Thomas Henson

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Getting Started as Big Data Product Marketing Manager

February 16, 2021 by Thomas Henson Leave a Comment

Big Data Product Marketing Manager

Ever wondered how to get involved in Big Data outside of Data Engineering or Data Science? Well let me tell it’s totally possible. All the roles on Big Data aren’t necessarily technical in nature. In this episode of Big Data Big Questions we explore the journey of Erin K. Banks into Big Data Product Marketing Manager. Data Teams and Product have many different roles that ensure they can achieve their mission. Product Marketing Manager covers a wide range of responsibilities and requires a unique set of skills.

Learn how to get involved in Product Marketing as Erin talks about her background as IT support into Sales Engineering and then Marketing.

 

Product Marketing Manager

The Role of Product Marketing Manager

Make sure to watch the full video with Erin K. Banks on the Role of the Product Marketing Manager.

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Filed Under: Career Tagged With: Careers, Marketing

What is a Chief Data Officer?

January 26, 2021 by Thomas Henson Leave a Comment

Learning about the Chief Data Officer Role

In the last 10 years a new role has joined the C-suite this role is all about making the most of Data. The Chief Data Officer now sits as a C-level leader within organization whose role is to ensure the company is has the right strategy for Data. The salary average for this role is between $118K – $300K+ depending on what company you land at. Here at Big Data Big Questions we talked at length about the different roles in Data but today we are going to focus on those leadership roles in Big Data.

 

Chef Data Officer

The Role of the Chief Data Officer with the Dean of Big Data

Make sure to watch the full video interview with Bill Schmarzo on the The Role of the Chief Data Officer.

Transcript from Otter.ai

Folks, Thomas Henson here with another episode of big data, big questions still jumping in that interview section where we’re going through careers today. Another amazing guest, folks may know this person is the Dean of big data, but bill Maher’s. Oh, if you’ve been following my channel for a while I’m done a book review. And if you’ve been following me, and you’re a data work summit, I think it was
2017 he was on stage, I had a breakout session, a lot of good stuff. So we talk about that and bring up some old memories talk about when we actually used to work together, and then talk about his newest book. So we’ll dive into his newest book. And let me just say this, this episode, make sure you tune in, he’s going to talk about what he thinks about data science, versus data engineering and the career output for those there at the end, I didn’t plan that it’s not that I’m trying to make, you know, a teaser. So you have to tune in which you should tune in the whole time. And then second off, if you’re interested in any kind of career, and maybe you’re like, Hey, you know what,
I’m ready to move on to the next stage in my career, or I want to get, you know, in the sea levels, or, you know, I want to be maybe less technical, but more, you know, business driven, listen to this episode, where he talks about, you know, career outlook for the chief data officer, you know, his thoughts around that, that vision, what companies are doing it? Well, how many companies aren’t so, so many different nuggets that you can take from this, make sure that you tune in. And then also my my request to everybody watching is one, put in the comment section here below what you think about these interviews. Second off, send me your ideas for who should be on maybe a tutor, maybe you want to come on in for the interview series, reach out to me, right, put it in the comment section here. And if you haven’t been tuning in, and this is your first time on this channel, you know, all about data analytics, but also about careers in it in tech. Right. So that’s what some of these interviews are, because data is actually touching all those I mean, we’ve had interviews on about marketing. So I will stop talking so we can get into this amazing interview.
All right, so 123 Hi, folks, thanks for joining today. We are excited. Super excited. We have Bill smartvault. On today, Bill, say hi to the big data. Big Questions. audience. Hey, Thomas. And Hi, big question. Big Data. Big Question. audience. Glad to be here, man. So I think the last time I saw you in person, we were on stage back when they used to have these things called conferences that you went to and I think you were you were coming offstage. You did a five and he did a little air guitar. back. I think we had it was hortonworks. What I think was the hortonworks conference data summit, something like that. So yeah, you’ve been? I’ve been doing well, I’ve been keeping busy and in trouble, which is what you’re supposed to do. Right? Right. Yeah, no, it’s good, man. So folks in the audience, if you’ve been following the channel for a long time, I actually did a review on bills. I think this was your second book bill. So the NBA. So Bill’s got a new book out. But bill for the folks who haven’t watched that amazing video, or haven’t heard of you? Why don’t you give us an intro, tell us who you are, what you do, and a little bit your background? Sure. So background wise, probably 40 some years in the data and analytics space. Lots of Forrest Gump moments in my life in the right place, right time. Not because I’m tall or good looking or from Iowa, sometimes you just get lucky be in the right place. So in the late 1980s, I was there at running a project with Procter and Gamble and Walmart that ended up being sort of the very first data warehouse bi project spent 25 years in the big data warehouse space when I was then recruited away from business objects by Yahoo to head up and be their vice president advertiser analytics that was at the time that Yahoo was developing this technology called Hadoop. And so I made the transition from a bi person to a data scientist. I teach at the University of San Francisco, where I’m an executive fellow, I also teach at the National University of Ireland in Galway, an honorary professor,
you know, and currently in between gigs, I just left my last gig where I was the Chief Innovation Officer, which was a most excellent adventure. It opened up all kinds of new domains and experiences, a lot of which I captured in my new book, things are just, you know, things I hadn’t thought about before they just all sudden became realized what was important. And in time as part of that realization was on the AI ml side, what we could do from an from an economics perspective, but also the power of team the power of empowering teams and how these go together.
So Bill, I want to jump right into this because you and I talked a little bit before this and we’ve talked in the past and you know
I think for my audience, we talk a ton about the technical and those are a lot of the questions that I get. But there’s so much power in, in understanding the business side. So first right in, you talk about the chief data officer for my audience, or for anybody what I mean, what does a chief data officer do? Like what is what is the CTO role? Yeah. So I think the chief data officer for most organization has become a minimi CIO. And what I mean by that is, I think the role of chief data officer and most organizations is not very fun, or creative or provocative. And I’m on a mission to actually change the nature of that role. I want that role become the chief data monetization officer, because I believe that organizations need to have a senior executive who single focus is figuring out how do I get value out of my data. So this chief data, so I’m on this crusade trying to get organizations to realize that, that there’s, there’s all kinds of unique economic value associated with data and analytics. And, and the Chief Data monetization officer probably doesn’t and shouldn’t have a technology background, I’m going to argue their background should be economics, because you think about what economics is about. Economics is about the creation of wealth and value, and how you use assets to create value. Well, that’s what that’s what data is all about. And you use analytics to convert raw data into valuable actionable customer product and operational insights that you can use to derive and drive new sources of value. So, so Thomas, my mission has been the chief data officer, you look like a CIO kind of person, don’t want that person, I want a Chief Data monetization officer who lives and breathes, who wakes up every morning and says, My job is to figure out how to get value out of that data, and is charged with with integrating across the entire organization, not just within it, but with sales and marketing and operations and engineering and everybody else to figure out where and how can we use data to derive new sources of value? No, I like that. So in this in, I’m gonna dig into this in a couple different ways. But first are all right, so in that role, so you get to decide this. So who does that person report to now? What well, who does that report to report to now? And where do you think that structure should be in the organization in Bills, Bills, most excellent adventure for your organization. Now, Bill’s most excellent venture, which is great. So I think this Rules report to the C e. o. Ci, correct right away. But I’ll see you tomorrow. So why don’t you just why don’t you make him the CEO themselves? Right? Well, I would do that. But I don’t want to get bogged down with all the crap that goes on with dealing with stockholders. No, this is a role that needs to sit at a level where this person can easily step across the organizational boundaries, and can help organizations to leverage and exploit, reuse, share, refine these data and analytic assets. If they’re buried underneath it, they’ll never get anywhere, because no one takes it seriously, from a business perspective. You stick them in finance or marketing or someplace, then you’ve automatically put them into a box. And this is the problem. Most organizations, we tend to want to put people in boxes. And once you’re in a box, it’s like a friggin cage, or you can’t get out of it. This person needs to have the authority to be able to walk across and show sales, marketing, finance, Product Management, engineering, how this one data source, for example, can power some of their key use cases, and can drive that collaboration across the different business units, so that they all can share and reuse the same data datasets over and over again. So in smartos, Most Excellent Adventure, this person reports high in the organization, and is charged with driving the overall across the organization use and monetization of these very valuable and economic or economically unique data and analytic assets. Let’s just draw on this for a second. I’m gonna go ahead and go this is why this is such an important conversation. This is why I think that this the person who runs this role, needs to have more of an economics background and a technology background. Here’s the reason why data as an economic asset, never wears out. Never depletes. And the same data set can be used across an unlimited number of use cases at a marginal cost equal to zero. Now think about that. Marginal cost equals your I have this asset. I can use it over and over and over and over again. It never wears out. It isn’t date is not the new oil. No date is like the new sun. It never goes away. It’s always provided energy for us. And so first off, from a data perspective, the thing that destroys and hinders the economic value of data, our data silos. If you can’t share data across the organization, I can’t take advantage of that economic multiplier effect. I can use that data over
Over and over again at a marginal cost equal to zero. So that’s number one is that data from an economics perspective, is unlike any other asset we ever seen in our life. And we tend to treat it like like it’s, we use an accounting mindset to try to put it into a box. No, don’t put it in a box, no boxes, we want swirls and let this thing swirl across the organization driving value. Now, here’s part two. So while data has this very unique asset that can be used over and over again, analytics is engineered correctly, will actually get more valuable, the more that they are used, right? Think about an asset you could have. Maybe it’s a car, maybe a Tesla, I love Elon Musk. When he made the statement, he made this provocative statement, which most people still don’t understand what he was meaning but as he says, he said, I believe that when you buy a Tesla, you are buying an asset that appreciates in value, not depreciate. Now, all the accounting people go What the hell does that mean? assets? you depreciate assets, you take them, you write them off? Yeah, real estate, 27 years, 27 and a half years over over time, right? Yeah, yeah. He said, Nope, wrong model, wrong frame, you’ve already lost the game, you’re thinking the wrong way about it, he say no, I can build an asset by the use of AI. And across a million Tesla cars, these cars are continuously learning, every time they turn a corner, every time they go past the path every time they go down the road. They’re continuously learning. And every night, the learning from each one of those million cars gets sucked up to the Tesla cloud in the sky and gets aggregated and then back propagated back to every car. So anything that one car learns about a particular driving situation. Now each of those million cars have learned it as well. That’s, that’s amazing, you can build these autonomous assets that continue to continuously learn and adapt. And many times the learning and adapting occurs with very minimal human intervention. So this is why your chief data monetization officer isn’t necessarily need to have a technology background, but needs to have an economics background and figuring out how how does. How do I leverage it? How do I use that to reinvent my business models? How do I use it to disintermediate customer relationships? How do I use that to totally redesign not just my business model, but the entire industries disrupt the entire industry business model? I love it. So today, right, I love the passion you gave me like five questions that I just wrote down that
you put you put a nickel, I mean, you got me going.
But so today, right. So today, we’re not in Bill’s excellent adventure today. And that’s, that’s why we’re here today to talk, hopefully, you know, to change that culture. So typically, the CDO will report to the CIO. And is that where we get challenges where it comes in it functions so marketing may be prohibitive, because they’re like, I don’t want to I don’t want to deal with the with CIOs, organization or engineering is like, we have our own kind of functionality. So it’s, it’s more of political or, you know, just just organizational structure. Bingo. Yeah, it’s it’s, you know, the the CIO, the IT organization has always been a cost center, not a profit center. And so the mindset around it has never been, how do I leverage that organization to derive new sources of value.
So you when you put the CTO or the Chief Data monetization officer into that spot, mean, they don’t look like a CIO at all, nothing they do looks like what the CIO does, but yet we’ve got this, this minimi CIO stuck in the CDO role, and they, and they think their job is to manage data. You know, your job is not to friggin manage data, your job is a friggin monetize it. So there’s, there’s a total mind shift needs to take place. Now, I’ll tell you right now, Thomas, there’s only a handful of companies out there that get this. Yeah, but but you know, if you look at the stock market, you look at the top five or six highest valuable companies out there. And you look at the amount of goodwill that’s stuck in those companies that’s comes from this monitor, you can very quickly figure out who these companies are, who have cracked the code are going, this is great. This leveraging data and analytics, to drive my business case use cases is like printing money. If no one else figures it out. That’s too bad. I just keep growing and getting more powerful. Yeah, so that was one of my questions is how many companies are doing it? Right? So for those, like you’ve got me thinking here now, so if if, you know, for those companies that are doing it, right and your CEO, and you know, I just my head goes straight to career career path and before I asked him the questions about, you know, what do we think a good CDO is the way that you’re explaining it to me is if if if our and I please, I do not want to be a CEO anybody so don’t get me wrong, but but you
You’re what you’re saying and the way that you’re kind of painting it to me like as you know, as an investor or as a board member, if I were looking at the next next CEO, I would want somebody maybe that came from one of those organizations, that that could be a natural step for a CTO or Chief Data monetization officer, similar to you see it. So it’s for so long, where you see you see CFOs being be moving into the CEO role. Is it fair to make the statement that oh, I could go to CEO? I think you look at I mean, probably the chief, the best Chief Data monetization officer out there is Elon Musk. Yeah. Right. And, you know, places like Google, these, you know, Amazon, masters of this, you know, apple, you know, Microsoft and parts of their business, not all parts of their business. These companies realize that they are in not just in the data business. They’re in the data monetization business, and they’ve cracked the code. I mean, think about for a second about Google. TensorFlow, I was joking earlier about TensorFlow, right? Exactly. The single most important technology that Google has, and the open source is now 99.99% of the financial analysts out there are going, What the heck? Why would you ever open source your most valuable technology? And here’s the reason why, in my humble opinion, why Google did it. It’s because in knowledge based industries, the economies of learning are more powerful than the economies of scale. So by having everybody out there using TensorFlow across a wide variety of use cases, TensorFlow just gets smarter and smarter and smarter. And who is the best at leveraging TensorFlow to drive data monetization? Google, so all their competitors are using their product or just helping Google to print more money? No, I like I love that. And it’s, it’s, I totally agree. And you’ve almost stoled my sales pitch or training pitch that I give to people. The the emphasis I put on it, too, is
you and I know you and I know what they what Google uses, right? They use TensorFlow, they’ve open sourced it. And it’s a it’s a great product, even if you and I were the most proficient TensorFlow people on the planet, and we didn’t work for Google, we don’t have the data stores that Google have. Right, right. And so that, you know, they’re, they’re able to get, they’re able to make their product better, kind of like what you were saying with Tesla make your product better, but you don’t have the data elements to act upon it. And they have the data sources, but they also have a different mindset. They’re their data scientists are not like normal data scientists, they give them a level of training, that I don’t think the average data scientists ever would, would ever appreciate. Understand. I mean, most of their senior data scientists at Google are taught design thinking, you’re gonna think, design thinking that has nothing to do with building neural networks, anything I said, you know, guess, right? It’s true, right? It’s they they understand, in detail, what it is they’re trying to do first, and then figure out what data they need not figure out not to say, oh, here’s our data I have. So what problems can I solve? No. How do you distinguish signal from noise if you don’t know the problem you’re trying to solve? And I think what you see from Google least in the folks I’ve met there, and I’ve not met a lot, but the ones I’ve met have been pretty impressive. They’ve got a laser focus on trying to figure out what is it we’re trying to do? And then what data do we need to support that? They’ve reversed the process? Everything’s about, obviously, to gather a bunch of data. And then here, tell me what’s valuable in data? Well, I mean, again, how you distinguish signal from noise and the data, if you don’t know what you’re trying to do. So they’ve taken it. And yeah, they got brilliant tools, and they got great datasets, but they have a different mindset is kind of like what Elon Musk did, he’s got this whole, I mean, if you want to change the game, change your frame, look at something different than your competition does look at it differently than than your competition. And you’ve got a chance of providing some very unique, differentiated compelling value. No, I think that’s important. And one of the things that, you know, I come more from the software side, as a software engineer. And you know, we we say this all the time, but we don’t act upon it. I don’t even act upon it sometimes. Right? Like, we we think, first and foremost, what is the new framework I can use versus what is the right framework for the job, same thing in data, right? Just just flipping, what do we want to solve? And let’s go find the data elements to solve that. So I’m going to give a homework assignment to you to your listeners, and I’ll provide the link for it. We developed a tool, design thinking tool called the hypothesis development canvas. And what it does is it articulates the problem we’re trying to solve before we ever put science to the data. So what problem you’re trying to solve
What are the metrics and KPIs against what you’re going to measure success in progress? Who are the key stakeholders need to buy in? What are the asset models you need to put in place? What are the decision you’re trying to drive the prediction support that data sources we might need. And even, even probably the most important part is, what are the costs of the false positives and false negatives. If you don’t know the cost of the false positive false negatives, you never know if your model is good enough. But yet, we sort of let that kind of flutter through. So we build this hypothesis development Canvas, we do it through an envision exercise, you probably remember me talking about envisioning exercises back in the day, I still do those, they’re still invaluable in driving alignment across the organization to figure out what problem we’re trying to solve. We put all that into the hypothesis development canvas. Now the data science team knows they’re trying to do they know how they’re going to measure success and progress. They know what decisions the customer they’re trying to make. They know what predictions they need to go, they now have a framework for figuring out which of the 1000s of different data sources are probably most important in solving that.
Yeah, no, I like that. And we’ll definitely make sure we link it here in the description, and in the show notes. So I’ll make sure that we repost that. So I want to I want to go back, I think we’ve set the stage. And you know, the way the way, I’m here in this conversation today, I’m pretty excited about it that, hey, there’s a career path for folks that like data that could possibly be CTO. Now, this is Thomas, and Bill saying that, but I think we have a pretty good a pretty good handle on it. So it may or may not work out. But what would you say to somebody who’s maybe they’re in college watching this, maybe they’re just wanting to change careers, or even you know, we even have some folks who are, who are just just moving into college.
Hey, this sounds interesting. I like the business side. I can be a peacemaker. What How do I become a CEO? So
I’ve done a couple of lectures in the last six months to a number different universities to to graduating seniors, basically with that same question, maybe even more broad, they’re like, Well, how do we? How do we future proof our career, we had COVID. Now, there’ll be something new next year, something new following that there’s always going to be change and challenges in front of us. Some of them may be digitally undo. Some of them may be, you know, healthcare induced or whatever pandemics we there’s the world is constantly changing.
I believe there are three skills that everybody needs to learn. When I teach my class. My class focuses on every one of my students, whether they’re a data scientist, an MBA student, software engineer, we work on these three skills, skill number one, analytics, you need to know what you can do with analytics. And you don’t necessarily need to know how to how to code, a TensorFlow or a neural network. But by golly, you better understand how it works and what you can do with it. Right? So you need to understand what are the things that I can do using reinforcement learning using unsupervised or supervised machine learning? What are the things that these things do so a an understanding of the application of advanced analytics is critical for everybody, whether you’re a nurse, a lawyer, a barista, a tech, whatever you might believe B, you need to understand it. That’s number one.
Number two, you need to understand economics, you need to understand where and how value is created. Or you need to understand how value is created with customers how it’s created with the operations within markets within products, you need to have a solid foundation, not in finance, as much as in economics, and understanding a lot of the basic economic concepts, you know, economic multiplier effect, and postponement theory and all these things supply and supply and demand a lot of basic concepts come to bear in the area of around economics, and then the third mixin. So you don’t do these simply mix these all together. The third one is design thinking, which is learning to speak the language of the customer. And the single most important tool that I think anybody should learn from design thinking is how to create a customer journey map. Think about your customer. Think about where they go through the path that they take, from the minute they have an epiphany as they want to do something all the way through the after goal. And then identify all the decisions that user has to make to support that journey. and identify in that journey, which are the points of high value or value creation right around which I want to make sure I’m monetizing. And what are also the points of that, you know, value destruction, the hindrances, because you might find that those those points have hidden hidden acts of hindrance are also monetization opportunities. And so what you do is you you really have to learn what a customer is because at the end of the day, the only person that provides value, the only person who provides who has ink in their pens is a customer. Yeah.
So I want to learn more design thinking Do you have a recommendation for a book or a couple of blog posts or anything that
Can oh god yeah, I, I’ve written a lot about it. I think my my third book, which is called the art of thinking, like a data scientists
goes into a lot on design thinking with respect to data science.
That That book is, I think it’s only available on my personal website, Dean of big data.com. I’ll make sure to link it here. Yeah, I’ll send a link to the reason why I self published that is I want to be able to see when you publish, which people don’t realize minute you publish, you give up distribution and pricing rights. It’s not in your control, I, there’s no book here, I had no say what this price that it was decided by, and how it’s distributed, right? And discounts and all that kind of stuff, I have no, say, I wanted to book, a workbook that my students could use that I could have at a price point any student could afford. And so it’s like 999, that’s $9.99. And also 900 900 $999. Right? My goal here is every time somebody buys that book that buys me two visits to Starbucks. So that’s my goal. That’s my Starbucks fix. But it’s got a chapter in there about the hypothesis development Canvas is a chapter about design thinking. And it’s really the entire workbook is how do you get people to think like a data scientist, again, regardless of your profession, whether you’re going to be a nurse, or a doctor, or physician or engineer, or technician, or whatever you’re going to be tomorrow’s world’s gonna require everybody to think like a data scientist. No, I think that’s so important. And, Bill, just a little, little information for the audience. Before we were kicking off recording, I told bill that was like, Hey, I think this is an important topic to talk about, just on the value of the business side. And, you know, Bill bill just gave, you just gave some of the points and my, my learning through through through my career as a software engineer, and in, you know, moving, you know, moving more into business development product side, my journey, my journey came because I wasn’t the best software engineer, and even even some of the best software engineers started realizing on the team, if they couldn’t explain to our customer and your customer, your customer could be, you know, I was, you know, I was I was a contractor then, but you may not have a customer, you may only do internal products, but you have a customer and so, you know, the things that bill’s talking about here are super important, because you’re always you’re always having to sell and having to communicate your vision, how else are you the projects that you want to work on, you know, the only way to continue to get those funded or to get those, you know, sponsored by your leadership or your customers, is to be able to show them your vision and be able to communicate that. So, Bill, I mean, that’s, I mean, it’s it, I think it’s huge to our audience, and I hope they’re still tuned in, when they when they found out we were going to talk business, right, like me, but that’s the point like this is, I mean, this is this is important stuff, you know, to be to be able to understand how to convey these projects, I’ve, I’ve had the great fortune of having managed a couple of data science teams, truly outstanding data science teams. And when I watch their, I know it gets them excited. And what gets them excited is when they’re talking to a customer, they’re able to help the customer solve a really wicked problem using data science. And then that solution gets put into operations. It isn’t just having a great idea. It’s seen it actually in the works. And that’s my teams I’ve worked with, I’ve been very fortunate they light up when they know that their ideas are actually helping, you know, help this company do this more, you know, more efficiently and better. So again, you to be effective, it is not just about having great ideas, it’s about being able to put those great ideas into work and provide value to people, that’s least for me, and for the people I’ve associated with. That’s where it gets fun.
So
part of our audience as well to you know, we do we do have folks that are, you know, executives or leaders or, you know, directors within their organization. Exactly. Get off your ass.
So, let’s say, Well, I mean, that’s where we’re going with this question. So say they’re watching this and they’re like, Hey, I like Bill’s excellent venture. I don’t think my company is one of the one of the handful that are doing it, right? What are the steps that I can put in place to start attracting talent? Let’s just start with the talent portion. How would great data we want to start building a great data team? How do I attract that talent?
great talent comes to organizations that empowers them.
great talent wants to be at the frontlines providing value and don’t want to sit in a box but want to be a part of a team that’s creating swirls I I call it
organizational improvisation. And what I mean by that promises that people want to work in a situation where all of their skills are being tested and pushed. They want to be in a city like a great soccer team, right? Think about the women’s us women’s Olympic soccer team. write poetry.
Ballet on the field, working in combination. Was there a coach above them Yellen. You know, Susie move here? JANET, go here, right? No, no, they had been empowered as a team and as individuals to accomplish their objectives. And that’s what they did. And so I think what it all starts with empowerment, I have a, I have a little thing when I finished my my, my video blogs in the morning, I always ended by saying hashtag culture of empowerment, yields hashtag culture of innovation, when you can empower people, when you can allow people to try things, test things, fail, learn and try again, you will get the best people. And better than that, you will get the best out of your people when you let them do what they can do. Again, this idea of I’m I’m very much against the organizational box, where he takes somebody who’s really brilliant, we stick them in a box, we put them in that cage, and they’re never let out bullshit, right? People are brilliant, they can learn they can synergize, they can blend, they can they can take one plus one equal three and seven, you can take diverse perspectives and diverse opinions, and you come up with something even better and more powerful that friction is how tires move, right? It’s all these things. But yet we senior management wants to go out and hire a data engineer and put a data engineer back by golly, no, right? You think about a data science team, you got data scientists, you got data engineers, you got an ml engineer, you’ve got business subject matter experts, my team is the design thinker amongst it, you have this team. And here’s the beauty of a team. Everybody, at some point in time, will be forced to have to lead. Everybody takes turns leading, depending on the task at hand, right? Everybody has to be prepared to lead everybody is prepared to work together, you’re you know, it’s it’s like playing.
It’s like playing a game
or childhood. Yeah, it’s like playing a game boy. Right. And, and when you play Final Fantasy legend to you soon realize it that way to win the game is to have a very diverse set of characters, who each will take different turns leading at different points in the game. That’s the way teams work. So senior executives, if you want to get the most me, you’re talking about hiring the best people, you probably have some pretty damn good people already in the company, you just had never empowered them. You’ve never given them the ability to try things. And by the way, if you don’t fail enough, you never learn enough. If we believe the economies of learning are more powerful than the economies of scale, then you have to let people fail. Just know you can’t fail stupidly. And you have to learn from those failures. That’s how you learn you try things out, you, you you nurture that natural curiosity to see what what happens. I blend this data set with this data set using the sort of ml AI framework blows up. All right, well, I didn’t work very well document share that. Try something different. That’s how we learn. I used to so one of one of my early managers in my career, and I love the saying in the way he kind of treated it. Because he said that, hey, I’ll only be mad at you. As long as you know, you know, no mistake or no making a decision, as long as you don’t make a decision that a first grader could make. Think I just blew the same. But I knew what he was trying to say. Right? Like, as long as it wasn’t like you said just something, something, something, something you could prevent, or something that you know, didn’t put a lot of thought so well. Here’s the interesting part of this talk. So I believe that greatness is in everyone. But every now and then you’ll have people who aren’t ready to step up towards that. Yeah, I, I had I had I had a really powerful data scientist, really, really smart. just could not step up, had to let him go. It wasn’t the right fit for him. He was struggling, he was not happy. He was bringing the team down. He had to be let go. And he came he went found another great job somewhere else that we could sit into a box and do what he wanted to do. He did he wanted to be in the box. He didn’t want to be in this world. He wasn’t ready. He didn’t want to lead. But I tell you, I I believe that greatness is in everyone. And it’s up to management, to basically unleash that greatness to let the best come up. And by the best managers out there. If you all your all your people are doing great things. Suddenly you look like a genius as a manager, when all that you’ve done is you basically have unleashed that greatness in your people. Yeah, no. So I like where we’re going here. So I’m gonna continue on this path. So for that director for, you know, like I said, Maybe we’ve got some executives of watch I’ve gotten I’ve gotten a few interesting questions and emails. So we know how to track that a talent and they’re on board where we’re doing the right things where
Empowering, we’re making sure that we have diverse thoughts and, and leadership within our team, everybody’s stepping into lead. How do I manage that up? Right? How do I maybe maybe this person is the you say the mini, the mini CIO or the mini mini CIO? If we’re stuck there, how do I make that? How do I how do I managed to get that cultural change so that once again, the goal is to become that handful of companies, right. So the The key is to find a friendly on the business side. That is somebody on the business side, who’s trying, it was in one of two modes. And I when I was in sales, I was a big fan of the Miller Heiman selling technique. And he used to say that they’re the people in two modes are the people who want to sell to people who are in growth mode. And people who are in trouble mode, people who are in growth mode, know there’s something bigger they can do, they just need help to get there. People are in trouble motor people who know they’re in trouble. Know, if they don’t switch things around, they’re soon going to be out on their tail. I like both those kind of people, because they’re now open to suggestion and trying to do things differently. And so you find a friendly, hopefully, probably have somebody more in the growth mode, somebody who’s No, I had a situation with a chief marketing officer, and a brand new to the company wanted to put his fingerprint on the company. We were releasing a brand new product. He wanted to we had you know, how do you focus your scarce sales and marketing resources to around the right customers for that product? Right. So we worked with him in his in his marketing team and the brilliant CIO, we had we put together a plan, we launched this and the first year generated an additional 28 million in revenue, right? Think about that 28 million revenue, from one use case. 28 minutes, like that’s like found money. It’s like walking down the street and Stumbling on a bag of money go, oh, here’s $28 million for you. Right? I’ve never found that on the street.
If I had, I wouldn’t be talking to you, I’d be saying that’s fine. But that’s that’s what it’s like, right? So you find a friendly, who has a vision, and you help them you make them a hero. Or you make them the champion. And then they’re out telling the rest of the seat that the CXO that Yeah, I just ran this use case. And I’ve gotten it’s my first one, he got 14 more to do the first one, you’ll have $28 million in additional revenue. And you’ll watch all the draw jaws and the executive go. What would you guys do? Right? Then what happens? Of course, success breeds success, which is interesting. So first off, find a friendly, make them a hero, make sure they’ve got a problem is big enough, right? I love the company and say, well, is my is my data big enough for big data? No, that’s the wrong question. Is your problem big enough, right? So $28 million.
Once you’ve done that, and you’ve started to convince the organization,
person by person is value, the next biggest challenge you’re going to have is on governance to make certain you’ve got a process put in place. So people are, are running their ideas through you. Because what you don’t want to do is have somebody go well, I’m you know, I can’t wait for your team to get to me, I’m gonna hire my own team and do my own thing. Well, data silos, right kills the value of data, orphaned analytics, I can’t I can’t continuously learn and adapt if I have orphaned analytics. So you have to have a rigid governance organization, which means this Chief Data monetization officer, not only are they a collaborative trying to step across borders and makes shit happen, but they also got teeth. And they’re not afraid to be a son of a bitch to walk across and say, no, we’re not going to have these bands or random data scientists in the organization, they’re going to sit in our organization, you’re going to work it through our governance process, because whatever you learn from the project, you just did, we want to make sure that every other part of the organization learns, because the economies of learning are more powerful than economies of scale. So the Chief Data monetization officer needs to be on one hand, very friendly, has the carrot can make you more money. Here’s $28 million per use case. It’s got a big old stick with a nail in it seen if you don’t play by the rules, buddy, you’re out of here. Yeah. So I hadn’t I hadn’t seen it from a governance perspective like that. So you know, building that value, I mean, and essentially what in my head the way that I’m kind of walking through it is, alright, so I build this amazing team. And we start you know, we find a friendly and essentially, you’re building your brand internally. Until, you know, you have you just have this rush of so many different opportunities within your organization. And then I guess I never thought about it from the perspective of organizations start to fall down when when when they start building their own. You can’t have four four centers of excellence, right. I know. Right? Right. We we saw this problem in data warehousing, right. We started off with data warehousing, but data warehouse was really hard to build. They were really hard to build so businesses couldn’t afford to wait for the data warehouse to get to them. So they built their own data Mart’s

Filed Under: Career Tagged With: Big Data, Careers

What is an Industrial IoT Engineer with Derek Morgan

January 22, 2021 by Thomas Henson Leave a Comment

Industrial IoT Engineer with Derek Morgan

Explore Career Path’s as Industrial IoT Engineer

IoT investments are projected to grow by 13.6% through 2022. There is a huge opportunity for developers to jump into a career in IoT and specifically as Industrial IoT Engineers. Data is at the forefront of skills needed for IoT workflows. In this interview I sat down with Derek Morgan to discuss the role of the IoT Engineer.

Derek has quite a bit of experience in IoT and has been focusing in Manufacturing space of IoT. During this episode of Big Data Big Questions we break down the skills needed to enter the IoT Engineering space and what certification matter.  Here are a few of the items we cover:

    • Tech Stack Rechner, Postgres, Python and Terraform
    • How C ++ doesn’t apply here
    • Cloud vs. Private Cloud for IoT
    • Security Challenges with IoT
    • Opportunities for IoT Engineers in this space

Make sure to checkout the full video below to understand the role of the Industrial IoT Engineer.

Industrial IoT Engineer Interview

IoT Engineer Show Notes

Derek Morgan LinkedIN
Terraform
More than Certified in Terraform Course

Filed Under: Career Tagged With: IoT, IoT Engineer, Python

My Journey Why I Chose MBA Over Masters in Science

October 5, 2019 by Thomas Henson Leave a Comment

MBA Over Masters in Science

My Master Degree Journey

Once again here at Big Data Big Questions we tackle a College related question. Today is a little different where I discuss MY JOURNEY in choosing a MBA over a Masters of Science. After less than 6 months into my first Software Engineering role, I decide to pursue a Masters degree. One of the biggest reason I acted so fast was advice from peers. The advice was simple knock out the Masters before you get too busy with life in general.

Wow was that good advice for me!

Once the decision to go back to school was made, I had to select as Masters program. In reality I’m sure I made the decision a lot harder than it should have been. Looking back after all these years I’m confident I made the right decision. Watch this episode of Big Data Big Questions to find out my process for choosing a MBA over a Masters in Science.

 

Transcript – MBA Over Masters in Science

Hi folks! Thomas Henson here with thomashenson.com. Today is another episode of Big Data Big Questions. Today’s question is another in the “how do I choose a degree” series I guess, that I’ve been getting in, and this one is more just around my personal journey. How did I decide to go with an MBA versus a Master of Science? It was a pretty big decision for me, so I thought I’d share my journey, because I know a lot of folks are looking at, even from an undergrad perspective, am I going to go more information systems, or am I going to go more computer science, or engineering from that perspective? How does it all go through, and what’s the thought process? I’m just going to provide my thought process to how I went through it, and maybe that can help you. Maybe you can give me some advice, tell me if you think I did the right decision.
I want to talk a little bit about my journey into choosing my MBA over choosing a Master of Science, just to give some thought process around that. I was not a traditional student in the fact that I graduated a little bit older. I had a career outside of tech a little bit before I really focused down, and buckled down, and went back and got my information systems, or CIS Computer Information Systems. It’s different at other places. Whenever I went through that, I made it, really, a focus that as soon as I graduated, I was really going to try to get back in. I think I only took six months off. During that six months, and even before then, I knew I wanted to go and get my Master’s, and so I was really struggling with the fact that, hey, what do I need to do? Should I go for a computer science or some kind of science master degree, since I had what was essentially a business degree in information systems, or should I continue on the path and go down the road of getting my MBA?

Sought out a lot of information from mentors, people I worked with. I was very fortunate in my first job, where they would pay for my college tuition for my Master’s degree. I was really excited about that, and also another one of the reasons that it probably really drove me within six months of graduating, getting a job, going back in and being like, “I want to work full-time and get my Master’s degree.” For me, sought out some mentors. My manager at the time, he had an MBA, and that was one of the things I asked him. I was like, “How did you decide?” He was like, didn’t really matter as much in his eyes from what he’s seen, and with him having an MBA, plus, a little bit biased. For him, he was like, “I liked it.” The thought process around having an MBA, being able to say MBA in your title, is a little bit different and has more of a pop, I guess, from his perspective. Another one of the mentors I talked to, actually he had a Master’s of Science, and he was upper-level director at the organization I worked for, and just talking with him, and he was along the same path of not necessarily saying that an MBA was going to matter. He was more that it matters that you finish. From that perspective, so really after I had some advice from that perspective, it really let me go in, and the way that I actually chose is like, all right, it’s not going to hurt my career path either way.

I really went through, and I looked at the programs. I compared the programs, compared how long it would take. It would take me a little bit longer to go through the Master’s of Science and really, it was more about some of the classes and some of the cool things. I’ve always had a knack for business. I really liked accounting when I had accounting classes previously in my undergrad. It really gave me an opportunity to dial in and look at some of the things across business from an economics perspective. Some of the computer information systems classes, because they’re still focused. You have an MBA. You have a focus. I still focused on that. Being able to do some things with more Java classes, because at the time, I liked Java. [Laughs] Some of the things with databases and information systems. Really, there were some cool things that were going on around healthcare that piqued my interest as well, too. Chose that path, so I know people have sought out advice, and looked around and asked. Should we do this? Should I go for a data engineering degree or a data science degree? It really depends on what you want to do, and I don’t think, just like with my journey, picking one or the other is going to hurt you down the road. Going back to the blunt advice I got from a senior director was, it matters more about if you finished it.

When you start out on that journey, make sure that you capture it and go on. That doesn’t say that anybody that’s watching this don’t think it’s a thing where you’re like, hey, you have to get a Master’s degree to be able to succeed within your role. We’ve proven that, especially in tech, so much in tech. There’s folks without Bachelor’s degrees, without Master’s degrees, and even without high school diplomas. It’s more about how creative you can be and how much you can focus, and just really pull yourself into your craft, whether it be development, whether it be analytics, or wherever you want to go. Just for me, for that journey, it was having me being a later student is kind of like, for me, I really wanted to go back and prove to myself that I could finish and stick it out. Being one of the first in my generation, between my family, to go and to have that Master’s degree, also was really awesome. Personal decision, but I’m sharing it with everybody here. Everybody’s situation’s different. Happy to give advice, happy to talk through it all, but that’s my story, my journey. If you have any questions, put them in the comments section here below or reach out to me on bigdatabigquestions.com. I’ll do my best to answer those, and I’ll see you again next time on Big Data Big Questions.

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Filed Under: Career Tagged With: College, Data Engineer Degree, Masters of Science, MBA

Data Engineers: Data Science vs. Computer Science Degree

October 2, 2019 by Thomas Henson Leave a Comment

Data Science vs. Computer Science Degree

How Do you Choose the Right Degree?

College is such tough time when it comes to choosing education paths. For most folks College makes the first time they are making huge decision about their futures. So it’s easy to get analysis paralysis because the decision means so much. Or does it? At the end of the day it feels bigger than the decision really is over the long term.

The difference between a Data Science Degree and Computer Science degree might impact career outlook in the short term. The long term impacts of which degree you chose are minimal. Look around at the number of position where degrees aren’t even a requirement. When I was working on my first Big Data project our Data Scientist didn’t have a degree in Data Science but he was great in that role. Now I will say that Data Science degrees haven’t been around that long so it kinda of make sense.

Find out my thoughts of the differences between a Data Science Degree and Computer Science Degree in the video below.

Video Data Science vs. Computer Science Degree

Transcript – Data Science vs. Computer Science Degree

Hi folks! Thomas Henson here with thomashenson.com. Today is another episode of Big Data Big Questions. Today’s question comes in from a user, and it’s all about, what specific Master’s degree should I get? Find out how I answer this question and what Master’s degree you should get or should not get if you’re going into data engineering.

Today’s question. If you have a question, make sure you put it in the comments section here below. Reach out to me on thomashenson.com/bigquestions. Find me on Twitter, whatever you want to do, and I’ll do my best to answer your questions right here.

Today’s question. I’m looking for a career as a data engineer, but I’ve got a Bachelor’s in IT, and I’m looking to get into a Master’s degree. Awesome! Congratulations. It’s a pretty cool thing to go through. I went through a Master’s program as well. Which is better for data engineering career? Thinking about that specifically. A Master’s degree in data science or a Master’s degree in computer science.

This question, for me, really keys. I remember what it was like going through, when I’m trying to figure out which kind of Master’s I wanted to go to. I had a similar situation. Specifically wasn’t in the data engineering from that perspective, but I was looking in, to see, what do I want to do to take the next level in my engineering career? I looked at an MBA with an emphasis in information systems versus a Master in Science Computer Science. I ended up choosing to continue on down the business path and getting my MBA in information systems. Pretty excited to have gone through that, and really happy with my decision. I feel like it’s been fortunate with my career. I understand where you’re coming from. I’m not telling you to get an MBA. That’s not what I’m saying. I understand how much you look, going back and forth, and you’re like, man! What do I do here? I appreciate you asking for my opinion, as well. Which one should you get if you’re going into a data engineering? It’s an easy guess for me, here, just to say, “I think computer science and the skills that are involved in computer science are going to help.” If I were in your shoes, I would look, and pivot more towards the computer science. I would look into, though, there are new universities and other programs that are starting to emerge that actually have a data engineering track. Just like you were asking about, should I do the data science? In my opinion, if you’re not trying to go down the data science path, you maybe don’t go into that. If they do have a tack specific for data engineers, so data science in a newer program, a lot of universities and colleges are having around the globe, so if they have a specific data engineering path, I’d look into that. Specifically, I’d probably stay with the computer science track. However, like I said, there are some universities that are putting out a specific, “This is not data science,” but a specific data engineering path, where you’re going to go through more systems administration stuff, where you’re going to be building out programs that are going to analyze data, and being able to really focus on distributed systems, whether it be from Kubernetes, and containers, to different clouds. No one had to do it in AWS. Building out good data pipelines and really understanding what you’re doing from that perspective. I think I’d look into that, and also make sure you’re looking at some of those degrees.

One more bonus tip around as you’re going through that. I would definitely, at the university that you’re looking at, have a conversation with some advisors, and even some of the professors in the data science world or in the computer engineering world, and see if you can cross over. Maybe there’s an opportunity there to do something inter-disciplinarian. Maybe you can take a couple of the data science courses, because they would be really good for you to get exposure to it, not become a data scientist, but exposure to what goes on, on the data science side, and have those packaged together, and go through some of those courses while you’re going through the computer science course. Maybe they, not asking you to take double load. Hopefully there’s a crossover there, where it’s like, “Hey, I can pick and choose some of these.” With data engineering and just the boom that’s going on with that as far as careers and, if you look at just globally, we need more data engineers. The universities will be pretty excited for, especially somebody standing out to do that. Worst case scenario, what are they going to do? Your professors may tell you no, but they see that you’re engaged, and that you’re interested in data engineering, so they’re going to be able to look out for, maybe there’s new classes that are coming up. What about internships, right? Some of these universities have really good relationships with corporations. Your name is already at the top of the list, and it’s shown that you’re showing initiative, that hey, I’m excited about the data engineering world, so any opportunities to learn more or any opportunities for future career growth, might be a good thing. Something as simple as taking an hour to reach out and talk to a professor may be investing in yourself and in your career for further on down the future. Definitely try that out. Should you get a Master’s degree to become a data engineer? You don’t have to, but like I said, I’ve got a Master’s degree, and I went through that for my own purposes. If you’re watching this video, you’ve made it all the way to the end, which I hope you’ve made it to the end. Everybody that starts watching it, this was a specific question where we were talking about different degree options for your career. We’re not saying that you have to get the Master in Computer Science to become a data engineer. Heck, you can even go through, you can do the Master in Data Science and become a data engineer. This is just my advice for what we’re trying to do. There are other data engineers that don’t have degrees. We’ve covered that quite a few bit on this channel, and so I just want to be specific to that. I don’t want people watching this course, especially if you’re in college, or if you’re in high school and you’re starting to think about your data engineering path, like, “Aw, man! I’ve got to go get a Master’s degree to do this. Be in it for the long haul. That’s not what we’re talking about here. We’re just talking about options. Let me know if you have any questions about degrees, certifications, anything data engineering or technology-specific, and I will answer it on the next episode of Big Data Big Questions.

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Filed Under: Career Tagged With: Computer Science, Data Engineer, Data Science

Speaking Skills For Data Engineers

July 15, 2019 by Thomas Henson Leave a Comment

How Important Is Public Speaking For Data Engineers?

Brand new question on Big Data Big Questions is around public speaking in Data Engineering. I’ve often heard that public speaking is the universal number 1 fear for most people. So many people choose to avoid it for various reasons. While no where will you see public speaking called out in Data Engineering descriptions, I believe it’s a skill that worth investing in. Find out my thoughts on Speaking Skills for Data Engineers in the video below.

Transcript – Speaking Skills For Data Engineers

Hi folks! Thomas Henson here with thomashenson.com. Today is another episode of Big Data Big Questions. Today’s question comes in from a user. If you have a question, find me on Twitter or put it in the comment section here below. Send me an email. There’s a ton of different ways to get in touch and have your question answered on the next episode of Big Data Big Questions.

Today’s comes in from Bobby, and it says, “Can you let me know which career path is better between data scientist or data engineers, which we’ve talked about, but this one is for a person suffering with anxiety or difficulty giving presentations?” So, thank you for your question, Bobby, and I totally understand where you’re coming from as far as having challenges that you’re trying to deal with. Trying to pick out a career path, like, we want to play to things that we’re going to be successful at and things that we’re going to be able to excel in. You’re looking for that career path. I’ll say just right off the bat, a couple things stuck out to me about it. I’m going to get to those as we talk about why I think presentations and stepping outside your comfort level are some options for you. Let’s answer your question first, before we dive into Thomas’s thoughts on some of that.

Depending on which was you want to go, it’s not going to matter. It’s going to be more about if you’re more technical as far as wanting to be code, and hands-on, and building out clusters. Maybe starting to play with Kubernetes, Linux, those types of systems. Then, being on the data engineer side, it’s going to be a good way to go, or if you’re more math-based and want to get into the specifics of, hey, some of these features or some of these pieces of data may be able to give us better insight into what we’re trying to solve, then the data science path is going to be there. Don’t let your anxiety or your difficulty giving presentations say that, “I must go data engineer,” or must go data science, because I think they’re both equal to give you the opportunity to not have to present and not have to have as much interaction as you would maybe in a different role where it’s more customer-facing and job-driven.

My thought process about how much you’re going to have to deal with in that situation is, I’ve worked with people who never had to present. When we were in that role, that just wasn’t their thing. They may be at the meetings. They’ll be at the meetings, but they’re not the point person. Maybe get a one-off question or something like that, but most of that’s in the confines of their team. You’re still going to have team interaction, but there’s still a ton of downtime where it’s like, “Hey, headphones on,” just banging out your own code, or doing your deployments, and stuff like that. There’s not a ton of interaction there. You may have some user interaction that you’re working through, depending on where you are in the stage of your project, but for the most part, I don’t think even outside of the questions here, most of your customer interactions, a lot of times, maybe not so much on the data science side, but it’s going to be nothing like you would think from a web development perspective or front end developer. Still engaging with the users, but more on the team atmosphere. Feel free to choose any of those paths to be able to deal with your anxiety and difficulty giving those presentations. I think you’ll be totally fine, and I think you can get away with never having to give a presentation, if that’s in your vote.

But, I think you should. I think you should try to work towards conquering those difficulties and those presentations, and I’m not saying that you start off going out, and being like, “You know what? I’m going to try to go to a conference and give a keynote.” I’m going to try to go to a conference and give a breakout session. That’s not what I’m saying. I think you should start a little bit smaller, and just on your team, and then if you find a new feature or new software tool, or just a new process that you like doing, present that to your team. I know it’s tough, and I know it’s hard, because they even did a study a while back about the number one fear most people said that they fear public speaking more than they fear death.

Let me say that again. They feared death less than they feared public speaking. Most people would rather die than do public speaking. Definitely it’s something that I’ve been working on for quite a few years, and I’ll be honest, I get nervous each time. I get nervous, start talking to people. I’m like, “Oh, I’m about to go on.” It doesn’t matter. It doesn’t matter to the fact that, maybe I’ve given a certain presentation 25 times.

Heck, every time I turn the camera on, and there’s nobody in this room, here, on Big Data Big Questions, I still get nervous, too. There’s going to be some amount of nervousness, and I understand that, there’s varying levels, too. I’m not looking over and saying that, “Hey, you know, everybody, you know, everybody can be able to do that.” I do think that you can work towards it, and so maybe everybody’s not going to be able to do it on the same is maybe what I mean to say. I think it’s something you should try to, because presenting is going to open up doors for your career. It’s going to make you feel good, too. Each time I talked about how nervous I was, I just spoke in front of over 1,000 people for the first time in my life. That was huge, but I didn’t start out that way all in one day. I’ll tell you, I was super nervous, and it was just for a short amount of time, but I was nervous the whole time leading up to it, and then afterwards, after you get it, it’s like, yes! You get that amazing feeling that you’ve done something. I don’t know if you’re into sports or something like that, but you feel like you’ve won. Even though, who knows, it’s the first time speaking to that many people. I’ll probably hopefully have that opportunity again, and I’ll be better at it next time. It probably wasn’t my best time, if you’re looking at it.

It’s something that you start to work towards. It’ll be interesting, how much networking, and how many doors are open by doing that, and it’s all about giving back to the community as well. To recap, I don’t think that you have to choose data science or choose data engineer to be able to not have to present and do some of the other things. However, I think most people, and if you’re watching this channel, and you’re really curious about career development, I do think that everybody should have some kind of presentation skills, and this is something they should practice towards, and I totally understand. There’s a lot of anxiety whenever you’re doing something like that. If it’s something that you can work towards, and you can conquer, then I think it’s going to be something that’s going to be amazing. One, for the community, because we need more voices. And then two, it’s going to be something that you’re going to be proud of, and you’re going to be able to work on, and it’s just another challenge, too.

That’s all I have today for Big Data Big Questions. Make sure that you hit the subscribe and ring that bell, so you never miss another episode of [Whispers] Big Data Big Questions.

Filed Under: Career Tagged With: career, Data Engineers

Will AI Replace Data Scientist?

July 12, 2019 by Thomas Henson Leave a Comment

WIll AI Replace Data Scientist

Will AI Take My Job?

Artificial Intelligence is disrupting many different industries from transportation to healthcare. With any disruptions fear begins to pop around how that will impact me! One question poised on Big Data Big Questions was if “AI Will Replace Data Scientist”. We are truly in the early days of AI and Deep Learning but let’s look forward to see if AI will be able to replace Data Scientist. Find out my thoughts on AI Replacing Data Scientist by watching the video below.

Transcript – Will AI Replace Data Scientist?

Hi folks! Thomas Henson here with thomashenson.com. Today is another episode of Big Data Big Questions. Today’s question comes in from a viewer. If you have a question, put it in the comment section here below, or you can reach out to me on thomashenson.com /big-questions. I’ll do my best to answer your question. This one came in around, “Hey, you know, is software going to replace data science?”

Whenever I think about software, specifically we’re probably talking about artificial intelligence. Artificial intelligence, or machine learning, or deep learning, or any of those models, are we going to be able to build models that can replace the data scientist?

This is a common theme, if you go out and Google anything right now, you can see, “Will AI replace lawyers?” Will AI replace doctors? All kinds of different things. Unequivocally, I think the short answer is no, but I’m going to talk about what I think are some of the reasons that I don’t think that AI is going to replace data scientists. Also, at the end, I’m going to give you some industry experts on what they think and what they’ve said about that whole concept.

Let’s jump in. Let’s talk a little bit about what a data scientist is, and then, talk about how we would even begin to look at how AI would replace that. Remember before, when we talked about data scientists in the past. These are the types of people that are trying to work on finding data that can build a model that might be able to predict an outcome. If we can predict the outcome, then maybe we can do something prescriptive. Hey, this is what’s going to happen, so let’s do this portion here after something happens. Think of if you’re creating, building a model to detect insider threats. You want to be able to decide, “Okay, does this user, maybe they’re potentially an insider threat.” Once you’ve identified that, maybe you can drop their access. Be prescriptive [Inaudible 00:02:04] it. Drop access that they have to certain directories, certain folders, and then also alert security.

We’re wanting to be able to build applications or models like that, that can be able to help. Can artificial intelligence do all that, kind of take the data scientist out? I don’t believe that’s the case. That’s very, very hard. If we really look at AI, and what’s going on right now, any time you hear the word AI, replace that with automation, and you’re like, “Okay, now I understand what’s going on.” Really, we’re not at the point where we’re actually building these super intelligent systems, kind of like what you see in Hollywood. I’m going to give you three different reasons around why I think that AI is not going to replace or software is not going to replace data scientists.

The first thing is, when we think about it, artificial intelligence has been around for quite some time. The term has, we’re getting better with our models. If you listen and read some of the books that I’ve read, we’re in that implementation phase where we’re putting these things out there. If you really look at it, even in the past, when we talk about the world’s best chess player versus artificial intelligence, we got to a point in the late ’90s where the world’s best chess player could win, or I’m sorry, the machine would beat the world’s best chess player. However, if you took a medium machine or artificial intelligence that was pretty good at chess, you paired it with a pretty good or an advanced human chess player, they could beat the world’s best machine learning model, or deep learning, or AI chess player. Same thing. What we’re doing, I think, the tools and the skills that you’re seeing being implemented for data scientists are about how we can help, right? What are the types of tools that can help us identify quickly maybe some complex algorithms that would work. Should I use a Generative Adversarial Network here? Should I used a convolutional neural network, or different types of things there?

Same thing that we’re seeing in the medical industry. Doctors aren’t going to be taken out of the loop, but doctors are going to be given maybe a voice assistant that you can prescribe and give the different, these are some of the symptoms that we’re seeing. What are some of the latest journal articles, and giving a summary to that, versus your data scientist or your medical, somebody in the medial field, they’re having to go out, and there’s always research, and research papers that they could be reading, and could be intaking, same thing here. You’re going to have assistants as a data scientist, to be able to say, “Look, what are…?” Run some stats on this, and let’s see what models might be good indicators here. I’m still in the loop. I’m still deciding what we’re going to do from that model, but it’s going to help me streamline and get faster, what we’re doing.

Number two, really simple, just go out there and look at the talent gap. We’re still looking for data scientists. That’s, go do a Google search, and you’re finding that there’s a ton of different open job applicants. If you go to any kind of symposium. There was a symposium over at Georgia Tech. One of the people from Google there was talking, and they were like, “Hey, man, I will take every PhD or even Master’s level candidate you have around data science and statistics,” and everything like that. There’s still a huge, huge talent gap there, and I don’t think it’s going to be cured by AI. Like I said, I think it’s going to be about automating, and then maybe AI can help us to train better humans that can fill those roles, but I think that’s another indication that, man, I don’t even know that we’re at our peak in data science. Just from a hype cycle perspective, either.

Number three, the industry experts. If you look at Andrew Neen, you look at Kai [Inaudible 00:05:41], you look at what their predictions are, data science is in one of those quadrants where it’s like, “Hey. It’s not a simple task that can be repetitive.” You’ve all seen the videos where it’s like, hey, robots, and AI can help on assembly lines. It’s a controlled environment. Data science is not controlled. It’s out there. It’s in the wild, and you’re having to, “This model,” or even ETL. We can’t even fix ETL. We’re still having to rely on human beings to help and automate, and make sure that we’re curating the right data sets, too. We’re still not at that point, and even if we do get to that point from an ETL perspective, still going to have to have data scientists. No, AI will not replace data scientists in the near future. All that’s subject to change. There could be advances in technology in 10 years that I don’t foresee. I’m not a futurist yet. Maybe, I don’t l know. I don’t have enough education, I guess, or understanding to be that. If you have any questions, put them in the comment section below. Make sure you subscribe, so that you never miss an episode of Big Data Big Questions. Ring that bell. Until next time, see you again. Big Data Big Questions.

Filed Under: Career Tagged With: AI, Data Science, Deep Learning

Web Developer to Machine Learning Engineer

June 25, 2018 by Thomas Henson Leave a Comment

Web Developer to Machine Learning Engineer
My Journey Web Developer to Machine Learning Engineer

How can a Web Developer become a Machine Learning Engineer? It’s a journey I had quite a few years back when Hadoop became a popular platform for analyzing large data sets in a distributed environment. A lot has changed in Machine Learning since those days but many of the concepts are still the same. Tensorflow with Tensorflow.js has opened the door for machine learning to be spread out on various machines. In fact with Tensorflow.js Web developers can train machine learning or deep learning models in the browser using Javascript.

In this video I will give you 3 tips for becoming a Machine Learning Engineer with your Web Development skills. Watch the video to learn more.

Transcript – Web Developer to Machine Learning Engineer

Hi folks, Thomas Henson here with thomashenson.com, and today is another episode of Big Data Big Questions. And so, today’s question I’m gonna tackle is about Web Development to Machine Learning Engineer. So, what I wanna talk about is how do you become a machine learning engineer if you’ve got a background with web development? So, not only are we gonna talk about my journey on it, but I’m also gonna give you three tips for making that journey if you’re a web developer thinking about that. So, find out more right after this.
And, we’re back. And so, today’s question that we’re gonna tackle is about Web Development moving into Machine Learning Engineer, but before we tackle that, I wanna encourage you everybody out there. If you have any Big Data-related questions or any questions in general, just go ahead, throw them in the comment section here below or go to my website thomashenson.com/big-questions. Submit your questions there. I will answer them here on YouTube. I really love the engagement. I love being able to get out in front of the community, so just keep bringing those in. And then, if you haven’t already, go ahead and subscribe to my YouTube channel, that way, you never miss an episode of Big Data Big Questions or some of the other cool things I’m doing with interviews, book reviews, and just general technology and awesomeness, right?

So, let’s jump into the question for today. And so, today’s question comes in about how do you transition from a web developer to a machine learning engineer? So, one of the reasons behind this is, we talked about it, I had a video where we’ve done, where we talked about what the machine learning engineer is, a huge, huge hot topic area, a lot of people are looking into how they become a data engineer or a machine learning engineer. So, there’s a lot of interest there in the web development community, maybe there’s a lot of people that are out there looking and they’re saying, “Hey, I kinda wanna be on that bleeding edge. I wanna see what else is going on with this machine learning engineer, how do I get into that, is that even possible?” I’m sitting here creating maybe some C# development applications or maybe you’re a PHP developer, ASP.NET, any genre there, maybe just a general JavaScript, maybe you’re a node engineer, and you’re wanting to really branch out and look kinda what’s into that.

So, before we jump into the three tips, I’m gonna give you three tips for how you can do that. I wanna tell you that it is possible. So, my journey into Big Data, I was an ASP.NET developer and so this was a quite few years back, it was in the early days of the Hadoop community. So, I was involved and I’m starting in Hadoop 1.0. And so, for me, I was a contractor and we were coming to the end of a project that I’ve been working on for many years, and I was looking kind of trying to see what else was out there, and one of the projects that was out there was a data analytics project, and I knew that it was kinda gonna be real heavy in research and kind of on the cutting edge, so I really started looking into it. As I got more involved with it, I started learning about Hadoop and I learned about some of the things that we’re doing as a community.

And so, I really got sparked from that point, but it is possible. So, it’s something that I was able to do. There was a lot of things I had to learn and there were a lot of things that if I could go back, I would have learned first, I would have done it a little bit different. But so, my journey here now is because of all those trials and tribulations that I went to, but the cool thing is, is I got to share some of those for anybody out there that’s a web developer trying to look into it or anybody who’s just getting started. But today, I wanna focus on the web developer, so your skills are gonna translate, right? But there’s gonna be some gaps and some things that you need to do. So, if you’re a web developer looking out there and you’re saying, “Hey, how do I jump in and how do I transition into a machine learning engineer?” And so, you want to be involved with the algorithms and some of the… and the day-to-day development activities for this huge massively machine learning projects or deep learning or AI, how can you get involved?

So, my first tip, you might not like this one, but the first thing you need to do, don’t cheat and don’t do the other tips first, take two weeks 30 minutes a day and start learning about linear regression and linear algebra, and there’s a ton of free resources out there. You can go to Coursera, they have some free courses on it. You can opt-in and take the certificate to get certified. A lot of things on YouTube, so you can go through some daily training on YouTube. There’s a lot of different blog post out there. Just take 30 minutes to go through the math portion.

So, the math and statistics are gonna be one of the gaps that you’re gonna lack, and so that’s one of the things. I didn’t take any of the formal courses, I just kinda went back and started looking at some of my old college notes just trying to figure it out, but I would go through… I would. And, if you have the time, so if you don’t… go through the two weeks, watch some YouTube videos, some blog posts, go to Coursera, sign up for a course. If you’re involved in a Pluralsight or any of the other online trainings out there, find many resources there, take it and do it for two weeks. Also, these are pretty big careers. Maybe sign up for a course. So, if you’re in college right now, take a linear regression course. If you’re not in college and you have the opportunity, outseam [Phonetic] why you would wanna do something like that. So, if this is a path that you’re kinda going down and really serious about.

So, I know it’s not the most fun part of it, but I’ll promise you, it’s gonna help you down the road understanding the terminology and the math behind what we’re doing. So, even if you’re not looking to be a data scientist, so we’ve talked about it here before, data scientist versus data engineer, this is more to data engineer, but you’re still gonna wanna have that math background.

Tip number two, you wanna take two weeks, maybe three, but two weeks for sure, 30 minutes a day, so you start and see your trend here, you wanna walk through the Hadoop or Spark tutorials. And so, you want to learn and understand how to kinda do those. Do the basic tutorials. I’m not talking about setting up your own full Hadoop cluster in your own data center. I’m saying go to the tutorials with the Sandbox. There’s a ton of resources out there. I have a lot of Pluralsight Courses that’s based around having just a little bit of SQL knowledge and a little bit of Linux knowledge, and that will help you kinda go through it, but there are other resources out there, too. Obviously, I encourage you and love for you to join in and watch some of my Pluralsight Courses, but there’s a ton of resources out there. This is a huge community.

Coursera has some baseline courses. You can find things on YouTube. I’ve got a ton of free resources on my blog that you can just walk through, so if you like walking through the tutorial, grab on some of the code. I’ve got a ton of tutorials and a ton of just basic command [INAUDIBLE 00:06:40] stuff that you can do to start learning the Hadoop and Spark. So, I would just take, like I said, 30 minutes a day, not too much [INAUDIBLE 00:06:48]. You can download one in the Sandboxes whether it comes from Cloudera or from Hortonworks, they’ll have Hadoop installed with it and also have Spark. You can run through some of the basic tutorials. Ton of resources out there, like I said, I’ve got a Pluralsight Course. We have quite a few Pluralsight Courses actually around that. But take the time and go through that.

So, we’ve gone through the math portion. And now, we’ve gone through just learning the baseline of some of the bigger data stuff. Now, let’s get into some of the machine learning. So, now, you get to use your skills, your background with Javascript and I would start looking into TensorFlow.js. And so, TensorFlow.js is a machine learning in the browser. So, TensorFlow if you’re not familiar, I’ve kinda talked through it here a little bit around why it’s awesome, but TensorFlow released by Google, it’s really, really popular right now in the Big Data community especially around machine learning and deep learning. And so, there are some really cool features in it, but there’s a lot of stuff that you can do with it from the browser spectrum.

So, this is TensorFlow.js. And so, this is where your background and you get to shine. So, go through the tutorials, but don’t skip the other steps, but go through these tutorials, start playing with the Pac-Man interface that they have, do the pitch curve, so is it a fast ball or is it a curved ball, and now you’re gonna understand some of that math because you did step one first, right, you didn’t cheat. I know everybody right now is looking on the browser. “I’m going to TensorFlow.js right now. It’s a really cool resource.” But you’re gonna understand the math behind it and this is gonna get you started.

And so, now, that you’ve got the math background and you’ve also got the ability to say, “Hey, if we wanted to send [Phonetic] this up and put this in some kind of distributed system whether it be Hadoop or whether it be just understanding some of the baseline on Spark, you have that knowledge background, too.” And so, you’ve done all this probably in six weeks and it gives you an opportunity to start looking and start understanding, and maybe there are some projects internally in your organization that you can look for or maybe it’s something that you’re trying to look for further down the road. Maybe you’re in college right now looking to do it. By doing some of these steps right now, you’re really setting yourself up for success.

Well, thanks again. I hope everyone got a lot of information from this. I hope you’re going out there and learning linear algebra and you can also play that Pac-Man game on TensorFlow.js. Any questions, make sure you submit them. Until next time. Thanks.

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Filed Under: Career Tagged With: BigDataBigQuestions, Machine Learning Engineer

Non-Technical Careers in Big Data

January 13, 2018 by Thomas Henson Leave a Comment

Non-Technical Careers in Big Data

Big Data Career Without Coding?

Do all career options in Big Data demand skills with coding or administration? Big Data projects are in high demand right now, but skill sets for these projects come from different backgrounds. If you are wanting to get involved with Big Data, but don’t have a technical background watch the video to learn your options.

Options discussed in Non-Technical Careers in Big Data:

  1. Data Governance
    1. How Timely is the data?
    2. What is the source of all this data? Garbage in Garbage out
    3. Explain one of my first jobs in IT
  2. Project Management
    1. Agile Development
    2. Scrum Master
    3. DevOps
  3. Compliance & Security
    1. Huge Data Lakes need securing
    2. Huge potential with GDPR General Data Protection Requirements -Plug Alan Gates Interview
    3. How many different breaches do we hear about on daily basis

Video – Non-Technical Careers in Big Data

Transcript

Hi, folks! Thomas Henson here with thomashenson.com. Today is another episode of Big Data Big Questions. Welcome back to the new year. Our first thing that we’re going to tackle today in our first episode of Big Data Big Questions for 2018 is going to be non-technical jobs or career options inside of big data.

It’s submitted in from one of our YouTube users. You can find out more right after this.

Today’s question comes in from YouTube. Remember, if you have any questions around big data or anything that you want to ask and you want me to answer, you can submit those in our YouTube comments below on any of the videos, or you can go to my website at thomashenson.com/bigdataquestions. You can submit any questions there, and I’ll answer them as best I can on air, and give you my advice on the Hadoop community, or big data, or data engineers, or any questions that you have.

Today’s question comes in from YouTube, and it’s from Shahzad Khan. He says, “I work as a change manager, and I don’t know anything about Java or Hadoop, but I want to learn this technology. Is it all right for me to learn, since I’m not into coding? Also, I’ve never been involved in a development team, please suggest.”

Great question. Thanks for the comments and thanks for watching. Continue to watch. My first thing when I look at this is, we’ve talked about the ability, and I’ve had a couple other videos that you’ve seen where we’ve talked about, that you don’t have to know Java to be involved in Hadoop. If you have any questions around that, you can check into that. Really, I think this question, I want to frame it a little bit different, and think about, just because you want to be involved in big data, and you want to be involved in the community and all the things that are happening, you don’t necessarily have to have a technical role to be involved in that.

There’s three roles that I want to talk about that are non-technical from the aspect of coding and Hadoop administration that you can do to still be involved in data or even big data. I’m going to put them together. These aren’t just specifically for big data. This can be around data analytics.

The first one is around data governance. When we talk about data governance, we talk about, what’s the flow of data? Where did the data originate? Everybody’s probably heard of the adage or the example of garbage in, garbage out. Where’s your data coming from? Can you trust, and can you automate, and trust the data that’s coming in? Data governance is about where that data comes from, but it’s also about, how timely is that data? You’re really involved with the sourcing of the data. You’re also looking at things around… I remember one of my first career options. I remember sitting around, and we have a couple different applications, and the heads of each application were together, and we were all there to talk about the different ways that we name things in our own databases. If you think about it, we were trying to merge everything into an enterprise data warehouse. This is a little more old school, but it still happens in big data, when we have these different data sources.

You might have an instance where data from one data set is named or has a different key than data in a separate data set, but you want to be able to merge those. Data governance is around, you can help find and help be a part of that, where the data’s coming from, so that’s one option. I would look into data governance if you still wanted to be involved in big data but didn’t have the technical skills or didn’t have desire to have the technical skills.

Another one is project management. We always need good project managers. Project managers, they’re the ones, the workhorses that really help bring the developers, bring the data scientists, bring the front-end developers, bring everybody together, and really gets that project going. Makes sure that we’re communicating. If you’re interested in project management, you can do that from a non-technical perspective. One of the things, though. I’ve got some stuff on my website where I went through and did the scrum master training. Think of agile development. Just like you would in traditional application development, big data needs agile developers or agile project managers as well.

Then, also look at the scrum master training, but also look at DevOps, and see where that is, if there’s any DevOps certifications, or anything that you can provide in that background to be able to help and manage these teams. Project management is a second one, and then the big one, the next one, compliance and security. We always need compliance and we always need security, especially now with the maturity of the Hadoop community and how much Hadoop is taking over and being used in the enterprise. There’s always compliance around it. You think of HIPAA, you think of some of the SEC compliance here in America. Then, you can also think of GDPR. GDPR, General Data Protection Requirements compliance, I would look at that regulation.

That’s something that’s really interesting to me, and if I was somebody non-technical, and I was interested in compliance or security, that is one area I would start to look at, because I think there’s going to be a growing need. Anytime there’s any kind of regulation, and this is a political statement in any way, but anytime there’s any kind of regulation or change in regulation, there’s a lot of things that go on behind the scenes as far as interpreting that and making sure that you’re in compliance with your enterprise, or if you’re working for some kind of public institution, you want to make sure you’re doing that. Anytime something like that, if you can become an expert and move to that, that would be huge as well.

For securing the data, too. It’s an ongoing, probably overused joke. How many data breaches have you heard about? There’s one every day. Big data is not, we’re not, immune to that. In fact, we’re larger, a larger target. Think about the three Vs.

Volume. How much data do we have in your Data Lake? Big data has big data, right? You need to be able to secure that. Those are the three areas I would look at for non-technical jobs if you still want to be involved in data. Data governance, project management, and compliance and security. That’s all for today. Thanks for tuning in. Make sure you subscribe, so you never miss an episode. I will see you again on Big Data Big Questions.

Filed Under: Career Tagged With: Big Data, Big Data Big Questions, career

Big Data Big Questions: Learning to Become a Data Engineer?

September 22, 2017 by Thomas Henson 2 Comments

Learning to Become a Data EngineerData Scientist for the past few years has been named the sexiest job in IT. However the Data Engineer is a huge part of the Big Data movement. The Data Engineer is one the top paying jobs in IT. On average the Data Engineer can make anywhere from 90K – 150K a year.

Data Engineers are responsible for moving large amounts of data, administering the Hadoop/Streaming/Analytics Cluster, and writing MapReduce/Spark/Flink/Scala/etc. jobs.

With all this excitement for Data Analytics and Data Engineers, how can you get involved in this community?

Ready to learn tips to becoming a Data Engineer? Checkout this video for tips to becoming a Data Engineer.

 

Transcript

Hi Folks, I’m Thomas Henson, with thomashenson.com, and welcome back to another episode of Big Data, Big Questions. Today’s question is: What are some tips for learning to become a better data engineer? Find out more right after this.

So, today’s episode is all about tips for learning to become a better data engineer. So, if you’re watching this, you’re probably concerned with, one, how can I start out becoming a data engineer? What are some ways that I can learn to become better? Or maybe you’re just looking to answer one specific question. But all those are encompassed in what we call the data engineer.

A data engineer is somebody who’s concerned with moving data in and out of Hadoop ecosystem, being able to give status scientists and data analysts better views into the data. So, we’re involved with the day-to-day interactions of how that data is coming in. Is it in how we’re ingesting that data? How are we creating those applications and tuning those applications so that the data comes in faster? All to support those business analysts, those business decisions, and data scientists in creating better models and having just more data to put their hands on.

And so, a lot of times what we’re always doing is we’re asked to take on a couple terabytes of data here, maybe implement and do all the configuration for your hives. You know, your hive implementation or HBase or anything that’s in that big data ecosystem. Some of the tips that I’ve found for just getting started, so if you’re brand new to this and you don’t know where to start, the first thing I would recommend is, go out and just download the sandboxes.

So, download Cloudera’s sandbox, or download Hortonworks’ sandbox and just start playing with it. Go through some of the tutorials. Stand up on your local machine in a VM environment, and just start playing with moving some of the data around. Find some sample data, so go to data.world. Also, I have a post and a video on where to find some data sets, so take those data sets in, start ingesting those. I have a ton of resources and a ton of material on just some simple examples that you can walk through with Pig, and some around Hive. So, go there and find some of those. But, basically what I’m saying is, just get hands-on. Start creating applications. Start trying to do some simple things like, ingest some data in, put it into Hive, and be able to create a table and pull some of that data out, and just maybe some simple Hive queries. And do the same thing with Pig, and just kind of go around to some of those applications that you’re curious about, and start playing with them.

Another thing is, is once you start playing, and sampling, and testing that data, get involved. By getting involved, just ask some questions, create a blog post, try to find a way that you can contribute back to the community. I mean, that’s what I did when I was first starting out. I started off with a sandbox, and what I did was, I took and made sure that every day for 30 minutes, I was learning something new in the Hadoop ecosystem. And so, that’s another tip for you too, is to take and try to do this 30 minutes a day, every day. Even Saturdays, Sundays. Don’t take a day off. And it’s only 30 minutes. And if it’s something that you’re passionate about, and you like doing, that time is just going to fly by. But over time, that’s just really going to give you more and more time in the Hadoop ecosystem. So, whether you’re doing this for a project at work, whether you’re already in the ecosystem and you’re just trying to improve, that 30 minutes a day is really going to help. And it’s something that I’ve continued to do, and continued to do, now, even though I’ve been in part of the community for three or four years now. It’s how I just continue to learn, so I make sure I’m always kind of pushing.

Filed Under: Career Tagged With: Big Data, Big Data Big Questions, Data Engineer

Why I am Creating more Content in 2015

February 2, 2015 by Thomas Henson Leave a Comment

I’ve started every year with the promise to blog more.

Each year I’ve failed!

So why is 2015 different?

Why have I increased my total blog in the past month?

It’s simple I want to accomplish certain things in my career and blogging can help me get there. So what exactly are my career goals:

  • To be known as an EXPERT

  • To SHARE my knowledge with others

  • To become an AUTHOR

  • To give a KEYNOTE a major conference

Photo by KROMKRATHOG at http://www.freedigitalphotos.net/
Photo by KROMKRATHOG at http://www.freedigitalphotos.net/

Who are you to say blogging more will help

Look at the first piece of advice Scott Hanselman gives in this get involved in tech course. It’s to start a blog. Scott Hanselman is a known author, speaker, and expert. He’s a known expert by software developers and routinely gives Keynotes at major conferences. So if he says I should start I blog it’s probably a good advice.

Around Christmas I got the flu and had a lot of time on my hands. Sitting around for a couple of days I was able to finish Soft Skills by John Sonmez.  Now John Sonmez is also a known software development expert, speaker, and author. One of the things John stresses in this book is to start a blog. In fact, John Sonmez attributes a lot of his success to starting a blog. Okay now that is two experts, authors, and speakers with the same advice. There must be something to starting a blog.

How to make it succesful

Having a blog is important but how can I make it successful?

How can I create content that will help the community?

I already had a blog but I wanted to make it successful. Creating a blog was one thing but making it successful was something else.

After a week of recovery I found a new email course on how to start a blog by John Sonmez. I was skeptical at first because I already had a blog. What I was looking for is how to make a successful blog.  I signed up for the course and started receiving the emails over a 3 week period.

The course was great it outlined the strategy for creating great content and where to focus your energy. Now I’m not going to spoil the course for you but I will tell what hit home for me was the consistency part. All the times I wasn’t consistent was the reason for my unread blog. Sure my content, style, and ability to connect with readers matters too, but you cannot get better at those unless you are writing the blog post week after week.

So that is why I have been able to blog more already in 2015 that other years past. I realized at of things that matter most its my consistency that will help me achieve my goals.

Experts are consistent

Authors are consistent

Keynote Speakers are consistent

I am consistent

 

Filed Under: Career Tagged With: 2015, career, Motivation

Why you should learn ASP.NET

March 19, 2014 by Thomas Henson Leave a Comment

Why you should learn ASP.NET

Developers get caught up in the which language is best to learn or the my language is better than yours mentality. The truth is there in no particular language that trumps them all. Odds are that over your career as a software developer you will have to learn many different programing languages and frameworks. For that reason I am going to explain why ASP.NET is the worlds best framework; not really but I do want to explain why you may want to learn ASP.NET. asp.net logo

Mature Framework

Have you ever spent weeks learning a new framework only to find out later that support has been dropped for that framework? Maybe the framework your working with hasn’t lost support, but have noticed releases are slowing down. Developers in ASP.NET don’t have that worry. ASP.NET is not a new framework in fact it was first released in 2002 by Microsoft. ASP.NET was released as an upgrade from the Active Server Pages (ASP). Since ASP.NET has been around since 2002 means it is a mature framework with many features and community support.

It’s developed by Microsoft

Did I mention that ASP.NET is a Microsoft product? Because Microsoft backs the ASP.NET framework developers can choose any .NET language that want to work with C# or VB.NET.  If you have a development team working a Webform application and half the team wants to code in VB.NET while the other half want to use C# that’s okay because ASP.NET was built on a Common Language Runtime (CLR). Microsoft also bring with it a large set of tools that integrate well with ASP.NET, for example:

  • SQL Server – Database
  • Azure – Cloud Services Platform
  • Visual Studio – Integrated Development Environment

In 2012 I switched from developing in VB.NET to PHP, from a syntax perspective I didn’t really have any problems.  PHP was a language I had experience with and found it easy to pick up, but the real issue from me was the IDE. I was so used to Visual Studio that is was hard to use a plain text editor like Sublime Text or Text Wrangler, but I did settle on using Netbeans. Netbeans is a great product, however I had spent over 2 years using Visual Studio and had a hard time adjusting. The same situation arose when I switched from SQL Server to MySQL, Microsoft just has these other tools beat.

Market Demand

I know Microsoft is seen as some evil corporation in developer circles but Microsoft knows how to write software. For that reason the ASP.NET framework is a top chose in government and financial institutions. These institutions want to their application running in a secure environment which happens to be a Windows environment. Careers in Government, Finance, Energy, and Medical industries make up a large part of the US economy. The good news is these industries are hiring and one of skills they are searching for is experience in ASP.NET. In simple terms, there are a lot more .NET openings then there are candidates to fill those positions. Take a look at the job sites:

  • .NET Developer search on Monster
  • .NET Developer search on Dice
  • ASP.NET search on Indeed
  • Craiglist Nashville – In Nashville I have some contacts who have told me there is a market shortage of .NET developers. Nashville has a heavy presence in both the Medical and Financial Industry.

 

ASP.NET Pays the Bills

Okay here is what you really wanted to know. How much do ASP.NET developers make? In the 2013 salary survey conducted by Visual Studio the Magazine, the average ASP.NET Developer’s salary is $94,784. The average developer in this study typically had a 4 year degree and 12.5 years experience. Salaries for ASP.NET developers have steady risen over the past few years and have fared well during the recession. The average household brings in around $50K, which means the average ASP.NET almost makes twice as much as the average household. So no worries about making ends meat in ASP.NET.

ASP.NET is a mature framework with a very large community and a ton of support from Microsoft. Developers have a wide range of tools to leverage that are backed by the Microsoft brand. Career wise ASP.NET developer are in high demand with an average salary around $94,784. My career has mostly centered around ASP.NET so I might be biased but overall I would recommend it as a option for a wide range of products.  What are your thoughts on the ASP.NET framework?

 

Filed Under: Career

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