Tim Ennalls Mixed-2.mp3

[00:00:00] A product manager collaborates with marketing and sales teams to develop roadmaps and strategies to sell products and analyze the performances of products launched in the past, Data sales manager would also collaborate with business stakeholders. By then, more business and more functional areas, depending on the company and what what areas the company wants to Data science manager to prioritize.

[00:00:40] What's up, everybody? Welcome to the artists of Data Science podcast, the only self development podcast for Data scientists. You're going to learn from and be inspired by the people, ideas and conversations that'll encourage creativity and innovation in yourself so that you can do the same for others. I also host open office hours. You can register to attend by going to bitterly dot com forward, slash a d. S o h. I look forward to seeing you all there. Let's ride this beat out into another awesome episode. And don't forget to subscribe to the show and leave a five star review.

[00:01:33] Our guest today is a data scientist who wears many hats.

[00:01:38] He's an MBA, CJP, PMP YouTube blogger and thought leader, though he has many identities, he has one passion, Data science. He's got technical chops to deliver business value products, sense to understand user experience and business acumen, to communicate and support top level decision making processes. Today, he's going to be on the show to share with us his products and his business sense so that we can be more informed when we enter the business world as data scientists. So please help me in welcoming our guest today, founder of Analytics explained Tim Annells. Tim, how's it going, man? Super excited to have you on the show. How are you doing today?

[00:02:31] It's going terrific. Thanks for the invite, Harp. This is one of my favorite podcasts right now and I'm excited to speak with you man, right now.

[00:02:38] And you guys should see the smile on my face. Man, thank you so much for saying that. I really, really appreciate that. Before we jump into, like, these awesome credentials you have and the path I kind of brought you to where you are today, why don't you just tell us a little bit about where you grew up and what was it like there?

[00:02:52] I grew up in Alabama in a small town called Anniston, Alabama. There wasn't much to do there except fishing. And football was the hardest conversation, the hardest conversational topic there.

[00:03:06] That's pretty cool, man. Do you have a favorite football team? Is it what? Alabama doesn't have a professional team, but has a huge on the college scene right there is the University of Alabama and also Auburn University.

[00:03:18] I never really chose between the two. I think they're both fantastic teams.

[00:03:23] How about for professional football? Like, do you have a team that you follow?

[00:03:28] I kind of admire the 49ers.

[00:03:31] Hey, dude, I'm not paying him to say this. Ladies and gentlemen, like literally San Francisco 49ers is my absolute favorite team. Like I'm drinking out of his forty Niners cup right now. I was born and raised in Sacramento, California. That's like state capital California. But it's the nearest big city with the football team was Oakland, but now it's San Francisco.

[00:03:50] But I always grew up San Francisco fan super. Not excited to see how they're performing this year, but absolutely still. Still my team, though, you know, got our present. So that's cool. Man So small town Alabama. Currently you are in Atlanta, Georgia. Some really interesting to talk about the path that brought you to where you are. So back in Alabama, small town, what were you thinking that your future would be like when you were when you were growing up?

[00:04:18] I had a faint idea that my career would involve both business and information systems functions, but I wasn't quite sure what exact career path I would go on later in life.

[00:04:29] That's cool, man. We had like a proper path for what it is that you thought of. Do I thought I would be in computer science? Like, that's what I thought I was gonna be in high school. But I guess I kind of got somehow into an adjacent ish field that that Data science. So for me, it's not too far off from where I thought I was going to be. But how about for you when you're growing up, like, what did you think, you know, your future would be like like how different is it now than what you imagined it would be?

[00:04:59] I didn't quite imagine that I would be involved in such a technical field, which is Data science. I did play around with database software back when I was really young and other software such as Microsoft Office and so forth. But I didn't quite imagine that I would use the Cisco methods and use programing languages to the to the extent that I am now.

[00:05:22] That's pretty cool. Like, it's always interesting how things change when you're growing up now. It's like significantly older than you imagine. There are some thirty seven. And when I was like in elementary school, like computers first started becoming a thing. And I remember my mom gave me a book and it was called Dos for Dummies. And so DOS is like the equivalent I guess of of, of Basche for Linux or whatever it is now. So that's cool, man. So what was the journey like coming from from high school to where you are now?

[00:05:56] I would say it involved a ton of skill development and the constant improvement of how hard that things both of my personal life and in my career. I started off in my career and mostly business related functions, and I gradually built skills that were more and more technical. I started off in telecommunications, which is a bit technical, actually. I then became a materials representative for a different company, which entailed inventory management, procurement, management and so forth. After that, I became a product manager for another company, which entailed analyzing both product and sales Data presenting to executives. Automating the product life cycles for products in our portfolio and so forth, I then became the first data scientist in that same company and I dealt with activities such as creating developing machine learning and NLP deliverables in Python, creating architecture diagrams, writing technical documentation, and helping to set up the Azure cloud platform from a project planning perspective.

[00:06:56] That's really cool, man. I had no idea you were the first data scientist at an organization, so I'm going through those challenges right now. So first data scientist and my organization. So we I talk a little bit about that a little bit later. I'm interested to see how you handle some of those challenges. So what would you say would be a experience that contributed to shaping who you are today?

[00:07:23] I can't pinpoint one experience that's shaped me into the person I am today, but there are a ton of experiences that played a small part each and influencing my subconscious decisions. I read a ton of books on business, technology, innovation, problem solving and other subjects which which influenced my thinking in ways that I probably couldn't really predict. Back then also also attended conferences about entrepreneurship, business and Data science.

[00:07:50] That's cool, man, that you were really involved in in this self-improvement self development of both professional and soft kind of personal leadership skills. Harp early on in your career. And I guess I really kind of shine through in a lot of the writing that I've seen because you've got some really great articles out there. One of them I thought was really interesting was an article on 10 innovation frameworks. So I think this is really cool. You know, having frameworks like this is really important for data scientists because it kind of gives you a model, a way of thinking. So I'm wondering if you can walk us through this article that you had written.

[00:08:30] Sure. I could describe about four innovation frameworks from the article. The first one is Blue Ocean Strategy that involves using methods to identify customers both outside of your industry and inside of your industry that are untapped and underserved. It involves using it involves researching industries outside of your current industry and specifically choosing certain industry attributes and making sure they're far above the level of industry standards. The second one is the S curve pattern of innovation. The basic premise of that is that systems evolve, iterations of systems evolve as scientific as basically each system delivers a certain function and ecosystem evolves until it reaches its limit. In regards to that system, the scientific principle, the second innovation framework is 10x thinking. This was popularized by Google and it went by the name of moonshot thinking at that time. And this was also popularized by Grant Gordon, who's one of the top sales trainers in the world. This involves thinking of a concept such as a product service and so forth, and thinking about that concept, trying to imagine that that concept, providing a function either 10 times faster or 10 times more effectively. For example, if you set a 10x goal for yourself, you need to rethink their approach is needed to attain that goal from a radically different perspective. And if you were to develop a product that's ten times more effective than what is typical for that product, you need to rethink the physical attributes of that product at the most fundamental level. And the fourth innovation framework is nine Windows. It's COMPRIS, it's a nine Wendle table, and it's comprised of the past, present and future for the columns and the system subsystem and super system as well as of that table. The system is what is used to deliver the primary function. The subsystem is the comprises of the components of that system, and the super system is comprised of the external environment and the components in which that system interacts with.

[00:10:54] Thank you very much, my friend. I appreciate that. I like that blue ocean strategy and I think that kind of would relate to the experiences that you might have had. And I know that I'm kind of having as the only data scientist in an organization. Right, because when a company doesn't have a data science team already there for you, it really is just a blue ocean. Right? So you're able to now find pockets of opportunities that you think you might be able to offer some services and provide some value. What do you think about that site?

[00:11:28] I think that's an interesting way to look at it.

[00:11:30] It's also a great opportunity to try things that the company never tried before and put yourself in the shoes of an influencer, sort of you need to get executives accustomed to doing things in ways that were never done before in a company which is which could be a considerable challenge, depending on the company's culture, their level of analytics, maturity and their existing Data architecture.

[00:11:56] I like that. I like that word. Analytics, maturity. Recently doing something at work where I had to kind of gauge the analytical maturity of our organization, I use the framework set out by Tom Davenport. I'm not sure if you're familiar with that now, but Tom's top never has this five stages of analytic maturity. And I thought that was some.

[00:12:16] A really interesting, very familiar with that work at all, I believe I've seen a diagram related to that on the Gardiner website or maybe even the Forester website, I'm not sure.

[00:12:27] Yeah. So when it comes to innovations like which framework do you think you've tended to fall back on the most with with respect to your work in in Data science?

[00:12:41] I would say the Blue Ocean Framework is the one I think about the most by far, but Data size is slightly removed from business strategy functions to a certain extent. It just depends on the company and how close Data scientists work with executives to influence their decisions. But I would say 10x thinking is the one I try to use most often and all of my work because I want to deliver work that is considerably better than what has been done before in a company, or at the very least provide Data insights that were never found before in a company using techniques that were never used in a company like that to man like.

[00:13:21] I definitely have the same type of philosophy when it comes to anything that I do. It needs to be different than what is already out there, whether it's working to doing an organization or self development podcast for Data scientists, some curious man. So like all this skill that you've managed to acquire with respect to programing and you mentioned NLP earlier, is this all just self-taught, like reteaching teaching yourself how to learn all these tools?

[00:13:47] Yes, this was all self-taught. I self taught my skills all throughout my career, and that's I just had an internal motivation to continually learn and upgrade my skills. That's why I have so many certifications and that's why I've taken so many classes past the point of just earning my bachelor's and master's degree and calling and calling it a day.

[00:14:08] Dude like that is the most underrated skill for a Data scientist. Probably the most important skill for Data scientist. If you want to really excel in your career is what Tim just mentioned, just the skill of not only just learning effectively, but the skill of having curiosity, being curious about things, and being willing to put yourself in uncomfortable situations where you might feel like you don't know what the fuck is going on and teaching yourself like teaching yourself, educating yourself, being resourceful enough to find the resources and then act on what it is that you found like that is a skill that I think people can greatly, greatly benefit from. You mentioned just a little bit earlier about how business strategy is kind of something that is missing from data scientist. And I definitely agree with that. I think we get too caught up in hacker ink and machine learning algorithms, at least the ones who are breaking into the field are new to the field. But at the end of the day, a man like you are hired by business to either make them more money or help them reduce costs. So it's hard to do that if you can't really communicate with them or understand business strategy. Some I'm wondering if you have we talked about innovation frameworks. Let's talk about business strategy frameworks that every data scientist should know. Do you have any that you could share with us?

[00:15:38] I think companies would benefit if Data scientists if you work from the perspective of their business stakeholders, as I describe about three business frameworks that may be applicable to data science, the first is the total matrix. This is a more advanced version of the swap matrix, which encompasses strengths, weaknesses, opportunities and threats. There are four strategies under the Total Matrix framework. The first is the strength opportunity strategy. This involves finding strengths and opportunities where you can exploit those strengths. This is also a great way to exploit your company's competitive advantages. The second is the strength threat strategy. This involves using your strengths to work against threats against your company. The second one is a weakness opportunity strategy. This involves trying to bridge your weaknesses so that your company can be in a better position to capitalize on opportunities. And the fourth is the weakness threat strategy. This involves the avoidance of. Of external factors that could negatively impact a company and then and for which you have no strength to counteract against the second is customer journey. This involves all the touch points that customers interact with when they first encounter your company or brand and then ultimately work their way towards purchasing your product or service. And the third business framework is competitive analysis matrix. This is a matrix where you compare your company against your competitors and in a table and you use various attributes such as prices, product attributes, marketing channels and so forth, to see how your company compares within your industry. And this type of information could be used to develop and implement the strategies that are most appropriate for your company.

[00:17:49] Thanks for sharing that, I think one important kind of theme throughout all that is having a understanding of your organization, like I think that is something that data scientists really need to spend some time doing it, not just understanding the data in the company, but understanding what real world processes and mechanisms generated that data that you are not analyzing. I think once you have a understanding of those real world processes, i.e. what the business does, that will make the strategizing part a little bit more definitely far more informed and you'll end up being a better data scientist for it. So what can a data scientist do to build and develop their product sense or their business acumen?

[00:18:40] Sure, I have a few suggestions in regards to that question. The first is to determine the areas of business that most interest you. For me, it's business strategy and for functional domains, which are our customers, sales, marketing and products. My second suggestion is if a company's public read its annual reports, annual and quarterly reports and look at the sections about your company's primary annual objectives, they're their product related initiatives and their financial performance. It also helps to look at functional analytics Harp points or presentations that were developed in the past so that you could use the information as a basis for the products you work on in the future, in which you would discuss with business stakeholders on a normal basis that give you a clear idea of what's been done before. That way, the work you produce in the future provides added value on top of that.

[00:19:47] Thank you very much for for sharing that, I definitely agree with that and just be as easy as asking your boss. Right. Show some interest in the organization that you're working for. But, you know, ask your boss. And if you see a senior guy walking around the hallways, just engage with him. Ask him a question about the business. If there is a press release out. A month something or a company wide email, just show interest in the organization and you'll definitely be better for it, and communicating, I think, with people in other departments is super, super important as well. I guess that's the part like I miss most about being in the office, is those random encounters where you bump into people and I go, what are you working on? Oh, well, I think I might be able to help in this capacity or that capacity. The end there. You mentioned making sure that the work that we do ends up delivering value. But I think it's kind of the well-known, quote unquote, statistics that 80 something percent of Data science projects fail. Right. So in your experience, what do you think are some reasons that Data science projects fail? And how can we as data scientist prevent that from happening?

[00:20:59] Sure, there are two reasons that I can go into about why I think Data science Data science projects fail. The first is a lack of emphasis on business impact. I've seen some people focus too much on the technology and not enough on the business stakeholders. Seth Godin has a quote that says, and I'm paraphrasing. It says, Focus more on your customers and I'm sorry, focused more on your customers than your products. I think it helps to have an entrepreneurial mindset when being a data scientist answers, look at your business stakeholders as customers and your deliverables as products. A second reason I could get into is inadequate data infrastructure. Sometimes that Data infrastructure just isn't at a level that where you could fully use it to produce Data sized deliverables at an enterprise level. I think it helps to have an established team of Data engineers or business intelligence developers that have already built a robust system that allows for the use of Data pipelines and reciprocal Data systems, which would then allow for the use of predictive analytics and prescriptive analytics at the enterprise level.

[00:22:24] What's up, artists? I would love to hear from you. Feel free to send me an email to the artists of Data Science at Gmail dot com. Let me know what you love about the show. Let me know what you don't love about the show and let me know what you would like to see in the future. I absolutely would love to hear from you. I've also got open office hours that I will be hosting and you can register like going to Italy dot com forward, slash a d s o h. I look forward to hearing from you all and look forward to seeing you in the office hours. Let's get back to the episode.

[00:23:10] I'm curious, man. So dealing with the same kind of stuff right now is that lack of that Data infrastructure, lack of Data architecture in the current organization.

[00:23:19] So you're talking about lack of infrastructure, lack of Data architecture, that's a problem that I'm currently dealing with at my job, being the first data scientist there. So there's challenges there to essentially reign in what seems like the Wild West in terms of Data to organize it. What are some best practices that you have that you can think of for those of us who are in, let's say, large enterprise, that is very kind of low down on its analytical maturity and we're trying to instill or install rather some good infrastructure for Data?

[00:24:01] I think it's a good practice to at least have a Data addition area so that employees know what the feels actually mean, especially new employees. I think it also helps to have Data related professionals involved in the creation of the Data infrastructure and to make sure they provide input in regards to what's wrong with the Data, what needs improvement and so forth. For example, the customer Data may have issues where multiple customer names are associated with each customer. I think that's an issue that many, many companies have right now. And I believe analysts can also provide feedback in regards to what executives want, because functional analysts tend to deal with business stakeholders on a more frequent basis than Data scientists.

[00:24:51] From what I've observed at Data dictionaries, huge man, my company or previous company, but the only company I've ever had where there is the adequate Data dictionary was as a clinical trial statistician, and that's because that is required by law to have stuff like that. But having a adequate Data dictionary like that, super, super helpful. I mean, you could spend one to two weeks just talking to people, trying to make sense of what all the different fields in the Data mean before you can do anything with it, because I think a lot of data scientists, when they're breaking into the field that are used to these nice clean data sets, just one CSFI, whatever, loaded into it notebook can build a model, have fun. But in reality, more often than not, it's these datasets are dispersed, multiple databases, multiple tables. You got to find a way to associate the to find the keys between the two and some of them. I have some really cryptic column names and you can't can't really decipher just by reading it. Nobody knows they're called means, but apparently it's useful for every report that the user or whatever. I mean, that's overstating the case. But I Data dictionaries are important. And I guess the moral of the story in my entire rant up until this point is just the need to really be able to deal with ambiguity and not just be stunned into inaction because things just seem so vague. I guess. So I really enjoy this article. You wrote 12 problem solving strategies. I know the audience would absolutely love to hear about them. Can you walk us through a few of us?

[00:26:27] Sure. The first technique is retrograde analysis. Work backwards from your goal with this approach, you determine the goal that you're trying to achieve and you work backwards from the goal to your present situation. I find this to be a great way to simplify a problem and that they have too much in the details that may not be relevant to the ultimate central objective you're trying to achieve. The second technique is to divide a problem and to solve problems. It's easier to solve one problem at a time rather than to solve try to solve the entire problem when the same problems are combined and reinforcing each other. The second technique is a root cause analysis. This involves identifying the root causes of the problem and addressing those root causes so that the problem doesn't occur in the first place.

[00:27:20] I find that it helps to identify the root causes of each of the some problems as a way to simplify the process. The fourth technique, and I make this the last technique, is the Phenix checklist. I think this is a this is a fantastic framework to consider because it encompasses 38 context free questions. This framework was developed, developed by the Central Intelligence Agency and the questions are segmented into two sections. The first is a list of questions used to define the problem, and the second is a list of questions used to define the plan to solve the problems. You can find a checklist by simply searching for it on Google. Do you have a favorite technique out of the ones you mentioned? I would say my favorite and the most applicable to most situations is to divide a problem into solve problems.

[00:28:08] I find this an easy way to manage problems and make them both easier to understand and and to solve in a more sequential matter about matter. You could solve one problem at a time and start with the easiest problem and eventually progressed to the most difficult and complex problem within the overall problem.

[00:28:29] That was going to be my favorite one that I was gonna say as well. It's absolutely my favorite technique is taking the large problem, just breaking it down into small, discrete chunks as possible. Actually, I was interviewing somebody recently. Maybe it was earlier this month, uh, Fred Pelleted, and he wrote the book How to Decide. I've got a well, it's not released yet. Releases in November doesn't pull out a PDF copy would have had. But that book is amazing. If you get your hands on that thing, I think you'll really, really enjoy it. So when you get into product management and Data science. So I guess let me ask you this question. What is the difference between.

[00:29:10] I mean, a product manager, just a Data size product manager and a Data science manager, sure, a product manager collaborates with marketing and sales teams to develop roadmaps and strategies to sell products and analyze the performances of products launched in the past. Data signs manager would also collaborate with business stakeholders by then more business and more functional areas, depending on the company and what what areas the company wants to Data sites manager to prioritize and in what ways you think the I guess phrase that what should the relationship between the Data scientists and their product manager be like? Sure, a Data science team could view the product manager as a business stakeholder, obviously, and help that product manager fulfill the mission of launching products that can garner the most sales possible in a market. The Data science team could could work on projects involving automating life cycles, perhaps use techniques such as customer churn, which could relate to improving the performance of products and so forth. Yeah.

[00:30:24] So what can Data scientists learn from product managers, product managers?

[00:30:32] Well, product manager is considerably more business focused than perhaps a Data manager, depending on organizational layout of a company. Whether Data science data science is what it is Data scientists are dilettantish. Lead are data scientist. Manager can learn from from a product manager is to make sure that their activities revolve around revolve around one central purpose, which is maximizing the business impact for the company and collaborating with the right business stakeholders, whether they're from sales, marketing or customer success group, to ensure that that you're that the deliverables are as relevant to the company's objectives as possible so that you've surpassed the pigs.

[00:31:14] I'm curious, what ways has your experience study for that exam that made you a better data scientist?

[00:31:20] Well, projects involve producing a deliverable or product within a set time limit. And I would say when I study for the PMP exam, it helped me look at the work required to create Data science deliverables as perhaps structured projects, even though there are other ways to look at it. There's the agile approach, obviously, which is growing in popularity at a fast rate.

[00:31:45] By the way, thank you very much. I appreciate that. So of this thread here now of strategic thinking and product sense and all that, I'm wondering if you could share some tips on what a data scientist can do to be more out of the box with their thinking, a data scientist?

[00:32:05] Well, it helps to view things from the perspective of your business stakeholders and make sure your activities are as relevant to the company's objectives as possible. It also helps to read books outside of the field of data science on topics such as business innovation, problem solving and so forth. It helps you also attend conferences as much as possible because the speakers at those conferences in many cases spent more than 10000 hours more than you on topics where you want to excel. And conferences also allow you to network with people who have similar ambitions and goals as you.

[00:32:43] Yeah, I definitely agree with that story going.

[00:32:46] And one last point is make sure to capitalize on trends and emerging trends as soon as possible, because you'll be ahead of the curve and you can make progress in the field before it fully establishes and build a great name for yourself before that field fully establishes. And it attracts a great deal of people who you may later meet need to perhaps compete with.

[00:33:14] Yeah, I 100 percent agree with that. I'd like read more books and something that said reminded me of the Jim quick quote, like when you read a book or when you hear somebody talk about the experience able to like download decades of information in days. I'm here. You mentioned Seth Godin earlier. Are you a fan of his? Like which which which works of his are you most inspired by?

[00:33:37] Sure. I read two books by Seth Godin. The first was the linchpin, which involved the concept and I believe you've mentioned this in your podcast before.

[00:33:44] I did that book is that book is phenomenal. That book is amazing. Hands on my book.

[00:33:50] And the main takeaway I got from that book was to make itself as indispensable to your company as possible. And even if your company doesn't fully isn't fully at the level where it could maximize its use of your skills, you could still use your skills in another company. And I just think it is extremely beneficial to your career, to. Maxed out the opportunities you have in your current company to help grow financially and to develop yourself as a person. The second book is Purple Cow Risk Involves, which is more related to entrepreneurship. And the main takeaway I got from that is to differentiate yourself in a market in ways that are practical and productive.

[00:34:31] Yeah, like linchpin is kind of about intrapreneurship where you're just kind of starting your own thing inside of a company. And like you mentioned about the entrepreneurship management is awesome. And that concept of like the resistance, the lizard brain. So in linchpin that that whole entire lizard brain and resistance concept, he brings up a book called The War of Art by Stephen Pressfield. And I just have it here on my desk for a little bit of motivationally. Each page is like it's like a little little passage and you just get get some good fuel in your tank to fight that resistance. At South Gordon's book, Linchpin is absolutely amazing. I highly recommend it because that's Data scientists like we are most time. We are entrepreneurs. We're doing something brand new and nobody's ever done an organization that a lot of people in the organization don't even understand or have the capacity to do. So you've got to come in and you really laying the foundation for years to come for this company. And if you treat it like your own little entrepreneurial initiative, but instead of a much larger organization, I think it's more successful at that. Do you subscribe to things like business newsletters or anything like that? Like what do you how do you consuming this information?

[00:35:46] Thanks for asking that. I meant to add that to the question about business acumen. The publications I most recommend are Gartner and McKinsey. I subscribe to the newsletters for those websites. Some other websites to consider are Harvard Business Review, Amitay Technology Review and and there are others I can't quite think of at the moment.

[00:36:09] I'll definitely look into that as well. I've got to get a separate email address that collects all my email newsletters and stuff like that. So I'll look into that and I'm kind of guilty of it myself. Like, I don't read a whole lot about business strategy. I mean, that's kind of not true. I read a lot about strategy in general and strategy and how to solve problems and leadership. But I've never really read anything from from my Harvard Business Review or Gartner or Forest or anything like that. So I guess that's what I'm kind of trying to say is like, yeah, I understand how business works. I understand the leadership and strategy, but I never read those type of publications. And I think that is one aspect of just a more well-rounded scientist. So thank you for those recommendations. I'm definitely going to be adding those to my newsletter collection inbox that I have to say, I think for that. So to talk to us a bit about emotional intelligence and why that's important for data scientists, there are three points that I think Data scientists should consider when it comes to emotional intelligence.

[00:37:10] The first is empathy. I think it's important to think from the perspective of the people you work around with and your business stakeholders try to consider their life experiences and how how those experiences shape them into the person they are and the values they have and their motives. The second point is kindness. I think it helps to let people know their unique strengths and contributions to your team and make them feel understood and appreciated. And it helps to, I think, also helps to do things like buy lines for people, at least that that was I think that was good advice during when times were normal. But it helps a bit. But that's for people just have genuine, genuine conversations with them. I try to go by the platinum rule when I deal with people. I'm sure you've heard of the golden rule, but the platinum rule involves treating people the way they want to be treated. And the third point is beyond the connections. I think it's great to talk about topics outside of work every now and then, just so people feel energized and feel good overall about going to work and and and associating positive emotions with work. I think it helps to build common ground and bring up topics such as sports, TV shows, favorite movies, things like that, so that you can just build a stronger bond with people and they you can establish more trust within your team. And I think that kind of that kind of approach and attitude can spread, can spread outside of a team into an entire department and build the culture in a positive way.

[00:38:47] Thank you very much for that. It becomes harder nowadays. Like you mentioned, the times are not normal anymore. But yeah, that's something I definitely need to up. My game on as well is reaching out to more people that work and be more connected with more people than than just, you know, my stakeholders and and people who I work with, especially tough ones, like the only data scientist in the organization, I think. In that sense, it's even more important to reach out and be in touch with people because there's lots of days where I got I can go a week and the only person I've talked to in that week, or at least message with or get on a on a meeting with is like my media manager and my manager is not a data scientist. I don't talk too much about data science stuff. So it's important to get to know the actual person, I guess. Thank you very much for sharing that. Really appreciate it. So last final question here before you jump into a real quick random round, it is 100 years in the future. Tim, what do you want to be remembered for?

[00:39:49] I want to be remembered as someone who built something great, whether it's a product or whether they're about a book or whether it's ideas, which I'm remembered for. I think building something greater than yourself is a great way to build a legacy and also to differentiate yourself from other people that that's probably how I would explain it.

[00:40:13] I expand it like that. So to my writing, because is that something that you've currently had in the works? Is that something you do? I know you write a lot for your blog.

[00:40:21] I did write a book in the past, but I've since published it. But I do plan on creating a digital product in the future, actually in multiple digital products, which which are likely place on my website within the next couple of months and over the next couple of years.

[00:40:41] Oh, nice. So a digital product like kind of could a course of some sort or e-book or something.

[00:40:47] I'm leaning more towards a course, but I'm looking at options as of now.

[00:40:53] Nice. Yeah, I've been trying to get more into writing now. I'm not writing a book or anything like that. I'm just just to exercise that that written communication skill. And, you know, the official website for the podcast will be launching early next year as well. And it can be accompanied by a blog. And I just want to write on topics that I find really interesting that take some of these really interesting ideas that I come across in books and I collide them with Data science and try to create a new perspective. Um, so I'm looking forward to that. Do you have a key? I know you said you're not currently working on a book, but you get your idea like a routine or like a creative practice that you have established for yourself daily.

[00:41:33] Sure. Whenever I began to write something or work on any type of video, I tried to find at least five high quality references and also status references at the bottom of an article. Right. Are in the video description of of a video and also try to explain topics in a way that hasn't been done before. I would say I have and I have a highly analytical mind. And I just I feel honestly, I feel energized by finding and communicating insights that weren't communicated before and at depths that weren't explored before.

[00:42:09] Yeah, man, I feel the same way. I just do creative things. I get super, super addicting these to really make these spent hours making these like Instagram carousels and cool graphics and all the stuff on the podcast just because it was such a joy of like colliding existing ideas together in a new combination of words, colors and presentation to create something that probably never existed before. My such an exhilarating, addicting feeling. Absolutely love it. So let's jump into a quick lightning round here. First question here is, what are you currently most excited about or currently exploring?

[00:42:48] I guess what I'm most excited about is the concept of entrepreneurship and trying to blend that with the time that technical topics I've worked with in the past. As I mentioned before, I want to provide insights in a qualitative manner in ways that weren't done before. And honestly, if I did not do that, I would feel like I would I would perform a disservice to myself by not chasing my dreams. Are these going for a side gig within my career where I can showcase my my strengths, which I'm perhaps not using in my regular jobs?

[00:43:24] I love that man. That's 100 percent true. And it's good to have a diverse talent stack, diverse set of skills and do something that complements or uses some other aspect of of your current work. So actually, I have a blog post that I've written but haven't published yet that is around this topic that you're just discussing. Definitely that you never publish it. What are you most inspired by right now?

[00:43:46] What I'm most inspired by is my favorite author and the personal development field. He spent years writing each of his books and his books contain more insights than even the current books written today. From what I've experienced, this author is Napoleon Hill and his book They Can Grow, which provided more insights. Stand then for more inside than I expected. The first time I read it and the main takeaway I got from that is that estab is associated with synapses in your mind. And as you change your thoughts, you change the physical composition of your brain and your actions, your subconscious decisions will begin to change as your as your mindset changes.

[00:44:35] And definitely check that out, been recommended more than once, actually, and it's actually available on Spotify, if I remember correctly. So I added it to my list. So there's like a free version of it, like audiobook version of it on Spotify. So if anybody is interested, I go check that one out. So what do you believe that other people think is crazy?

[00:44:57] I believe that misinformation is one of the biggest is becoming one of the biggest problems our society is facing. Somebody could use a tool as simple as Microsoft Paint to influence the decisions of thousands of people, which sounds crazy, but it's it seems to have happened this year. And I think technology such as deep faith and voice replication software could exacerbate that problem. And it'll be interesting to see how well governments and public corporations deal with this issue as as it perhaps grows more prominent over the next few years and definitely agree with you on that.

[00:45:36] When the defaqto super, super scary, if you could have a billboard placed anywhere, what would you put on it and why on that billboard?

[00:45:46] I would probably for the message, such as just not doing what everyone ever not doing what everyone else is doing, try to work on areas where your strengths, your passions and your society's needs overlap. And also, I think another benefit is these types of goals. I'm sorry, I better start over doing this helps you, helps you find the purpose in life that is perhaps the best fit for you and the one where you can make the most progress and both and also enjoy the journey as you're own, the progress of fulfilling that purpose.

[00:46:20] Thanks very much for that. And so what are you currently reading?

[00:46:25] I'm mostly focusing on listening to a variety of podcasts at the moment, but the last high quality book I read was The Ride of a Lifetime by Roger Eiger, who was the previous CEO of Walt Disney. He describes his experiences with Marvel Studios, Pixar Studios and so forth. And one of the takeaways I got from that book was a part where he describes analytics team and how it was so bureaucratic that it's dramatically slowed down the decision making processes among executives. That was an example of analytics done wrong. And I think analytics employees and managers can learn from that specific example provided in that book. And also towards the end of that book, he provides 20 principles on leadership, which I think every manager should aspire to.

[00:47:20] I definitely add that to the reading. Listen to the show notes. So what podcasts are you listening to?

[00:47:26] Some podcasts I can mention are Pratico A.I. and in fact one of their hosts for that used to host meet ups at Georgia Tech on Deep Learning, which I went to almost every month. Another podcast is Hardcore History, which delves into historical events throughout American history and other countries as well. And another podcast is Tim Ferris's podcast, and of course, yours.

[00:47:52] Thank you. Thank you very much. And I appreciate that. Yeah, temporaries is I like his podcast a lot. He he got some of the most awesome people that I was listening to over the weekend, that episode with the latest episode he did with one of my idols. And I've already got, though he did an interview with a couple of years ago, two or three years ago, but recently just did another one with the Super Bowl. I don't know if you're familiar with him, but they've already comp is the man. So what song do you have on repeat?

[00:48:20] One song I have on Repeat and which I've liked for a long time was ordinary people like John Legend.

[00:48:28] Nice woman, nice one. So I'm going to go ahead and open up the random question generator now and pull this up here. Gozman, what's something you wish you figured out sooner?

[00:48:40] I wish I got into a more technical field earlier in my career because technical fields tend to have higher demand, although they require considerably more effort when it comes to continually learning new topics, especially in fields such as Data science, where the landscape changes on almost a monthly basis.

[00:49:03] Yeah, I definitely agree with the Data sciences. A field for lifelong learners absolutely got to be got to be lifelong learning to thrive and make it to the top in this field. If you could live in a book, TV show or movie, what would it be?

[00:49:18] Great question. There's so many books, TV shows and movies to choose from and a lot of them provide so many conflicts that that it's hard for me to imagine.

[00:49:26] I would want to live in certain environments like the real world that the real world is is getting like real world isn't the one we live in at the MTV show format. From a while back, I PDF what is one of the great values that guides your life?

[00:49:41] Great value that guides my life is continuous learning.

[00:49:45] I believe job of learning isn't done after you obtain your high school diploma, bachelors or masters. The world is a huge place and it's a good practice to to learn as much as possible about topics related to where you're trying to go and get an awesome the last one here.

[00:50:04] Let's do pet peeves. Do you have any pet peeves?

[00:50:07] Sure. I guess one pet peeve I have is probably lack of integrity, actually lack of character. I think character plays a bigger role and an exception in in leadership than than many people consider up until present times where there might be more, more, more obvious. I think character is perhaps the top trait of any leader or or any employee you're considering to hire on hire onto your team.

[00:50:40] Absolutely. Great man. Integrity, high integrity, strong character. I mean, part of the reason that I became a student of stoic philosophy was just to learn how to better train and discipline my character, which I think is definitely an important skill to have him. How can people connect with you or can they find you online?

[00:50:59] You can search for my name on LinkedIn. You can also find me on my website analytics. Explain dot com. And my name is also searchable on YouTube as of now where you can you can find my YouTube channel.

[00:51:12] Right. And I will most definitely include links to that in the show notes. Tim, thank you so much for taking time out your schedule to. Be on the show today, really, really appreciate having you here. Thank you.

[00:51:23] Thank you so much. It was a great honor to be invited to your podcast.