Noelle’s Notebook: [EP 2] How Did I Get Here?

Hey everybody, it's Noelle. I am so excited to be here with you today. Um, it is Friday. This is the second episode of Noelle's Notebook. I actually am going to see if I can grab the actual notebook that I am referring to. Let's see, here it is. Oh goodness, I think it might be by my bed anyway. But I have multiple versions of them. I've taken these notebooks, I've written in hundreds of notebooks in my life. I started at the age of, I think I was 13, or that's the first one I kept long term. I actually have a bunch of my content is, or a bunch of my books and everything that I had when I moved to D.C., Washington, D.C., right before the pandemic for my role at National Public Radio. Hi, friends. Right before then, what that, you know, having that opportunity to go there, of course. But then when I had an opportunity to move to where I am now, I ended up leaving everything in storage. So I'm finally— it's all arriving tomorrow. And one of the most exciting things is these notebooks that I've collected over the last I don't know, I won't say how many years, 30+ years. So many, so many notes that I have taken. But today I'm actually going to do something that I've spent the last couple of weeks doing, which is I am in the process of onboarding as a new executive at IBM. So exciting. And one of the things that I have done— hello, friends— one of the things that I have done in doing that is meet with people. And I'm finding myself say the same things over and over and over again. So I just thought that same story I would share with you. So I'm going to do that now. I don't know if you caught it, but if you didn't, if you're here live, awesome, welcome, thank you so much. And if you're catching this on the replay, which hundreds of you end up doing, you can go ahead and hashtag replay so I know you were here. And you can also take a look at the beginning because I have a new intro. I'd love to see what you think. I like it. I think it's kind of cool, but would love to see what you think. So as I was mentioning with the move and getting all my notebooks, I over the last few weeks have been, you know, talking to a bunch of people and giving them my same backstory. So I thought today I would start with that backstory because many of you might not know it, but also for those of you who are interested in building a brand, and in, you know, creating and starting to message about what you do, what you care about to the world, a very important part of that strategy is your backstory. And that honestly can be the very first thing you post if you've never posted anything before. You could just get on camera or write it down or open up your mic on your phone and record, you know, how you got to where you are. So that's how I'm gonna start The first few minutes of this will be my backstory of what I've done and how I got to where I am today, which is a crazy, fun, cool journey. And then I'm gonna talk to you a little bit about what I'm learning. And actively, this is literally things I've written down in my notebook, on my whiteboard, things I'm actively working on. One of the most interesting emails that, and you know, it's funny 'cause people often will say to me, Email's dead. I never read my emails, you know, like especially marketing ones, right? And there is one marketing-ish email that I read all the time, and that is Tim Ferriss on Fridays. Today he sent out an email called Five Bullet Friday, and I love it. And I love it because it shows me 5 random things like what he's reading, what he's eating, what he's thinking about, who he's interviewing, who he's watching, you know, like these random 5 bullets. And so I'm not going to do Five Bullet Friday, but I am going to tell you a couple of the things that I'm focused on this week. And every time I do one of these, I'll tell you what's top of mind and what I've been involved in. And hopefully that will be interesting to you all because maybe it's not the same. So, so again, thank you all of you. Hi for joining and catching this live. And for those of you on the replay, I really appreciate you spending some time We won't be here too long. So let me go ahead and get started with my backstory. I do want to share with you. I'm going to stick to the professional side, though my personal life was wrought with interesting, you know, e-Hollywood story type adventures. But I'm going to stick to the professional side of my career and of course sprinkle in when necessary or needful any personal history. But for the most part, when I first got into the field of technology, I actually didn't know tech. I was not classically trained in engineering or technology. I went to a university that focused on aviation, which I do still know and love. But I wasn't classically trained in the art of computer science or programming. However, I was told by my advisor, and thankfully she did sit me down and say, if you're going to do anything, it was Y2K, pre-Y2K, if you remember this, she's like, if you're going to do anything, just go into tech until you figure it out. If you don't know what you're going to do, do that. You won't go wrong. And needless to say, she was not wrong. And she said, go into— go teach yourself coding. And so I started by doing that. My very first job though, was building training for a government contractor for the Kiowa Warrior. It's a helicopter for those of you military people out there and know it. It's why I went to a fair recently and saw a Chinook, and I was like, oh my gosh, a Chinook toy. It was a toy. And I was like, it's a Chinook. And the guy looked at me and he's like, you know, like, not very many people know what that is. Not very many men know what that is. And I laughed. But I love aviation in general. And so I got to work on training components and training simulator components for the military on the Kiowa Warrior, as well as the Chinook, as well as Hueys. So loved that kind of generation of aircraft. Anyway, so that was my first job ever, training component development for a government contractor. But I very quickly realized, again, with the help of my advisor, that there was other opportunities in tech. And so I then got very interested at this time, it was basically Smalltalk was kind of the first language that I, object-oriented language that I got involved with in early in my career. And Java, Enterprise Java. So at the time it wasn't Enterprise, it was just plain old Java. No one knew this language, it was relatively new, was coming on the heels of Smalltalk, and IBM back then was teaching it. And so they needed people who could learn it and then deliver it and teach it to the masses of people that would come through their training. And I taught tens of thousands of people Enterprise Java, IBM WebSphere. I ended up having 40 different classes in my portfolio that I could teach. I ended up teaching those classes and consulting over for 12 years, right? And, and here's what was interesting about that is that, A, I learned a skill I didn't necessarily have any reason or background knowledge in learning. I wasn't classically trained, nor did I have an affinity to math. I did have an affinity to science, but not so much to mathematics. Um, but I could learn a language. Um, and so I learned Java, and I learned it so well that I could teach others. And then I learned a product that embodied that language so well that I was one of the best in the world, and I maintained one of the highest CSATs of all instructors year after year after year. And it was, it was one of the things that I think made me successful was that I took something that I love to do, which is speaking and talking and sharing my experiences, and I combined that with something that the world needed, right? So How can you do what you love? Like find what you love and align it to something the world needs. And at that time, the world needed people to teach them about Enterprise Java. So that's how I started. My first 12 years were teaching workshops and consulting and really helping people understand the world we were moving into. And from then it was like client-server into the world of microservices. And I went to all sorts of clients, Bank of America, John Deere, Wells Fargo, you name it. Financial services, retail organizations. I was at Macy's. I taught at Harvard. I remember getting a sweatshirt and being like, I'm totally going to tell people I taught at Harvard because I did. I taught their IT staff WebSphere, but still, I have since then taught AI classes at Harvard. But, but back then I was like, you know, in my 30s going, woohoo, I just taught at Harvard. I taught at Notre Dame to teach their technical staff WebSphere. I got a chance to travel the world and do amazing things and align it with something that I love to do, which was speaking and talking, but not just speaking, right? Like sharing my passion. I want to get you a book that I recently found. Okay, hold on, I'm going to get you this book. Hopefully— oh yeah, I even tabbed it. Um, oh gosh, don't, don't mind the noise. Um, this is it, it's called Sparked. I don't know if you've heard of it. Jonathan Fields, I think it's an awesome book. But what it, I love these books that have, I think it's, there's a website, I can't remember what it is, like Sparked, look it up, Sparked Assessment. And it's an online assessment that you can take and then it will categorize you. And, you know, these are personality tests, give it, you know, give or take, right? Whatever, it just kind of depends on whether or not you're one of these types of people likes to do this. Like what Muppet are you? What, you know, what Disney character are you? So this is similar to kind of that Myers-Briggs type personality test. But you take this test and what it uncovered for me was very interesting. And the goal of this, Sparked, is that if you align with the things that this book identifies about you and you align that with what the world needs, specifically in data, AI, technology, you could be very, very successful. And I feel like I did this without knowing this book, but this confirmed a lot. So I'll just tell you the two things that I ended up dropping into. The first one was called The Maven. And the maven, it says, basically the slogan for a maven is, "I live to learn." Yes, I totally do. So I, it's all about somebody who's constantly learning new things and is invigorated and energized by learning new things. Yes, me all the way. But then my secondary supportive kind of personality trait is the Sage. And the Sage's slogan is, "I awaken insight." What? Which means like, I find joy in teaching, in watching light bulbs go off, in extending my knowledge, in learning hard things and making it easier for the people who come behind me. Holy mama. That was amazing. I was like, yes, all of this, yes. It's why I do what I do today. So I spent 12 years at IBM. Doing exactly that, right? Tying my desire to learn new things, get hands-on experience with complex activities, complex enterprise architectures, and then simplifying them and sharing them and educating the world about them. Uh, after that though, Red Hat came along. At this time, IBM and Red Hat were not the same company, um, or I guess related, uh, but Red Hat came along and was like, you don't want to work for Big Blue, you want to teach, you know, open source, the same concepts, middleware and application development, but you want to do it the open source way. And it was a critical time in my career because I learned, I mean, I basically got my stripes, cut my teeth on one of the biggest enterprise software, you know, projects in the world, IBM WebSphere at that time. And then I went to Red Hat, pre-billion dollar, right? Pre-acquisition. It was in this time when open source was just really starting to take hold, not just with Red Hat Linux, but also with middleware, also with application services and data services was just starting to come into its own. And I spent 3 years advocating for the use of enterprise-hardened open-source technology, and I still do today. Wait, I love what Red Hat has done because they take the work of all of us who want to help, want to build better software, we contribute it to open-source projects, and then they make it so that that software works in the enterprise, is hardened, secured, right? All the hard parts of using technology in production, they make that possible. And I think it's really, really an amazing organization. So I spent 3 years there as a solutions architect, eventually a senior solutions architect, and then VMware came along. So now think about this, right? This is in the 2000s, so early 2000s. I'm teaching at IBM everyone about IBM WebSphere and enterprise application technology, and then the next thing I'm doing is talking about open source and the power of open source and virtualization started to become a main topic. And then the next thing is that I went to VMware at a time when they were thinking about, of course, virtualizing hardware. This was the beginning of the cloud, right? How do we make the most of our data center, let alone get rid of it, right? How do we make the most of our data center? How do we increase utilization of the hardware we purchased? Um, this is like, again, back before cloud was really a thing yet. And I was in a position, I was leading education, leading certification on how do you make the most of the investment you as an enterprise have made. And it was really interesting because at that moment, Pivotal, taking Cloud Foundry, Pivotal Cloud Foundry and Pivotal as an organization was created, spun off, and then created to actually, hold on, Hi honey, it's amazing, really good job, thank you. It's a teacher planning day, so everyone's home. Great, but I'm, I'm gonna— okay, thanks, bye. Um, so at VMware, we spun off Pivotal. I was part of that organization in its early days, again pre-IPO. Um, super exciting times though, right? Because we were applying, um, all the things I had learned so far. Every role I took was an evolution of the previous the easiest role. So now we're talking about virtualizing hardware. I'm focused on big data, Hadoop, Pig, you know, all of these, like, if you guys remember some of these technologies. And some of us, of course, I spent years encouraging enterprises to invest in these technologies. The challenge though, you know, I spent again another 5, 7 years saying, save, you know, save all your data, preserve everything, you know, make sure that you have a system that can hold as much data as possible because soon a magical unicorn-like creature will come and make sense of all this data that you're saving. So I spent years just telling and creating master data management profiles and making sure that people had an infrastructure that could hold all the data that they wanted to preserve. Great. Enter AWS. I joined AWS in 2013. And was there in order to be a— I was literally hired to help with training, to help bring on instructors and deliver training in North America. And it was actually a bit of a step back in my career. But some of us who have done this before, and maybe it's just me or maybe it's just people like me, but we're given an opportunity and they will say to us in an interview or even at the offer stage, So I'd like, even though you've proven you've done this in the past, I'd like for you to consider taking a less bigger role, a slice of what you currently do, but as an opportunity to get into an organization and do more. Now, this could be a red flag for some. For me, I'm a bit of a yes person. I tend to look at opportunity, do my due diligence, and then say yes if it's right. And I did that with AWS. At that time, just to give you an idea, the stock price was $382 when I joined AWS. It's not that anymore. So it was a good idea. I ended up joining AWS. I was there for a year as a leader in the AWS training organization. And then Alexa was born, right? So Alexa was launched in 2014. Right before that, they were looking for new people to join that team and help create it. I was part of the organization, like employee 10, right? Very early part of Alexa. There were just a collection of us. It was run by a director. There was no SVPs. There were, you know, it was a very small little project, a pet project, if you will. And I got to be part of that at the very beginning. Really, I was focused on devices at first, which might make sense with Alexa, right? I was focused on Alexa Voice Service, where you would embed Alexa into devices. And very quickly, here is my first Oh, sorry, Alexa, stop. Hopefully it will stop soon. I just asked it about hardware, I think. So I did have this amazing opportunity, but within about a year, a VP saw me speak in front of a bunch of people and was like, you should be an evangelist. And I was like, okay, I'd love to be an evangelist. What does that mean? Right? And I learned very quickly what happens when you are very— when you are an advocate for your own skills and you put yourself in a position for people to see you at your best, miracles can happen. People can pick you up and drop you into the right role. And that's exactly what happened. I didn't have to interview. I didn't have to, you know, go through all the traditional things. Like, I literally was picked up and dropped into a role where I could be really successful. And that was that evangelism role. I was the second evangelist to do that work. And it was really exciting. So after I did that though, this is Noelle Russell. I will be very vulnerable and transparent with you right now. If it doesn't stay super exciting, engaging, and you know, like I will, there's part of me that will yearn to learn something new, right? And so I uncovered this world of data science in Alexa and it was a relatively new team. And I was like, I want to go and be the TPM, technical program manager for Alexa data science. I want to, Go in there and help build the team that will be the glue between engineering and business to deliver on the promise of data science and on Alexa. And I got to do that. I went, I applied. I did not necessarily have those because I had been a TPR, but not at this scale, but they gave me that. But I was a team builder and I was an affirmed leader. So they gave me a chance to do that. I ran that team for 2 years, built it from myself to, we had 14, I think TPMs at that time. We then built the code pipeline, right, for Alexa. Oh gosh, all my devices are going off. We built the code pipeline for, you know, hardening how the data science work would get delivered into production where before it was a bit haphazard or manual, which is even worse. It's why now I'm very passionate about operationalizing data science because I know what it's like to be a data scientist and have to manually deploy or manually create your own environment. Like you could spend so much time in your day doing things that are not directly related to building good stuff, right? That are auxiliary. And so there's platforms for that, tools for that, but most importantly, there are people who can help you with that. Anyway, so I did that. I then was recruited by Microsoft after I was at, Amazon for 4 years, deployed 4 language models, and Microsoft was like, hey, we're doing a similar thing with cognitive services. We have a bunch of research projects that we want to turn into cognitive services. Would you like to participate? And I was like, absolutely, yes, I would. So I ended up leaving Alexa and going to learn a new thing, which was all of these different AI models. And of course, natural language, NLU, was my specialty. It's where I again learned all about artificial intelligence, but I very quickly got enamored with the concept of applied AI, of building models in computer vision, OCR, you know, language, not just spoken language, but actually written language, semantic analysis, search, like all these different AI models that were being worked on. So I got to be a part of the amazing team that launched a bunch of those Cognitive Services in that year. And then, as many of you know, I was evicted from my position at Microsoft. But it all works out, right? I ended up having a very traumatic event happen right after that. I went dark on social media. I took time for myself and my family. And then when I rose again, I was the VP of Engineering at NPR. So many people ask me, you know, like, how do you pick your next thing, right? And I will say that because I am an advocate, because I like now am talking to you realistically about my life's journey, people know what I'm good at. People know where I can help them. And so when they need something, they know they can come to me and be like, hey, are you— do you happen to be interested in this opportunity? And that's exactly what happened. I got an email from a friend who said, oh my gosh, I don't know if this is the right time, but we we could use your expertise at NPR. I said yes, interviewed across the board, you know, got into the organization, and I think, I hope, made some significant changes and differences there that helped that organization. And of course, now here we are. Red Hat last year recruited me back to Red Hat as part of IBM. Now, as opposed to being really an advocate for middleware, as I was in 2008, '08, now coming back and being able to leverage my understanding of AI and cloud, really thinking about how does OpenShift, Red Hat OpenShift, play a role there. And it was really exciting. I've done a bunch of YouTube videos and webinars and things like that that you've seen over the last 6 months. But then again, someone who knows me, who's seen me, said, oh my gosh, we could use your skills. Can we evolve your position This doesn't happen unless you're out loud saying, here's what I care about, here's what I like, here's what I do. And so I out loud have said it all year long, became an MVP for Microsoft in the area of AI because of that advocacy and championship. And someone at IBM was like, hey, I saw you. You would be great in this role. This is an evolution of the same thing you're doing right now. And that's exactly what happened. And so I'm now in my dream role as an executive, talking with some of the biggest companies in the world and helping them apply all the things that I've talked about over the last 7 years. I've done so many Art of the Possible discussions with companies only to have them say, "Yes, awesome, let's do it," and then not really be able to be part of the solution, part of the team that does the work, that implements the change, that, that does what I actually talk about all the time. And so I'll tell you a little bit about this in a second, but You know, I'm working on some very interesting projects today that are literally an evolutionary step, a step up. I always tell people, I don't know if you saw it at the beginning, but you know, if you're gonna go big or go bigger, right? Like, look at what you're doing today, and if you want to do something different, go bigger, right? Like, if you're going to do something different, like, look for bigger opportunities. And the more confident you can be, and that's really the fuel for my career is the confidence to do the thing. Like, I, even if I don't know anything about it, like Alexa, I knew nothing about Alexa when I started, but I went heads down, bought it, you know, got involved in GitHub, built a bunch of repos, like created a bunch of applications, replicated a bunch of people and their work in trying to learn. And then I was able to do it myself. And so I think there's a lot to be said about learning by doing., but also by stepping into an opportunity, by saying yes to an opportunity, even if you're going to acquire some of those skills on the job. So that is my career in a nutshell. I'm sure I missed some things, but, but that's kind of me. And it's interesting now because the customers I'm talking to today, I literally talked to 20 years ago about the evolution from maybe mainframe or procedural applications into the world of microservices. Back then we were calling it service-oriented architecture, but the same idea of breaking up monolithic code, you know, making, reducing fragility in your enterprise applications. And that was 20 years ago. I was teaching people like, hey, it's important to break this up. Now we're talking about how do you evolve the little pieces of code that you've got now that you've broken up your major applications, or maybe you're in that process now. How do you collect data, analyze data, create AI models that can serve those application pieces even more, right? And that's where we are now. We're in the world of predictive enterprises. That's the future, right? It's today actually. Those enterprises that are investing in becoming predictive are the ones that will win because we have the data, we know the answer, right? It then takes a very, I would say, mindful team to take that data and do something with it, something that can help their customers in a way that we couldn't help them before this data was available. So I will end with a couple of things that I wanted to share with you, kind of my 5 Bullet Friday. I wrote them down so that I wouldn't remember. One, what am I reading? I know I talked to you guys about Sparked. I loved it because I was like, that's me. It felt so good. But one thread that's come up over and over again, and during this time of the great resignation, you might know, like a lot of people are changing jobs. And so what I have been— I was suggested in two separate conversations was this book. And some of you have already changed jobs, probably know it, is The First 90 Days. And a book really isn't a book until you like spill something on it. So mine's broken in. But The First 90 Days, this book, I have it in Kindle, Audible, and in hardcover, mainly because there's like one of my favorite parts. I think I dog-eared it is, you know, go in in a data-driven way to learn about your organization. And it has these like, hey, talk to, of course, your leadership, your direct reports, but also others in the organization and ask them the same 5 questions. You can get the book to find out what they are. But it's not just what the questions are. Of course, that's extremely important to ask the right questions. But then the power of asking everyone the same questions, right? And putting, like documenting your answers in a spreadsheet and analyzing similarities and differences and gaps. Huge, you know, for us who— people who are data-driven, it's a huge, like, difference in onboarding and in understanding an organization that I can gain just by, you know, interviewing the people that I'll work with every day. And then from that, I can learn so much. So really cool. So that's what I'm reading right now. I'm on chapter 6. So happy to book club it with anybody who who's also reading it. The other thing I wanted to share with you was some new tech. So I got— I don't know if you see it, but this is my new Oura ring. O-U-R-A. And I love it. It basically— their ad campaign is like, ditch the smartwatch, which I also have, ditch the smartwatch and go with the ring. And the reason I like this ring is number one, it fits really well. It's also— I thought it'd be weird to have it on my finger like this, but it I totally— I hardly know it's there, but it, um, calculates and collects a bunch of data about your physiology, right? So how you're sleeping— it has what's called a readiness score, which is kind of the combination of how you're sleeping and your heart rate and your recovery and how your body is assimilating to the types of activities that you're doing every day, how much mindfulness you're doing, how much, you know, like heart rate management, REM sleep, deep sleep, all these things. And it's really interesting because it starts over time to start making really cool suggestions like, hey, your best time to go to sleep is at this time because you get the most efficiency in your sleep. Now, I don't sleep a lot. Most of you probably can guess that I don't sleep a lot, but when I sleep, I sleep hard, right? Like, I sleep hard. So really interesting, um, you know, uh, insights that I've gotten so far so that I can get the most efficiency I mean, my efficiency in sleep is like 90, in the 90th percentile. So I don't get, you know, 9 hours of sleep. But when I sleep, I sleep very efficiently. And I think that I'm excited to have some data to back that up. The last thing I want to share with you, I don't know if you know this, but I am prepping for my 7th Disney Princess Half Marathon. So that is at the end of February, February 27th. I will be at Walt Disney world running the Disney Princess Half Marathon, 13.1 miles. I will be dressed as a princess. I won't tell you which one yet, but I will be dressed as a princess. But I recently got a Tread as a gift. Thank you. And I was on it yesterday for the first time and it was incredible. So really exciting. So I'll be documenting that journey on Instagram. The entire time if you want to follow along, things I'm doing, things I'm eating, how I'm running, how I'm resting. But yeah, that's an exciting thing that's coming up for me. Oh, last thing. Okay, well, I'm only 2 minutes kind of over and I started 2 minutes late, so I think we're out of time. But the last thing I wanted to share with you was something that I'm working on at work. So in the last 3 weeks, I have Obviously they shared with me like, read this book and do what it says. And that there's a 400% increase in your success in your new role if you do some of these things. One of them is at 30-60-90. But one of the things I uncovered, and some of you know that I'm very passionate about AI and ethics and trustworthy AI. And I have, of course, law of attraction, been able to to find the team within IBM, specifically my organization, AI and Analytics, that's focused on trustworthy AI. And what makes me happiest is that we've identified these kind of 5 pillars of AI, trustworthy AI. Some of these terms you might know, right? Transparency, resiliency, security, privacy, right? So you'll understand these pillars as I'll probably do a webinar on them sometime soon. But what's more interesting to me is that we're actually offering, like we're doing work with customers to make AI, ethical AI, a reality, right? It's not just like, this is what I used to do. I used to get up and I'd tell a bunch of CEOs like, hey, you should do this. And they'd all nod along and be like, yeah, good idea. But there would be no real tangible way to follow through, no real tangible way to get those types of learnings, design thinking applied in an enterprise. And so when I found this team within IBM Consulting, I was like, you're doing this, and they're doing it successfully with some of the biggest brands in the world. So I'm just excited to find out that this is a real thing, that AI and ethics is not just something we talk about. It's something that real companies, large companies and small companies, that they're doing. And I'm excited as we move move a little bit more forward and start delivering results for these customers to share those success stories with you. But it was really, really cool, really heartwarming and affirming to me to know that sometimes it's not just about the slide presentation and the webinar, right? Results, um, delivering on the promise. Like, I'm finally at a point in my career where I get to be part of that side of the story, and I couldn't be more excited. So thank you so much for joining me. Thanks for catching me on the replay if you catch the replay. And I am just so excited about this year, as you can tell. Oh, last, last thing. I am actually going to be starting to do some technical sales type of, you know, coaching/training once a quarter in my own hometown. I'm going to actually do a live event where I'm going to have people people come and you can just learn kind of what I do, what I do with customers, what, how I learn, all the things. Just get my head into those who decide to spend a couple days with me. I've partnered with Grant Cardone and the 10X Nation, um, in order to do a lot of this. So I'm— if you don't know him, definitely look him up. He wrote, uh, the national— the bestseller, um, 10X Rule. And so I'm part of that organization now, and I'm going to be delivering— really, it's like 10X Tech, right? Like, how do we 10x our tech careers? How do we, you know, create generational wealth? Um, how do we just do things a little bit differently? And specifically, especially for women and people of color. So super excited about that too. So much I always have to tell you guys, but thanks for joining me. I hope you have an amazing Friday, an even better weekend, and, uh, we'll see you next week. Thanks so much. Talk to you later. Bye.