to be analyzed thoroughly. So no real-time component is needed really, right? But the calculations or what you call the attributes that they're calculated, they could be done much faster. So talk about the properties of data, obviously, with Internet of Things and or probes and or in data in general. Passive data, people are used to.  What are active data? The role of active data, because with mobile devices, with edge devices, if you will, people or things, you have a lot more activity going on, engagement or things. So active data is coming in and you have passive data, a lot of data, passive data. So what's the relationship in your mind and how do you look at that paradigm, active versus passive? So what I understand, so when the drilling process is taking place, then we are collecting a lot of active data, right? It may not be everything is real-time, but it's still pretty active data, right? But the seismic study was the passive data. But at the same time, we are also collecting seismic data when an activity is happening. Connecting them can actually help us actually optimize the operations. And what happens is it's not always, depending on where the field is, what the concept is, people talk about digital oil fields when the data becomes really more real-time, then the challenges will show up. So right now we don't see all those big challenges from Internet of Things yet, but I think as more and more sensors are deployed, then we will see a lot more of those. Is the evolution of Internet of Things in your mind bottlenecked by smart devices, the connectivity, or just state of the evolution? So I started saying that we should not call the term Internet of Things. Internet of Things is good, but I think it should be what is called emerging technology devices. Because some may be connected on the Internet, some doesn't have to be connected on the Internet, right? Because all of them actually contribute to it. So the term Internet of Things narrows down the focus. So in terms for digital oil fields, I think these devices should be called emerging technology devices. Because it could be modification, what is called the bottom hole assembly. It could be what you call the sensors on the wires. So many properties we are measuring. So what device will come out, we don't know yet. It doesn't have to be really connected through the Internet, but they have to be. So most of these are emerging devices in your mind. I mean, Internet of Things is just kind of, but you say that's more of a buzzer. People get up on that. But that's a broad term, but so is emerging devices. So how would you classify the difference with emerging devices more generic or emerging as a non-old, new? No, I think it's the new things. Because as we learn about how to predict something or learn what other attributes are necessary, if those metrics are kind of programmed into those send devices, then we can get much more rich data, which will be very helpful. This is why I call emerging technology devices, because it's not only just about creating a sensor, because we have to learn about the data bits as well. I wonder if you could comment on kind of the market and the vendor ecosystem out there. There's a lot here at Strata Hadoop World. There's, I don't know how many vendors out on that floor. As a practitioner, how do you view what's happening in the market? Obviously there's a lot of marketing battles going on between all the different companies for mind share and that kind of thing. From your view as a practitioner, do you listen to all that noise? Does that matter to you? What is your take on how the ecosystem is evolving? And is it evolving you think in a healthy way? So I've been doing this big data since 2005 or something. So I do keep up with what's happening in the industry because it's important to watch what kind of tools and technology they are deploying. Some are mature, some are not mature. But at the same time, what interesting part that one has to look as a data scientist is what combination of tools can you use? You cannot be tied to one particular tool, right? Because especially with the technology evolves very fast. Especially if you look in, since we are in Bay Area, things come up very fast, right? Earlier two years back, nobody would talk about streaming analytics that much, but now there are so many funded companies, well-funded companies. So the thing is, can I deploy that or not? Is it useful for me? So those are due diligence processes that one has to take and it's not always easy because one is the hype that is created around a process and if you think of it like somebody says, oh, social media, I can do analysis, but does it actually apply to me? Right, one of the concept that I talked yesterday was that value in the data is important and how does the value come, right? The value, for example, seismic is much higher when the volume is high and the value is much higher compared to social media where the volume is okay compared to seismic. The velocity is much faster, but the value in terms of total business value is much smaller compared to a oil and gas industry. So I cannot take a solution that has worked 100% in a social media analysis that will actually apply to my seismic or drilling. But a combination of these tools can work for it. So in order to create that combination that would work, you have to be aware of what's happening in the industry. Well, that's also a challenge, right? Because you've got so many different players. And I think part of my thinking around this, having talked to a lot of practitioners, is that in fact, having to cobble together the different solutions is a challenge for most enterprises. I mean, we're seeing the global 1000, the really big enterprises are out there adopting Kadoop and some other tools. But when you get beyond that, I think a lot of companies don't have the expertise to do that, they're not equipped for that. And I think they're waiting for the market to mature a little bit, so you'll see some consolidation, you'll see a little bit more of a platform built out versus point tools. How do you approach that? Are you comfortable bringing together the different point tools, or would you like to see a more platform approach? So the business unit that I work on, we also build platforms. So we know the platform business well, and then we're one of the leaders in that sense. But I don't think some of us who have been around, I don't think we wait for somebody else to build the platform. I think we know enough what was needed from a business point of, one of the things one has to look as a data scientist, a chief data scientist to actually know the domain. Because if you don't know the domain, then you can pick up tools and do whatever you want, but that doesn't make sense. So if you know the domain, then you can actually find out what's the right tools and technology to deploy, and then connect it. And this is important, right, for any data scientist for that matter. It's not about just what you call coding and running the algorithm, right? It's about, I think a full data scientist is one who understands the business also. Yeah, talk a little bit about that. Obviously you need the analytics skills, the data science skills, but you've got to have the domain knowledge, and people talk a lot about the communication skills as well to kind of get across. You find a great insight in the data, you've got to communicate that to the business. Talk a little bit more, expand on that to kind of what you see as the key characteristics of a successful data scientist. So successful is one, one is of course, if they know computing well, then that's a great value. Second is understanding the domain, and the third is visualization of patterns, right? We all can recognize patterns, but everyone else looks at the patterns differently. And then of course, communicating what you see in the patterns for actionable insights. An important part is actionable insight, right? You can see a pattern, but there's just no meaning, right? What do you do? And so how do you understand whether it's actionable or not? That's important. So that comes unless you know the domain, you can't talk about it. So these four things are needed for a data scientist. And of course, communication, the communication is multiple layers, right? One is able to communicate with their peers in the data science realm, where you're programming or running predictive models or building predictive models. Second is talking to your product people, software, right? Or the business people, sales people, because they are the ones who are going to sell these solutions, right? So if you can't explain to them, we have a challenge, right? And then of course, communicating to the executives, which is also a challenge, because if a data scientist will say, okay, I can solve this problem, and they'll say you have 20 days, you really can't do that, right? You predictive models don't build in a time frame, so. But the ability to communicate in that 20 days, what you can achieve, what is my far side vision of the problem and what's my near side? What can I solve in the near side? That is what you want to solve first and show them some results and then go forward. And then simplifying the visualizations, right? When you create patterns, okay, fine. You can create this complex fractal diagram, whatever, right? But then how do you explain it, right? Bringing it down to what I call the kindergarten level, right? Sometimes helps, because that is what most people can understand, that okay, what I'm, because eventually we are running a business, we are not an academic institute in that sense. So for, especially in business world, you have to really bring it down to a level that everybody can understand and see the value in terms of profit, innovation and revenue and cost. Because eventually you have to boil down to that. So those are, that was a great, great description of what's required of a data scientist. But of course we know there's a shortage of those types of people out there. What's your view on how we're going to expand that pool of data scientists? Is it a training issue? Or is it, do you have to come at it through real world experience? Or can you learn it in the classroom? What's the approach you think is going to be needed to really expand the pool of data scientists? Yeah, so I think in my past life I've built some data science teams. So I don't know if it's a real shortage, but yes, yes, it looks like a shortage of people. But I think a lot of people who have done PhDs and all, they can actually be very easily trained into being a data scientist and given with the business knowledge. The other concept that I say is that complete data scientist could be two people or three people, right, a business person, a computer person, and a product or whatever you want to call it, but those three people can combine, right? So the team approach? Team approach, because that is very important. Otherwise, you can have a lot of people doing computing, right? Nowadays if you know like in Silicon Valley there are so many companies will claim, okay, data science as a service, right? Or you can download this package and you can run it to your self machine learning, fine. You can learn machine learning, but what do you do with it? Right, so how do you scale that? So look, that's a good setup. Now, companies that need to scale that. What's the strategy there? What do you see as the best practice advice that you'd offer? So some of them bringing some PhD level people, right, with you and then bringing, partnering with them, the people who have spent their career in business intelligence, for example, right? They have been, some of these people have done a lot of analytics work, but they have been doing in sort of, question has been given to them, they find the answers. You have to flip that model saying, here are the answers, how do you ask the question? Right, so it's a reverse order, basically, but that means you're forming the patterns and then saying, why do I see that? So they can go to the business guy or a product guy and ask them, I see this pattern in the data, can you explain to me? So a lot of business intelligence people could be actually converted into data scientist. Yeah, well, that's interesting because I've noticed, if you look at LinkedIn, a lot of the people that were previously calling themselves business intelligence professionals, changed that wording to data scientist, and it's, I think it's definitely possible, but it's not as easy to just flip the board. It certainly offends real data scientists, right? I mean, so it's like, what do you call data scientists? I mean, is there a test? Is there a... Well, I think it's, obviously I want to get your point. Certainly I want to get paid. I think it's results, I think it's results. Can you... So I think data scientist is not a new term, but it has become more popular. But if you think of quantum physicist, quantum chemist, computational chemist, a lot of atomic physicists, they were all data scientists because they were generating huge amounts of data. My background happens to be quantum chemistry of a PhD in that, and we did a lot of data, and from there we were trying to whatever predict whether a drug molecule is interacting with the protein. It's all about analyzing the data. We talk about visualizations. We were all looking at this structure of DNA, trying to figure out where this molecule will connect. So we were all data scientists, but we never used the word data scientist. We were called scientists, right? But nowadays people have just put this label on top of it called data scientist.  Certainly your pay just went up in your job security. So, but no, there's an element of hype around it. It's legit, there's data growth going on. Absolutely. There's levels of science. But certainly the mindset is needed in terms of knowing the answers and then asking the questions. That is certainly a flip side of it. From a business intelligence to data science. It's essentially the chemistry back and we're looking at real reactions and that's a mindset that's really compatible with how data is, because there's a lot of fusion going on around interactions of different data sets now. Integration is a big part of it. So final question as we get down to our time here. What do you like in the market right now? As you look out in the landscape, you're here kicking the tires. You're here at the Big Data SV event looking around and looking at all the content at Stratoconference and all the different startups that may or may not be around. If they don't get sales and there's some good technology tracks, what do you see and what do you like and what don't you like? Oh, that's a tough question. It could be anything like, so I think that... Obviously you don't have things we know you don't like that we just talked about that. But in general, what are you liking? What's getting you excited? The thing that is interesting is that yes, a lot of startups especially and some companies are trying to make it easier to use machine learning. Machine learning is nothing new. It was done in 1960. So we're not doing anything special, right? But what it has enabled is leveraging all these open source technologies which people or even organizations were not ready to it. So what Silicon Valley has done is this, bringing this open source technology, making this hard looking science easy to use is a great trend because it can benefit a lot of companies, right? Because one of the things... And compute is now available. And heavily compute is available easily. You don't have to worry about buying servers and all. So it's making that layer very easy, right? As they call it, like starting a startup is not a problem. Technology is not the problem, right? It's the idea and then its execution is the problem, right? So that is a good part of it. Difficult part is that when these solutions are coming up, if you don't know the domain expertise or how do you apply, how do you interpret, then it looks like, oh, we are wasting so much energy. So understanding the real problem, but that coaching doesn't come from this, many of this so-called application or what I call tabs like technology, application, products and solutions, right? The tabs, creators, that training doesn't come for the domain, right? They will pitch in their solution, saying, oh, we do this fantastic algorithm and it can create these beautiful graphs, right? But then how do you interpret? You need the domain. Yeah, you need to integrate that in. And that education gap will be the problem. So you're excited about the technology growth, the opportunity, but yet there's a practical integration. A practical integration. For solutions. The products aren't necessarily solutions. So, absolutely great. Dr. Satyam, thank you so much for coming on theCUBE. We really appreciate your candid practitioners perspective. We love to talk with folks actually implementing and this is really the reality. It's where the rubber hits the road. That's where the values created and a lot of great opportunities. Thanks for coming on theCUBE. We really appreciate it. We'll be right back more with live coverage here in Silicon Valley after this short break.


Hey what's up everyone welcome to another video my name is Dylan and I'm a cycling coach at CTS and today we're going to be talking about training with heart rate versus training with power. I'll talk about some areas where heart rate falls short and how power solves these issues but I'll also talk about some instances where heart rate data comes in handy and how looking at heart rate data and power data together can be really useful. The overarching theme of this video as you'll see shortly is why you should be training with power but at the end of the video I'll talk about how sometimes power data can get in an athlete's head and actually be a detriment and I'll talk about who shouldn't invest in a power meter so be sure to stick around for that. If you're new to this channel I make weekly training topic videos going over tips and tricks that I've learned in my 12 years of racing experience that have gotten me to the top of the ultra endurance mountain bike game in the US and as a cycling coach at CTS. I also go into the science and your training questions so if you want to learn how to get faster or just more about sports science in general be sure to subscribe and hit the bell notification so you don't miss anything. If you have a training question or a topic you'd like to see me cover in a future video be sure to leave it in the comments section down below. I do my best to get to all the questions in the comments. I train athletes using both power and heart rate but whenever one of my athletes asks me whether or not they should get a power meter my answer is always yes. In fact I recommend that you upgrade to a power meter before you make any other upgrades to your bike. Man Dylan I don't know power meters are just too expensive and I'm trying to upgrade to a pair of zip carbon wheels. They're supposed to be 10 grams lighter and 3% more aero than last year's model. If you know how to use a power meter it'll make you as a rider significantly faster and fitter while upgrading other equipment on your bike will just make your bike marginally faster. Hmm yeah you know that is a good point but um you know I think I'm just going to get the zips man. Now to be fair if you're just starting to get serious about training and all you can afford is a heart rate monitor then that's a lot better than nothing. Heart rate at least gives you something to base your efforts off of and you can train off of heart rate zones. However there are a few major flaws with training with just heart rate. For starters heart rate can be extremely variable. Heart rate is affected by sleep, fatigue, caffeine intake, altitude, temperature or even your mood. What this means for your training is that even if you're in the right heart rate zone your power could be completely off. People who regularly train with both heart rate and power often find that their heart rate could be completely different from one day to the next for a given effort. Let's look at a quick example from my own training. On this day I was fairly fresh and well rested and I did a five hour endurance ride and my average heart rate was 147 and my average power was 199. Now let's look at a day when I was more fatigued. Same ride five hours endurance and very similar power this one being 205 watts but look at the heart rate I averaged just 129 beats per minute so we're talking about a 15 to 20 beat per minute difference for the same workout on different days. This is the kind of difference that could have you completely training in the wrong zone as heart rate zones often span just 10 beats per minute or less and it appears that heart rate is fairly inaccurate at determining what zone you're actually in. A study looking at power output in stage racing in pro-road cyclists took six subjects and measured their time and zones during a six day stage race. Using a three zone model they found that heart rate underestimated time spent in zone one and zone three so very hard and very easy and overestimated the time spent in zone two. The study concluded by saying that the benefits of measuring workload through power output include better quantification of intensity compared with heart rate measurements. Training specificity can therefore be improved through an optimized monitoring of performance. This gets amplified as you get more fatigued in training because fatigue has a big effect on heart rate. A review on heart rate monitoring during training and racing in cyclists stated that during overtraining maximal heart rates as well as submaximal heart rates may be decreased while resting and in particularly sleeping heart rates may be increased and this is exactly what we saw in my example data. When I was more fatigued my heart rate was significantly lower for the same effort. This is definitely a reason to use power over heart rate for monitoring your intensity but it's also an argument to use both power and heart rate together. A lot of cyclists ditch the heart rate monitor all together when they get a power meter but the heart rate monitor is still giving you some useful data. The article also stated that heart rate may be a better indicator of whole body stress after training or competition and that heart rate is very useful in the early detection of overtraining. How do we put this into practice? Well if you're riding at a certain power number and your heart rate should be this level but it's 10 to 20 beats below that then that's an indication that you're fatigued and you need some rest. This shouldn't be confused however with changes in heart rate as you get fitter. As you get fitter your heart rate for a given power output will decrease over time. However if you go into a hard three day block and you're holding 200 watts at 150 beats per minute at the start of the block and then on the last day you're holding 200 watts and your heart rate is only 130 beats per minute then you're probably just fatigued and you need a rest day. Another thing to consider when training using heart rate is that heart rate lags. Power is instantaneous you start pedaling and you can see exactly how much power you're doing but with heart rate it takes a few minutes for your heart rate to climb and then level out. Because of this using heart rate to pace shorter intervals under five minutes is practically useless and it can also be problematic in longer intervals. Because of the lag in heart rate you could overshoot your zone at the beginning of the interval. Heart rate will also drift up as you ride. This phenomenon is called cardiac drift and according to the same review article cardiac drift can change the heart rate power output relationship drastically especially in hot environments or at altitude. If we take an example from one of my own workouts again we can take a sample from the beginning of the ride and we can see that from this section my average power was 207 and I had an average heart rate of 124. In that same ride if we look at a sample from the end of the ride we can see that my power is almost identical at 208 but my heart rate is 11 beats higher at 135. As far as using heart rate to pace yourself during a race you might as well chuck that heart rate monitor out the window if you're going to use the same data that you use in training. During a race your adrenaline is going to pump your heart rate a lot higher than it normally is. Shoot I'm usually in zone two just standing on the starting line of a big race. I would still wear your heart rate monitor during racing just so you can analyze the data afterwards but chances are your heart rate is going to be much higher than normal so pacing off of it becomes problematic. That being said there's sometimes during a race where heart rate can be useful. For example if you know your max heart rate and you look down and see that you're really close to that max heart rate or at that max heart rate that's a good indication that any second you're going to blow up. This can be even more important when racing in altitude when it becomes even harder to get that heart rate down. Also if you're doing a long race cardiac drift can be an indication of dehydration. A review on cardiovascular drift in prolonged exercise stated that approximately half the reduction in stroke volume which in turn increases heart rate is due to reduced blood volume from dehydration. This is more of something that you might look at in the post race analysis than during the race but if you're racing and you see your heart rate creep up for the same given power output then drink up you probably need it. Heart rate can also be helpful when mountain biking or racing cyclocross when power is all over the place. If you can look down real quick you can get a quick snapshot of where you are and as you race more with a heart rate monitor you can determine whether or not that number is too high or too low. Power on the other hand is an absolute game changer for pacing especially on individual efforts or long climbs. Let's take the 40 k t t for example if you know your ftp then that's a good number to shoot for and guess what it's going to feel way too easy in the first five minutes that's called proper pacing. Without a power meter you can easily overshoot this. You can also use power in road mountain and gravel races as well for example in a road race if you get in a solo breakaway once you're clear of the group you can assess how much further you have to the finish and then determine what power you can hold for that duration. This also works anytime you get on a long climb. I often have my ultra endurance guys shoot for zone three on climbs and zone two on flats during a long race. Finally power allows you to compare your current fitness to your past fitness it's easy to see whether or not you're hitting better numbers than say a month ago or a year ago this is something that you can't do with heart rate. You're also able to compare your power to others this can be both a good thing and a bad thing. If you can get an idea of what power you need to be at to beat your competition for a certain race then this gives you a good target to strive for for example you can do this by looking at the winning time of a certain climb and then determining what power you need to produce in order to beat that time. This can be a slippery slope though constantly comparing your power or your ftp to others can get in your head and start to negatively affect your results and there are so many other factors in a race that affect the outcome other than your ftp. Oh you've got a four watt per kilo ftp yeah man i'm i'm right there too um i just got to lose like 10 pounds and you know just add like 30 watts yeah i'll be there by this summer for sure i like to call it ftp obsession for people who have an extreme type a personality or a perfectionist this can really start to psych you out for these people as much as it pains me to say it you may want to take a brief step back from your power meter and work on your own self-control and realize that not every single workout can be a personal best power day and don't get me wrong ftp is extremely important but obsessing about it and constantly comparing your ftp to others isn't healthy and in the long term will probably hurt your performance also if you just ride for fun and don't compete or you don't take competition too seriously then a power meter becomes much less important if your goal oriented and you like seeing your fitness go up then a power meter will add value but if you're more of the stop and smell the flowers type then there's really no need for a power meter then again if you do stop to smell the flowers kudos to you on making it this far into such a training focused video your internet attention span is truly impressive all right let's review real quick for starters if you can't afford a power meter then training with heart rate is a lot better than nothing but there are a few problems with training with heart rate that are solved when you use a power meter for starters heart rate is extremely variable and is influenced by a whole host of factors this means that you could be training outside of your targeted zone fatigue affects heart rate and using power and heart rate together can give you a good indication of how fatigued you are if you're struggling to get your heart rate up for a given power output then you probably need a rest day heart rate lag during intervals can have you completely overshooting the beginning of your interval and using heart rate to pace short intervals is practically useless heart rate is close to useless for pacing yourself during a race while the exact opposite is true for a power meter using a power meter to pace yourself is an absolute game changer especially in individual events or really long ultra endurance events finally a power meter allows you to compare your fitness month to month and year to year so you can see whether or not you're improving thanks for watching and i hope you guys found this information helpful let me know in the comments whether you train of heart rate or power or both and what advantages or disadvantages you've experienced with each if you like this video be sure to give it a like share it with a friend and subscribe for more training tips if you want to see more coaching content be sure to follow me on instagram twitter and facebook and if you want to follow my training leading into this upcoming season be sure to check me out on strava finally if you're looking for a coach shoot me an email at djohnson at trainright.com


And welcome again, everyone, to this third day of the FT's global boardroom event. We are delighted to welcome to this Tech Asia keynote interview Audrey Tang, Taiwan's minister in charge of social innovation. Audrey is well known as a luminary in their field for famous for spearheading a vision of open government in Taiwan, which we will hear about more in a couple of minutes. In the public sector, Minister Tang served on Taiwan's National Development Council's open data committee and the 12 year basic education curriculum committee and led the country's first rulemaking project. In the private sector, Minister Tang worked as a consultant with Apple on computational linguistics with Oxford University Press on cloud lexicography and with social text on social interaction design. And in the social sector, Audrey actively contributes to Gov Zero, a vibrant community focusing on creating tools for civil society with the call to fork the government. So, Minister Tang, a warm welcome. And just to start with, I wonder if you could recount a little bit of the saga of the mask maps in Taiwan. I think some people will be familiar with this. But in essence, I think this initiative in which you were a prime mover is seen as an example of open government and open data. And it helped get Taiwan ahead of the pandemic in the very early days when the virus was spreading and me based in Taiwan was, you know, wondering what our fate would be. Could you just explain what happened at that time, please? Certainly. So, last February, early February, when we discovered that the R value of the new variant of SARS is quite high and without more than three quarters of people wearing masks properly, we wouldn't be able to solve it using contact tracing alone. So we decided to start rationing out masks because at the time in a country of 23 million, our mass production at the time was less than 2 million per day. So it means that if we do not ration out the mask, then people would hold the mask and it will not protect anyone. But even before we roll out this rationing method, the civic tech community of zero, as you just mentioned, the G zero V community came up with their own idea of visualizing the mask availability in the nearby pharmacies and stores and so on. So that people queuing in line do not have to queue in vain and can distribute among themselves to the place that still have some mask available. So the mask rationing map is a what I call people public-private partnership where the people who came up with this idea of a visualizing map and we do a kind of reverse procurement. So early February in a time span of three days, we started providing the kind of real-time open API, updated every 30 seconds so that when you queue in line, you don't have to wait for the person queuing before you to update. There are numbers on the map, which is what the civic tech originally did, but actually a pharmacy which more than 90% of the fiber optic line back to the National Health Insurance Administration. So you can buy some mask and immediately after 30 seconds is like a distributed ledger, more than 100 different tools, including chatbots, mobs, voice assistants for the seeing difficult people and so when all gets updated in real time. So first people trust this system as a distributed ledger so people can see as being rationed out rather effectively and second of course one does not have to queue in vain and the OpenStreetMap community for example can also co-create better distribution and pre-registration algorithms because everybody can analyze the data including the bias independently. So indeed it is a kind of shiny case of how real-time open data helped to counter the pandemic and by April by May last year we're largely gone to zero COVID and state that way for a very long time. But this may of course when the alpha and later on delta variant hits high one we face our real first and only wave. And so it's the same bunch of people who co-create a mask rationing map also created a SMS based contact tracing tool. And it's not like in other countries where the data is aggregated to the state like making a server and state, but neither are we kind of trusting solely the Google and Apple of the world, the multinational companies from coming up with their exposure to the notification app. The third way is having the civic society, civil society creating the tool based on open standards in this case SMS and QR code. So you don't have to download any app just you're built in phone you're building camera point QA venues QR code and pops up automatically SMS pre-composed with the 15 random codes representing the data you just hit send. So everyone can check in in just a couple of seconds and within a week basically adopted this design from the graph zero community and more than 2 million venues began printing those QR codes and to date there's more than one quarter billion SMS sent this way that reduce the contact tracing time it takes for each infected case from 24 hours to less than 24 minutes automatically and that's why for more than one month now we're back to zero COVID. Wow fascinating just just a point on that to clarify so so does that rely on the individual that has got COVID to report that the fact that they have been affected infected does it rely on that. So basically the way it works is that the SMS that representing the check in is stored in the telecom operator of your choice right so whomever hands you the SIM card stores that check in for 28 days. And if in the same venue there's anyone confirmed as being infected then it is the contact tracers that's then use this platform to notify automatically all the people who have checked into the same venue it doesn't need to be a static venue could be a same taxi or things like that so they can get exposure notifications and if the contact tracer finds out who exactly is in the same venue and so what then of course the contact tracer can proactively notice the persons and put them in quarantine so on. And so this is both proactive but also has a passive mode as well but if there's no case in the 28 days in any given venue then the telecom companies just rotates deletes the checking records and the contact tracer never access that deleted records. So to date there's only been I think around 10 million or so 12 million or so accesses to the checking records so the vast majority of the quarter billion were just deleted with no privacy compromises. Fascinating it's really a world leading example I think but but more broadly in Taiwan I mean we know that Taiwan's government has been a leader globally in open government and you have a national action plan in this regard could you just explain you know what are the main aims of the plan and what are the main steps so far. Definitely. So the open data part is the transparency pillar of the open government national action plan which runs the same length as Dr. Tsai Ing-wen's second presidential term so we still have a couple years more to implement more open data when it comes especially on environmental data climate sensing air pollution sensing and so on and the other pillar is the participation pillar which costs for not just increasing the bandwidth of democracy as in as I said open data also reduce the latency meaning that people do not have to wait for four years to vote but rather continuously every day there's a petition platform going called join and people can say their own agenda after collecting 5000 signatures the ministries and including interagency issues twice every month we meet face to face with those petitioners and multi-stake holders to figure out how we can work together and the same electronic signature infrastructure also extends next year will start to do joint e signatures for our national level referendum so not just the administration level but also binding to the legislation level as well as part of our referendum work and also we're committed to be inclusive so including people who are not even 18 years old can still set the agenda through the use advisory council what we call the reverse mentor to the cabinet as well as starting their own petitions on for example banning the plastic straws of our bubble to take out and things like that so it's not just to make the entire administration more transparent and accountable but also make a decision making process more participatory and inclusive. Right. It's so I understand so basically if 5000 people get together and they have a proposal for the government. Then the government is duty bound to consider that proposal is is that the way. That's correct and we have a dedicated system called participation offices or POS embedded in all 32 ministries in charge of engaging the public. For example in 2017, when a service designer named sort of petitioned that the tax filing experience is horrible and I quote explosively hostile to taxpayers and of course, then it's not just this negative energy of demonstrating against but rather the participation office engaged him immediately and say let's co create next year's tax filing experience together with the people who feel the most anger about this particular issue. There's a real sense of urgency there but I think a very creative thing of the PO network when it comes to the joint platform petitions is that the breakout groups were not facilitated by the tax agency. But instead by the coastal God, for example, but when we deliberate about the facing the ocean kind of ideas of surfing amateur fishing and things like that. Then maybe it's the tax agency or the financial ministries participation office facility that break out groups and the theory is that once they're outside of the silo the tax collector also serves in spare time and coastal God probably files the tax to. So they take automatically the position of the citizens and can work on true kind of mutual partnership because they feel the need of citizens themselves. It's, it's really an interesting initiative I imagine there are plenty of countries around the world where people feel anger when they have to file their taxes and, and they, you know, cooperation with an authority to make that process easier is surely something that I well I personally feel the UK could could learn from just on a different topic though. I know that you have been involved with the issue of misinformation and toxic commentaries in social media and in other places could you explain what you're doing in that regard. Finally, so Taiwan's way to counter that this information is not via takedowns we don't take down anything from the administration but rather investing in public infrastructures pro social social media in the digital realm. For example, the PTT, which is the forum like Reddit for free of advertisers or shareholders is squarely in the social sector for the past almost three decades now funded by the Taiwan academia network and that's the place when really when last message spread to Taiwan, 2019 December that there are and I quote seven starts cases in the Huanan Sea from market and of course, got triaged overnight in 24 hours it resulted in us getting the health inspection for all five passengers coming in from Taiwan to Taiwan and that shows a pro social social media a digital equivalent of a town hall can really contribute to early discovery and response when it comes to pandemic and other urgent issues and then the joint platform again is a investment by the public administration in the national development council so starting 2016 basically we classify such investment in the public infrastructure in the digital realm. Infrastructure money this is a previously things made out of concrete like concrete buildings are can now be made in bits and then we have the digital equivalent for the citizens to have a pro social conversation but of course the digital equivalent of nightclubs does to say Facebook's and so I was loud music and smoke the room in private bounces. Of course these are still around there's a lot of conversation going on there, but they are by and large we use the idea of human over rumor, making sure that people are understanding this information as discovered by the journalists the fact checkers the zero people the covax project meets in just a couple hours a very comedic response from the participation officer from each ministry so that people can just laugh about it laugh the tension off and then focus on the policy not the outrage. How do you deal with the issue of opinion. I mean, if somebody has an opinion that you believe is based on faulty information. How do you deal with that because I think in many societies people would say well that's my right, you know I, I feel that way, and my interpretation of the reality is this yours may be different so how do you deal with that. So I use a method called notice and public notice. Indeed, as you said, if they read something that has been manipulated but think that's the fact that's the truth. If you take it down if you impose some top down finds or something that actually it feels the outrage or it feels the conspiracy theories actually make the more polarized, but especially leading up to election that's when those conspiracy theory are in their kind of full height. But in Taiwan, what we did is basically the contact tracing idea applied to the disinformation kind of like a virus of the mind. So we invite, for example, the middle schoolers to fact checked the three presidential candidates during their debate and forums and so on. And it's not just a classroom assignment or exercise, they, along with anyone who want to contribute, once they discovered something that doesn't match can actually contribute to the live stream when the debate was still happening. So making a positive impact to the entire conversation and during the tallying, for example, the election process, we use paper based ballots for presidential elections and mayor elections. When people start discounting process, they show the paper to the YouTubers in the audience, and there's dedicated apps in each major country that does the tallying concurrently from multiple angles. So basically this is a way to make sure that anything that is disinformation is met with participatory audit that that particular person can trust a YouTuber that's more aligned to their political affiliation to actually dispel the rumor about the miscounting and things like that. And maybe next round they will participate in the fact checking work themselves. Right. Okay, very interesting. We've got some really good questions that have come in from the audience so perhaps I could take them one by one we've got about eight minutes left so have to be fairly brief but the first one is are you concerned about the dominance of big platforms like Google on the internet. Yeah, as I mentioned, the ideas of civic technology like based on SMS contact tracing QR codes and so one is based on the idea that the local norm, what's considered normal in the cyberspace is co created with the entire society. So for example, what's the right balance between the right to know that a contact tracer has access your data and your voluntary contribution because you can always go back to pen and paper we never said that's out of the question right is a voluntary choice. So the idea is to build such data collisions there's partnerships with the people, not just for the people. And once people understand that the norm around contact tracing or about masquerading of course we welcome Google who very graciously waived the Google map fees of the initial operation name of the mask prototype. And they also contribute a lot of the design into the contact tracing tools, including the line is the messenger in Taiwan. They actually adjusted their QR code scanner of adding friends is like what's up in Taiwan to support a contact tracing effort but what's important in people public private partnership is that the norm is not set by those large companies. They actually just implemented local norm as set by the civic tech community along with the people. Right. Excellent. The next question is is related I believe but I it's still very interesting. How does the web 3.0 initiative by which this question and means the decentralization of the Internet fit with your vision of open data and open governance. Yeah, I think web three with its call to bring code to data rather than aggregate data to code works very well and indeed I'm working with Vitalik buttering as a fellow board member in radical exchange to explore what kind of blockchain inspired governance where three inspired governments can be applied in Taiwan. So for example our presidential hackathon where every year five social innovation teams are blessed by the president to become a presidential promise for the next fiscal year with all the budget regulation and personnel requirement fulfilled by the state that got from the blockchain governance the idea of quadratic voting for the past three years we use this new voting method to unlock more signal around synergies and things like that and that's something that we learned directly from the decentralized governance community and SMS based contact tracing by storing the mapping between the venue code and the venue in one place and then the check in the SMS records in various different telecom companies. The end result is the telecom knows nothing about which venue you have been to and the venue owner knows nothing about your phone number or indeed anything about you. And so this what we call multi party design again is a key part of the federated architecture of the modern web three imaginations. I think it continues to be an inspiration and we will continue to work on the zero knowledge on the homomorphic encryption differential privacy federated learning all those great privacy enhancing technology that affirms the democracy ideas that share with the way three can be missing. Interesting. The there's a couple of questions come in you just mentioned blockchain. I would also be fascinated to know your views on blockchain and crypto with the background of course that quite a few countries are restricting crypto in some of its iterations. What's the view. What's your view personally what's the view of Taiwan. As I mentioned, I think it's like a research are which provides the inspiration of the central as governance for us to work with but not for the people. So I think this is we have great partnerships with the blockchain communities. Now, mostly we use the distributed ledger ideas for example for masquerading the 100 different application developers maintaining their own ledger along with crowd audited input. That is directly from the ledger like technology auto is not technically speaking a blockchain or, for example, last year when people decide they don't want to collect the rationed mass for a couple weeks because maybe they have some spare. They can actually use a kind of data sovereignty to the national health insurance app and as I express to say that's the ration that I did not collect. I would like to dedicate it to international humanitarian aid and provide a signal to foreign service either anonymously or mint a NFT right now fungibly I would like to say that this particular person has dedicated this number of masks. So a lot of people contributed charitably, I think more than 7 million mosque initially were distributed this way to around the world and again this is similar to an NFT although it's not technically run on blockchain technology. Very quickly, in the last two minutes, would you be able to answer this one. What is the state of digitalization at the smaller medium enterprise level in Taiwan, and what support is the government providing for digitalization. I know this is also an issue in Hong Kong where I'm where I'm based. So what's happening in Taiwan. Well, this year, for example, we have the T cloud, the Taiwan cloud project at keep T cloud, the GOV, the TW says, if you want to try out any of the digital transformation tools that are cloud based as a small or medium enterprise, then the state subsidize 80% of that money. So you can actually try like five times more than your originally planned to invest right so maybe different payment methods, different way of organizing your supply chain and things like that. And without incurring a lot of initial capital investment on it so that's the T cloud project and we also have a P for talent the three T talent transformation training project that match makes the young students who were freshly graduated from undergrad level study, and then assign five of them at a time as digital ambassadors to a local SME or a night market community, and then work on the theory of change to digitally transform them so they're not really interns they're more like reverse mentors that are willing to share their first and experience of getting those digital tools, and again stay subsidized so without incurring much cost to the SMEs and we've seen a lot of uptakes, especially during our, what we call 5,000 quintuple stimulus voucher where more than 4 million people choose to redeem the voucher in an electronic way through mobile payments. Well, Minister Tang I'm afraid that's all we have time for but thank you so much for such a tour de force it's really been fascinating listening to all the developments underway in Taiwan and I'm sure many of the audience will will will take away a lot of this it'll be new to a lot of people who are listening thank you so much. Thank you live long and prosper.


Well, I've been mayor now for about 45 minutes. So I haven't had a lot of time to reflect on what's happened today, but we've had a lot of challenges in the city, a lot of divisiveness, and I'm hoping that I can help bring the town together, focus on the city instead of on individual interests and problems. There is a lot happening in Crosby right now that's extremely positive. We have a lot of new businesses, we have new people moving into town, we're getting a great reputation from the, well, around the nation for sure on the biking in this region. We've seen some property values begin to rise, and so I think we have a lot of really positive things happening, and I would just like to see if I can contribute to that process.


For all that we call the Marines a bunch of jarheads and crayon eaters and all the other derogatory terms we use to insult their intelligence They do seem to write the best military manuals Hello everyone Dylan Schumacher Citadel Defense and we are back with another edition of tactical book review Today's book is warfighting by the United States Marine Corps This is the second Marine military manual I have read probably out of about I don't know around a half dozen military manuals. I've read or tried to read or read parts of maybe It's more like ten somewhere between a half dozen and ten. I don't know the exact number But this is the second one of the Marines I've read and I have to say the Marines so far get the award for the best Military manuals. They are clear. They are concise. They are to the point. They are poignant They are fantastic manuals that you can actually take and read and get something out of Right now the other one that I read which I also reviewed in this channel was one dash three Tactics, this is one dash one MCDP one. I think dash one is the technical designation warfighting and this one is like tactics This is the even more so foundational work of the United States Marine Corps This is the manual that they want to base all their thinking off of in the United States Marine Corps All their other manuals warfighting or excuse me tactics The other one I read refers back to this one often But this is it. This is the foundational work that lays the the foundation for how the Marines are going to do warfighting So that really explains their process of thinking how they want their command structure to run and how they want things to go And this just isn't like a okay Well, you know Bob reports to Jerry who reports to whatever and and like a some kind of boring HR organizational chart It's nothing of the kind It's philosophical frameworks for how they want you to think and that is its value That's what makes it such a good read and it's a short read. I mean these pages if you look at them, right?  I don't know probably takes about two to four hours of reading time Depending on your level and if you're taking notes and all that stuff But I think this is a fantastic book and would highly recommend it to you Because again, it helps you get to that foundational level of thinking. How are we going to think not, you know The answer to two plus two is four But how are we gonna think about adding two and two if that makes any sense if you're trying with me there? So if you're looking for a book that's kind of kind of help you think through Tactics and warfighting and then how you should think about problem solving in general I would highly recommend that you get your hands on warfighting by the United States Marine Corps You can just download the PDF and read it on your computer or print it or do whatever I'm sure you can find all kinds of printed copies on Amazon if you want but I would highly recommend you check this book out because as Combat leaders we need to be combat thinkers, right? that's very very important and Books like this are gonna help you be a good team lead and help you think through problems and Think for your people well. So would highly recommend it. Hope that's helpful. Do brave deeds You


In the previous lesson, you'll learn how to create your own store policies and legal pages, which you need for your own protection, and which will also help you close more cells. In this video, you're going to learn about the navigation menu, which will create links on your website on the top menu and the footer menu with the links to those legal pages that we added in the previous lesson. And by the way, good job on making it this far, guys. It really shows that you are serious in what you're trying to do and you will succeed if you continue learning and continue implementing. So now let's head over to the online store on the Shopify dashboard and then go ahead and click on navigation. Now, what you're seeing here is the footer menu and the main menu. On your side, it looks like this. The main menu is here on top and the footer menu is what we see here on the bottom. So let's go ahead and fill them in with the relevant pages and links that we just created. Go back to the navigation menu. Click on the footer menu. Now on the footer menu, you can see that there's currently a search function. Let's go ahead and see exactly what that is. So go back to your website, scroll down to the bottom to the footer menu. And here you can see a search quick link. It's not very friendly to have a search link on the bottom of your page. So at this point, what we're going to do is click on delete and remove it. Don't worry, you can always add it back later if you want. Now there's nothing in your footer menu. So what you need to do next is click on add menu item. And now we're going to start adding links to those legal pages, which are boring and we don't want it to be in the center of our website. So we're just going to have it on the bottom over here under the quick links. So click on the text box under link. Scroll down until you see policies. Click on that. And here are the policies which we already created. So let's go ahead and start adding them. Privacy policy. The name is privacy policy. So we're just going to go ahead and click on add. And here, as you can see, it was already added. Let's go ahead and fill in the rest of those policies. So refund policy. Add menu item. Policies. Shipping policy. Add. Add menu item. Policies. Terms of service. Add. See, it's not really that hard once you get the hang of it. So click again on add menu item.  Here you have the three pages that we created. About Us, Contact Us and Frequently Asked Questions. What we want to put on the bottom is Frequently Asked Questions. Click on add menu item again. Go to the pages. And we will add the About Us also on the bottom. The Contact Us, I think, can be on the top menu instead of on the bottom menu because when a viewer sees that they can contact you, it usually helps them get more confidence to buy from your store. So we're just going to add the About Us as the last page for the footer menu. And as you can see here, all six of those pages and policies have been added to the footer menu. Now go ahead and click on save. And go back. Now we're still on the menus, only this time we want to edit the main menu. So go ahead and click on main menu. Here you can see that you have two links, a home link and a catalog link to see exactly where they are and what they look like. Just go ahead and head back to your website, scroll up to the top. And here you can see the home link and the catalog, which will take you to your products. If I click on that, it'll take you to the products. That's the catalog link. So you have home and catalog. Now what you can do first is change the name catalog maybe to products. Okay, so we have home and products. Now let's add a menu item. Back to those pages that we created. And we want to have that contact us right up there. Click on add. And that's it, that should be good. Now go ahead and click on save. Now let's see how everything looks on our website. Okay, so here we are back on the website. Let's go ahead and refresh it. Here we are, home, products and contact us. The products will obviously take us to the products. Let's go back. And the contact us is going to take us to that contact us page that we created. So here we are, it's alive, it's running and it works. Now if we scroll to the bottom of the site, you can see the rest of the quick links that we just configured. Privacy policy, refund policy, shipping policy, terms of service, frequently asked questions and about us. And if you click on each and any one of them, it'll take you to that text and here's the link to that contact us page. Click on that, back to the contact us. So everything works and it's already starting to look good. The website is starting to look professional. I changed the text up in the announcement bar to just make it a little bit more friendly to the reader. But as you can see, everything here already looks good. There's just one thing that I would like to edit from here and I'm not sure if you guys noticed it but I don't like the fact that we have a subscribe up here and a subscribe down here. So this one over here looks good. We don't need the one on the bottom and it is not on the footer menu. This is the footer menu over here. So in order to delete this newsletter block over here, what you need to do is head back to your Shopify dashboard, click on online store and under themes, you have to customize your theme. So wait for this page to load, click on customize and here on the footer, which is exactly the newsletter that we want to delete, click on that and here's that newsletter. Click on it, three dots, remove newsletter. Boom, that's gone. We have the quick links on one line which looks good, it's in the middle. So is the subscribe to our newsletter, the products. As you see, step by step, our store is starting to look really good, really professional and we're that close to launching it and start making sales. So that's it when it comes to navigation menus and linking them with your pages and your store policies. You can create as many pages as you want for your website and set them up in your navigation menus. It doesn't only have to be legal pages, but use your creativity later for that subject. Your assignment for this lesson is to set up your navigation menu and link up all of those pages and store policies that we have created in the previous lesson. Once you're done with that, I'll meet you in the next lesson where we are going to talk about store currencies and payment providers. See you there.


Hey y'all it's Tammy with Colored Valley Cooks. If you're not a big cook and you want to take something really good to an office party or a family, get together for the holidays, making your own sour cream and onion dip is so easy and everybody will rave about it. This recipe is in our Volume 1 Cookbook. Now this recipe, it seems like why would you go to the trouble? Well let me tell you, you'll be glad you did. Something that you can have some company and they'll go for that onion dip and they're going to think wow this is delicious. Now if you can't get chives at your local grocery store, I got these at Publix, then just use some green onion and cut the green tips off of green onion.  We're just going to chop them in small pieces and remember the recipe, the amounts are always at the bottom of the description post. You can also get chives in the dehydrated herb section too if you want to get them that way. We're going to get these in our bowl and we're going to chop up a little onion. You're going to use a sweet onion. If you can get a videa you can do that but now this is just a sweet onion so you're going to chop up an onion and get it in your bowl as well and when you chop your onion try and get it as diced as you can so you're not going to want big humps, small pieces so it's more like minced or diced onion. I don't go all the way through the onion and that way it stays attached on one side and it makes it easier and you also cut off a flat side too so that it stands up on your cutting board and that makes it easier so cut it as thin as you can and lots of times I don't worry about using the entire onion. I leave what's left at the bottom to save for other recipes like if you're making a roast or soup or whatever in that way or chili that way you've got some onion that you can use and you can use the easy part to chop so it doesn't hurt even if you did a good job to go ahead and try to chop it a little bit more. We're going to get these onions in there and then we're going to add a little garlic salt we're going to mix this up it's going to be really good. Okay now you're going to use garlic salt which I hardly ever use in any recipe but there are some recipes I do use it for most of the time I use garlic powder but this is good with garlic salt so you're going to add your garlic salt and then you're going to add your sour cream and what I do is I just put it right back in the same container and that way it's ready and you can just mark the top with a marker that it is onion dip and that way you don't get it out and put it in something else. It's really good to mix this up and use it in your cream potatoes too so if you got some extra in the refrigerator use it at night in your dinner dishes it's good and it's marinate a little bit and gets more of a flavor if you've had it mixed up overnight as well. So now what we do is we just put it back in our container and usually you have a little extra so we're going to eat the extra today me and Chris and we'll taste the extra and remember to mark your lid and remember it'll be good until the date on the container so go buy your sour cream date if you want to know how long to keep it in the refrigerator but it's going to get strong so remember that too you don't want to make it a month ahead or anything crazy. I'm a Lay's potato chips gal I love them and I actually like the low sodium ones better than these but now Chris loves ruffles so whatever kind you like bomb. It's really good fresh and remember it's even better the next day but it's delicious. Yo me I'm so easy. Now that's a burst of flavor in your mouth if you're on the date you might need a breath mint. Thanks for watching Colored Valley Cooks where we cook like momma did. Bye y'all love ya