This Week in Enterprise Tech 526 Transcript
Please be advised this transcript is AI-generated and may not be word for word. Time codes refer to the approximate times in the ad-supported version of the show.
Louis Maresca (00:00:00):
On This Week in Enterprise Tech, we have Mr. Brian, Chee, Mr. Curtis Franklin back on the show today. Now, this might be the year for ai. That's why we go deep and talk about chat G P T and just how disruptive it might be. Plus, we have a great guest, Becky Trevino. She's Executive Vice President of Product Management Snow Software talk about enabling CIOs and IT pros alike to have complete visibility out of their software and hardware and even hybrid environments. Who wanna save this year? Organization might just get the visibility it needs. You definitely should miss it. Quiet on the set
Podcasts you love from people you trust. This is TWiT.
Louis Maresca (00:00:49):
This is TWiET. This week in Enterprise Tech, episode 5 26, we record January 13th, 2023, snowstorm of Data. This episode of This Week enterprise Tech is brought to you by aci Learning respected companies and government agencies around the globe turn to ACI learning year after year to help maintain their competitive edge supporting organizations across audit IT and cybersecurity readiness. ACI Learning keeps your team at the top of this game. Visit ACI learning.com. Learn more am by ZipRecruiter. Your company has goals this year. Find the right people to help you achieve them with ZipRecruiter or four out of five employers and a quality candidate within the first day. Try it free at ziprecruiter.com/tw.
Welcome to twt This Week in Enterprise Tech, this show that is dedicated to you, the enterprise professional, the IT pro, and that geek who just wants to know how those worlds connected. I'm your host, Lewis Mareska, your guide to the big world of the enterprise. And Boyd, do we have a packed full show today? But I can't guide you by myself. I need to bring in the professionals, the experts in their field. Sorry, the very on Mr. Brian Cheese Net Architect at Sky Fiber. He's a board member. He's a network expert. He's an all around tech geek cheaper. It's always great to have you on the show. What's what's been keeping you busy this week?
Brian Chee (00:02:16):
Hey man, I've actually been eating Learning Curve on Windows 11. I bought a real inexpensive little machine and discovered very quickly that I used to buy Chrome boxes because they're inexpensive and now of a sudden Chrome boxes have gotten insanely expensive and the Windows 11 machine was under half price of what the Chromebox was. Wow. Charging. Wow. So that might become my new kiosk machine. We'll see.
Louis Maresca (00:02:47):
Now what's the power on that machine? Is it like a, like a, kinda like a arm machine or is it, is it like low power?
Brian Chee (00:02:54):
It's a, it's a reason to be low power machine. Okay. It got bought because it was cheap <laugh>, it was under 200 bucks.
Louis Maresca (00:03:02):
Brian Chee (00:03:02):
And that included the Windows 11 license. So I'm not complaining. I just wanted something so I could eat the learning curve and get used to it. It, it eventually is going to get bolted onto the visa mo on the back of a monitor and become the machine to drive my 3D printers.
Louis Maresca (00:03:22):
Fantastic. We'll have to hear how that goes. Thank you. Sheer good to see you. Well, we also have to bring in the man who has the pulse of the enterprise real. He's Mr. Curtis Franklin and he's senior analyst at AMD Curtis. So he is great to have you on. Now you've been neck deep in research, haven't you?
Curtis Franklin (00:03:38):
Yeah, the research continues. We're looking at all kinds of things. I'm finishing up finally my research on cybersecurity awareness training, and then I'm gonna go be going into some risk quantification and also looking at couple of things. One's the issue of the AI in technology, especially in security. Just published something on that for Omnia subscribers this week. And I'm also gonna be looking at friction. In other words, when you apply security, how much does it cost you in terms of making things more difficult for your users? So lots of interesting stuff going on. Can't wait to dig deeply into it.
Louis Maresca (00:04:25):
Sounds good. Sounds good. Well spoke. Speaking about digging deeply, we have to dig deeply into This Week in enterprise news. Now, AI is gaining some serious momentum. It's fact, it's impacting all aspects of the industry. Well, op Open AI has had a couple of disruptive technologies, including the most recent arrival of chat G P T. We're gonna get into what that is and how it might just impact the entire world. I'll tell you, we are gonna talk just how you can have visibility into your software, your SaaS offerings, your hardware, and even your cloud. Today we have Becky Trevino, she's an executive vice president over at Snow Software, and we're gonna talk about enabling those CIOs and those IT teams to have complete visibility of software and hardware, your infras, even your apps, regardless of whether it's OnPrem the cloud, or even in hybrid environments. So definitely stick around lots to talk about there.
But first, like we always do, let's go ahead and jump into this week's enterprise news flips. You ever wondered about the, some of those pitches that are going on in Shark Tank? You know, even those incubators or even those shows at Tech Crunch where they claim they have, you know, a ton of users on their service, right? They already have a ton of users. I tend to wonder where they come from, don't you? Well, turns out from this entrepreneur AER article, they may have been actually fabricated sometime. That's right. J P m Morgan Chase filed suit against Charlie Javis, the 30 year old founder of FinTech startup, Frank, which the bank purchased for 175 million. Now, JPM charges are saying that Javis misled it regarding Frank's value by faking a massive list of customers to convince J P M it was a worthwhile purchase. Big no, no, no.
J P M filed the suit in Delaware naming Javez a fellow and fellow Frank Exec Oliver Mar. Now court documents depict an alleged deception that began in 2021, where Javis approached the bank about an acquisition. Now claiming that Frank had about 4.2 million users. That's a lot of users. The company had just under 300,000 users at that time. Big deception there. And of course, Javis is fighting back actually because of being fired due the whole ordeal with the separate suit. To me, this is like a method to project the possibility of being innocent. That kind of counter suit the suit notes that J p m requests, that Javis proof Frank had the number of subscribers claimed. However, Javis first refused citing privacy concerns, then invented not only the names of fake customers, but also added addresses, dates of birth and other personal information for 4.265 million students who did not actually exist.
JPM only found out something was wrong when they actually launched an massive email campaign campaign to those email addresses and found that 70% of them were undeliverable. Yikes. Now, I'm surprised by just more than a couple things, but I'm gonna talk about just a couple things now. The first one being that JPM didn't investigate this user base earlier before the acquisition. This is very weird that they didn't do this. I'm curious to hear more about that. But number two is that Javis even thought that she was get away with this whole scheme. Like I, there's no way that this, that they would ever get away with something like that. And, you know, going through that whole ordeal is really interesting. I guess it goes to show you bots or fake accounts alike will always blow even the best and worst of services.
Curtis Franklin (00:07:46):
Well, you know what, it's like you try something new, find some success, get all excited, and then spend all your time on that bright, happy new pursuit. But before you know it, the shine is worn off and you're looking for something more exciting to replace what has now become routine and boring. Well, that seems to be what's happening with a key group of malicious actors who have left their traditional direct attacks on the enterprise to focus on fundamental enterprise services, identity services like Okta. Last pass, developer focused services such as Slack and GitHub, and now development pipeline service providers, circle CL have suffered very public rather successful attacks that have struck at the heart of the enterprise application supply chain. An article published on dark reading, Lori McVety, a distinguished engineer and evangelist who also happens to be a former colleague of mine, says that the glut of attacks on core enterprise tools highlights the fact that companies should expect these types of providers to become regular targets in the future.
Now lately, attackers have been focusing on two major categories of services identity and that access management systems are one developer and application infrastructures are another. Each of these services underpin critical aspects of enterprise infrastructure. Developer services and tools have become another frequently attacked enterprise service. For example, in September, a threat actor gained access to the Slack channel for the developers at rockstar Games where they downloaded videos, screenshots, and code from the upcoming gram theft auto six game. Why are attackers suddenly focusing on these services? Generally, it's because identity and developer services give access to a wide variety of corporate access from application services to operations to source code. And compromising those services can be a skeleton key, allowing access to the rest of the enterprise. Now, no one is suggesting that companies suddenly stop using these service, but it's time to make sure that they're being used safely. Experts recommend a zero trust approach to the identity and DevOps infrastructures and suggest that a fast method for rotating secrets, you know, things like cryptographic keys held in cloud-based repositories could turn out to be a true bargain when the inevitable breach takes place.
Brian Chee (00:10:21):
Well, this is something travelers might cringe at, and this is our from our friends at ours, Technica. Apparently over about a thousand planes have altimeter listening to signal signals in the wrong frequencies. The federal aid deviation administration will give airlines another year to fix or replace airplane altimeters. That's radio altimeter that can't filter out cellular transmissions from outside their allotted frequencies. Now be careful allotted FIEs in a notice of proposed rulemaking released yesterday, the F A a proposed a deadline of February 1st, 2024 to replace or retrofit faulty altimeters, which are used by airplanes to measure altitude. Out of 7,993 airplanes in the US registry, the f a says it estimated that approximately 180 airplanes would require radial altimeter replacement, and 820 airplanes would require the addition of radial altimeter filters to comply with the proposed modification requirement. The total estimated cost of compliance is 26 million. The requirement could finally end the dispute between the aviation and wireless industries, which has prevented at and t and Verizon from fully deploying 5g.
On the C-band spectrum licenses, the wireless carriers purchased for a combined 69 billion, that's billion with a B airplane. Altimeters rely on a spectrum from 4.2 gigahertz to 4.4 gigahertz. But some can't filter out 5G transmissions from the carrier spectrum in the 3.7 to 3.98 gigahertz range. In a quote from the f a says, some radi altimeters may already demonstrate tolerance to the 5G CB band emissions without modifications. Some may need to install filters between the radi altimeter and antenna to increase a radi altimeters tolerance. For others, the addition of a filter will not be sufficient to address interference susceptibility. Therefore, the radio altimeter will need to be replaced with an upgraded radio altimeter. Now the bottom line is, this is the big complaint is against terrestrial towers, but really people, you, you turn off your phones when you're on the plane. Come on. We've been doing this for decades now. And with more 5G phones coming out, that means you're gonna be putting out signals that are closer to that radio altimeter. Please turn off your phones when you fly.
Louis Maresca (00:13:14):
Speaking of the f a and planes, this past Wednesday, if you were traveling, I'm pretty sure you probably didn't make it too far. There was an outage in the notice to air mission system notice as noam, which warns pilots in advance to potential hazards like weather and runway closures. And according to this Forbes article, the 30 year old system was not responsive and it looks to be due to a damaged and corrupt database file. Now, there's an investigation in what caused the corruption, and at this point, they aren't sure whether it's due to human error or malice. Now, still looking into it now, what has been uncovered as part of the investigation was that at least one of eight contractors have had had access to the system and data and could have corrupted the file. Now, the resulting impact was that travel in the US pretty much came to a halt over 10,578 flight delays and about 1,353 cancellations.
Now, coincidentally, on the same day, Canada's Noam system also had some disruptions and interruptions as well. But it will say exactly that will stay exactly that, a coincidence. Now, we all know even some of the most advanced systems and services in the world are subject to disruption due to mistakes. But we've seen some of them in recent years that have taken down places like Amazon, a w s and many other services that are out there. We've seen it. Imagine a system that's been around for, you know, 30 years like no tam. It can be fragile and a lot of different aspects. Now, it's unfortunate that they weren't able to restore the database fast enough to impact flights and not cause groundings. Now, according to the F a A, they've actually earmarked some of their 23.6 billion in funding this year to help modernize this particular system.
Now, I guess if a single corrupt file can take down a critical system flight system like this, the system, you know, may need to be upgraded and hardened just a bit. Well, folks, that does it for the blips. Next up, we have a great discussion. We're gonna talk about chat G P T, but before we get to that, we do have to thank a really great sponsor of this WEEKEN Enterprise Tech and that's ACI learning. Now. If you love it and you're an IT professional, it's a given. You will love ACI learning. They have a lot to offer you. A c i learning has fully customizable training for your team and formats for all types of learners across audit, cybersecurity, and IT from entry level training to putting people on the moon. A c i learning has got you covered. According to the Robert half, 62%, 62% of tech managers plan to expand their teams in 2023.
And 88% said it's challenging to find skilled workers to field available roles. Now take advantage of these opportunities and let a c learning help you get your IT and tech career started with ACA learning. Gaining these certificates and certifications and increasing your salary has never been easy. Now, the most sought after jobs, tech jobs that is for 2023 are here. This cloud engineer, database developer DevOps engineer, these sound familiar, don't they? Front end developer help desk tier two and three, network administrator, network security engineer, software developer, software engineer, and systems security Management of the average salary for an entry level network administrator is $75,000. Think about that. The A C L Learnings Network Support Specialist program provides the training needed to pursue or advance your career in networking, preparing you to take the CCNA certification exam and Comp Ts security and network certification exams. Now, ultimately the big difference in salary comes down to specialization.
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That's ACI learning.com. And we thank ACI learning for their support of this week. And enterprise tech Open AI's technology is at the heart of some of the trends and interests in the so-called generative ai, what we call the, you know, the whole term encompassing the algorithm that can generate text, images or other data. Now we're gonna talk a little bit about something you may have heard of just recently. Now, in December, open AI brought their technology downstream to consumers to help highlight the power of their latest work that they wanted to debut called Chat, chat, G P T probably heard of it. What is exactly chat, G P T. We're gonna get into a really gr conversation about it, but I wanted to just get into just the baseline of it. Now it's short for generative pre-trained transformer. Pretty clear to you now. Well this is a AI model that uses deep learning to generate human-like text created by open ai.
Now the name itself requires some unpacking. Let's talk about what that means. Generative actually refers to the ability to generate text. Pre-Training means it uses a model from one machine learning task to train another. Similar, how we as humans draw our existing knowledge out when we learn new things. In this case, G P T involves pre-training on a much large amount of text that's out there. And transformer is a kind of neural network that actually historically learns about relationships between all parts of data sequences and is seen as a breakthrough, really a breakthrough actually, because it understands context and nuances better than previous approaches. Now all of this is powered by G P T three. It's a text generation algorithm that was developed by open ai and it has actually fed massive amounts of quantities of text data and in fact, research from the web and other sources.
Then given additional training on actually how to answer questions, it's pretty magical. Now, the underlying check technology is the same that powers other chat edges like chat Sonic, which making is actually making an interesting version of chat. G p t itself a chat. G p t doesn't use the internet to locate answers like some of those other assistance that shall remain name let's say, you know, that the ones that you know give you recipes and definitions of weather. Yeah, they, this is a little different than that. Instead of constructs a sentence word by word selecting the most likely token that should come next based on its training that chat. G P T was trained on the, the collective writings of humans across the world past and present. And this means actually con contains some of the same biases that exist in that data as well.
It also appears in the modeling as well. Now even before chat G p t went viral, there was G P T three playground. It was actually something that was a platform that was public. Way before. Unfortunately, the tool didn't create, create as much buzz because this is more consumer based. Now, there, there are other implementations of this in fact view.com, which is a search engine that was bootstrapped by two Salesforce engineers. It's actually powered by GPT T3 as well and is trained as a train based chat bot. And they also have you write was actually exists earlier than that, which is a GP three text generator for writing emails and other documents. Now I want to bring my co-host back and be, before we get to that, the interesting thing about this is the fact that this particular service will weed through all the data that's out there and it'll find statistical patterns and text rather than connecting words to meaning.
So if I ask it for how do I do a recipe, it's gonna go connect that to a particular recipe on the internet and gimme facts. But in the case of chat, G p D, it's gonna do more than that. It's gonna explain some things to you. It's gonna explain facts and figures that it finds it's explain text and, and it's gonna say give you some research on how to do those things right. However, some of those things you might find interesting because some of them are actually seen fabricated, you know, facts and figures because it might actually misunderstand your question. It might exhibit bias because of some of the training data. So there's some, some improvements that need to be done out there. Now the interesting thing here is the G P T three uses about 175 billion data points. And that the thing about that is it's a massive factor difference from its previous predecessor or J P T two, which only used 1.5 billion. Now, I wanna bring my co-host in here because the tech industry is really excited about this, but they, a lot of the consumers out there, people out there are really kind of shadowed. It's shadowed by the parlo tricks that it can do and some, but some people are actually saying that's this is gonna really disrupt the market. What do you guys think, Curtis? Brian, you guys have anything to say about that?
Curtis Franklin (00:22:35):
Well, I've got a couple of things to say. First of all, it depends on which markets you're talking about. I think there are some markets that this is beginning to have an impact on. Some are fairly predictable. We know that there are students who are trying to use chat G p T to write their research papers. I find it amusing that there are also AI-based systems that try to detect AI generated papers. So you've got this battle going on. So we've got that a little higher up. That particular food chain. We know from an article that was recently in nature that when they submitted about a hundred AI or chat G p t generated summaries for hypothetical academic papers human reviewers only picked up about 60% of these as being AI generated. So we've got that aspect of it.
We also have the knowledge that there are news organizations. I'm going to do bunny air quotes, news organizations out there that are doing articles based on AI generated texts. So you do have some markets that are in the process of being disrupted. I think the real question is the extent to which two things happen. One is that people recognize that they are dealing with an AI generated text stream based on the text stream. In other words, can they look at it? And in the same way that if you listen to one of the AI generated narration on a video that you find on a social media site, you know that it's generated because of the lack of inflection and things like that. And how many companies will tell those who are listening that what they're listening to was generated by an ai. It's that second one that I think is a more interesting question. And, and the one that I, I'd like to come back to after we hear Brian and what I'm sure is gonna be his very powerful opinion on this,
Brian Chee (00:25:18):
Right? Well, actually, I'm gonna set the Wayback machine just because I love to do that. And say approximately five years ago, thereabouts, Curtis made a prediction in one of our end of the year shows. And his prediction was basically that the predominant human computer interface of the future was going to be voice. We all basically agreed with them, but one of the things in the back of my mind was where are the stepping stones? We're not going to immediately jump into, you know, a computer that can talk to us and hold meaningful conversations. One of the stepping stones needs to be something like what chat G p T is providing, the ability to build multiple, multiple, multi more complex answers and conversations to query the person talking to it on how to cl asking for clarification. So a lot of the examples were saying like could you tell us about Christopher Columbus arriving in America in 2015?
Well, that's a nonsensical statement. It's can't possibly be true because Christopher Columbus didn't arrive in 2015. But the chat G p t example said, well, it, he didn't arrive in 2015. You know, maybe you want something along this line, or maybe it's someone else named Christopher Columbus. These are the pieces that I think are really missing from the human computer voice interfaces. So if anyone's used something like, you know, the Amazon or the Google voice assistance or even any of the natural language systems out there, one thing you find very, very quickly is, I need to learn their nouns and their verbs. And most of the systems, even when you start building them by typing them in, they only can handle one verb and maybe two nouns. Well, this system, because of the way it's working, it's allowing you to have a much more natural interface.
Something that doesn't feel quite so stilted. And I think this is one of the big reasons why academics and some other people are kind of freaking out because now for the first time, or maybe one of the first times, we can start having much more natural responses that could potentially be mistaken for a human's work. It's going to be really interesting. I think like any advanced technology, both good and bad, can be done with it. I, for one, want to think about the good things because I really want, when I'm old and frail and confined, maybe to a wheelchair or a walker, I don't wanna have to walk all the way across the room to be able to just turn on the lights. I want to be able to have a conversation with my home to tell it what I would like to do. And I would like to use a natural language interface, which I hope this was a stepping stone for.
Louis Maresca (00:28:44):
I agree. I think this is, this is gonna be a very interesting technology. I know a lot of organizations out there are very excited about the potential. Now they're, we're talking about a little bit like Curtis had talked about potential disruptors here and what some of the markets it can disrupt. Now I think we should go through some of the other ones, cuz there's some pretty interesting, obviously the most prominent one that I see quite often is the fact that Google seems to have the, the, the throne of being the most used search out there today. Now they obviously provide based off their own ranking algorithms and and so on. But the interesting thing about this model and this, this, this, this system is that it can produce data to in one single place without you having to go and search around the world. Do you guys think that this is something that we're gonna place the common search that we have today?
Brian Chee (00:29:38):
I don't know that I,
Curtis Franklin (00:29:42):
I I don't know that it's gonna replace it. I think what it's gonna do is give us a new way into it. In the same way that I'm going to show my age. I remember when before there was Google, there was the reference desk at the New York Public Library, and every writer that I knew had the number for that desk in their, their daytimer or in their Rolodex. And you would call and you would talk to a human being and they would sometimes go away for a few minutes. Sometimes they would call you back and give you an answer. I suspect that what we're going to see is a lot more human questioning and voice response to simple queries. You know, a lot of us base our expectations for this kind of interface on what we saw on a handful of television shows and movies.
Star Trek being one of the most frequently cited, if you recall, what we have there is that people talk to the computers for commands and fairly straightforward answers to analysis. When they wanted to get deep, they were holding onto a pad or looking at a screen. We have different ways of looking at data when the data is of varying complexities, varying length and varying densities. And I think that's gonna remain. But I have to say, I do love the idea that at some point I will get to the, the place where my main interface with my computer and the internet is voice rather than trying to see just how accurate I can be with my thumbs.
Brian Chee (00:31:46):
Well, I'm gonna add to that only because I actually took real honest to God college, well actually graduate level classes on search methodologies because those reference librarians were using access to some databases, which were ungodly expensive. You know, these database companies charge an amazing amount of money for what is actually pub sometimes publicly published. Being able to search things was a skill that you had to learn and librarians were very good at. I actually took classes on how to do that. It actually got down to the point where I was using natural expressions, you know, those weird little characters that you'd mm-hmm. <Affirmative> use and see and so forth. I was starting to use those so that I could string together multiple verbs and multiple nouns so I can get more accurate results. <Laugh>, well, Google and so forth started making that a lot very redundant. And now what this is doing is it's going to make it a lot easier to get very sophisticated answers without having to know a specialized language. I think that's gonna be the key, not needing that specialized language to do queries. And I, I am I for 1:00 AM very much looking forward to that.
Louis Maresca (00:33:15):
Agreed. Agreed. I think, I think this is gonna be really interesting to see how it will disrupt some things. I, I think some organizations will, especially they use chatbots today to help with customer service, with sales, with marketing. I really feel like this more natural sense that will be able to not only bring in data from the company and from other sources in a much more natural way, will, will definitely help with that experience. The question is, how fast can they commercialize it? Now, I I think there's one other one before we we move on cause we do wanna get to our guest is the fact that the cyber security industry, they're a little worried about this. And I think it's just because of the fact that the, this particular technology has the ability to you know, mimic things you know you know, potentially mimicking, you know, user content or, or, or users themselves. They can generate content that might even look legitimate. Like for instance, emails from a particular service or, or application. You know, in this case they are considering it almost harmful content. Do you guys feel like this is adding to the problem that's already out there that we call fishing and cybersecurity and ransomware and that kind of thing?
Brian Chee (00:34:35):
Yeah, I, I'm looking forward to a computer being able to sell me. Which squares have bicycles in them?
Louis Maresca (00:34:42):
<Laugh>, <laugh> do it for you, huh? <Laugh> those captions, right?
Curtis Franklin (00:34:48):
I, I like this, but I, I do think that this has the the, the possibility of impacting especially something called A B E C or business email compromise. That's where someone has compromised a, an email account so that they are sending out spearfishing messages, not from a spoofed email account, but actually from a legitimate email account. When you can also spoof the content that that particular user might send or using some of the voice spoofing technology that is now coming online, do a deep fake of their voice and include an attached e voice recording that ask the recipient to do something. We're starting to get into a whole new level of potential fraud. So I think this is going to put the onus on fraud detection training and fraud detection technology. They have their job cut out for them for the next couple of years at least.
Louis Maresca (00:36:00):
Agreed, agreed. Well, I think, I think we've probably talked about that. And Brian, you have anything else to add to that? I don't.
Curtis Franklin (00:36:07):
No, I'm good,
Louis Maresca (00:36:08):
Thank you. Okay. All right. So I, I definitely think there's gonna be more to come. I obviously we've seen a lot of interest from different players in the industry and we might just see some some potential funding in that area to help play out some of the technology, but it'll be really interesting to see how this all plays out in the, in the coming months. Umm, let's looking that 2023 might be the year of ai. Well, to see, well folks, I'd say that that is done for the bites, but before we get to our guests, we do have to thank another great sponsor This Week enterprise Tech, and that is ZipRecruiter. Now, this is the busiest time of year for really any business, and you're building your goals for 2023. And all this means is finding the right people to help you accomplish them.
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Becky Trevino (00:38:52):
Hey, excited to be on. Great to meet you all.
Louis Maresca (00:38:56):
Well, thanks so much for being here. We now we have a large audience and they have different, they're all at different points in their career and a lot of them like to hear about people's origin stories. Can you take us through a journey through tech and what brought you to Snow Software?
Becky Trevino (00:39:09):
I'm an engineer, like a lot of you. I graduated from the University of Michigan with a dual degree in business and engineering. Started off my career in engineering at Dell and just, I think I've just progressed with the servers. I started at Dell when primarily we were working on premises and shadow. It was the big thing. Then I moved on to Rackspace when we were, I call it the Pepsi to Amazon's Coke because we had launched OpenStack and we're founding that and I was just at the onset of the cloud with that really strong adoption when we were really moving in from the cloud being just something that was test and dev into now being something that was production. So I went through that era with Rackspace and then I joined Snow at what I call the end user era, where we're now seeing this shift in buying patterns from, you know, B2B technology. Where it moved from being the C I O made all the technology decisions, then it was lines of businesses and now we're seeing end users, whether that's DevOps or product led growth that are really deciding the use of technology in their organizations. And that's what we help do at Snow is really manage the technology that organizations are using and help get it the visibility they need into what software and cloud services are running in their environment.
Louis Maresca (00:40:23):
Okay. So I wanna dig into that because I think a lot of, there are some organizations out there that sound like they're doing something similar, but my guess is they're not obviously a lot of organizations, they are trying to juggle things like automation, data intelligence, you know, trying to handle all these different multi-cloud environments. It's pretty challenging if you ask me, especially for smaller IT organizations and the the CIOs of the world. What is, what is Snow Software really doing in this space? What, what kind of technology are they bringing to the table to help with all of this?
Becky Trevino (00:40:54):
We started off really helping customers manage software asset management. And that was a big question that organizations had in terms of enabling you to know how many licenses of Oracle s a p Microsoft you really needed in your environment. And that's where we really built our database. We tell, we tell the world that we have the world's largest database of, of software publishers in the world. Mm-Hmm. <affirmative>. And so we built that knowledge really helping organizations rightsize their technology. And back when organizations received audit letters and some still do, we helped them, you know, really establish their license position. That was empathy of what we did. But in the process, what we got through that was a really strong identity around we know what organizations run in their environment, so we have a good idea of on-premises technology. Then we started to grow and understand, well SaaS is becoming this really big thing.
So we extended that definition to build, building our database and understanding and recognizing and identifying all of the SaaS technologies and organization runs. Then as the cloud began to take on, we made an acquisition of ambos, which was a leader in hybrid cloud management and then got to understand more of the hybrid cloud environment, the cloud pieces. And most recently built some partnerships, infin ops to really help an organization understand, well how much are you spending on Azure a w s, et cetera, when it's just a public cloud environment and not extended into your hybrid cloud environment. And so that's how we've kind of just grown our business from starting off with Sam into growing it into SaaS and then now having a broader view into hybrid cloud and pH ops.
Louis Maresca (00:42:34):
So let's talk technology for a second. The, now the interesting thing I did to hear this, all of this is built, especially the, the Atlas platform, the Snow Atlas platform that you have is actually built on Azure. Now are you, were just ingesting all of this data from these organizations, maybe building some data lakes here and there and building some analytics around them or there, is there some policy engines around it? How, how is this all kind of, without giving too much of the, of the secret sauce away, how's it built?
Becky Trevino (00:43:00):
So a lot of it we end up getting ingesting a lot of that data and it's really up to the customer how they choose to do it. So for example, we use often a lot of our customers use Microsoft S E C M to bring in the data themselves. Mm-Hmm <affirmative> into it. Oftentimes what most customers do, because they want that granular data, in particularly if you're working with it, you end up using the snow agent. So we'll deploy an agent into an environment where you really need to understand how much Oracle am I running in my particular data center. Other times we use technology like a browser extension that enables you to understand that true usage of sas. Cuz what a lot of organizations will do for you is say, well, I can look just at your financial ledgers to understand how much sass you're using, but when you look at it from a shadow SASS perspective, which a lot of IT people are really interested in, you wanna really understand usage. So that browser extension enables us to really understand who actually lasts access a particular SAS site on technology. And then oftentimes with, you know, finops, we're really just ingesting in data in from a W S Azure and how much you're actually billing statements are in there. So we get permissions there in and we bring that into our data and then we end up being able to ingest that and make insights and recommendations from there.
Louis Maresca (00:44:18):
Interesting. So I, I want to just kind of pivot just to for a second. I know that there was a recent report brought out by snow of the 2023 IT proprietors priorities report. And I know you guys surveyed a bunch of IT leaders across the industry, so there's some interesting things in here. Can you maybe give, take us through maybe an overview of some of the background and the findings that you saw in that report?
Becky Trevino (00:44:39):
Right. So a big part of Snow, what we end up enabling customers to do is just maximize their technology. We want customers to be able to spend more in the areas that actually work for them. So part of our IT priorities report is really that lens is from an IT organization, where do you wanna spend more? And ideally what we found from customers that in an ideal world in 2023 where we believe that our purses are gonna be a little bit tighter because hey, we don't have as much budget to spend. We see that CIOs are already understanding that they're spending too much post pandemic. A lot of us thought that organizations would just right size technology. Instead, they invested a lot more in technology. Think it was Satia nadela that sell said that he saw two years of digital transformation in two months. And as a result of that, what the IT priority report shows a lot of tech stacks are bloated right now you're in general, most IT organizations are spending too much on their technology.
And so there's a vast majority of CIOs today that are looking at, well, how do I reduce how much I'm spending? Not in critical areas, but in understanding that I probably bought too much. I'm probably not getting that return on investment. And so the priorities report really tells people is, well where exactly do you wanna do that spend? And a lot of it is really around application rationalization, which is, Hey, I want to be able to really go back to my organization and understand, do you really need Smartsheet and Trello at the same time? Or do you actually, can we just consolidate one and generate some savings? The other part of it that we uncovered is that there's this balance for it between, I got a right size spend in my organization, I have to grow digital transformation, but I also have to keep security top of mind.
And what's going on in technology for a lot of CIOs is, I think we've known for a long time that this technology spend decision making has moved to lines of business. And as that's happened in particularly with the growth of DevOps and product led growth, is that most CIOs and most IT leaders no longer know what technology is running in their environment. And on average organizations have 190 SaaS applications running in their environment. And with that, most of that's being procured without the knowledge of it. So what's coming out of IT priorities report is that shadow SaaS where it just does not know that this is being procured or used. Particularly that usage component where you can't really rely on that financial statement is becoming a bigger deal for CIOs and IT leaders.
Louis Maresca (00:47:17):
It's amazing how many I I, I've been an organization but sat sitting with organizations where people were using applications and their IT department didn't even know they were using it. It's pretty interesting to see <laugh> to see some of these organizations use these things. It's compliance problems too. I do wanna bring my coho
Becky Trevino (00:47:32):
But also buying patterns as well, right? Like if you think about it, you're a technologist, you know, what does DevOps wanna do? No one wants to take the sales call anymore. And then once you take the sales call, you have to engage procurement and the way that people wanna buy an I I'm gonna try it out. Okay, I tried it out, it was successful, then I'm gonna have my buddy over there, try it out, then I'm gonna have that engineer tried. But pretty soon 10 people are using it and then, okay, now I'm gonna move from the community addition to the paid addition. It's really hard for IT to know that without some type of usage mechanism of what org of what the people in the organization are using. So it is a big compliance and issue and, and just a security issue at large beyond just the, the cost issue that we're unaware of.
Brian Chee (00:48:11):
Louis Maresca (00:48:12):
Right. Well I do wanna bring my co-host back in cuz they're jumping the bit. You wanna ask some questions? I'll throw to you first, Brian,
Brian Chee (00:48:18):
Well, you actually answered part of the question in that shadow SaaS or shadow IT in general is a huge problem. I saw it back when PCs started sneaking in the door and SuperCalc came in and all of a sudden mainframes weren't, you know, yeah. Were it, you know, it was, it was crazy. But what other things can you folks help with? Because cleaning up behind orphan or zombie project is a big deal. You know, how much further can we go? Is it just finding orphaned microservices and virtual machines, or can we actually start looking at maybe underutilized things or thi or what have you? What other tools do we have in cleaning up Shadow sass?
Becky Trevino (00:49:12):
I think a big portion is that what we call at Snow that big, the first part is really getting, establishing that level of visibility. You wanna know what's going on in your environment. Then the next two parts of our software, really what's insightful for customers is first we give you that insight in terms of what do I do with that? Like, this is being underutilized, that's being used by this department. So we really help the help our users hone in on where to spend their time. So that's the insight. And then we give them recommendations on, you know, particularly around application rationalization of you have too much of that, let's get rid of it because that's a, that's a risk area for you. And then, hey, this particular user didn't use that. Maybe they should get rid of it. So we try to drive automation and recommendations into the process to enable people to do more than just, Hey, we recognize a bunch of mess for you. We try to identify the areas in which they can actually hone in to just drive value, whether that's from a compliance perspective or that's from just a cost perspective where they just like to streamline and just get less bloated.
Brian Chee (00:50:18):
Yeah. I'm I'm also really curious, do you folks actually help on the social side? You know, trying to tell someone that you are over utilizing something or you're bloated, that's a tough thing to buy, especially if you are really invested in it. Does your, do your people actually help with that to help, you know, change the personality of the IT organization?
Becky Trevino (00:50:48):
I think a big part for us in the organization is really empowering it. So I think at the end of the day, it, it really needs to work with the lines of business in terms of, hey, you don't wanna be the police anymore, right? You don't wanna actually tell people, Hey, get that away. But what we try to do is, is give a C R O that insights that say, here is how much you're spending on technology. Your technology spend is up X percentage year over year. Is that really where you wanna spend your dollars? And so that's, and then here would be some of our recommendations as to where we would streamline that budget. So I think that's the social perspective at the end of the day, particularly when it's workloads and applications that sit outside of it, you know, that's really a sensitive topic because you can't really go to other parts of the business and tell them, Hey, I'm gonna reduce your budget without that action.
So we try to enable just the data so that our CIOs could have really good conversation with their peers and really just elevate the IT function to be able to say, look, at the end of the day it is responsible if something goes wrong with technology regardless of where it was procured. So we give them the data to have those good conversations with their peers. And sometimes the decision is to keep a tech source of technology. Like at Snow, for example, we used our own technology to identify that we should streamline on teams, but at the end of the day, while the data suggested that our organization as a whole said, Nope, we actually want to use both teams in Zoom, but the data within the product enabled us to have the decision and have the conversation about whether or not that was the best use of our resources and dollars.
Brian Chee (00:52:34):
Curtis Franklin (00:52:36):
Thank you so much. Well, you know, one of the things that, that I'm curious about, you, your company uses a word that you see an awful lot in the industry these days, and that's visibility. And one of the questions I, I have is why A, is that such a hard nut to crack and B, are there so many different approaches to visibility? It seems like the sort of thing where there would be one best way into it, but instead we seem to have a thousand different ways of approaching telling a company what they actually have.
Becky Trevino (00:53:12):
I think most companies don't know what technology they're running. And frankly, it's probably the, the biggest challenge that CIOs have is that you really don't know and you live with a certain level of ambiguity and you just have to because at the end of the day there's this, there's this never ending battle between governance and agility, right? You wanna be able to lock things down, but then the other people wanna, you know, I was actually one of those engineers that used to work at Dell and was part of the shadow IT problem because I would, I would work around Dell it because I wanted to work faster than what the Dell i t roadmap or policies would let me do. And so I think with that, it's just this constant battle within organizations and we've just enabled ourselves to live with it. And there is no perfect way to, to derive visibility and to know what assets w are exist in your organization.
And there's plethora of it. I think, you know, I don't know, we, we've solved that problem just because I don't know that it's been the big priority for organizations. We know that I t s asset management, for instance, which we're a leader in, sits at the foundation of the NIST framework. But, you know, sometimes we decide, hey, there's other avenues or different problems to prioritize. So I think that has been the challenge is that while we know it's important, we know that visibility is there. We haven't always prioritized it as it made to the top of the list of, of things that we need to action on. And that's why that challenge is, is so robust and remains in the industry and why you have 20 plus different ways of, of achieving visibility.
Curtis Franklin (00:54:44):
Well, since I brought up the many different ways and, and you answered it, I'm, I'm gonna take the next step and talk about the fact that it's a market where there is a huge player company that sounds a lot like the hobby of going into caves. Do you find that your, your customers look to you to solve different problems than they would get from that massive market player? Or are they looking to solve problems in a specific way? What, what sort of special things do you bring to the market?
Becky Trevino (00:55:26):
I think most customers come to snow to solve an IT asset management problem, which is, look, I need to know what I'm running in my technology so that I can help my IT organization to maximize the value of what we're spending. In the end, once you have that data, there's endless possibilities of what you can do with it. We've had customers that have built sustainability dashboards using that data that can help quantify that moving from shutting down a data center has reduced their carbon footprint by X, Y, Z. We've had customers that have used our data to identify how much Oracle, all the Oracle footprint in their environment to make their shift to off from on-premises to cloud faster. We've had customers that have used our data to actually sit there and you know, improve how they do it service management because they've now had their C M D D was a mess and they used our data to actually clean up ServiceNow.
And so the use cases of what you can do when you have a robust set of data that really almost this intelligence layer of all of the data running your technology are really endless. You know, our specific use cases of what we help customers do is to reduce their technology spend, manage risk, whether that's from an audit or that's through m and a that you've acquired a company and you don't know what they're running. And we definitely help companies do that. We help customers to, you know, have higher ROI from their investments and ServiceNow and B M C by through improved C M DB data quality. Those are somes that we've productized, but our customers have used this for endless use cases that they've either used our services team or they've just extracted that data themselves and have been partnering with their IT teams or other of our channel partners to get that end result for them.
Curtis Franklin (00:57:24):
Well, we're getting down towards the end of time, but, but I wanted to to ask a question. Is there any sort of commonality or, or a trend in the department or the job title that tends to approach you first from an organization? It sounds like once you are in an organization, your data and your services are gonna be used by a lot of different groups, but is there a, a single way that you tend to get into a group?
Becky Trevino (00:57:54):
I think one is whether that's a software asset management professional, a pH ops professional IT director who's looking to understand what exactly am I running from a SaaS per SaaS, like how much sass am I running? I need to bring down my SAS costs, or hey, I'm spending too much. So that, whether that's it t s asset management and IT manager or finops professional, one of those three titles is usually coming to Snow for some level of support managing their technology footprint.
Curtis Franklin (00:58:28):
Very good. Well, since we've now identified who asked, how does someone do it, if, if one of our listeners or viewers wanted to get started with Snow, what's the first step that they should take?
Becky Trevino (00:58:42):
I think a good one is to really go to our website, identify information. There you can just download some content within it. Some of our products either offer a demo or a free trial. What a lot of customers are using us right now is a lot of cloud providers have, you know, raised their prices. So right now, for instance, a lot of folks are coming in to snow to talk through a seven day assessment that we help customers understand how much are you spending in on your Azure or are your a w s and let's sit here and talk about how we can right size that for you.
Louis Maresca (00:59:16):
Fantastic. Well, it's amazing how time flies when you're having fun. Becky, thank you so much for being on this show. Great conversation. Since we're running low on time, I did wanna give you the chance to maybe just tell the folks at home where they could maybe learn about some of the events or things that are coming out from snow First coming soon, maybe some events that you're gonna be attending to.
Becky Trevino (00:59:36):
I think a big one for us is really coming in. Follow us on LinkedIn, on social media or you can go to our website and LinkedIn to where we'll be. I think that's a great way to just find out where Snow is and, and if any point someone on your shows is interested in our products, wants to learn on something, just connect with me on LinkedIn and I'm happy to talk through what a good next step would be.
Louis Maresca (01:00:00):
Thanks so much for being here.
Becky Trevino (01:00:03):
No, you're welcome. Good to meet you all.
Louis Maresca (01:00:05):
You as well. Well, folks, you've done it again. You sat through another hour of the Best Day Enterprise and IT podcast in the universe, so definitely tune your podcaster to TW it. We wanna make sure I thank everyone who makes this show possible, especially to my co-host Arthur very on Mr. Brian Chi Cheever, what's going on for you in the coming weeks? So where can people find you?
Brian Chee (01:00:27):
I'm building cabinets with Kurt <laugh>. I'm actually doing a lot of stuff. I discovered that sadly some of my really cool cell modems are 3G and that's gone. So I guess I've going to dump those and see about what I can get. I, I really wish I had paid attention on and did an inventory because it was almost six years ago when I got the first notice from my at and t rep offering trade-ins on my 3G modems so that I could make the transition to LTE Cat M or M one. I'm going to poke fun at the city and county of Honolulu only because they got the same kind of notice and now all their parking meters were 3G modems in 'em don't work anymore, so free parking. If you haven't traded out your 3G modems, I'm not sure there are trade-ins left, but you should check with your rep Anyway.
I rant about things like that on Twitter. And my Twitter address is A D V N E T L E B advanced net Lab. I would love to hear from you. We throw out all kinds of interesting things. If you were following me, you saw pictures that I took on Remy's Wild Ride. That's where Remy came from. And looking forward to hearing from you, especially show ideas. Some of you have thrown out some amazing show ideas. I desperately try to shuffle around and play show Tetris to try and keep the threads going and make sure we cover different things. We try very hard to also get startups so that we can have them represent themselves and get known by the general public. You're also welcome to use email. My email address is scheiber spelled C H E E B E R T TWiT.tv. You can also throw email at TWiT tv and that'll hit all the hosts. We'd love to hear from you. Everybody. Stay safe and have a great time.
Louis Maresca (01:02:47):
Thank you. Cheaper. I definitely think some of the health device organizations out there should deal something with their 3G modems cause I have a couple at home here that still have 3G and they just stop working. That's a good thing for health devices. Anyways, thanks so much for being here, Zeibert. Well, we also have to thank our very own Mr. Curtis Franklin. Curtis, what about you? What's coming up for you in the come weeks? Where could people find you?
Curtis Franklin (01:03:13):
Well, as for me, I, I mentioned earlier, I'm going to be writing about a number of different things. I've got pieces coming up for our omni subscribers as well as some pieces coming up on dark reading in the next week or two. Continue to do some things on LinkedIn, both some writing and some LinkedIn live. If you wanna keep up with me, you can find me of course on Twitter at KG four gwa. If you're someone who is a Twitter refugee, you can find me on Mastodon. I'm KG four firstname.lastname@example.org. You're also welcome to follow me on LinkedIn or Facebook. I'm just all over the place still trying to figure out all I can about the world of cybersecurity and share what I learned with other people.
Louis Maresca (01:04:09):
Fantastic. Thanks so much for being here, Curtis. Well, we also have to thank you as well. You're the person who drops in each and every week to watch and to listen to our show, to get your enterprise in it. Goodness. Wanna make it easy for you to watch, listen and catch up and your enterprise and IT news should go to our show page right now. Tweet that tv slash twi. There you'll find me all the amazing back episodes, the show notes, the co-host information, the guest information. Of course, of course the links that we do during the show as well. But more importantly, next, the next to those videos right there will get your helpful subscribe and download links. Support the show by get your audio version, your video version of your choice. Listen on any one of your devices or your podcasters applications cuz we're on all of them.
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I want to thank everyone who makes this show possible, especially to Leo and to Lisa. They do this, they support this into This Week in Enterprise Tech each and every week. And we really couldn't do this show without them. So thank you Leah, Lisa, for all your support over the years. Of course, I wanna thank all the engineers and staff on at TWiT. We couldn't do this show without them. Of course. Thank you to Mr. Brian Chief one more time. He's not only our co-host, but he's also our tireless producer. That's right. He does all the bookings and the plannings that we do during the, the show that he does for the show. And we couldn't do with this show without him. So thank you Zeibert, for all your support. Now, before we sign out, I want to thank our editor for today cuz they make us look good after the fact. Of course. And of course, thank you to our TD for today. Mr. Anthony, thank you so much for all your support, Anthony, and to making this seamless for us. We appreciate all your support. Until next time, I'm Lewis Maresca. Just reminding you, if you wanna know what's going on in the enterprise, just keep twi.
Rod Pyle (01:08:44):
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