Is the Data Analyst Role Dying Out? with Mo Chen, Data & Analytics Manager at NatWest Group

Richie Cotton

Hi Mo, welcome to the show.

Mo Chen

Thank you so much for having me, Richie.

Richie Cotton

Great to be chatting with you. But to begin with, I can't. Every now and then, I assume here that the data analyst role is dying out. So tell me, is that really happening?

Mo Chen

My answer will be no, because I clearly still have a job. So, ChatGPT, I think it became super popular in through November. I believe, and we're speaking at the time of this recording in September I still have my job. I actually progressed in my job as well, so I'm making more money. I am more senior than I used to be.

Mo Chen

So I would say no. I think AI is changing the skills needed in the industry right now. So rather than remembering everything I think it's about learning the foundations well and then using AI to produce even better work, even more complex work. And there are many things that, at the beginning of my career, when I started in so I'm getting relatively seasoned now.

Mo Chen

There were many things that took me quite a while to do, maybe even a day or five days. Now I can do it in probably minutes, maybe an hour. But then again, you have to know how to prompt. You have to know what to look for. You have to know how to combine various AI workflows and tools so that you can actually do the thing you want to do.

Mo Chen

The example I usually bring up is data. Imagine if I'm just a small coffee shop owner, in the sense that there's nothing wrong with coffee shops, but I have nothing to do with data. So if I'm a coffee shop owner and I need to build a data engineering pipeline, even with all the AI that's right at my fingertips, I won't build it as fast as a data engineer.

Mo Chen

So that's it. It's just one of those things that when you tell me that I will take away my job. I don't think so. I think you go to work and it's different skill sets compared to years ago. Let's say you used to go to a hotel. You had the you had the man who used to press the button in the lift.

Mo Chen

And then, you know, one to whichever floor non-existent anymore. Let's say automatic cashiers. Right. The self-checkout. It eliminated that specific position. But I'm pretty sure those people were able to do something else within those grocery stores. Add more value by doing other activities. I'm not a big grocery store expert, so I'll, I'll leave that to you.

Richie Cotton

Okay. Well, that's very reassuring, but, the data on this role is somewhat still persisting, but you mentioned quite a few ways. It's changing. I'd love to get that in a bit more depth. Can jump into which areas of data analysis is going to be or am I general like really good for and which bits are still more human oriented?

Mo Chen

Debugging code fixing code super good, super fast. Better than me for sure. I'm not an amazing Python coder or any type of coder in the sense that I'm pretty much self taught. And the way I see data analysis is that I add the business value because I have domain knowledge and I know how to analyze data. It's not that I'm really good at Excel.

Mo Chen

I'm really good at Excel, and I'm really good at data analysis. If I'm just good at Excel, I'll be good at product demos. But the thing is, when you go to work and that's the differences between social media data analysis. And like when I go to work, the real data analysis, because when I go to work I have to solve business problems.

Mo Chen

So just using Excel and being amazing got it is not enough. So the bit I think I right now is really good at is sequential debugging for code or fixing code, or at least what I use it for when it comes to technical things. And you didn't ask any non-technical use cases, so I'll leave that for now. Maybe you'll ask me or not.

Mo Chen

So technical use cases, SQL, Python, and those are really, really good use cases because it actually works from my experience. And then we have the best tools. Power BI, Dax measures, Tableau, Calculated Fields, QuickSight right now for example, or even in Excel functions and formulas because you have more flexibility and it's not that sequential in the sense that it's hard to hard for the AI tool to see everything you've done.

Mo Chen

I think it doesn't work that good. So this is what I use it for mostly. But for coding, honestly, I never code from scratch anymore because I'll dive down to a super simple example and super exact example. Let's say you're using the seaborn or the matplotlib library in Python. By the time you annotate the chart with labels and titles and all of that, I mean, remembering that syntax takes up so much memory, whereas nowadays you can just prompt whatever ChatGPT or whatever model you want to plug into, and it'll do it in no time for you.

Mo Chen

So that's just so, so, so quick. That's one example that works extremely well.

Richie Cotton

Absolutely. I'm certainly writing code for generating plots, even if the AI doesn't get it quite right. It's just going to generate a silly plot. But you'll be able to see that it's done something wrong. So very easy to correct that eventually. Since writing code is like the strong point, it's been a bit of a trend for data analysis and for business intelligence to move, like from sort of coding from R and Python to using tools like power BI, Tableau, like all the rest of these kind of point like interfaces.

Richie Cotton

Now those also have AI help us in them. But do you think the fact that AI is good at writing code, is that going to reverse the trend? Or do you think it's something to make a difference.

Mo Chen

With the VS tools? I don't think AI is there yet, and I don't see it's going to be there just because there's so much flexibility. And the way I see it is that a good data data pipeline for me is something that is within the ETL process before it actually gets into the BI to all. The code is so clean and neat that basically you only need to do simple drag and drop, and then it's just about answering the business question.

Mo Chen

So I actually think in those vis tools, anybody who does really, really advanced things, it goes against my data engineering ETL best practices. But then you can, you know, it's yeah, it's up for debate. So whoever's watching this or I don't know how the interaction is going to be, you can let me know if you don't agree with me.

Mo Chen

But the way I see it is that I think by the time the code gets into a schema database and the tables, those tables need to be really, really simple in the sense that if I visualize something in Tableau, it's mostly drag and drop, and that's about it. And I think this is again, biggest difference between social media data analysis and at work.

Mo Chen

At work I produce bar charts, on line charts. Hands down, that's it because it's simple to read, easy to understand people are after insights. People are not after the shiniest thing. Obviously make it look nice. But if your dashboard doesn't even tell a story, it doesn't matter how nice it looks. And this is where I think AI is not going to replace anyone because you need that human touch.

Mo Chen

I really think you need that human touch. I cannot solve the complicated business problems. I know for this for a fact because say for example, I've been working on so many data projects recently, so I help other people create their personalized data projects in various domains and I tried prompting ChatGPT and perplexity. I have perplexity pro using all of the models.

Mo Chen

I tried to give myself a huge shortcut, let's say analyzing customer churn rates or looking for good investments using stock data or or looking at health care data within the US to see which hospital is actually efficient in terms of how much money they spend per patient. And I'm telling you this right now, ChatGPT Microsoft Copilot, whatever Claude, Google Gemini.

Mo Chen

They could not do the analysis, even in the sense that they couldn't even get me to a point where they could give me the right questions to ask.

Richie Cotton

Thus interesting. And it's worth knowing, like what's not really possible yet as well as what is possible. So let's unpack that okay. So first of all, I mean, you talk about how they're actually only two plots. You have a drawer. And this actually reconciles with my experience as well as like for business like plots. Whenever you're basically drawing line plots, you just like, does this metric go up or down and you draw bar plots like what is the most popular thing out of these categories?

Richie Cotton

Which is amazing because, I mean, we teach all kind of different data visualization version. You are really a long way with, two different plots. So, yeah. Do you want a bit more through your putting strategy? And then maybe we'll go back to some of these AI topics.

Mo Chen

My plotting strategy I have to say this is where I'm going to, I am going to give a shout out to a company, The Economist, because I love their visualizations. There's something so simple and neat about the way they do things, and they mostly use guess what bar charts and line charts. So my plotting strategy is make it interactive.

Mo Chen

So if it's a dashboard, make it as interactive as possible. And then there are going to be times many, many times when you work for a large organization that people want you to build a dashboard that they can extract in a PowerPoint format or a PDF format, it's just the way it is. If that is the case, obviously you cannot make it interactive, so then you have to fit everything onto one page.

Mo Chen

But my advice here is that try and go for only one big story point per visualization or per dashboard. Because if you have too many things and if you cater for too many, asks if someone comes to that and says to you that, can I put this in the Excel pack? Can I put this in the committee meeting and you say yes to everything, you'll end up with no joke, different metrics on a single page, and then nothing will be meaningful because everything will be lost.

Mo Chen

So number one is focus on fewer things. I would say one per visual, even on a dashboard, I would go for let's say five six visuals. Then there's five six things you want to focus on. Use the headline or the subtitle to tell the story, give some context. So instead of saying customer churn dashboard, I'll just come up with an example.

Mo Chen

You could say that the highest churn rate is in the age segment, to because of whatever the reason is. So you give a bit of the story and then you guide the audience's attention. Color contrast would be another really, really good one. Don't use too many colors. Use every single color with a purpose. So don't just choke on a color because you feel like it's you know it's good if there's no purpose to the color.

Mo Chen

Keep everything blue. And then if you keep everything blue and you make one thing pop that, that thing will surely pop. Don't go against traditional conventions. Let's say we associate blue with cold and red with hot. So don't go against that. Or let's say red, amber green. Right. Red bad, Amber better. Green good. If you start color coding the good things with red and the bad things with green, I'm already confused just trying to say it.

Mo Chen

So imagine how confusing it could be for your audience when they're looking at it. I would say these would be my top tips. So just to recap and make your job easier as well, focus on fewer things. Ideally or things per visual. Focus on context and storytelling using your headline or subtitles. Use colors for contrast and always use colors with a purpose.

Mo Chen

And don't go against traditional conventions. Let that be naming colors or anything along those lines. Another good example for the traditional conventions would be the way you plot the time, right? You always put the that the time on the x axis because you want the line chart to kind of flow through from left to right, because it would just look weird the other way around.

Richie Cotton

That's what I feel like. We just had like a whole plotting masterclass in five minutes. And so there's probably gonna be like one physicist in the audience going, actually, blue should be hot, and but you be cold because it isn't in astronomy. Oh is it? Hey. Yeah.

Mo Chen

While clearly, clearly, I'm no astronomer or physicist.

Richie Cotton

Yeah, they do things back and forth. But then again, you're right. There's a there's a tradition and you need to, to follow stuff. One thing you mentioned before, you're talking about how once you're in your BI tool, you don't want to be doing all the fancy transformations or whatever that should happen in a data pipeline beforehand, which is interesting because all the like the power BI enthusiasts are like, oh, look at all these like cool transformations I can do.

Richie Cotton

So this feels like an important lesson on like how you approach, like learning how to do good, bi and like how you set up the infrastructure. So talk me through like basically if you think you shouldn't be doing like fancy transformations and you and your power BI or whatever BI tool, what should you be learning about in order to good by what skills do you need?

Mo Chen

First of all, let me just put it out there. This is just my opinion. There is no right or wrong. There's advantages to both sides. So let me just put it out there in terms of what you should be learning, let's say when it comes to power BI. So I think you you need to learn the basics well.

Mo Chen

And first of all, you need to feel confident enough with the tools. So rather than focusing on how do I do this, you focus on what do I do? I think that is the first part and whatever that means to you. Because if you are working, let's say in a data analyst role where you do tons and tons of visualizations, it'll be a little bit different from a data analyst role where you do some business analyst work as well.

Mo Chen

The level of technical expertise will be different, so it is really hard to pinpoint what exactly you should learn, but in terms of how comfortable you should get, I always give people the advice that instead of focusing on how to do things, focus on what to do, and then if you get to that point, it's on off. That's that's all you need.

Mo Chen

And in terms of just going back to which engineering is better, where you do everything in the BI tool and you have complete control, or you just have a pretty neat data pipeline in the sense that in the BI tool you do drag and drop, there is no right or wrong. I think it depends on the size of the organization because imagine a startup ten people, people, right?

Mo Chen

The power BI guru is going to be the data guru is probably going to be the data engineer, the data scientist as well, maybe a bit of business analyst as well. But then if you work for a super large org, I think you want good processes in place because you want reusable things. And the only way you can have reusable data, reusable code, reusable visualizations, reusable insights is if everything is clear along the way and documented and simple, rather than living in someone's head or living in one massive power bi fi file, or one massive power BI data model that nobody except the person who created it understands.

Richie Cotton

That's really interesting. So basically, as organizations get larger, you're going to want to focus more on less, like solving individual problems, more on like, how can I solve similar problems over and over again? So reusability becomes more and more important, I guess, is your, your, analysis needs growth.

Mo Chen

Yes. Yeah. I've only worked for a super large organization. And, again, this is just my opinion and experience, but the way I think things should be done at a large scale is you need to make them reusable because you need to be efficient. You cannot create command dependencies when you have a startup with ten people, you're probably you probably all know each other quite well, and it's just the way things are.

Mo Chen

Like startups, they just move so, so fast. You do a little bit of everything and then from idea to product, you could do within, I mean whatever, or weeks. If, if you are super, super fast, depending on what the product is. But at large organizations, you have sign offs, you have committee meetings, you debate things. And I think the way it should just happen is that you have clear roles and responsibilities, and you produce neat and clean and trackable code so you can trace it all the way back to source, wherever the data is coming from at source, and you track it through all the way, wherever it goes in the organization.

Mo Chen

So you know where exactly your data ends up and how you're using your data. It's just good data management practice.

Richie Cotton

Absolutely. And that's a very important, point of code is that you can, just store in GitHub or wherever, like it is very traceable. You can track the changes. And so you do have like, that audit sort of built in which you don't get for the point and click interface before you were talking, about, tech use cases of, I, I suspect you want to talk about non-technical use cases as well.

Richie Cotton

Like, how are things changing for, data analysis in the non-technical sense?

Mo Chen

In a non-technical sense, I think it makes writing super, super easy. Email writing, I think, is one of them. Easy to check for typos, easy to rewrite your emails in a certain tone, easy to rewrite the same message to cater for different platforms. So I'll give you an example. I send out an email to many, many people. I think an email has a different tone compared to we use I.

Mo Chen

Actually, I'm not going to disclose what we use anyway. It doesn't really matter in the sense that it's volume gauge. It's not a big deal. Actually. We engaged is like Facebook for work. Whatever. It's a social media platform that you use at work. And obviously when you make a post, it needs to be a little bit different. Use some emojis, you know, make it more social media, like, whereas if I start using those emojis in my email that I send out to people, it's going to look a little bit weird because I think there is a specific comms platform for a specific purpose.

Mo Chen

So the same message, I can just ask AI to rewrite it for me. One catered to a social media post, one catered to just the traditional, more formal email, and then I can just type in whatever I want to in or sentences, and then I will quickly generate two versions. And that immediately saved me, let's say minutes of writing.

Mo Chen

That could be one of them. Audio transcription recording meetings nowadays, who takes minutes anymore? Nobody. I remember when I started my career, I used to sit in the meeting room taking minutes, obviously because I was quite new and I was quite junior, and I'm sitting there with my notepad, taking the notes, taking the minutes, whereas now, I mean, there's no need.

Mo Chen

Meetings are timestamp. You have audio transcription, you have live transcription. Even better. And then after data you can just ask whatever tool you use. It can be Microsoft Teams or it can be zoom. You can ask the the tool itself to summarize the meeting for you, give you the action points. So I think this is one of those things where it just helps so much eliminate the really, let's be honest, boring tasks.

Mo Chen

Who likes writing minutes? Probably nobody. If you do, I don't know. Comment below. How does it work? I don't know, but let me know if you do so these are the things I use I for a lot. Just writing, summarizing meetings. Copilot is really good, so I use Microsoft Copilot a lot at work, and it's very good at finding information across my mail, across my teams chats.

Mo Chen

For example, let's say if we've been chatting for a while and you send me a hyperlink to whatever something a long time ago, I can just ask copilot, hey, can you find me that hype? The last ten hyperlinks that Richie sent me and they're all just listed?

Richie Cotton

Absolutely. Yeah. I'm right there with you that I absolutely hate taking, notes for meetings. So. Yeah, having that done automatically real, a real blessing that. And yeah, I'm sure there are or people who really love, taking notes, but, yeah, I guess you still can take notes if you want to use. Can I get that?

Richie Cotton

Yeah, I do it for you as well as a backup. Okay, so I do have the idea of, like, anytime you're dealing with text, you know, changing like, styles or one of the communication styles and that, that works really well. So I guess, on that note, do you see, communication aspect of, data analysis changing, like, do people need different communication skills?

Richie Cotton

You need to worry about communicating differently. What do you need? What communication skills do you need? As a data analyst.

Mo Chen

I always say this, and I may sound like a broken record if you watch my content, but you need to be able to deliver any type of insight in a way that a six year old could understand and in a way that would satisfy me, or even someone even more technical as well. So if you really know your stuff, you can really dumb it down, and you can make it so complicated that honestly, only the people who are really, really up there in terms of technical expertise can understand.

Mo Chen

And I think that's it. If you are not an expert in your field, then you're just going to be talking left, right and center. You know, say, oh, I did this, and then you did this and then this. Whereas if you know what you're doing, it doesn't matter how complicated the analysis is, you can break it down super, super simple and you can make it really difficult as well, in the sense that you can really elaborate on the details.

Mo Chen

So to me, it's the ability to provide information to both technical and non-technical audiences.

Richie Cotton

That's wonderful. It's kind of like the holy grail of communication, being able to scale, depending on the level of technicality. Do you have any tips then on how you might go about, converting, a technical explanation into a non-technical one or vice versa?

Mo Chen

Well, let's go with customer churn, because I think it's simple to understand and everybody does it. Or probably most people care about customer churn enough. At an executive level, I think people only care about the top level figure. So you probably want to tell them which segments churn the most, why they turn the most, and then you give your exact options.

Mo Chen

So option A we'll do this. Option B will tackle this and option C will tackle this. That's all they want to know. They don't care about how you got to the numbers. What calculation you use. What's the formula for a customer churn for example. And then if you go a level down people will be interested in the details.

Mo Chen

It's like how did you create this Tableau dashboard? Where did you get the data from? Is that like did you validate the data? What calculations did you use to calculate customer churn? How did you segment your customers? What was your technique? And then this is the way you dive a level down and then explain specific things. And a level further down would be you take one segmentation and what you did with that specifically.

Mo Chen

Like let's say, for example, you segmented the customers by age and something else, whatever that be. Let's say for a banks it can be product for example. So credit cards, loans, car loans, whatever mortgages for example. And then you dive a level down and then you take that analysis apart. You show the specific code used. You show exactly which system you pulled the data from, how you pull the data from.

Mo Chen

Did you use SQL or did you use some kind of third party system? Did you use reference data to validate? Did you do data quality checks? You know, accuracy, timeliness, all of those things. And that would be taking it really, really low level because people who do the technical bit, they'll be interested in that. And then people at the very high level, all they care about who churns the most, how can I make them not churn?

Mo Chen

That's it.

Richie Cotton

I love that example. Definitely something I've fallen over in my career. Like I want you do not spend ten minutes talking to a CEO about data cleaning. They do okay. They've they appreciate it. But, yeah, the lower down the organization, the closer people out of the data, the more the work, you know, about, the technical details and the how rather than just, the outcome.

Richie Cotton

Are there any other soft skills that you think are important beyond communication? If you're working in data analysis.

Mo Chen

I'll say this, soft skill that would apply, I think, to anything you do in life, treat others the way you would like to be treated. I think that's it. I speak to everyone at work with the same respect, the same patients. It doesn't matter what level they are can be. The CEO can be someone else. It doesn't matter.

Mo Chen

Obviously, if the CEO was asking me to do something, there is clearly more weight to it. But just because the person is the CEO doesn't demand more or less respect. Then let's say when I go to work and someone, is, you know, cleaning the desks, I treat them with the exact same respect as my CEO, because it's the job where humans and I think at the end of the day, as long as you're a good human being, that matters probably the most.

Mo Chen

So if you're likable at work and you're genuine and to achieve that, I think just simply treat people the way you would like to be treated. I think that's a pretty good I don't know if it's a soft skill to have or not, but I think it's a good characteristic to have and it'll take you far in your career if you're genuinely just a nice person.

Richie Cotton

Yeah. Certainly everyone spends a lot of hours at work. You're going to be interacting with your colleagues a lot, whether you want to or not. So, yeah, that's a bigger person, I think, that's a treating people respect as good a life skill rather than, even just a career skill. So, yeah, I do like that idea.

Richie Cotton

I've. Let's talk a bit about, getting a job as a data analyst. So I guess, the first thing is qualifications. What sort of qualifications are necessary to, become a data analyst?

Mo Chen

Now, I'll talk about my qualifications. What I have right now, I actually have to think about it, because once you have experience qualifications, they really go into the background. So I am currently a AWS certified AWS Cloud Practitioner certification. I believe that's what it's called. That's the official name. I also have a Udacity Data Engineering Nanodegree. I'm also a NIO Forge Certified Professional.

Mo Chen

And for those of you who don't know what Neo Forge is, Richard, do you know Neo Foggia?

Richie Cotton

That's the graph database.

Mo Chen

Of course you know. Yeah. Okay. No way I can catch you out. But yes, Neo Forge is the graph database, so we use that at work as well. So I thought, what's going to make me more credible than get certified? Right. Nobody cared about it work because I already have a job, so honestly, nobody cared.

Mo Chen

So these are the three certifications I have. I also have the CFA level one Chartered Financial analyst from I think it was a while ago because I was working in investment banking, and I thought that was the way I was going to go. So I think these are the four qualification I have. I believe I have a master's degree in finance and economics.

Mo Chen

If accounts qualification, how important they are when getting a job. I don't want to lie to people and say that they are the most important because I don't think they are. I think experience beats anything, and if anybody says otherwise, they can find me. I'll put my hands up. And I think experience does beat anything and everything. But if you don't have relevant experience, certifications are fantastic in a way that recruiters know what certifications to search for for specific roles like, let's say you have the power BI plus certification.

Mo Chen

That's why you have the Tableau certification, or that's why you have numerous AWS certifications, because recruiters know that if you have that certification, you'll be at least be able to do the technical bit. Maybe you don't have the business knowledge, maybe you won't be able to come to work and solve problems immediately. Business problems. But if there is a business problem that people tell you about, they can have the confidence that you'll be able to use your technical expertise to solve that problem.

Mo Chen

So this is where I think certifications come in, where if you don't have relevant experience, certifications are the next best thing you can do and achieve. And get.

Richie Cotton

Okay. So really it sounds like, the certifications, qualifications these much a lot more at the start of your career when you don't have the experience to get that first role, and then after that, the experience is much more important during the experience. I guess the best way to demonstrate this is through a portfolio. So talk me through what you need to have in terms of a portfolio to become a data analyst.

Mo Chen

I would say go for anywhere between to projects. Choose tools that the company you want to apply for, the industry you want to work in use so for finance it'll be Excel, for health care it'll be, I think, a mixture of excellent power BI, I would say, and SQL is always handy. So SQL is one of those that if you have a database and you cannot get the data out of the database, then you have no data.

Mo Chen

So SQL is super, super important. And then this is where I think depending on the industry, the best tool will widely defer. So I find in marketing you know it's more a SQL plus. Tableau skills are really really good. You can use Looker Studio. But that's just because of, you know, Google as an engine. And then, it really depends on the industry.

Mo Chen

So it's, it's so hard to say which, projects you should build. Exactly. But in terms of from a high level to projects, pick the tools your industry actually use. I would say for a data analyst role, if you know Excel, SQL, power BI, Tableau, you're covered. Because Python, in my opinion, is quite specialized in the sense that most data analyst roles don't require Python.

Mo Chen

They say it's a nice to have, and genuinely it's just a nice to have at work. Python makes my life super easy because there are many things I can automate and just make it quicker. And if you use code, it's reproducible, so it just helps me do pretty repetitive tasks. I see it way more as a data science data engineering skill set, way more not a data analyst skill set.

Mo Chen

When presenting your portfolio, build something that is super simple on the front end. So don't use a GitHub page with lines of code. That's one thing you should definitely not do. For example, I've seen so many bad for portfolios. Honestly, I don't want to go into it. I'll go into some good ones. So let's say a clean Google doc, at its very simplest with, clear title subtitles and then you add your analysis into it.

Mo Chen

I myself use, notion and my portfolio is in notion. So I like using notion a lot. And actually my best, product and my most popular product that, has the best, best feedback. And I've been offering for multiple years now. It's the ultimate data portfolio. It is built in notion. And it just it's got such a nice front end to it.

Mo Chen

And the back end, you can store the code, you can tell a story. And it caters for both technical and non-technical people as well. So even if you know nothing about data analysis, if you look at my portfolio, you'll know exactly what's going on. Because on the front end, it's basically just like a medium article. Super simple, easy to understand, no jargon, no heavy data analysis concepts.

Mo Chen

It's just this is it. This is what happened. I actually have a Stack Overflow project that I wrote on medium where I analyzed, I think, the Stack Overflow survey responses, and it's so simple that, I mean, even if you know nothing about Stack Overflow, data science or data analysis, you would still be able to understand it.

Richie Cotton

Okay, so I like that idea. You've got some, you want to have some, demonstration that you can do SQL, you want to demonstrate that you can use a BI tool, and then you just want to have it somewhere where it's easy to read, I guess, regardless the audience and probably not too heavy on the, technical jargon because you want to be met by like, hiring managers.

Richie Cotton

You may not be that technical job, right? Yeah, yeah. Are there any other tips you have for, making your profile stand out when you're applying for a data analysis job.

Mo Chen

Resume on LinkedIn, resume is a must. A good LinkedIn profile is, just super, super nice to have. The way I look at the resumes is that it's around where you can only fail because you never get the job after passing the resume around, but you can easily get eliminated for such silly things. You don't need to have a great resume to pass the resume round, because mostly I mean many companies just use the ATS system and many companies have people who spend actually check this.

Mo Chen

So you can quote me on this. I think it's seconds or seconds that a recruiter usually spends on a resume. And if I think about it, I've looked at resumes as well at work. It's probably about right, because if you know the skill set you're hiring for, all you're going to be looking at is experience.

Mo Chen

Quickly looking at the keywords to see if you can find what you're looking for. And then if you don't find it, boom, you're on to the next one. That's how people look at resumes. Nobody actually reads the whole thing, I believe. Or people at least, who are, you know, into hiring because you have a limited amount of time.

Mo Chen

So the best advice I can give on the resumes is that only put in relevant information. A lot of people just think more is better. I don't think so. Less is better if it's relevant. So start with the relevant thing for it. Relevant experience that you have on the top. Underneath that I would put relevant education or relevant projects.

Mo Chen

If you don't have relevant experience certifications, those are really, really good. And then the skills you have. So let's say power BI, Tableau, Excel, what else is there. Let's say cloud skills Google Cloud. Or it can be Microsoft Azure or AWS. Make sure you throw in as many keywords as possible, because the people who glance through your resume, if you just throw in or keywords here and there, it's easy to miss them.

Mo Chen

But if your resume is loaded with those right keywords, then it's so much easier to spot, right? It's almost like I cannot even miss it, because if I read the top or the middle or the bottom, I can see that this person, let's say this person, is applying for a powered by developer role. I can see in the projects, in the relevant experience, in the certifications, in the education, in every single section that this person knows power BI, and that's something with power BI.

Mo Chen

The second thing is quantify things. So instead of saying, I did this, say I did this and it led to whatever this, you identify the business problem, you did something, you did something, not your team, your wider team, not we supported someone or I that or we led this Workstream I did this, I supported this, I had an impact.

Mo Chen

Highlight your impact. The recruiter and the hiring manager. They're not interested in what your team, what your organization that they're interested in what you did. So focus on yourself. I think this is pretty sensible advice. Hopefully you agree.

Richie Cotton

Yeah. I mean, I have to say while that that recruiters only look at your resume for seven seconds, like I feel like I've spent long, it's like reading the back of like a food carton to decide whether to buy the like for a trivial purchase has been longer than like that seven seconds. So, yeah, that that's, completely crazy.

Richie Cotton

But it does, I guess, highlight the importance of making sure you just have the important information in there. I do like the idea of talking about what you've done. Just a little simple, like, language change. You say I did this. That seems, very important. So I guess, suppose you do get that about first run interview.

Richie Cotton

Like, what is the into what is the interview process for a data analysis role to play?

Mo Chen

Both depends on which company. So I'll pretend that this is a generic company. Usually you would get some kind of first round HR interview. That's usually the case where someone steps in from the HR team, and all they're gauging is, are you roughly the right candidate. So this person from HR, they're probably not going to be technical, but they work for the company.

Mo Chen

And they've been hiring for tech for many, many years. So they know what to look out for. So let's say if I don't nothing about data analysis, they'll be able to tell. Like if I have no idea what I'm talking about, then if you pass that, you usually get a second round the interview. This is where things get a little bit more technical.

Mo Chen

So this is where someone, usually from the hiring team will come in and sit in with or HR reps. Again, hypothetical scenario. Let's just say this is a typical process. They'll ask you probably more technical questions. Now some companies make you do technical things live as in I've even done some through zoom and whatnot. They give you a task just to test whether or not you can actually use the technical tools.

Mo Chen

And with those technical exercises, I do just want to say here two things. First of all, they want to put you under pressure. So don't expect to finish everything because you won't, because the whole idea for them is to give you so many things that you will never finish it, but they just want to see you under pressure.

Mo Chen

Now, the second thing is that I like the companies who let you use whatever you want to Google. I ChatGPT like use the resources that are available to you, because otherwise it's not really a real life scenario. When you go to an interview and you have to memorize everything. So those are the two things about. But if you pass the second round, the interview, sometimes they give you a third round where you speak to the head of the team or someone who's quite senior at this time.

Mo Chen

Now this is where the interview can go anyway, because sometimes they just want to judge your character. If you if it's a smaller company, they want to see if you're the right culture fit. Sometimes you just get completely roasted. It happened to me before. I just sit on the interview and I'm like, what is this person asking me?

Mo Chen

And you know, you sit there and you're.

Richie Cotton

Like, oh my God.

Mo Chen

What is going on? But those, to be fair, I have to say, I like the interviews where they test intelligence rather than interviews where they ask me, tell me a time when you've overcome a challenge. You know those cliche HR template questions. So I like it when people throw me curveball questions that are difficult, questions that test truly just my intelligence.

Mo Chen

Because I feel like nowadays any skill can be taught on the job really quickly. If you're smart, the learning curve is steep, but with AI and everything available now, it's quick. So that's, I would say, a typical interview process. First round, HR second round, probably more technical. Maybe they'll make you do an exercise. That's my experience. And then I can give you a different one for how I started my career.

Mo Chen

Because in the US as well, you have these, early career opportunities. And in the UK, where I live and where you're from. Richie. So, we have these, grad opportunities, and those work a little bit differently. And those are specifically for career starters. So I can expand on that if that is something you're interested in.

Richie Cotton

Sure. Yeah. Maybe we come back to graduate opportunities, in a moment. But I feel like maybe you're a bit of a glutton for punishment. Enjoying the hard questions. But I do think it's important for people to realize that if you do feel under pressure during, a job interview, then that's kind of the point.

Richie Cotton

And it's okay not to just freak out. They just want to see you. Can you work under pressure? All right, talk me through how to graduate. Opportunities differ then.

Mo Chen

So grad opportunities in the UK. Speaking from experience, I know pretty much how it works. Super competitive. First of all, you fill out probably hundreds of these and then you complete some online assessments. There's some logical math and reasoning tests once you do that. In my time I had to answer some culture fit questions and some case study questions.

Mo Chen

But it was very weird because you speak to yourself, so you have this online interview, but they give you the question. They they give you five minutes to answer. You look into the camera like this and then you speak to yourself. You answer the case study and then you move on from there. You usually move on to an in-person interview, and then you have an assessment center where they put the best against each other.

Mo Chen

And let's say an assessment center would be a whole day event where they test you on so many things. Ten people go there and let's say three people get the job offer. So this is a typical grad experience from my time. Bear in mind that that was in So it was quite a while ago. But I believe, speaking to other career starters, it's roughly along these lines nowadays as well.

Richie Cotton

Okay. Yeah. I imagine like the the whole interview format hasn't changed that much over over time. Like you're still going to want, a similar sort of a setup where you have go from sort of low level, just air interview through to some kind of assessments and then that end up with, maybe a more senior interview at the end.

Richie Cotton

Okay. So I guess, we talked a bit about, maybe getting your first job, in data analysis. What about for career switches? So suppose you've had a career in a different area and then. Okay, I really want to get into working with data. How does that transition work?

Mo Chen

I always recommend people lean on their domain knowledge. So I've had so many people who had way more experience than me, which is crazy. I had actually someone specific who had a PhD with years of experience in health care or something like that. They had more years of experience than almost I've been on this earth for, which is crazy to think.

Mo Chen

And they had insane business knowledge, and I just asked them to build on that business knowledge, really lean on it, learn some technical skills to supplement it, and then go into a role. Because when you go into a business, the ability to solve business problems and identify them is so valuable. Common sense and intelligence. I feel like it's so underrated nowadays in the sense that everybody's talking about implementing, implementing, implementing.

Mo Chen

But then imagine if you don't even know what questions to ask. Like you just go to work and you literally have no clue what to look for. I will not solve that problem for you because every business will have its own unique problems. So let's say if you work in finance and you're looking for good investments, ChatGPT will not tell you what's a good investment because every bank is going to have a different portfolio.

Mo Chen

It may point you to the right direction in terms of what to look for, but it won't be like when you go to work. It'll just be completely different. And obviously, large organizations don't feed their data into OpenAI's GPT models because it's just a black hole. They develop their own models. So then again, the GPT models are not smart enough to actually, I think, deal with real life hard business problems.

Mo Chen

They're fantastic at so, so many things, which is why I think for these people, the career switchers, that knowledge that they've built up over five years, ten years, years, that is invaluable. That is something that only other people who also want to transition into data analytics and have also worked for five, ten, years in that specific industry have which when you think about it, it's not many people.

Richie Cotton

Yeah. I suppose even if the AI can answer the question, domain knowledge is still going to help you ask the question to the AI to help refine your knowledge as well. So yeah, that's the same thing. Incredibly important. Are there any challenges you think, there are associated with, with, getting into data later in life?

Richie Cotton

Like, what do you need to be worried about?

Mo Chen

Yes, absolutely. So I won't lie, it's it's not easy, but data jobs are out there. They are available. And I think when people say that it's hard to get a job. Yes, it is because data jobs are high paying roles. So if you think about formula one drivers, Premier League footballers, NFL players, NBA players, it's hard because it's highly paid.

Mo Chen

So I'm not surprised that to get a six figure job you have to compete. If it was easy, we would all do it. So I think the difficulties of transitioning into data or getting a data job, it's the same level of difficulty as getting any other job that is also highly competitive. That's the way I think about it now.

Mo Chen

Covid was a different time, obviously, because during Covid, I think a lot of companies thought that because we had extra data, we needed X. The data resource, which in reality is not true. And then people got laid off. And now with AI coming in, it is it is hard to get a in your job because companies think they can just automate everything, which is probably not the case.

Mo Chen

But it is really hard for career starters to get into data. But I think for career switchers, I think the difficulties lower, like I said, because they have invaluable experience. Once you have that invaluable experience, you go to work and the people who can solve the problem, think about the problem, ask the right questions and build a solution.

Mo Chen

Those people are so, so valuable to the organization because you'll find that many people are really good at managing money. People are really good at doing, but the people who are really good at managing and doing, which means you can think, you can find problems, take the initiative and build the solution yourself. Imagine how valuable that is. I can only speak for my wife.

Mo Chen

For example, the senior business analyst study at a, medical technology. I believe that is a sector, that's what it's called system. It's an American company. So she's a senior business analyst. What makes her senior? It's the fact that she can identify the business problems now, and she can work with people to solve those business problems. And on top of that, she's a power BI expert.

Mo Chen

So she runs the whole teams power BI model, which makes her invaluable.

Richie Cotton

Okay. Yeah. That does seem very important. You've got you got the balance between that can do technical things that can do management things. And also, yeah. I've got the domain knowledge not to be able to ask the right questions. So, you mentioned how things are tricky for junior analysts. So I've heard different opinions.

Richie Cotton

On if I can do very junior things. It is not on hiring junior people. You only want senior people in your organization. And then there's the sort of a counterpoint that actually junior people don't have that sort of preconceived notion of how workflow should be. They're much, keener to adopt. I would like, because, you know, the, they don't know the old way of doing things.

Richie Cotton

So do you have a sense of, like, if you're a hiring manager, should you be focusing your efforts on recruiting junior people or more senior people, you know.

Mo Chen

Depend on your team? I the thing is, I'm not a hiring manager. And, I was never in a position to lead big teams ever. So it's really hard for me to comment on something like this. I think if you're hiring someone who's, who has a relatively less experience, I think you hire, potential because nowadays, like I said, I feel like intelligence is so underrated.

Mo Chen

Everybody just thinks, okay, you've been doing this for years, so you'll be great. But the way I think about it is that you've been doing this for years, which means you're not up to date. You're set in your ways. Not everybody, not the majority. But this will be the case, right? Like so, having lots and lots of experience in the same field, in the same team is an advantage in the sense that you have so much business knowledge.

Mo Chen

But sometimes these people don't bring in fresh perspectives. So the way I see it is that the way I do things is nonconventional at work, because I haven't worked for long enough to do the conventional thing. I've only been in the corporate world for seven years, and I say only in the corporate world. I see some dinosaurs who can literally I see people at my company, you know, who had their th anniversary with the company.

Mo Chen

I'm not even I mean, you know, so it's it's one of those things that I think when you hire junior people, you must hire, potential because people can really amaze you with what they can do if you just give the right person the right opportunity. And when it comes to a little bit more senior roles, I think it's very different for, smaller organizations where you're looking for someone who's super hands on and can do everything, the role will be different compared to large organizations where you're you're looking for a people leader.

Mo Chen

When you're looking for a people leader, I think experience matters so much because dealing with people at work is probably the hardest thing. Surprise, surprise, right? Like you go to work, you have all these amazing technical skills. But then the hardest bits about working is actually attending meetings, getting the by, and convincing other people that this is the right thing to do.

Mo Chen

So it really depends. Richie, I'm sorry, this is the most honest answer I can give you.

Richie Cotton

No, That's fair. Under different circumstances, it's going to be different needs that. But yeah, I do know that it. Well, you know, you feel like you have hard technology problems for your colleagues or even that or a problem.

Mo Chen

Not not a problem. No, no, no, I wouldn't say a problem. The way I approach this is that I like working with everybody at work. But there are certain people I like working with more than other people. So I like everyone to work.

Richie Cotton

It feels like there's an undertone of gossip there. All right. To, wrap up just to leave, what are you most excited about in the world of data at the moment?

Mo Chen

I'll say I'm excited for the use of Genii in a way that you don't need to use English to prompt anymore. So I'm excited for a Genii solution that can sort of just look at what's on my screen for this is just an example or a Genii solution that can sort of look at my data sets or my work pattern, my workflow, and what I want to achieve as a business goal and just give me the solution.

Mo Chen

So rather than me prompting something along the lines of give me the top five best performing investments over the last six months, how about Genii? Just figures out that I'm getting this data set. I'm trying to like, look for bar charts and companies, and then just from that, it can suggest something to me. I'm excited for something like that.

Richie Cotton

So not even having, to write the prompt, the prompt just appears automatically. It's like, okay, would you like to do this? I guess it's like, the old Clippy style thing from Microsoft Office, where you start typing and then it pops up and says, oh, would you like to write a letter?

Mo Chen

There's some there's something along those lines. So that's what I'm excited about. Unfortunately, there's so many things I'm worried about with AI, and I think it outweighs what I'm excited about by a mile.

Richie Cotton

Okay, go on. What do you worry about? Oh,

Mo Chen

So, I mean, I think and there's already proof of this is that I can choose to lie, choose to hide, choose to scan. Which is a problem because I know currently we're using AI to test whether or not it can penetrate systems. Right? Create fraud. But there have been case studies where the AI model penetrated a system.

Mo Chen

And then when asked, the AI model said it didn't do it. And instead of trying to reveal all the security flaws, it hid the penetration. So if you think about it like this, this is my biggest worry, is that if we make JNI truly to be like humans, humans on average, I don't think we're good in the sense that that's why I live in I wouldn't say the middle of nowhere, but you'll know this that I live at the National park in the UK and I love animals.

Mo Chen

And the more people I meet, the more I love animals. So this is why I have a concern around the AI. And just on this note, I think this is why there are going to be different fields that are going to be emerging. And I already see that you guys provide training around this as well. So AI ethics, AI governance super important because if you don't understand that the models are biased, which most of them will be because it needs to feed on some kind of data.

Mo Chen

So all models in my opinion will be biased to some extent because it just depends on what data you feed into it, but it's how you refine the model over time to close the bias. That's what's important, because if you do it wrong, it'll just expand and expand and expand, and then eventually you end up with terrible models that give you terrible results that will make you do terrible things.

Richie Cotton

Oh man. I'm sure as we're ending on that pretty dark note there. But, yeah, certainly. I think, having, a sense of, responsible AI, of AI ethics, this is going to be incredibly important skill to deal with, particularly as, as Germany becomes more and more widespread and in use, it's going to be, of core skill for everyone.

Richie Cotton

Okay. I see, do you have a happy thing to end on? Maybe.

Mo Chen

Oh, yeah. We need to end on a happy thing. So I think let me just end on this note, because I think this is close to people who use Datacamp as well. So your audience and my audience as well, I know it's really difficult to get a data job, but I personally know of successes, and I know it's one of those things that if you just put in the work and you persevere and you're consistent, it is completely achievable.

Mo Chen

It is one of those things that when it gets difficult, don't just do and endless tutorials. For example, go to datacamp and you can you can do projects, do real life hands on learning, do something proactive. If you just keep on watching things forever, I don't think learning will stick. But if you actually do one or or projects where you have to think outside the box and do something a little bit differently, then I think getting not just data analyst jobs, because the role of the data analyst is changing and will shift over time.

Mo Chen

It's the ability to work with data using AI nowadays. I think the opportunity is there. So anybody who tells me otherwise, like the people, hundreds of them who said in nd November that I will lose my job, I'm still here standing and improving them wrong in a way that I didn't think my job was at risk in in November, because I knew that at the end of the day, it's just a different skill set that I'm going to have to learn and adapt to.

Mo Chen

And the way I look at it actually, I welcome using AI because like I said, who likes writing minutes? I no longer have to do it. Who likes debugging code? Hundreds of lines of Python code and I have to find the problem, whereas now I just does it for me. So I in general is good. I think the only thing I wanted to highlight here is that the ethics around it and the governance is super, super important as well.

Mo Chen

If we can do that right, I think we can create amazing things and we have already done so, so many amazing things. So I know I said penetrating systems, but also fraud detection is so much better with AI. Easier, better, more efficient, quicker. So that's just one example. And there's so many other use cases, not just within finance but within healthcare.

Mo Chen

I see companies already use AI to proactively try and forecast who might be at risk of something, so you can do something about it. Years ago, before the actual thing happens, right? Like once you had stage four lung cancer, there's nothing you can do. There's no AI model that will help you. But if you can actually utilize technology, use the data that you have and build a model that will predict it accurately.

Mo Chen

Who is more prone to having lung cancer? Then you can do something about it years before that and then boom, you just gave someone else another years of life. And how can you put a value on that? So I think this is a pretty happy note to end on, Richie.

Richie Cotton

Absolutely. Yeah. I mean, lots of great things there. Yeah, it is still definitely possible to get a job in data analysis. There's lots of cool stuff and frankly, lots of interesting new things to learn. The as the as the job changes. And yeah, I do I don't like AI for healthcare, like improving people's lives. It's yeah, just, an unambiguously good thing.

Richie Cotton

All right. Wonderful. Thank you so much for your time.

Mo Chen

Oh, thank you so, so much, Richie. And I'm sure we'll speak soon.