236. Combining Creativity with Data: Inside the Future of Journalism at the FT

What does it mean to evolve your career in the Digital Age?

Meet Janina Conboye, a Financial Times journalist who added data skills to her storytelling talent. This episode is a masterclass in smart career transitions.

Listen to learn:

• What data journalism actually is and why top news organisations are investing in it
• How to think strategically about your career evolution
• Why the "creative vs technical" divide is holding people back
• How to use AI tools like ChatGPT to help you learn new skills
• The reality of career transitions - from the tears to the triumphs

Timestamps

00:00 Introduction

03:06 Understanding Data Journalism

05:49 Intersection of Traditional and Data Journalism 

09:00 Data for Storytelling 

12:12 Data Skills 

15:07 The Future of Journalism and Data

17:51 Data Journalism for Readers 

21:03 Transitioning Careers 

23:49 Long-Term Investments in Career Development

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Transcript

Sophia Matveeva (00:00.204)
I'm confronted with a fork in the road. One is promotion, more responsibility, but continuing to do what I already do, or basically turbo charging my employability for the longterm by looking to the end of And that's the road I decided to take.

Sophia Matveeva (00:19.513)
the Tech non-Turkish podcast. I'm your host, tech entrepreneur, executive coach at Chicago Booth MBA, Safiya Matil. My aim here is to help you have a great career in the digital age. In a time when even your coffee shop has an app, you simply have to speak Turkish. On this podcast, I share core technology concepts, help you relate them to business outcomes, and most importantly, share practical advice

on what you can do to become a digital leader today. If you want to a great career in the digital age, this podcast is for you. Hello smart people. How are you today? Happy New Year. I hope that your 2025 is off to an excellent start. And in this episode, I am going to introduce you to somebody doing a job that's a mix of the old and the new. And that's a data journalist.

Yanina Konboye works at the Financial Times, is one of the world's best newspapers and I am a proud subscriber. And Yanina began her career as a traditional journalist. And in fact, we met years ago when she interviewed me when I was running my first tech company. But in the last year, Yanina decided to update her skill set for the digital age and transform her career.

So if you are thinking about how to take your career to a new level this year, then this is both a very inspiring and a highly practical episode. You'll hear why Yanina took this approach in the first place, how she went about changing her career, and also you'll learn what data journalism is in the first place. So if you are thinking, I'm creative and I can't do left brain work, then this episode is 100 % for you because

Yanina's example shows you that you do not have to throw away your existing skills to thrive in the digital age. It's all about building on your existing skills and talents. So whether you're a founder who needs to understand market data or you're an investor looking for patterns that others miss or somebody considering a career pivot, then this episode will show you how to think strategically about your next move and also what skills you actually need to learn.

Sophia Matveeva (02:39.117)
But first, if you're enjoying these episodes and finding them valuable, then please take a moment to leave us a rating and a review because it makes me feel warm and fuzzy. But also your feedback really does help other smart, ambitious people like you find us. And that'd be a wonderful thing to do to get yourself some karma points for 2025. Okay. And now let's learn from Yanina. So Yanina, what is data journalism?

So data journalism is essentially finding stories in essentially large data sets, analyzing those data sets to see what they reveal. also another sort of example of data journalism is you may find several trends in different data sets that are related to one another, but they create a narrative and you can do an analysis.

So examples of data journalism I've been doing recently is looking at global fertility rates and projections. And when you look at that, what you can see is fertility rates over the next century are falling absolutely everywhere. I'd say the vast majority of countries are already below replacement and the countries that aren't are heading that way. So that's one example.

I think I saw that on Instagram. think because I remember reading that story and kind of having mixed feelings about it. Yeah, so it's actually a move on from that. So we did that because that was a particular set of projections by some academics and their very highly regarded stats. And they're not that dissimilar to the ones we've used for this new piece, which comes from the UN population prospects, which is a huge data set, which has all the data for all sorts of

indicators like live births, population, total fertility rates, death rates, and it's all broken down into countries, regions, low income, mid income, high income. So you can see all different trends across all different scenarios. And that's a huge data set that's updated every so often. Yeah. So we've done another story just to talk about what the future could look like and how countries need to try and cope with basically shrinking populations.

Sophia Matveeva (04:56.596)
And so how is data journalism different from a data scientist, I don't know, working in a company, for example, because, know, let's say you work at L'Oreal, I know that L'Oreal really take data mining really, really seriously, and their aim is basically figure out what lipsticks are going to be in in the next two years, so then they can create the right pigments and

create the right marketing campaigns and so on. So they also have a bunch of data and they're kind of looking for a story. So is that basically the same thing? You're just not doing it to sell lipstick, you're doing it to tell stories? I think the difference, I don't know the ins and outs of exactly what a data analysis within a company might do. I kind of get the gist that they're working on very specific data related to that company is very specialist.

And I guess maybe data journalism is a little bit different in that you have to be a generalist. You have to know how data makes a story. And the interesting thing is sometimes you might get pitched data stuff from a PR and it's of interest, but you want the data set because you might analyze the data set in a different way and find top lines that are more suited to an FT news story or wider analysis piece.

And I think data journalism is probably different in terms of how you interpret the data, what you want to do with it, what stories you're trying to tell. And then also the added skill is you have to be able to write and know what you're looking for. And also for me, going into data journalism, I already had a traditional journalism background because part of data journalism also is

being able to have contact, you still have to have contact, still have to ring people up, still have to speak to them and know how to get the information from them that you want. So yeah. interesting. So it's like journalism for the digital and for the AI age, because it's taking the traditional journalism skills that were people were practicing in like the 19th century, mixing with the data. Interesting. And so let's

Sophia Matveeva (07:04.666)
Keren, exploring this fertility example because we all understand what that means. We all understand the implications. When you get this massive, massive dataset, how do you know what to look for? Do you start with a specific question you want to answer? What do you do when you just have this massive records? I think sometimes you might go into it with a specific question or several questions. That's helpful when you're

facing a big data set. But one of the key things about what data journalists do is you will get your data all clean, you will familiarize yourself with it, you'll find your way around it so that you know what you're looking at, what stats are what. And then it's about getting it into a shape where you can then look at the visual. So you chart it essentially and look at it and see what it looks like. And that will help you.

find your story. For example, the fertility stats in the first story I did, I did a map and it animated across time so that you could see by country, the shade would change so that it will become lighter as the fertility rate fell. And it gave people a global sense of what direction those numbers are going in. then that's it. Can you tell us are the Europeans dying out? I expect so. we?

Is that the end for us? That's another thing. So when you dig into the data and then you speak to the experts because you're like, okay, I found this pattern and I think it's telling me something. And then you go and speak to people who really know this stuff inside out. So myself and another colleague who I'm working with on this new fertility piece that we're looking at, you go and speak to people and they'll tell you that, okay, you've got a total fertility rate of say 1.7 in some European countries.

And then you've got a fertility rate of below one in South Korea and China. yes, I've heard in South Korea, there's a trend. I've learned about it on TikTok. Now, the interesting thing is, it's both below replacement. But for China and Korea, it's much more of a crisis than it is for European countries. Because one, we're already used to and adjusted to, to a certain extent, not entirely, an aging population. We have certain structures in place.

Sophia Matveeva (09:26.916)
place. We know they're going to have to be adapted, they're going to have to be made more flexible, people are going to have to work longer, whether they like it or not. But in places like China and South Korea, or this probably applies more to China more than anything, and also some Latin American countries, their age dependency ratio will go stratospheric in double quick time. So it's more difficult for them to adapt quickly. And that's

what makes it difficult for them is they've got a shrinking population and then like a huge aid to apprenticeship ratio in like double quick time. Whereas in Europe, it's more of a drip feed situation and therefore it's easier to handle. it's getting your figures and then looking deeper and thinking, okay, well, what do they mean? Because it actually really depends. So interesting. So I guess kind of the lesson here is that when you just have a massive data set, the first thing to do is just turn it into pictures.

Because a picture tells a thousand words. Yeah. So if you just see like a massive bubble versus a tiny bubble, then you just know, okay, we need to investigate this. Interesting. You you can use a really big data set. So obviously the context of my career transition. So I'm a traditional journalist and then I've moved into data journalism. It's been made easier for me because I work in an organization where

Data journalism is a growing area and they're investing in that while also wanting to provide people with career opportunities. So first thing to say is I've had a very supportive manager in my old job who was helping me and now have a very supportive manager, managers who not only help you learn on the job, give you the space you need to learn very specific skills.

So one of those skills is learning R, which is essentially a sort of coding program for analyzing data. And that's been a huge learning curve. And at times I thought I might weep, but I got there. Yes, we had to learn it at business school. I have loads to learn. have incredible colleagues who are so good and also they are really willing to help. And it really is just very collaborative, very collegiate atmosphere.

Sophia Matveeva (11:43.468)
I'm curious because traditional journalism, it's all about writing, storytelling, speaking, and it sounds like traditionally what we might call right brain creativity. And then you're talking about learning art and I've had to do it and I completely, I remember I ate a whole kilo of chocolate when I was trying to learn it because it was just like I just needed some

something to keep me going. My teeth didn't fall out and I did manage to pass it, it was hard going. And so what do you think of this? Is it that we are thinking about ourselves in a completely kind of wrong way that we shouldn't be thinking, I'm a right-brained person, I can't or I shouldn't do that? Is it that we can all do it or is it that you think, okay, well, I had to learn the skills, but primarily I am

I'm still a storyteller, it's just I got a new tool. Yeah, it's definitely you're still a storyteller, you've got a new tool, but also the whole right brain, left brain thing is just like a load of rubbish, in my opinion. If you use one predominant, predominantly fine, but it doesn't mean that you're not capable of engaging the other one. And also, for example, I play piano. And I think as far as I'm aware, piano is very good for using both sides of the brain equally. So I think some of its myths.

And actually creativity, I think one big myth is people think that some tech techie stuff is not creative and that's not true. And I think that we really need to get over that because the thing about R for example, is once you get quite good at it and you've got your big data set and you need to sort of visualize what you've got so that you can figure out what you're going to do with it and what sort of narrative it might be giving you. And you can do that in R and you can then

all of a sudden have all these like charts alongside your code. And then you're like, okay, well, that's interesting. That's interesting. And then you can code a little bit more to think, okay, well, if that's doing that, maybe it would be interesting to have a look at this or this, and then you can sort of change it. And that's basically a lot of trial and error. Sometimes you might have a data set and think, this could be cool. Actually, doesn't tell you very much. actually, it reminds me of the artist in Renaissance Italy. I've been reading a lot about that recently.

Sophia Matveeva (14:08.821)
Because the great artists like Caravaggio and so on, they essentially wouldn't just make the paintings, but a lot of them created their own custom paint so they would only know how to make a specific pigment and that pigment was their secret, they wouldn't tell anybody else. Which is something that I learned recently and so essentially they would be masters of the tool so they would know exactly how to mix a specific paint with some sort of secret ingredient that they got from somewhere.

And then they would use that specific tool to make a beautiful painting that we are still marvelling at today. So essentially, creative people have always been playing around with technology, essentially, with technology to tell their stories and to create works of art. So I do love this example of that. Actually, let's think of ourselves as ambidextrous as opposed to left brain or right brain. Yeah. No, yeah, I definitely would agree with that.

I think, and also there are benefits to obviously I was predominantly using my right brain, you could argue as a traditional journalist, and then doing data analysis and stuff, it's possibly made my writing more concise. So they help each other. Yeah, well, it's sort of like, you know, organizations today, they are so siloed, and our education system makes us really siloed. And I think the education system plus corporates don't really help us become these Renaissance people, because

Again, back then people would be masters of, don't know, Greek and Latin and write a sonnet and, I don't know, wield a sword and maybe less useful today. I don't know. Okay. So the Financial Times is obviously investing in this skill set for their journalists. And is this where journalism is going?

in general, or is it because the FT is basically so successful that they can go and experiment and do these crazy things? I think data journalism is part of the offer. I don't think it's going to be just about data journalism because the best stories really are good data journalists teaming up with really great correspondents and they produce really some of the best stuff. I think some of the big publishers like New York Times,

Sophia Matveeva (16:27.509)
data is quite a big thing for them. I think any big organization, news organization worth its salt, you'll find is they're investing in data journalism and they have quite big data journalism teams. And for any youngsters who are thinking about being journalists, it wouldn't hurt to consider data journalism because the skill sets are really in demand. And also, you'll probably get paid more. good to know.

So what about readers? I was just checking my FD app today, as I do every day. I'm trying to spend less time watching cat videos and spend more time reading serious press, or at least on Duolingo, anyway. And so can readers tell? How can readers tell that it's data journalism? Is it just when we see a bunch of charts? Or are there any other telltale signs that your work is behind it? That's a good question, actually.

I mean, obviously if there's charts, that is definitely obviously data journalism. guess any story where you're looking at the top news lines have come from basically an FT analysis of X, Y and Z. And then I'm trying to think of some specific examples. Like for example, I did an analysis last year of this is a bit niche, but basically we were looking at

UK constituencies and their average NHS waiting time. interesting. And the only way you can do that is actually this is a really good example of like quite hardcore data analysis that's very sort of newsy. So the ONS has a data portal. And the ONS is the Office of National Statistics. That's great. Yeah. So they have this data portal where they have essentially like coordinates for regions for

councils for lots of different like subgroups of stuff, constituencies. And then you can map them on top of one another. So in my case, I needed these things in the UK called sub-ICBs. And basically they are what used to be called care commissioning groups, which is all to do with the NHS and who's responsible for what area and what hospitals are in those areas. So I took these sub-ICB groups.

Sophia Matveeva (18:48.759)
and I matched them with constituencies using a program called QGIS. And it allows you to take the coordinates of the constituencies and the coordinates of the sub-ICB groups and map them on top of one another so that you can see which constituencies are which in sub-ICB groups. And then you can, what you'll find is that it's then overlap. So you might have one.

UK constituency which might be covered by two of these so-called sub-ICBs or care commissioning groups. So you're like, okay, well, which one do I pick? So that's where you take population data and then you are then able to map the population data and generate essentially a CSV, which is like a spreadsheet. And you can then allocate your constituency to the sub-ICB that covers the majority of the population. And then you can look at

the whole picture of which areas have the highest waiting times, which constituencies. And then of course, you can analyze who's in charge in those constituencies. Are they conservative? Are they Labour? Are they Liberal Democrat? So we did analysis around that. it wasn't that charty. That's the other thing about data journalism. You can write around what you found because it might not necessarily be the perfect chart.

it's more about just getting those top lines and you might get some really great top lines out of a data set and then it's about going off and reporting more broadly about what those things that you found. it's kind of like the beginning. I mean, you could take down governments with this Yanina, just from this example. yes, the power. So kind of data journalism can be the beginning of a story. So you see a pattern.

And then you go and you investigate and you say, why is it that in your borrows, that's the worst? How fascinating. so when, you know, there are a lot of people who listen to this podcast who want to transition into something to do with tech, because, know, that's where the money is, there's a lot of opportunity. Frankly, the work is interesting. They don't necessarily know exactly what it is that they want. And so I think that's why people are.

Sophia Matveeva (21:03.849)
listening to try to find out like what are the opportunities and also how do I find out and how do I capture these opportunities. So I'd love to hear your story. So how did you go from basically being a traditional journalist writing at the FT to even knowing that data journalism is a thing and also then deciding that, okay, I'm going to go and take this really radical step because

You know, you're making it sound so easy, but I'm assuming most journalists at the FT are still quite happily just doing their traditional thing and being like, I'm a journalist at the Financial Times. I don't really need to do anything like life's pretty good. so basically, I was at a juncture in my career. So there was a job opportunity that arose and I thought that's not for me. So where can I go instead?

And I had two options. I was like, where are the growth areas in the FT? And the two growth areas I was particularly interested in were newsletters and data. There's already a system in place for people to get OFe with very basic data tasks. So really understanding Excel basics, that kind of thing. And everyone's encouraged to be able to do that. And also we have a chart tool which allows most journalists to basically do their own basic charts.

Well, too complicated. So there's definitely a campaign for much better data literacy. And then it's kind of, how do you then take that on? I'd kind of gotten a bit familiar with some data stuff. Like I'd been doing some surveys in my job on the working careers desk and worked with the data team. And then I, that's when I started to think, okay, how do you start to get your head around being able to do that?

I was working with someone helping me analyze the surveys and I was like, okay, how do I learn to be able to do the analysis myself rather than getting someone else to do it for me? And I spoke to the head of the department. He was, it's as much to do with attitudes as it is just learning stuff. So I was in a position where I was being allowed some like serious career development opportunities and

Sophia Matveeva (23:20.631)
There were managers on the data team who were quite keen to get people from traditional journalism backgrounds onto their teams so that they become less siloed. So was much more of an understanding between the two. And when I joined the team, they were like, oh, don't worry. You've got basic Excel. It'll be fine. You'll learn all this stuff in no time. And I was like, you've got to be joking me. And the first three months, three to six months were really, really hard. I enjoyed it.

but it was really, really tough. Partly Yeah, I think I didn't see you then. You just disappeared into a work. Yeah, it was really, really hard. Last Christmas, for example, I had a few quiet days and I used those to get to grips with our basics. I was like, okay, I think I might cry. But I came out the other side and I still got absolutely tons of stuff to learn.

After a year now, I'm starting to notice that the certain things I can now do a lot quicker. I can get to grips with data set faster because I know what I'm looking for. When I'm going to colleagues to ask for help, I've already managed to get myself to a certain point. And then the help that I need is a bit less. the other revelation is we have access to chat GPT.

Excellent. Brilliant. Because if you know what you want, I think in terms of learning R, I probably needed a human, my colleague, who I booked endlessly and has been of great help just to lay the foundations. And then once you kind of have an idea of what you want to be doing with a data set, you can ask very specific questions to child GPT and do it in stages and take it one step at a time.

And that's been really brilliant. It would have taken me longer without it. And what do you do to make sure that it doesn't lie? Because it does love to tell poor keys. It's because I'm not asking it to fact check. I'm actually asking for specific code and I'll know that it's lying because the code won't work and I'll ask it again. Like this doesn't work. please tell me what's going on. So you literally just get some code from chat GPT and then you run it through your model.

Sophia Matveeva (25:42.846)
Yes. So I literally, I use Chat Sheet PT simply. will, I might want to filter something specific or have the data sort of constructed in a certain way. Or I'll be trying to figure out how to build a function. So when you build a function that you can then apply to several sheets, for example, so it will just do it faster. So you're not taking endless time to sort through Excel spreadsheets.

And ChatGPT is really good for trying to construct that. And it's trial and error. Like it might give you something and it might not work. And then you'll explain to ChatGPT, this isn't working. Why isn't it working? And then it'll be like, I think there is a rogue comma or something and it will help you correct it. So it's a really great coding assistant. So the pattern that I'm seeing is that, okay, you decided to make this.

interesting transition and then you basically paid in six months of pain. So six months of pain, but it still wasn't a full time coding course. So I think when people are thinking, should I go and do some sort of like full time thing? Like, I just want to point out that actually, you weren't learning all full time, you were actually going to your journalist job and writing words.

I'm a super, right? It's partly because also you don't need R for absolutely everything. I was learning it because it's a useful skill, particularly when you're dealing with really big data sets. That's where R is very handy. There are also elements of my job where it really is very quick spreadsheet job and it'll take you 10 minutes. So we have a system where, you know, someone will want a chart with using a specific data set. And it might be the journalist has already gotten hold of the data set from a PR person or whatever.

and it's relatively clean and you just have to tidy it up. And then really, rather than working too much with the data, you're thinking more about the actual visualization and you're putting it into our chart tool, playing around, what's the best way to present this and think about the illustrative side of it. Awesome. And so when somebody is thinking about making this transition, like I want them to see that there is going to be a sort of a painful learning curve because that's what happens whenever we're trying to do something new.

Sophia Matveeva (27:59.696)
I am attempting to learn Arabic right now and it is going very slowly, very slowly. But I just keep on picturing myself actually being able to have a conversation. even if that's in five years time, it will be fabulous. So there is a painful learning curve, but actually it's not that bad. Like it's not that long. It's not that intense. And within just a year's time with the help of chat GPT, you're actually feeling pretty good and pretty confident and also actually

being able to do work and meaningful work. And so what would be your advice to people who not, you know, maybe not, they're not necessarily journalists, but they're thinking, okay, 2025, that is the year I'm going to transition my career. I'm working something very traditional and I want to essentially future brief my skillset. So what are you going to say? But they didn't know necessarily exactly what they're going to do, but they know something to do with Turkish otherwise.

Why would they listen to a tech from techies podcast? So what would be your advice to this person? I think so in the context of myself, so I had something to bring to the job, even though it was going to be a massive learning curve. A lot of my skills were already very useful to that job. Anyway, I was able to write. I know how to be a journalist because being a data journalist is still being a journalist. You are not just a data analysis.

a data analyst, should I say. And that's the key. So I wonder if anyone considering a transition should build on your strengths of a semi-transition where you're going into a job where you can offer your skills that you have with an opportunity to be learning other skills that you want, which is exactly why I made this move because I was like, okay, I am confronted with a fork in the road. One is promotion, more responsibility, but continuing to do what I already do.

Or basically turbo charging my employability for the long term by looking at you. And that's the road I decided to take. Long-term investment. Exactly. didn't feel like promotion was the right move for that time in my life. And for the long, for the long term, I thought if I'm really going to be exceptional, I have to actually take this other road. And I also mentally paired myself thinking,

Sophia Matveeva (30:24.564)
going to be the hardest thing you've ever done in your life, but it's okay. Just go with it. So kind of when you know that you're paying for this cost, you know, it's similar in a way that I remember when I graduated from business school now, what like eight years ago, God, and everybody was going to work at Goldman Sachs and McKinsey. And I was like, I'm going to start a company, which basically means I'm not going to really make any money for quite a while. And, know, people were just like, yeah, let's, let's meet up for lunch at Nobu. And I'm like, absolutely not.

off, not now. And now it's working out. Like now it's good. But it was one of those long term investments that it was like long, was pain in the short term and you see other people kind of essentially living much easier lives, not putting them getting promoted and getting bonuses. And I was thinking, oh, okay, well, I'm just torturing myself.

But then eventually it pays off, but you unfortunately have to take the hit if you want to have that long-term success. Well, thank you so much, Yanina. I am really, really proud that we've had you on the show. You've shared something completely new and something completely different. And I think this is a really, really good episode to kickstart January with, especially a bit of advice is, okay, if you're to be exceptional, that means you need to think strategically and not just take juicy opportunities.

just because they look nice without really thinking through. Thank you for having me. It's been fun. Did you enjoy this episode? Did you learn from it? If yes, then please leave the show a rating and a review because honestly, it would make such a huge difference to me. And on that note, thank you for your time and your attention today, my dear smart person. Have a wonderful day and I shall be back in your delightful smart ears next week. Ciao.