Hey folks, this week I'm in Washington DC at the American Society for Microbiology's annual microbe conference. Earlier today I was lucky enough to receive the inaugural ASM microbiome data prize. I was so proud to receive it. This prize was also sponsored by the National Microbiome Data Collaboration, the NMDC. They do a lot of great work to encourage folks to release their data, make their data publicly accessible, but more than that they really put in a lot of great effort to helping people think about metadata and how we can add more value to our sequence data and other type of data that we generate in microbiome but also more generally. So I hope you enjoy this talk and again thanks for all the well wishes that I've received and really appreciate the support for our efforts to engage in open research. Thank you very much to the organizers and the NMDC and ASM. It is a great honor to me to receive this award. I hope I'm not reading too much into it but it's a particular honor to be the first recipient of this. Those of you that engage in doing open science sometimes you wonder if you're fighting the battle that's not worth fighting and so receiving awards like this really really does make it worth it and is a good indicator that to our trainees that this stuff is important. So thank you very much. Question that I want to ask you all to motivate the rest of my talk. I don't know if you're familiar with a guy named Simon Sinek but as a book and probably Ted talks out the at your ears that basically asks you to start with why. So why do you do science right and so I don't want to know but if you're like me when I answer this question I do science because I am on a search for truth. I want to know how the world is put together how things work and and I think the world is beautiful right and I want to understand how that beauty translates into ways that we can help each other and how we can help the environment right but to do that I can't do it on my own right. I have to build upon the work of others I have to stand on their shoulders so I can see further and I know then that I have a responsibility that others have to be able to stand on my shoulders right so I need to make broad shoulders I need to try to be tall so to speak right maybe we can push this metaphor a little too far but we all build upon each other and if you're doing closed science if you're making everything proprietary saying the data are mine they're not yours then you're really narrowing your shoulders right you're making it harder for others to stand on your shoulders and to help us to see further. We have big problems I don't know if you've noticed that over the last couple of years but we have big problems and science I think is at the heart of solving those problems so I've got a number of reasons and this is a very partial list for why I think open science is important and we'll spend a few slides talking generally about each of these points so I think open science is important for the rapid release of information I'll start with a picture here on the right so three years ago when I think we were in San Francisco I was walking around with just a huge weight on my shoulders because I had just been diagnosed with Hodgkin's Lymphoma and I hadn't told anybody and it would be a few more months till I tell anybody and it's just this just huge burden on me because what would it do to my family and so the next week my wife and I went to go see our oncologist and she brought in this conference abstract that had not been peer reviewed and had this single figure on it okay and my doctor said and I looked at it and like okay that's a survival curve I don't know what all those abbreviations mean but the red line and the black line that you can see kind of tracing each other the red line is radiation alone and the black line is radiation plus chemotherapy and she said based on this 20-year study that was just presented at a conference without peer review I don't think you need chemotherapy okay for the type of cancer I had right so this impacted my life dramatically right it meant that I could go into the pandemic without having to recover from chemo right and it meant that my family wouldn't be so burdened and so releasing data affects people and if you haven't noticed right there is a pandemic going around right and so the availability of preprints and open access publications is all instrumental in getting information out to our colleagues right imagine where we'd be with a vaccine or therapies if all these scientists hadn't been releasing their genome sequences right we would be just in a horrible position right and I'm not saying that preprints have solved all the problems for they probably caused some problems but I think it certainly helps to move science forward and really allows us to build upon each other and we also need to think about this idea of developing these community resources like sequence data and like the great microbiome data that we've been working with I also think it's important for reproducibility and replicability when we do a project in my lab we always start with the last project as our starting point right and I think that's kind of what we all do in a way but I want to see that people in my lab and myself can recreate the last result we had because then we want to build upon that right so if we develop a new tool well we want to see what people did previously and we want to validate it using their data because we don't want to you know do a bait and switch and make their program look worse with a different data set but we want to be able to reproduce what they've done and so this table comes from a paper that I published in in bio a few years ago talking about reproducibility replicability robustness and generalizability and so we can think about same methods but we can also think about same data or I'm sorry same methods and and going across we can think about different data right so same data or different data right so I can look at the biomarkers for colon cancer in my cohort but I could and so that would be reproducibility right and then I could look at other cohorts and that would help with replicability right and so then as we as use different methods different angles of looking at the problem we can think about robustness and generalizability but that all starts with getting the data right to expect me to generate data for multiple cohorts worth of samples it's just a waste right it's just ridiculous and that would be tremendously selfish for people to not share the data because and that slows our progress forward and of course none of us have all the good ideas right like you all are smart you're here after all right um but but we don't have none of us each have the good ideas and so ways that we've thought about this in my lab is as I've mentioned benchmarking new methods and the ability to benchmark those methods with diverse data sets I'll talk more about that here in a few moments we come up we're constantly coming up with new analytical methods right and so being able to get access to previous data sets or previous samples is really important and something that I've been dependent on using samples for biomarker studies that the PIs had no idea what the human microbiome was but because we had a new strategy we could go after those and then of course we all have new questions right questions that the original investigators could not have foreseen I think open data is also really important because huge huge investments have been made for every paper that gets published right we talk about the kind of out of control article publication costs just to publish a paper of a few thousand dollars well if you look at NIH funding and the number of papers that come out of each NIH grant can serve things like indirect costs it's about a quarter of a million dollars for a paper to get published right when you consider personnel money reagents getting samples sequencing publishing right that is not cheap and so beyond the financial costs there's also intangible costs so again if I go out and I recruit a cohort of people with and without colon cancer I'm going to go up to people that have just figured out they have colon cancer right so how many times do you think we want to do that to those people right we want to minimize that as much as possible also we have we're working in environments that I would consider kind of scientifically sacred so whenever I see somebody has gone off to the beautiful Galapagos Islands I wonder does the Galapagos Islands needs another person traipsing around screwing up that environment probably not right so we need to be efficient with the data that we use from these scientifically sacred sites and I think what this also comes back to is what does a published paper represent right do you see the papers that you publish as the final answer on whatever you are studying and so if you think that and I don't know I think maybe you need to drop your perspective a little bit because really we're all on a step right towards something next and even Nobel Prize winning science is a step to something more right and so what our papers I think represent is an advertisement an advertisement for ideas for our data for our methods for our reagents for our tools right and so if we think of our paper as an advertisement well I want to sell my ideas right I want people to read my papers I want people to use my data and so we really need to get out of this proprietary framework that I've published this paper you don't have rights or access to the data or methods in those papers that it's mine right so needless to say not everyone agrees with this and including the editors of the New England Journal of Medicine who a few years back wrote this editorial on data sharing and this is like one of the things that Twitter just loves to go nuts about that in here they have a quote there is a concern among some frontline researchers that the system will be taken over by what some researchers have characterized as research parasites so I know people use words loosely but I consider myself a microbial ecologist and a parasite is an organism that benefits at the expense of something else right so it's a positive negative relationship so if I make my data available to you does that hurt me no right that helps me that helps me with citations again that helps me to get my ideas out right it's a mutualistic interaction not parasitism needless to say like this kind of blew up on them I don't know how much they've retracted but it did give rise to another type of award called the parasite awards celebrating rigorous secondary data analysis which I think is just the the appropriate response to claims of data parasitism and one of the awardees of this is Julie Dunning-Hutop who's an outstanding microbiologist at the University of Maryland and she has a series of work that I absolutely love where she basically takes throwaway data and reanalyzes it so in this study that was published in plus computational biology she took cancer genome sequence data and instead of throwing away the bacterial DNA as a contaminant she included that in her assembly and lo and behold the bacterial DNA assembled with the human DNA primarily in the chromosomal DNA and if you haven't looked at her studies they're fascinating there's another where they find entire chromosomes of a wabakia in Drosophila genomes and then they go back and they do further studies of course not just sequence analysis to biologically show that this is the case and think about mechanisms right so the data that they're borrowing again is a case where the original PI's authors had no concept because of their expertise right but Julie is interested in bacterial genomics horizontal gene transfer evolution host microbe interactions and so she sees the world differently than the oncologists right and so this is one example of many of course within our own microbiome field this is I think a fairly popular editorial at this point available upon request is not good enough this is an editorial that basically is a takedown of a european study that published thousands data from thousands of samples that they then kept behind a paywall and would make PI's jump through tons of hoops to use I've never used this data set because I can't get access to it so if I can't get access to that data set I'm not going to cite their work right and so what what others have found is that when authors say available upon request it's almost like you don't need a study to do this because we've probably all emailed an author for data that 93% of the authors who say data available upon request do not provide the data to the person asking for it right so this is just a non-starter so what are we talking about in terms of scientific data so the NIH has instituted a policy that any proposal submitted after January of 2023 so seven months or so that all that PI's have to have a plan for data management and sharing and they define data as this that the recorded factual material commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings regardless of whether the data are used to support scholarly publications right so I think this is a really ambitious definition uh as someone that's received this prize now perhaps I shouldn't admit this but that last clause regardless of whether the data are used to support scholarly publication I'm not there yet right I need to improve I need to get to the point where we're submitting our data even if it doesn't go to a publication right we all have the best of plans to publish something perhaps it turns out to be a negative result and those you know get pushed down a little bit further in terms of our data release but we need to do a better job of getting all the data accessible and I'm proud to say that within ASM's journals uh microbiology resource announcements is very happy to publish data so that people can get citations for that even if it's a negative result in fact you're not even allowed to describe results for a microbiology resource announcements paper within ASM journals we have a data release policy um where we say that it's expected that this data will be released to the public no later than the publication date of the final article I would prefer to see this at the time of submission because then we can put the enforcement of this on the reviewers and editors rather than the editorial staff who doesn't have the domain level expertise to go through and kind of make sure that you know your accession numbers link up and everything and make sure that all the data is actually there so in my closing moments I want to talk about the the efforts that my lab has taken to both promote open science as well as benefit from open science so a number of years ago um the SRA was a royal pain in the rear for uploading data to and I think it probably still is but I don't realize that because we put a really cool function within our mother software package called make dot SRA that basically streamlines the uploading of the data using min marks data package standards and we did this in collaboration with the folks at SRA and um they were only too happy to help us do this since then we've also created tools in mother so you can directly download and use the SRA formatted sequence data and so that's a tool that that we have in mother and many have benefited from also because I recognize we're bringing a lot of people in from other fields into the microbiome world that there's a lot of training that needs to happen and so over the years I've created a set of resources at riffamonis.org um I have a long tutorial series on reproducible research that has a heavy emphasis on data accessibility and data availability and if you look at the numerous youtube videos that I've been making about uh reproducible data analysis you'll see in there that we're also using a lot of open data sets to promote reuse of data so I also find that these um data data being available is tremendously useful for benchmarking new methods so this is a figure from a preprint that will hopefully be published later this year looking at the effect of removing rare sequences from your community analysis and what effect that has on downstream processing right and so in these um simulations we use data from 12 different types of communities and imposed an effect size and basically what I was able to show was that as you remove basically like singletons double-tons and rare sequences you lose statistical power to detect differences between different treatment groups right and so but what you can see is that by using 12 different data sets it's not always a clear cut picture right but by looking across a large number of data types we're able to get a more rich more complete story and and going back to my original bioinformatics tools of developing a tool called mother or daughter rather before mother um we've always relied on the generosity of others to make their data open so that we can do these types of analyses over the years we've also engaged in doing meta analyses and so in a meta analysis uh one such example is looking at the relationship or association between microbial community structure and obesity status in humans and so this was a observation that was originally reported by Peter Turnbaugh when he was a student in Jeff Gordon's lab and so uh it's been a bit controversial in the field so what Mark C a former postdoc in my lab did was to collect data from 10 different studies these studies most of them had nothing to do with studying obesity but because in their metadata they made height weight BMI obesity status publicly available Mark was able then to go through these 10 studies study them separately for this question of association between obesity status and diversity to show that well two of these 10 studies did we see a significant effect as you can see the effect sizes are miniscule and then statistically like a meta analysis pull the effect sizes to show on this bottom row that they're overall yeah there is a difference in obesity of lean and obese individuals is that biologically significant eh I doubt it right and then when we look at things like ratio of vector eddies or formicities we see nothing so Mark then went another step and so like I mentioned before that making data available also allows us to do new types of analyses so what Mark then did was take these 10 data sets and separately to generate 10 different random forest machine learning models to predict obesity status so for this first one the first column of Baxter on this black circle is the model performance of the model the accuracy of the model based on the Baxter data set he then took that model and ran the nine other data sets through that model to assess accuracy to predict obesity status right and so what you can see is it's very noisy and we do a really bad job of predicting obesity status based on the types of organisms in these microbial communities again this this would not have been possible without these PIs making their data accessible and making their metadata accessible as well and and there were studies that we did not include because data were not accessible and metadata were not accessible or they were the data were available but no metadata was available not even the data that would be necessary to reproduce the original work in the studies they basically checked off the mark the block saying they made the data available even though it was completely worthless and so as I mentioned providing the metadata is just critical right if you're submitting sequence data but not providing any other information about it it's worthless you're just you're wasting storage space and so one effort was proposed about 10 years ago min marks it's the minimal information about any marker gene sequence and and so just for each different type of environment what is the minimum information that we should be providing to add context to our data more recently within the human microbiome field the storms checklist was created at again is a checklist a tool that we can use as researchers to enhance the utility of our data and of course the MMDS has great resources as well for other ways of improving the utility of our data and so what I want to close with is a point of encouragement in a way that we can look forward so one of the big concerns people have about making their data publicly accessible is that it will allow our competitors to scoop us well first of all I think you have a five to ten year head start on your competitors and if you can't publish your work in that time span then maybe different male maybe you deserve to get scooped a little bit I would say if you release your data you're going to slow down your competitors right because now they need to analyze their data as well as your data to give more context to their data this is something that we're not doing a great job of in the microbiome field right there are numerous colon cancer data sets out there so if I publish a new colon cancer data set I should get in the practice of analyzing it in the context of these other data sets as well and so I think that will further help move things around long but it's only going to be possible if we release our data and release our metadata and so finally whenever awards like this are given I feel like it's it's sure it's to me it's my name but it's really the people that have been in my lab and have believed in this mission of open science throughout the years I remember when we started kind of going down this path I think people were a little bit uneasy right am I giving away my career are they giving away their career by releasing their data and I think awards like this the tremendous citations we've gotten on our work the prestige people hold all my trainees is a testament to the fact that it does pay and it moves science forward more so thank you all so I hope you enjoy the talk that I gave to you today and we'll see you next time for another episode of Code Club


So, are you ready for our first speaker? Yes!  First, as Bill Gates used to say, your most unhappy customers are your greatest source of learning, he said. So tech companies that forget that, no matter how cool their tech are bound to fail. Steve Jobs put it like this, he said, you've got to start with the customer experience and work back towards the technology, not the other way around. He goes on, he said that he's made this mistake himself many times. And it seems it many continue to get it wrong. Studies routinely show more than 80% of analytics, big data and AI projects will fail. Our first speaker is a consulting product designer, host of the Experience Data Podcast and the founder and principal at designing for analytics. And he firmly believes that to build innovative machine learning and analytics solution, it requires a people first technology second approach. So there he is, let's welcome Brian O'Neill. Hello Brian, how are you? Hi, I'm great, thanks for having me. It's great to be here. It's lovely to see you, Brian is connecting from Cambridge, Massachusetts in the United States very close to Harvard and he was telling me behind the camera that it's a very nice weather to get in Chile and that he'd rather be here with us. But maybe next year, Brian, it's lovely to have you tell us about all your secrets, what you came here to say. Yes, I'm going to jump right in here. We're going to talk about technically right effectively wrong, the data products that customers don't want. And so what do we do about this, right? So first, let me jump in here with the story. This is a story about a six-year project that it created thousands of artifacts and outputs of different kinds, diagrams, specifications, all kinds of that good stuff. And technically this solution worked. And what did it produce? Well, this is an example of output of the system, ZDA-110-3-15-1. This is the answer from the technology. This is the solution, so to speak. More specifically, the message that was actually presented to this customer in the software was FAA, Attic, NAVE, Sacristy, ZDA-110-3-15-1, aspirating framework, which is my favorite part of this message. So my question for you is, does this feel like human-centered decision support? And so much of our work with data products and analytics and data sciences is decision intelligence or helping people make decisions with information. So my question is, how compelling does this information feel? And this is a story about Notre Dame.  The software data output here was to identify the requirement was to identify the ID of the fire detection device and display it, right? So there's different smoke detectors, fire detectors, all these different telemetry inside Notre Dame. The human goal, the business objective, or whatever you want to call it, the organizational objective, was to prevent and stop fire. So the question is, did this technology work or not? And I think we know what happened here. What would you do with this information? So I want to talk to you today about why design matters. Bad design is not only expensive, it can be damaging, it can be harmful, and this is especially more true as we move into predictive technologies with machine learning, et cetera. And I'm sure many of you know the high profile cases here. But again, what would you do with this information? And so today, what I want to talk to you about in Hammer Home is that if humans are in the loop using data, your mindset as a leader, and I'm assuming most of you watching this, are leaders of data product teams, analytics teams, or software products that use intelligence or insights. You have to shift from producing outputs to thinking about outcomes. Outputs are nouns. They're things. Outcomes are results that we get from our outputs. And decision support and decision making is one of these outcomes, and it's one of the key ones that so many of us should be striving for, but we're not. So what might they have done here? This is just a very fast mock-up by three together, but I want to try to hammer home some of the points, right? The context here was completely missing from that solution. There were no visuals here. It's hard to look at a message like that and know without memorizing what this code means. Where is this fire? How far away am I? A fire is a group experience. It involves multiple people, the public, the fire safety people, the employees, the people that may have been on site there. This is an ecosystem, right? So simply technically focusing on the fire suppression device or the detector that was triggered is insufficient. It's not a complete solution. It is a very technically right solution. It might have pinpointed exactly where the fire was first detected, but if you can't go stop the fire, we didn't really provide the information that was required there. So you need to be thinking about holistically how the people will interact with these solutions and not just on what the technical output might be. This is especially true with when we think about machine learning. The simple prediction itself is not the end of the journey. It's probably the beginning of the journey. It's the beginning of the solution. So we need to change the culture here and put people first and the data and the technology second. And I know this isn't always a natural place, but as you heard Helena said in the introduction, it's hard to fall into this. It's very easy to jump to what we can do now because we can do so much more with cloud and all these new technologies that are now here. The assumption is we can throw data science and talented software people onto these problems and magic will come out the other end. And they are absolutely integral to this part. But if there are humans in the loop with your solution, there's more to it than simply getting the data part right. Okay? So why do I really think this matters? What are other examples of this? Well, this is a conversation I had on LinkedIn and this is from a guy that he sells a piece of software and he was saying how, you know, I've heard this many times, Brian, I went into this bank and the production solution was based on a database and a batch run of R scripts. And when David asked about, you know, he was asking this customer, well, what about the people who don't know R or SQL, which is 100% of the people that would use your solution? And this data scientist said, deadpan, those without technical skills are not relevant and will not be helped. So besides the fact, I think this is a management failure. If you're hiring people with this attitude where their job, they think their job is only to focus on the technical quality of the output. This is a culture problem here. I mean, it's a poor attitude to begin with, but the culture really has to change here, right? And so he kind of jokingly said, well, I'll just, I'm going to call back in a year or two after this guy's done enough damage that senior stakeholders will notice.  The time for that is going away and business expectations are growing around what are we getting for our investments in data, in innovation. And a lot of the expectation is that new innovations are going to come out of the work that data science, analytics and engineering teams are doing. So if you're not producing outcomes, you're at risk, right? So what do we do here? We need to change this mindset. So again, outputs and outcomes, we cannot just measure the technical outputs, the amount of code we shipped, whether we put a model into GitHub. That is not a solution. Operationalizing the model is really all that matters because until it gets in the hands of the people who are going to use it, the work is not done. The work is just getting started when you get to that point. When it's actually being used by the people who have to take action on it, who have to make a decision with it, this decision support thing. So who am I? I'm a professional musician in Boston. That's my formal training. But I'm also the founder and principal designing for analytics. I'm a consulting product designer. And I specialize in helping companies create innovative machine learning and analytics solutions. And some of you may know my podcast, Experiencing Data. Feel free to check that out, where I interview leaders in this space about what they're doing to produce more human-centered data products and analytics solutions. So design matters. We've talked about this. Bad design is expensive, and it can even be harmful. But I want to talk to you about what leaders can do today with their teams, especially if you don't have product people and designers on your team right now. There are behaviors that you can start practicing now. And you don't need to be a trained designer to do all of this work. There's a lot you can do today. Okay, so I'm going to give you about eight different activities, behaviors, or techniques that I think strategically need to be part of the DNA of your culture. If you're going to be producing software that's going to be used by people. Whether it's analytics in mind or date price and machine learning or AI or whatever, at the end of the day, if you're producing a digital software application that's going to be touched by humans, these principles most likely apply and will help you. So the first one is this mindset, right? And this is that analytical people and our data science friends and that this community of technical experts we have need to adjust to the great mushy world of people, behaviors, emotions, people that are illogical. Why people are motivated to do the things that they do. There's a lot here and I think most of you probably know that people don't just make decisions based on data. We're not robots, we're people and there's different incentives for why people may decide to turn right or turn left when presented with the fork, and even if the machine learning algorithm says to turn right, there's going to be times where they turn left. And so part of our job is to understand, well, why are they doing these other activities? Why are they turning left when it says to go right? What do we need to do to get the company or our customers to embrace this technology and to feel like it's been designed for them and it's not abusing them, it's not failing them at the time where they need to be successful in their work. So we have to start embracing the non-analytical mindset that comes with dealing with people. This gray world of design, design is all about embracing this mindset. And the second one here is more practically is embracing empathy, particularly routine one-on-one customer research and getting into the mindset of falling in love with problems. And so not in love with solutions and there's plenty of technical problems to go work on. There's always going to be data cleansing and getting enough data sets and running all your different recipes to see which models work and on and on and on and on. There's plenty of plumbing to create. This is about going out and really getting to know the people that are going to use your solutions, not relying entirely on quantitative data but real qualitative data. I would say this is probably the number one thing that your team can do to start developing real empathy. And this is easy if you are working at an internal, if you're an internal applied data science or analytics group or BI team that's servicing operations or other employees, you don't have thousands, well you might have thousands, you probably don't have millions or tens of thousands of customers. Your customer base is an internal employee. You can go out and start to develop relationships with these people. You can do write-along interviews. You can go learn about what it's like to be in sales or in marketing. What is it like to do their job? And how does our work fit into that? And more importantly, you can start to, instead of just waiting for a ticket to come in and Jira that says I need a model that gives me X, Y, and Z or I need a spreadsheet with the following columns of data, you're going to start asking why? And you're going to start honing in on what the real problems that these people actually have so that you can start producing outcomes and not just the outputs that they literally asked for. Because a lot of times the problem is not fully articulated. This is really important to understand. When someone says what's wrong with you, it's just like going into a doctor and saying, doctor, I need surgery on my arm. A good doctor does not pull a scalpel out and start cutting into your arm. They diagnose the situation. They're going to get to understand you and what kind of goals you have and how long it's been going on, et cetera, et cetera. They're not just going to give you the thing that you asked for. And I think this is something that teams need to do too, is to understand really what's that last mile look like when this person gets the solution. So the third one is this is kind of the activity of synthesizing all this stuff down into human-centered problems that are tied back to business needs. So when I talk about human-centered, it's important to remember that the business is formed of people. So the business, just like government, is people. They're still human beings, right? So some of you, again, you may work on solutions that touch external customers who pay you for goods and services, or you may be servicing other employees, suppliers, or whatever, more internal friendlies that aren't paying for your solutions necessarily. You still need to come up with clear problem statements that address the business objectives, but they also address the literal day-to-day problems that this person has. If you're servicing a salesperson, maybe you're trying to help them understand who is going to not re-subscribe next year or not re-up their subscription, right? And instead of them kind of running through their own algorithm in their head about finding in the CRM which leads they think are most likely to cancel, you need to understand not only who is like, not just to predict who's likely to cancel their subscription, but what's that process like for being a salesperson? How will they engage with this person? What's the next information they need? How much evidence do they need when they pick up the phone and call this customer and say, hey, we just wanted to check in with you, etc., etc., etc. What kind of evidence does this person need to have an effective conversation to actually prevent them from canceling? If you can't express these problems in a clear way that your whole team can get around, you're not likely to put out a great solution, and you may find out that this salesperson in this example just says, you know what, pass, not going to use your solution, I'm going to do it the old way because I know how that works and I trust it. And I don't know how you came up with this solution because I wasn't involved with it and I have no idea who these names are, I've never seen these names before. And at that point we failed and it's not because your model is wrong. Your model might have tested really, really well against the test set, but it doesn't matter if Mr. or Mrs. Salesperson doesn't decide to use that information at the point of the last mile where the technology matters. Another exercise here, I really like this one, is journey mapping and service blueprints. And these are tools that we use in the design world to understand how people move through a process because most of the time with decision making, it does not come down to one screen, one report, one dashboard, one visit, et cetera. There are design and user experience typically happens over some period of time. And so mapping out either the current journey or even an aspirational journey and it's much better to start with auditing your current customer journey, understanding that makes it really easy for the team to get honed in on what part of the phase are we working on? What are we trying to improve here? And when you can see what's wrong with it now because you've actually audited how people go through that, it's a powerful way to get alignment on where we can be strategic with the data and the insights that we bring and where we insert technology into the process. And service blueprints, I'm not going to get super detailed into this, service blueprints is really more of a back office. It's looking at the operational side of the business instead of the paying customer experience, which is more of what the journey mapping tool is for. Onboarding, honeymooning, and many of you know what onboarding is through using apps and this kind of thing. Honeymooning is another, is the period of time for me, which is after that first touch with a new product or software application, but you're kind of in the first couple of weeks, so the first couple of months and you're getting used to it. Maybe you're collecting data, maybe the algorithm has to learn for a while. This experience also has to be designed with intent. Sometimes it doesn't matter. Sometimes you can go right from zero to one. Other times there's steps along the way and that experience really matters because that's when you're bringing someone into the fold to start saying, hey, we have a new way of doing this. We have a new way of presenting insight and you've actually designed that experience to bring them along instead of them having to understand, oh, the gravy is going to come three months from now, but in the meantime I look at a blank dashboard because there's no data and you're like, well, just wait three months and it will come back. Well, they may just decide not to come back because you haven't alerted them that the system has collected the data it needs and that there's finally some insights here. That experience needs to be designed and thought through. The fifth one here is understanding that design is a team sport, right? And so being able to lead what I call design jams or design sessions, this is where we get low fidelity. This is also where I think algorithm design planning can come in, especially when we talk about the human parts. For example, if you're looking at the way humans do something now, let's say, what's the sales team's process again for predicting which customers are going to churn, right? They probably have some methodology that they use now which doesn't involve predictive analytics or something like that. This is the time where we might understand how do we model that behavior? How do we integrate that knowledge into the software so that they will actually trust the new version of this when we put this thing out and they will adopt it and they will say, yes, please give me more. This was really helpful. Your team is really valuable to me and my organization. I have 10 more requests for help. This is the point at which we start building those relationships down the road. I like working in low fidelity. I think teams should work in very low fidelity, getting visual, getting off of heavy written requirements for things. It's a little bit harder with coronavirus, but tools like Miro, et cetera, exist to do whiteboarding exercises and working in low fidelity early. The sixth one here is where we get more visual and we talk about interface design, MVPs and prototypes. Probably the key thing I want to talk about here is that visual design matters more than just aesthetically because the visual attention that you give to your artifacts, your dashboards, your software interfaces, all this kind of stuff, they say a lot about how much you care, how much attention you put into the work, how much does the user matter. And when it looks really complicated or it looks poorly constructed, it says, it sends a signal that not a lot of care went into the technology either. And I'm sure most of you have seen this before when you see something that looks really shaky or unstable, you might be questioning the engineering that's actually behind that as well. Even if the technology is rock solid in the background, the visual part of it can actually communicate a lot about the trust in the intent and the usability and the utility of the solution that you're going to present to them. I like finding ways to get stuff in front of people very quickly and I think sometimes we assume that there's no way to work quickly with some of these technologies, especially machine learning. And I agree that Agile and some of these traditional ways we do software, well, it's not traditional for everybody, but Agile is not new anymore. Some of these things don't translate well when you have large amounts of infrastructure required just to get to the point of doing modeling work. But there are ways to prototype. There are techniques you can use to prototype predictive analytics. You can present a prototype to a customer. Let's say it's a scenario planner, where it's going to be running simulations. You can come up with 10 different predictions for a different scenario that are completely fake, but they're somewhat realistic. And by showing people these different scenarios, I would use this example, like planning grocery store produce purchases. How many carrots should each store in the region purchase for next week? Well, maybe there's some kind of tool there that looks at past customer volume and some other signals or whatever. You don't actually have to have the real data to test what that solution might look like, because you could run someone through a bunch of scenarios and ask them, what kind of information would you need to make a decision to actually purchase or take the next step here? Would you buy carrots at that price? Would you buy that many carrots? What if it said, you know, buy the carrots at 10 cents a carrot instead of 4 cents a carrot? Would you pay that much? And by having a conversation with them over the design and evaluating it, this is where you're going to hear things like, well, there's no way I'm never going to pay more than this. And I have no idea how the system came up with something like that. I would have to know X, Y, and Z before I would ever do that. And when you hear this emotionally-charged language and they say, I would have to hear X, Y, and Z, the customer has just revealed a bunch of really important insights about how they make decisions. And so when you present your solution to them, you might find out that the actual features and the stuff you need on the screen is not necessarily super technical. It's not advanced or anything, but it reinforces the decision-making and the human behavior that you know is there.  There's some way to challenge the model. There's some way to give a different kind of feedback and say, well, let's adjust for this other variable and rerun the simulation and see what we get because you talk to this customer, the purchaser, the person that buys the carrots. And you know what they're doing during their job because you've been out, remember back in step one, you've been out getting to know these people and the way they do their work so you can anticipate some of this stuff. And through this prototyping, we can learn a lot before we commit to the big work of building out these large enterprise data products, which tend to fail. And so anyhow, low fidelity, moving into high fidelity, but always thinking about small prototyping, getting feedback early, right? And so there's also the form of testing these user experiences before we ship. So this is more and again, some of this can happen prior to having a working code, but the point here is there's kind of more the qualitative research aspect here. This step number seven is really about actually going out and running people through different scenarios with the software to understand, will they do this? Were they able to accomplish a very discrete set of tasks that we gave them? Can they get through them? And where did they fail? And then understanding why did they fail to complete here? Was it a lack of desire? Did they not trust the information that was there? Was it too difficult to drill down into a bazillion different analytics and metrics because the dashboard didn't present any type of conclusion or opinion. It just presented all the evidence and it asked them to put together a conclusion on their own that these kinds of questions, we put these together in a protocol and we go through and test them. And I think this step has become more important when we move into machine learning and predictive technologies where we're putting out solutions that are probabilistic, where it's no longer that there's three scenarios. It's either true, false, or no response, something like that with traditional software. We know what all the different states are and we can kind of plan for all these and test them. We have to test more different scenarios that might come out of a system that's probabilistic like this or when there's a disruption like COVID-19 and the models are still learning from the data but there's been a catastrophic change in the business climate, et cetera. How do we test these ahead of time so that we build the resiliency and not the data resiliency but we build the customer loyalty or the dependency on the software, the trust has been built in because we anticipated these changes. We studied it, we actually went out and ran them through different scenarios to understand what works and what doesn't and we recycled that feedback back into our product development cycle and we're constantly getting better at it. So we're not waiting until the end to find out whether something works and just saying, next year a ticket please, I'm on to the next project. That is not the model we want to have and the final strategic thing that I want leaders to take away is you have to sustain this behavior as a normal way of building indispensable data products. So it's not a one hit thing, you just do this one thing and then you go away. This is again, when I talked about changing the culture there has to be a mindset shift here and it's really about telling yourselves are we going to measure the quality of our work based on the outcomes we want to get or just the outputs that we're able to put out. We migrated to the cloud. We plumbed all this new data engineering pipeline, etc. These are easy to measure things. They are not the outcomes. They're not the human piece. They're not the decision support. They are not the last mile. So we have to sustain these as regular behaviors that our teams do and there's different ways to do that. There's training. There's bringing in people that help change the culture. Designers help with this kind of work and remember a lot of the work of design even if you're not a designer is understanding this as a team sport. We're facilitating the knowledge of our SMEs, our data scientists, our analytics people, our software architects and engineers, our product and business sponsors. It's a team sport and making sure the right people are in the room at the right time and also say just with the machine learning and AI piece, there's also not... And the reason I don't talk about UX so much is that UX doesn't include the people who are not users but who may be affected by the solution. So we think about un-serve populations that may be affected by the use of the technology. So you think about facial recognition and some of these kinds of services. Well, the guy at the security desk might be the one that's looking at the screen but me going to the store, I'm technically part of that ecosystem. And so human-centered design levels us up and it changes our mindset to think about all the parties that are affected. And I know that not everybody... Some of this doesn't matter.  But I think it's important to be aware it's not just about UX and the business. There can be third parties that need to be part of how we approach this as well. So the act of participating in design and bringing design and a product mentality into your process helps us know who needs to be in the room, okay? So will traditional companies embrace this product mindset of making human-centered data products? And why did I put the slide in here? Well, for a lot of the non-digital native companies and there's probably a mix here of people that come from software companies and traditional companies where you're working on software inside the business, a lot of these in the latter category, they want the results of what the startups and Silicon Valley and all these digital native companies are doing great things with data. What they're not doing is copying the way these organizations build software. They're missing steps and they're missing roles. And some of those roles are what I call the data product manager, product and user experience designers. And by not having them often, what it means is the SMEs and the proper business sponsors are also not in the room because no one is championing that cause. The technical people are focused on the technical work only. So we're missing, we don't even have the right team in place because we don't have someone in the room who knows how to figure out what the right team is and what the activities are that the team needs to do. So we need to copy not just the results and hope it's kind of like saying, well, we don't have any money for design but we want to be like Apple. And it's like, well, if you want to invest and have results the way a company like Apple does with design, you have to invest in those things too, the people, the processes, and make a decision that it matters. So this mindset here, you can either ride this waiver or you can create it and I think this wave is already coming and I just wanted to tell you that this isn't just my thing. Drew Smith from the International Institute for Analytics, they see this product thinking approach instead of projects and outputs but thinking about data products, even the stuff we don't sell to customers, like if you're servicing your internal business units, et cetera, a product approach to how you produce technology is very different than a project approach because you're thinking long-term and strategically. So they think this matters. Karim Lakhani from Harvard Business School who's an author of this great book, Competing in the Age of AI. I did a deep dive podcast with him which you can check out on my show but he said developing a product focus mentality is essential to the AI-centered operating model, especially this data and analytics product manager. I think sometimes what's missing here is that the expectation from the most senior levels and executives in our organizations see the possibility of what we can do with data as a source of inspiration. There's some fear there as well but they think it's going to be a source of innovation for the company as well. But if we're treating it as operations and cost savings and these kinds of things, then that's only half the battle and those may be some good places to start. But if you want to be innovative, you have to change your approach to building solutions there. And I think the expectations are going up for what we can do with things like AI. And I don't think the teams are necessarily keeping up with the expectations. And so you may say, oh, well they have inflated expectations. Well, part of the job is to kind of meet the two organizations in the middle to help them understand what's possible. It's to be going out and doing the work that produces innovation instead of looking at as I wait for a year ticket. Someone says, I need a churn model. I deliver a churn model. I go on to the next ticket. This is not how it happens. This is not how it happens at digital companies. And it's not going to serve you well. Gartner also in their chief data officer model, they put out this different model for what this role is. What was it in 2019? Moving from project-centric to product-centric, right? So this theme of product keeps coming through. And so today's big takeaway, your analytics, machine learning and AI solution will fail to produce value if people can't and won't use it.  I suggest you go there, check that out. There's a self-assessment guide. If you're developing your own analytics tools, processes, you can run your product through my guide and my assessment and start to learn what you might change. You can get the perspective of a product designer or user experience professional looking at that. What might I change? What might be wrong? What questions am I not asking? I'll also send you a link to the podcast, my insights mailing list. And if you'd like 30 minutes with me, you can book a call on my calendar. If you'd like to learn how I can work with you, just quick here. I run training seminars called designing human-centered data products for private teams and individuals. I also run it publicly twice a year. So in March, it'll be coming out again. I also do design strategy road mapping. I have an audit service. If you have an existing product that you know, it's not easy to sell. It's hard to use. It feels like an engineer builds it. Customers aren't seeing the value of all the IP that you've put into it. I can help you do an audit of that. And then there's custom design projects as well. So again, designingforanalytics.com slash big things, if you'd like to grab some of those free resources there. And I'm glad if it looks like I finished early. So here's the URL, my email address. I'm not super active on Twitter, but I'm at Rhythm Spice, if you'd like to hit me up there. And feel free to just shoot me a note. I'd love to have you in the community. And I hope that you all have a great 2020. Wear those masks, stay safe. Questions. Thank you so much, Brian. My God, I am exhausted. Lots of stuff. All the things you do. When do you have time to eat? All these things. I was eating actually during that, so. Offering. You're going to have so many people contacting you now. You know, we're seeing worldwide. So don't offer yourself so much because, my God, thank you so much for this super interesting talk, Brian. Starting with that photo that we know so well in Europe of Notre Dame in Paris caught in fire. What a good example of, you said, bad design can be harmful, damaging, and can be expensive. So probably design can change the world for good. And you mentioned, very interesting, the change of mindset that we have to shift from the output to the outcomes into the decision support frame. And you gave us these eight points to follow, which everybody took very good notes. Somebody's asking you a question in this regard. We have a question here from Nicola asking you how to show, convince people, that their decision making process is wrong. So there is no point to support it by any data technology. Well, so this is difficult, right? Changing the culture about the way things are now. It's hard and I think you have to understand that some of these things are going to take time for the culture to change. So what I often advise people to do, and this is kind of a different skill set, but what makes this person tick? What problems does this person have now that you might be able to help them with to build trust such that over time, they may start to listen to you more and take the recommendations that you give them. And I think attacking people and saying, well, your decision process is wrong. Let me just give you an example of this. Which shirt is the right shirt to buy when you go to the store? And how did you come up with the decision to buy that shirt? This gets back to the point that data is not sufficient to make decisions. It's not the only thing humans use to make decisions. So chances are, in that scenario, my assumption there is there's probably a good chance that this person is afraid that if I start adopting this thing, like the need for me and my job or the role, the thing that I think I'm valued for is going to go away and I don't want to let go of that thing. So this could be a problem with management or the way that the business has talked about our strategy and how our culture is going to change when we start using these kinds of tools. So I think if you can find another problem that you can help them with and start there, I think another thing is to learn to understand how do you make decisions today and what informs those kinds of things. You can present them with alternative options there and use it as a process of getting feedback and talk to other people as well and see if the culture is making those wrong decisions the same way. Is there a repeated pattern there? Because a repeated pattern of making the wrong decisions may suggest the culture. Something is reinforcing that model of making decisions and maybe management needs to be involved with this and you say, well, look, we can build this thing for you but guess what, no one's going to use it. Is this still a good use of our time and our budget and our resources because the evidence suggests they're not going to get used? You also mentioned actually that designing is a sports theme so maybe you're talking also about maybe this is that if this person, Nikolai, was asking us from a corporate point of view, I guess he's in a corporate. Is it more of a top to bottom approach, that bottom top approach or is it more a transversal kind of cultural change of that mindset? I think bottom up always works better with this kind of stuff. It's not something that's going to be dictated from below and I think, again, one of the best ways to get adoption for why this kind of stuff matters is to show the current state and show what's wrong with the current state and give visual examples of that. Like one of the classic things that the tricks designers like to use or use or experience people is we run through people using a piece of software now that we already know is bad. We run the video camera with their permission, of course. We run them through a study and we watch them sit there and struggle and we record these moments and we share these moments. We bring the engineers in the room. We bring the executives in the room and I'm telling you, if you want to convince somebody to change, just let them watch someone struggle with the thing that they think is just fine. This is one of the best ways to show that the culture needs to change and that's not about your opinion. It's like, well, just look at this is what we saw and there's something very powerful when it's people and not just data and report a study. Well, we run out of time, Brian. It's so interesting. You actually said that we are not robots. Thank God I was having my doubts and that we are not... Oh, I actually am. Well, yes, you could be because as I say, you don't eat. You have no time, so I wonder. But we're just to say that obviously, designing designers are important to get more visual. As you said, get visual in your 0.6, not only in MVPs and prototypes everywhere, be more visual. Designing is important. And I'm just going to leave the audience with a question, the one you said, are you riding the wave or are you creating it? Thank you so much, Brian O'Neill, for joining us on The Attic today. And we hope to see you next year. And stay with Aram in The Attic for more interesting chats. Our love and kisses to Cambridge, Massachusetts. Muchas gracias. Keep yourself... Ah, andale, orale, and we'll see you very soon. So the audience in The Attic will see you in one minute. We'll be back with our next speaker. Thank you very much. Bye.


Can you quickly bring us up to speed as to what's happening now in the state as to the jailbreak? Thank you very much. It was an unfortunate situation. There was an intel before the attack was carried out and that led to beefing up security around the correctional center at Canberra. We had soldiers there and also mobile policemen and that was why they were able to repel the attackers and that led to a situation where we did not have too many casualties. We lost a soldier unfortunately and another mobile policeman but also the attackers were seriously attacked by the security agents around the place. So many of the many of the emits from the correctional center that escaped have been re-arrested. I was briefed a couple of minutes ago. We have one or eight back to the correctional center now and many are still coming even on their own. So that is the situation right now.  Could it be that the security that you beefed up or the men that you sent to the correctional facility were not enough to repel these people as to for the jailbreak to even take place because if they were able to repel them they couldn't have even been able to get into the jail cells in the first place? Yeah this intel are always very limited in terms of information they give to you. You don't even know where they are going to attack. There was an intel that they were going to attack. Places to take over. Another places in the state. So we beefed security because we have soldiers there and we have a movement there but the attack was a very highly coordinated one and it was a very serious attack. So I think they were unable to stop them from attacking the correctional center but they reacted very swiftly because of the robust security architecture that we had in the state and because also the governor gave the marching order to the state security advisor to ensure that immediately the security agents were mobilized to support those on ground there to be able to ensure that we record less casualty. We're able to achieve that largely because of the security network in the state not just the conventional security agents alone but the hunters the local vigilante and all of that were able to track the inmates to be able to bring them back and also the attackers went with very serious gun wounds. We know that many of them will be arrested soon. We have already made play to health institutions not to treat people with gun wounds until they get clearance from a security agent. So we are tracking them and we are very sure for this not a place you can come to do such a thing. We are tracking them and in a couple of days we are going to apprehend them and bring them to justice. I'm curious and I know that you may not have as much information as the person who runs the correctional facility but who are the people who are in the custody of this correctional facility and why would why do you think that such heavily armed gunmen would want to make a hit on that correctional institute? What exactly do you think that they were after? Thank you very much. It is on record that very high-profile criminals are in that correctional center. Some of them are bandits, some of them are Boko Haram people that were arrested, some of them are standing trial, some of them have been convicted. So it is most likely that it is these organized criminals with very serious network globally that have been able to come there to wreck this kind of havoc on that correctional center. It is too early to go into conclusions but we know that they definitely came there to rescue some of their colleagues. Has there been any such similar case in Kaba or even in Kogiste before now or is this the first of its kind and what what is to say that this might not be a test case and that they might want to come back and what measures is the government putting in place to make sure that that never ever happened? This is not, it happened a couple of years ago at Koton Karofi, the Koton Karofi Correctional Center and when you look at it it is about national issue now because a couple of months ago we witnessed the same in Nemo states and now we are witnessing it also in Kaba. So it shows that the federal government will have to do more to strengthen security around their own agencies so that they will not be vulnerable to this kind of attacks and when we have this kind of attacks they should be able to have enough security on ground to be able to repel it and we as a state we as a state we were able to master a lot of security strength to be able to keep these at bay but you know it is a federal government institution and expect that the federal government will be able to do more. Our intervention was to support the federal government and also to ensure that our people are not caught in the crossfire so we will continue to support them but they have to play the leading role in ensuring that all correctional centers across the country these are things that have been happening. They have seen it in Nemo a couple of months ago and that is the right signal to the federal government to ensure that these centers are seriously secured. Kingsley Farnwell is the Honourable Commissioner for Information called you state. Thank you very much for speaking with us. Thank you very much. And don't forget to hit the notification button so you get notified about fresh news updates.


All right, let's talk about communicating with the boys. Hey everyone, Dylan Schumacher, Citadel Defense. And we're gonna talk about comm setup here today, specifically as part of a Minuteman setup and what that looks like, what would maybe make that different from a non-Minuteman setup or something like that.  I will put a link in the description or maybe put one of those fancy little cards in here before, but I've talked about this before about theory of radios in general, and if you, as an individual, should have a radio on your kit at all, that's a different discussion. I've done a different video on that, but let's just move past that.  Now, in this example, what we're talking about is a radio on my rig to talk to other members within my team. We're not talking about a radio to radio back to a talk or to talk to a different team that's a mile or so away or to talk to some LP, OP that we put out there or something like that. We're not talking about any of that. We're just talking about radioing two members in my team who I'm probably not gonna be separated from by more than 50 to 100 yards at a stretch anyway, okay? So you gotta keep that in mind because that explains how this is set up because the first thing on here is a high loss antenna, okay? So this is just a bail fang, waterproof radio thing. It's the, what is this? GT3WP, if you can see that down there, okay? It's basically a waterproof UV5R is essentially what it is. And what I put on here is a high loss antenna, right? Just a short little stubby antenna that's gonna have a high loss. Now, why would you do that? I did that because I don't want my signal to go far. I don't want to be sending out radio signals, hundreds and hundreds of yards, maybe up to a mile or two, right? I don't want to be doing that. I want this signal to be very short range and to die out very quickly so that I can reach the people I need to reach and then try to reduce as much as possible our team radio signature, right? So that there's not transmissions going outside of where they should be. So that is why the high loss antenna is on here, okay? Is to, again, try to reduce that radio signature so that my radio is hopefully only going a couple hundred yards to my teammates and that's it. And then after that, you can't pick it up because it's just the high loss antenna is not allowing that. So that's the theory behind the high loss antenna. This is on a BNC adapter, so you could swap it out, right? So if you start to say, okay, well I only want to talk to my team, I pop this on and then I keep another antenna in my pack maybe that I can rig this up to a jungle antenna or a much longer antenna for when I do need to punch out to that LPOP or whatever, you know, you can do different things there. But I do like having the BNC connector so that I can swap those antennas out in general. Again, when we're talking about just inner team comms, this is the high loss antenna is gonna stay on there but that way, again, I have flexibility to swap out to other antennas. I'll put a link to these in the description box below if you're into that kind of thing. All right, now coming up from the high loss antenna, I think Minuteman kit should be set up in one of two ways, okay? The first thing I think you should go to is a shoulder mic push to talk mic, okay? I think that for a couple reasons. One, it's as much easier to mount on your kit, right? If I'm in a vehicle, if I'm on foot, whatever, I can always talk into this thing rather than digging my radio and actually talking into the radio, right? That's just a pain. So this just makes life considerably easier and you should have one of these. So, we're gonna, we set this up this way for a reason, right? So that if I grab this chest rig and I don't have my helmet, I can still have comms, right? I can still have comms with my teammates. If I didn't get my ears, didn't get my helmet, didn't get anything else, just got this, I can still run comms, no matter what, I'm equipped to run comms. That's important because as a Minuteman, right, you might have to throw on your kit at a minute's notice and you might not have time to get your other stuff or you might forget your other stuff or whatever and the reason I like this setup is no matter what, I have my radio, right? As long as my battery is charged, I have a radio that functions and I can use it. Now, what we wanna do is we wanna layer this system, right? And what I mean by that is to be able to take what we got here, this is totally functional, works by itself and be able to layer it into other systems so that we can increase our, should we say, security posture and we can increase our ability and ease of use of comms. So the way we're gonna do that is we're gonna take a little three and a half millimeter jack, right, we're gonna put it into our push to talk and then what we'll do is we will route this into our helmet, right? So we'll take this that gets routed up and then I have this little lead cord here coming off for my ears that gets plugged into there and then for me, this wraps around here and plugs into here. You'll say, hey Dylan, why didn't you just plug this jack right into that ear? Well, this ear is on my left side of my head, right? And I run my comms on the opposite side, right? So, excuse me, this is on the right side of my head. My comms are on the left side of my head. So it has to go around to the other side of my head and I've just found this is a simpler system rather than trying to route this whole thing through here. It's just easier to plug it right in here.  But maybe you'll just find ears that actually plug in on this side or you'll run your comms on the right side or whatever. Again, maybe you can figure out a better system. But here's the point. By plugging our ears, right, connecting our electronic ear pro to a radio, now we've increased our security posture and I put that in semi quotes because when my radio is on, it's gonna route directly into my ears, which means that I'm not blasting radio traffic out of my mic, right? It's just routing all the radio directly into my headset so that I can still hear it but nobody else can. Now, I think overall, right, that's the preferred way to go, right? If I get to have my full setup, I get to have my druthers, I get to get all my kit on, this is how I'm gonna run comms. I'm gonna run them plugged into my ears so that I can hear it no one else can and then I'm gonna use this push-to-talk mic to be able to talk to my friends, right? Now, at one point, I did have a cheapy head mic coming off here, that's what this Velcro was for. So it's a mic running right to my mouth and then I had a push-to-talk fancy push-to-talk button on my chest and then I could keep my head up and I could talk directly into the microphone. And that's a good way to go if you know that you're gonna be able to get all your kit on. I ended up moving away from that system for two reasons. One, it was just a cheapy Amazon one so it didn't work super well. But even if it did work all the time and it was perfectly reliable and I had an expensive $3,000 setup here or whatever, I still don't like that because it necessitates having the helmet. Now, do I always wanna have my helmet on? Yeah, preferably, right? However, being realistic, there might be times when I only have my chest rig, right?  Well, then I have my rifle, my chest rig, I'm not bringing my helmet, but again, my radio is still actually usable if I needed to use that for whatever reason. So I like the ability to have an independent system that's tied just to the chest rig, right? With just the push-to-talk mic and the actual radio. And then I like being able to layer that on top of my helmet or my ears so that I can plug it into my ears and then I can have a more integrated system and then again, like I said, hear my own radio better. Especially again, when things get loud, having those radio comms plugged into your ear pro is gonna help you a lot because you can still hear the traffic even when things are bad and explosions are going off and stuff like that. So, big fan of this kind of system. This is my current system of how I run comms and how I have everything set up. Maybe you have a better system, if so, tell me about it. But this is how I would suggest you do something like this, because again, it offers that layer, that layering system in order to make it effective for you and no matter what, it's always gonna work on your raygazes. So, that's how I currently run my communications and radio set up. I hope that's helpful. Hope that gives you something to think about. Do brave deeds and endure.


Did you know that Microsoft Word 1997 introduced the office assistant called by its nickname Clippy? This tool looked like a little paper clip that popped up when Clippy thought it could offer a tip about how to use Word. Far more people hated Clippy than loved him. In fact, Brody, the author of Word 1.0, compared his feelings for Clippy to a cat's feelings about taking a bath. Microsoft retired Clippy as it rolled out Word 2007's Ribbon and most users were glad to see him go. Burn to Learn has a lot of tutorials about Word. Subscribe today!


So would you recommend this experience to anyone? I'd recommend it to anybody that's interested in any engineering for a science major Any advice? Apply. I recommend applying to this. Anything else to say about the program? It's a wonderful program. It's a wonderful experience and you won't forget it.