SDS 083: Leveraging your experience into Data Science

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Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today and now let’s make the complex simple.

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Hello and welcome back to the SuperDataScience podcast. Super excited to have you on board, and today we’ve got an interesting guest, Emma Whyte, who is the Head of Centre of Excellence and Customer Advocacy at The Information Lab. So the two main things you need to know about this podcast is that Emma never actually studied anything technical. So she came from a background in History and Politics and now she is rocking it in the world of data science. So that’s going to be very inspiring to learn about.

And the second thing is what Emma actually does. So Emma started off by providing consulting services in the space of Tableau and Alteryx, and now her role has grown and developed over time, so her career is progressing, and slowly she’s moving to a more strategic, I would say, consulting point in her career where she is helping clients implement data-driven business decisions in their companies, and data-driven culture. So a shift from the old-fashioned way of thinking about business to the new way of thinking about business. And in this podcast you will learn not only how she does that, but also why it is so important in this day and age for companies to do that.

So that’s what we talked about on this show. And I can’t wait for you to check it all out. Without further ado, I bring to you Emma Whyte from The Information Lab.

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Welcome everybody to the SuperDataScience podcast. Today we’ve got an exciting guest, Emma Whyte, on the show, who’s the Head of Centre of Excellence and Customer Advocacy at The Information Lab. Hi Emma, welcome on the show, how are you today?

Emma: Hi Kirill, I’m good thank you, how are you?

Kirill: I’m very well, thank you so much. And you’re calling in from just 30 miles north of London, is that right?

Emma: Yeah, that’s right, in Hertfordshire.

Kirill: Yeah. And it was funny, I actually just mentioned to Emma that her title is so long it should have an abbreviation. It’s Head of Centre of Excellence and Customer Advocacy. What do you do, Emma, at The Information Lab with a title like that?

Emma: I’ll take recommendations for a shorter title. It is quite long. But what that boils down to is I help our customers. So I work with our customers to train them up, so to make sure they’ve got all the best skills in Tableau and Alteryx, the two products that we work with, and I’m there to help guide them through their roll-out of these pieces of software and to help them move to a data-driven culture as well, as an organisation.

Kirill: Ok. Very interesting. And we’ve actually already had, for our listeners, we’ve already had one guest from The Information Lab on the show, and that was Rachel Phang, and that was a couple of episodes ago, so a lot of us already are familiar with the name The Information Lab. But just in a nutshell, in a couple of sentences, can you give us an overview of what The Information Lab is.

Emma: The Information Lab is a Tableau partner and Alteryx partner. And we help our customers see and understand their data. It’s all about connecting to data, making it useful, creating great analysis with it, and trying to drive decision making with data.

Kirill: Ok, that’s a really cool description. And what I wanted to clarify here is that sometimes it happens on the show that we have a certain topic many times in a row. So we’ve had several guests on AI, now we’ve already had several guests on Tableau and visualisation. So I just wanted to clarify. Is what you do just about visualisation? Or is it also about analytics?

Emma: Oh, it’s definitely about analytics. So we partner with a tool called Alteryx as well, and that has some really great stuff in the box. It works really well with spatial data, it does a lot of drivetime analysis, it does a lot of forecasting as well, or predictive analytics, using R. We do the whole package, really.

Kirill: Ok, gotcha. Totally understand. Alright, well maybe let’s start with your story. As we all know, this is a podcast on careers. Tell us a bit about you, your background, what did you study at university, what were you interested and passionate about, and how did that bring you to the career that you have now?

Emma: I don’t really have a traditional background for an analyst. So I actually studied History and Politics at University, and I did an MA after that in International Relations.

Kirill: What’s an MA? A Masters of Arts?

Emma: Masters of Arts, yeah, in International Relations. And I think what I learnt from studying those subjects is that I just love finding out why things happen. I think I’m really inquisitive, and I really enjoyed doing the research part of my degree and my Masters as well. So coming up with my own question and exploring the answer to that, doing lots of research to find out the answer to that question.

Kirill: That’s really cool. So even though you studied something completely different, and just to repeat, it’s History and Politics, and then International Relations, completely not related to data science and analytics, but through that, you understood that you have a passion for being inquisitive and getting to the bottom of things, finding the answers, and that is now something that you leverage in your career in analytics. Is that about right?

Emma: Yeah, that’s exactly right. I went from my degree to be a researcher. Maybe it wasn’t so data heavy, but I think the whole idea behind data science is finding answers to things as well. So I can really relate across the two.

Kirill: That’s a very good example. I love that. A lot of the time, people who want to break into the space of data science, they have this question, like, “I studied something completely unrelated. How am I going to leverage my background in politics in data science?” Well, maybe it’s not about what you studied, but about how you studied, or how you approached it. There’s always something to find in your own personality and your traits that you can leverage to your advantage. I’m really excited that you found this. Tell us, what was your first role in the space of data science? How did you break into data science?

Emma: I became a researcher at a company that did a lot of surveys of people. They actually worked a lot in the health care sector, so surveying doctors, surveying patients through e-mails or phone calls. We would write the surveys, but we would also collect all the data, so collect all the responses, and then analyse them and present them back to the companies that we work for.

So, my early introduction to data science was really collecting all of this survey data and then having to analyse it in different tools, mainly Excel, SPSS, and then eventually I found Tableau as an alternative to these. So, you know, you have to copy and paste data into Excel and you’re quite limited with what you can create chart-wise. The idea was that we wanted something interactive that we could then present to our customers, to present their survey data back to them, and that’s where I found Tableau and kind of went from there.

Kirill: That’s really cool. I actually have some similar experience. When I was at Deloitte I was doing some survey analysis and Tableau works fantastic for that because for surveys, sometimes you need more advanced visualization techniques than you can easily get in Excel, and one of them was Likert charts that I used in Tableau. And even in Tableau it takes some time to create, it takes a couple of hours or if you’re really good, maybe it takes you something like 40 minutes to create a Likert diagram. But once you create it, and because it’s also interactive, that really makes a huge difference. So I can totally understand that. If anybody out there has a visualizing surveys, Tableau is a very good tool for that. That’s one of the big advantages that it has. So you started using Tableau and then what happened?

Emma: I actually went to a Tableau conference in San Diego. That was quite a few years ago now.

Kirill: That big one that they have?

Emma: Yeah, the one they have every year over in the U.S.

Kirill: Okay. This year I think they had it in Texas.

Emma: Yeah, they had it in Austin last year and this year it will be in Vegas.

Kirill: Okay, so it moves around? I’ve never been.

Emma: Yeah.

Kirill: Okay. How did you feel about it? Did your company send you or did you go by yourself?

Emma: It was part company-funded, part me-funded. I went as a learning experience because they have a ton of hands-on training sessions and sessions where you can go and find out more about how Tableau works at the conference. I kind of funded it as a learning experience, but while I was there I actually met up with Tom Brown, who set up The Information Lab, and Craig Bloodworth and Robin, from The Information Lab as well.

Kirill: Okay. That’s really cool. It’s very inspiring that you decided for yourself that this is something you need to do and went all the way over to San Diego. Of course, meeting these people was life-changing for you, but apart from that, was the conference worth it? Did you learn as much as you thought you would learn?

Emma: Yeah, definitely. I actually did a training session the day before conference, so I went out and I did a whole day on Tableau Server, and then the conference itself is just incredible. I’ve been lucky to go to quite a few of them. And each time they have a lot of the same content, so if you’re a new person, everything will be relevant to you. But they also keep adding new content as well, so if you go over and over again, you’ll find something new to go and see. And I think the networking is a really important part of conference as well, just getting to meet other people who use the tool, getting to go and meet the speakers—once they’ve done their session, you can go and meet up with the speakers and ask them questions, so I think that’s really valuable.

Kirill: That’s so cool and so inspiring. We are actually running our own conference in November this year in San Diego for the first time ever, and just hearing you talk about how you were inspired by that conference is making me think about how many people will be inspired by our conferences. It’s very, very exciting that there are people out there who have this mind-set. I hope you keep it up. You know, even though you’re successful in what you do already, I think it’s still important to keep attending events and seeing things. Do you have something in mind that you’re looking forward to attending in the near future?

Emma: I’ll be back at the Tableau conference later this year in Vegas. I think when I go now, what I really look forward to is just catching up with people that I haven’t seen for a year. There’s people that I see once a year at conference, so I really look forward to catching up with everyone.

Kirill: That’s so cool.

Emma: Yeah, it’s really cool. I’m really lucky I’m a Tableau Ambassador as well, so I get invited to some of their community events, which are really good as well.

Kirill: That’s so cool. It’s like you’ve built a whole social life around this conference, catching up with friends. That’s very inspiring. Okay, cool, so there we go. That’s how you got into not only Tableau, but then how you took it to the next stage, and you met Tom Brown and people from The Information Lab. So what happened next?

Emma: I was lucky enough to be hired by The Information Lab. I became a Tableau consultant and learned how to use Alteryx as well. It just went from there where I did a lot of consulting. I became a certified Tableau trainer. I did all of my Tableau certifications as well. You can do exams with Tableau, so I did Tableau Desktop and Tableau Server exams, so I’m pretty qualified to go out and do all these sorts of things as well. Yeah, I’ve been there for nearly 4 years now and just learning new stuff all the time as well, because Tableau keeps developing, Alteryx keeps developing, and you just keep learning and learning.

Kirill: Yeah, I totally agree, the tools are changing very quickly. And how do you become a Tableau Ambassador? What does that even mean?

Emma: I am a Social Media Ambassador, which basically means I spend far too much time on Twitter. (Laughs) And I also have a blog as well, and I write articles about Tableau and Alteryx on there. It just came out of that really, it’s just being an active person in the Tableau community on social media.

Kirill: So they reached out to you and they were like, “Emma, we’ve seen too many visualizations from you. Here you go, here’s your Tableau Ambassador title.”

Emma: Yeah, pretty much. Every so many times a year, they add some new ambassadors to their program.

Kirill: Okay, that’s really cool. I’m looking at your Twitter right now. I haven’t gone into detail here, but you’ve got some interesting visualizations here. It’s @EmmaWhyte.

Emma: That’s right, yeah.

Kirill: You’re so lucky to get the actual name/surname combination, that it wasn’t taken before. Or did you have to change your surname to get it?

Emma: No. (Laughs) No, I didn’t. I actually joined Twitter when it was very new, so I was lucky that way.

Kirill: Okay, that’s really cool. All right, so you’re a Tableau Ambassador and you’ve been with The Information Lab for some time now. Tell us a bit about some of the work that you do there. You mentioned before we started the podcast that you’re starting to move your focus from actually working with Tableau and Alteryx to more helping companies at a more strategic or cultural level to shift their thinking to a data-driven culture and data-driven business decisions. I think that’s very exciting. That’s a big consulting challenge to help companies make that shift. Tell us a bit more about the work you do.

Emma: Yeah. I work with our customers who maybe have been using Tableau and Alteryx for a little while, but are still in that journey towards really getting a culture change. So when you go into a boardroom, you’ll see dashboards and executives will make decisions based on the data that they’re seeing. That’s kind of the goal of the program and how I work with customers. That is multifaceted. There’s lots of different parts that make up that journey. So, I’ll go to a customer and I’ll ask them lots of questions about how they’re using Tableau, what’s their data like, is it in a good place, is it well-structured, is it easy to access, can people get hold of it, all the way through to what skills do your staff have in Tableau, how many people use your Tableau Server and consume everything that you’re making, do executives use your Tableau dashboards in meetings. We also ask lots of questions about that and then we come up with an action plan based on lots of knowledge and experience of working with other customers get to a data-driven culture, and we just try and help other customers on that journey as well by giving them some action plans to think about to make sure that journey is successful for them.

Kirill: Okay. So, in that sense, when you ask all these questions, do you have a template in mind of what they should be doing and then you ask the questions and you, like, check off the boxes that they’re doing and the rest is what they need to do? Or is it the case that in every situation you have to come up with a new set of activities that you think are right for this client? So, basically, is your approach tailored to clients? I know this might sound like a question with an obvious answer, but nevertheless, is there some standard blueprint that all companies, in your view, should be following, or minimal blueprint that they should be following in terms of data-driven, or is it always a tailored solution for your customers depending on what they’re doing, what their needs are, and how they want to develop in the future?

Emma: I think it’s a little bit of both. We would use the same set of questions for every customer to give us a way to benchmark answers as well. And from the answers to those questions, the answers are ranked between 1 and 5, like you would experience in a normal survey, the idea that 5 is the best example of a data-driven culture and 1 is the worst example. And then we use the answers to those questions to come up with high priority actions. But in some companies, getting to a 5 on a particular question may not always be a higher priority item for them. So I guess that even if the questions that we ask would be the same, the actions that we would come up with would be tailored depending on what is important to that customer and what they can actually tackle right now.

Kirill: Interesting. Can you walk us through an example, like a sample question and a sample answer and a sample recommendation for that?

Emma: Yeah, sure. If we’re talking about Tableau as a desktop tool that analysts are using, then there would be questions about what training do you offer for your analysts in Tableau. So, ideally, for that sort of question they would send all of their analysts onto classroom training or do the virtual training with Tableau. And then they’d have ongoing training as well, so you’re not just kind of given the tool and then forgotten. They actually have a program for training. I think that’s really an example that you’d hope every company would do, you know, you’d hope they would all get to the point where they have a training program in place for their staff.

Kirill: Okay, cool. And somebody might say, “Oh, well, we have a training program, but it’s 3 out of 5.” And then you’d say, “Okay, cool, so this is what you need to do to make sure it’s a 5 out of 5;” and then they decide if they want to progress in that direction or not.

Emma: Yeah, exactly. We’d have a series of questions to do with training. And we’d also probably use an end user survey as well, so actually survey the people who use Tableau on a daily basis and see what they think about their training program as well.

Kirill: Okay, cool. So it’s like you’re approaching from both ends, both the top of the company and the bottom to make sure the answers are consistent?

Emma: Yeah.

Kirill: Okay, that’s really cool. So you provide the recommendations, and do you help them actually implement these recommendations?

Emma: Yeah. We would have regular reviews with them as well to see how they’re getting on. We don’t just leave them alone with an action plan to go on with it. And we would also probably give ourselves some tasks to do as well for the customer, so we could offer to come in to do a training day or a workshop day to help them with certain things or give them support with getting the data right, or give them support with upgrading the Tableau Server environment, things like that.

Kirill: Okay, cool. I can see how this is working. We’ve made a clear understanding of how it’s working, how you’re doing this. Now let’s talk about why. Why is it important to do this? You mentioned two things: the business decisions and the culture. Let’s talk about both. Why is it important for companies in this day and age from your point of view to shift to data-driven business decisions? Why can’t we just keep doing things the way we were doing and just have a strategic approach, go top to bottom, not really focus on data-driven business decisions, but rather the old-fashioned way of business decisions?

Emma: Yeah, I guess old-fashioned business decisions would be with your gut, so you’d be like, “I think we should do this.” In many cases, it can be that data backs up those gut decisions. But I think in today’s climate, business is really tough. You have to be competitive. Lots of companies try and work out where they’re making lots of money, where they could maybe cut back, maybe looking at where are a lot of their expenses. A lot of customers have actually found when they’ve done some digging around with their data and done some data analysis that they can save a lot of money in some areas, you know, something comes to light and they realize they can save a lot of money in some areas as well.

I think that’s why, to me, having a data-driven business is important. I think it helps you be more competitive and it helps you understand your business better as well. I think if you rely on these gut decisions, or at least go with the gut decisions without seeing if the data backs it up, I think that can lead to some bad decisions.

Kirill: Okay, that’s interesting. And what do you think about starting a business? Should people who are listening to this podcast and considering starting their business, have a business idea and want to become entrepreneur and start some sort of venture, do you think they should start right away with business decisions being data-driven, or do you think they should start the old-fashioned way and then switch to data-driven business decisions at some point? What are your thoughts on that?

Emma: I think as a startup it just makes so much sense to collect that data and analyse it from the beginning because that’s the perfect way to start. You probably don’t want to start a year down the road or two years down the road and you’ve got all of this data and it’s everywhere and it’s messy and now you’ve got to start looking at it and analysing it. I think, especially as a startup, the most obvious things are looking at your finances, how much money goes in and out every month. That just kind of seems like an obvious thing to start with as well.

Kirill: Okay, this is going to be exciting. Do you want to do a quick improv session? I’ll be, like, a person who wants to start a business and you’ll just give me some consultation on what you think I can think about when starting the business. Do you want to do that?

Emma: Yeah, sure.

Kirill: Okay. I want to start a bakery. Hypothetically, I live in let’s say San Diego, since that’s come up already, and I want to get a loan for $100,000, I want to put a bakery next to my house so I don’t have to move far and I’m going to be cooking bagels and they’re going to be vegetarian. That’s my gut instinct, that’s what I want to do, that’s what I feel like doing and I have no data-driven decisions behind that. What are your recommendations? What should I consider before doing all of this?

Emma: Well, I think you should probably look at things like information about the area that you want to open the bakery up in. You could have a look at do people live there; if they do, how many people live there, do they have lots of businesses in that area that people may come on their lunch break and come to your bakery, or come before work or after work. I think there would be lots of information that you could capture or find if it’s already existing out there about that area.

Kirill: Even before I start the business, there is a lot of information.

Emma: Yeah, definitely, because you don’t want to end up opening in the wrong place.

Kirill: Okay, so kind of like market research. That makes sense. And then let’s say I’ve already started the bakery and now the first customers are coming in. Things get more interesting here, right? What kind of information would you say that I need to collect in order to better help drive my business decisions going forward?

Emma: I think you would definitely need to collect information about your sales, so how many products you are selling each day, what they’re selling for. You should also collect information about what you’re purchasing as well, so obviously you will need to buy the ingredients to make the bagels and you’ll need to buy equipment, and you’ll probably need to maybe pay some staff as well. That can give you a good idea of, are you spending more than you’re actually selling, what are your bestselling products, is there anything that you should stop selling because it doesn’t make you any money. Yeah, I think there’s loads of stuff that you can start collecting.

Kirill: And even some stuff about customers like male or female, what age, what demographic, and so on, where do they live, what do they do for a profession if you can get that information by talking to them or something like that.

Emma: Yeah.

Kirill: It actually reminds me of a really cool example. I was in Lisbon a couple of months ago, in Portugal, and I went to this store which is like a grocery store, but it’s a bit more expensive because it’s all natural products, all natural ingredients. And the owner there, or one of the owners there, Diego – by the way, hi to Diego if he’s listening to this – he had this amazing technique of collecting information, amazing because he didn’t have Tableau or any of these advanced tools, he just had a paper and a pen, so he had a table where he would say, “Okay, how much did the person spend? 0-20 Euros, 20-50 Euros or 50 Euros and over?” And those were the columns, those were the rows, and the columns were “Is this person a local, is this person a tourist, is this person an expat?” That was his segmentation. He was doing it on paper and pen. And that was so inspiring to see that, you know, he doesn’t even have these tools such as Tableau, etc., but at the same time he can see the value of the segmentation, so he knows who to market to, what kind of labels to put on the products, what kind of products to get into the store, what kind of prices to set and so on. That’s a great example of somebody who just started a business and is already seeing the value of data-driven business decisions.

Emma: Yeah, that’s a great example. You can’t really underestimate the power of pen and paper for data analysis as well.

Kirill: Yeah, totally. Okay, thank you for that. Even though it might sound obvious to a lot of listeners, a lot of people don’t really think about those things or rush into business. You know, if you extrapolate that into a bigger picture, you can see how these data-driven business issues are important. Now let’s talk about culture. You mentioned that part of your role is to transition businesses to a data-driven culture. What does that mean and why is it important?

Emma: I think that a lot of businesses that have been around for a long time may not realize that data is a really important part of their business. A lot of the tools that we use to visualize data are fairly new and I think that because they’re still fairly new, a lot of the time they’re not included in a decision-making process. You may find, especially in large organizations, that they do have BI teams and their job is to respond to questions and requests for information from different parts of the business. But quite often, they can be easily overlooked as well. Changing the culture may mean shifting the importance of the BI department as well. It may be that they end up being asked into really high level meetings to answer questions in the meeting on the fly, and it could just be that they get their work seen by executives.

Quite often it may be middle managers would see dashboards or see data on a regular basis because they’re reporting to the executives, but actually the executives don’t always look at those reports themselves. It’s all about changing the mindset of how companies may make decisions, it’s about making that data analysis work visible across the organization as well, and making sure that people who don’t really spend a lot of time with data, it’s also about educating them about how to understand data and how to look at a dashboard and start interacting with it and answering their own questions.

Kirill: Okay. That’s a pretty interesting description. And do you get a lot of pushback from people who you approach and you want to help them understand the data better but they just say, “No, this is not something I’ve been doing. I don’t need this in my role, leave me alone,” that type of thing? Do you get a lot of pushback or do you find that it’s pretty straightforward and easy and that people are accepting?

Emma: Yeah, I think you can get pushback. You can get pushback because people think, “Oh, it’s just another job for me to do. It’s just something else I’ve got to look at and I don’t have time for that.” I think when you come across people like that, the important message is how can this dashboard, how can this data visualization make their job easier. You know, they probably have something that they have to produce on a regular basis, some report or some questions that they need to answer all the time, and I always think it’s important to go, “How can we make that easy for you? How can we get you to look at this one dashboard and it answers all of your questions?”

Kirill: Okay, that’s really cool. So you kind of want to promote self-serve analytics into the company so that you can not only help them not be in the dark, but also help them to be able to get these answers themselves rather than always asking somebody for help and asking somebody for a report or something like that. And it’s usually a good idea because that means, as you mentioned, more people have visibility of what’s going on, more people can come up with ideas on how to improve things and make things work better. Yeah, that’s pretty cool.

Do you find that people, once you promote this culture, that once they get it, they become inspired and then they start helping each other and promoting the culture within the company? Do you find that you create this viral effect that once 3 or 5 people know about this, and then they start telling their friends or colleagues inside the company and so on and it’s much easier to move that change?

Emma: Yeah, definitely. I think it helps massively when you have buy-in from leadership as well. I think you see the biggest change when a leader of a company or even a department of a large company really gets the importance of data and data visualization. It’s just so easy for them to make that sort of culture shift, to make that change. Even if you don’t see that sort of high level buy-in straight away, the best thing that you can do is just go around to someone’s desk and show them something that you’ve made and show them how it can help them with their day-to-day job. I think that working from the bottom up also works as well. You just have to work a bit harder maybe.

Kirill: Okay, that’s interesting. Do companies mostly come to you for the business decisions part of things, data-driven business decisions, or for data-driven culture?

Emma: I think they kind of go hand-in-hand. Yeah, I think they have to work alongside each other. I don’t think you can really do much with just data-driven decision making if you’re not changing the culture of the business at the same time.

Kirill: And the other way round, right? If you have data-driven culture, what’s the point of it if you’re not having your decision based on that?

Emma: Yeah, exactly.

Kirill: Okay, cool. So, we talked about the business decisions being data-driven and the culture being data-driven. And out of all the work you do right now as a data scientist, what would you say is your biggest challenge that you face or you have faced in the recent past?

Emma: I think for me, my biggest challenge, because I don’t have that sort of data background, or an IT background, or a maths background, my biggest challenge was learning how to work with data and learning how to reshape it and clean it up and manipulate it and stuff like that.

Kirill: Okay. That’s a very interesting one. So how did you go about learning it? I can imagine without an IT background that could be quite challenging. What were the steps that you took?

Emma: I taught myself some SQL, so I did a couple of online courses to learn some SQL. At The Information Lab, we use a tool called Alteryx, which helps you work with data, you know, it can transform data, it can clean it up, it can do all sorts of things with data, and I had to use that for my job, but also in my spare time, I’d find datasets that I found interesting as a bit of a hobby. At one point I was training for a marathon, so I had all of this running data from Runkeeper. There’s been other datasets, I saw a really interesting film, so I’d just get film databases, just working with them in my spare time to play around with data and just to create things as well.

Kirill: Okay. That’s really cool. So you learned mostly through your own hobbies and through your own applications while having fun. That’s the best way to learn, isn’t it, while you’re actually enjoying what you’re doing. What did you learn about your running? Can you share something interesting that you learned from all that visualization?

Emma: Yeah. I kind of learned while I was marathon training that I was probably actually training a bit too hard. I learned that from the data, that my heart rate was really high and I was just pushing myself too hard, too fast. I probably would have gotten injured if I didn’t actually look at that data and go, “Actually I need to slow it down a little bit while I’m running.”

Kirill: Wow! That sounds like a sci-fi movie. It’s like you prevented your own injury. What was that movie called with Tom Cruise? “The Minority Report,” right? When they prevent crimes based on flashbacks that these people have. So it’s similar to that. You prevented your own injuries and saved yourself time and cost of the hospital bills. That’s really cool.

Emma: Yeah. It’s something that football teams do a lot with data tracking. You know, they track the health of their players and how hard they’ve been training and things like that.

Kirill: Yeah, but football teams have money and analysts that are doing that. You just did it for yourself. People should do this more often. People listening to this podcast should go and download Tableau and just look at their running, eating, sleeping and other habits and see how they can help prevent things or improve their lifestyle. This is really inspiring. It’s really cool.

Okay, that was an interesting challenge. And what would you say is your biggest breakthrough that you’ve had in your career? Obviously transitioning from something completely different to data science, that’s a huge step. Among all these things that you’ve learned, the tools that you’ve mastered, the jobs that you’ve had, what would you say has been your biggest breakthrough and biggest life-changing event that has happened in your career?

Emma: I think it was becoming a trainer in Tableau. I’m not particularly fond of getting up in public in front of people and speaking, but being a trainer and training other people in a classroom really breaks down that fear. You know, you’re up in front of people—I teach two-day courses, so I’m up in front of a group of people for two days teaching them how to use Tableau. I think that was a really big challenge for me.

Kirill: Yeah, of course that would be stressful if you’re not used to teaching. I remember my first courses that I was teaching online. Whenever I’ve been on stage I was always nervous presenting and speaking to people. So why did you decide to do that and push yourself out of your comfort zone?

Emma: It’s something that I really enjoy. I really enjoy helping other people. And training became the best part of my job, because although it’s scary to get up in front of those people, at the end of the course when someone is connecting Tableau to their own data and making really great stuff with it, that is such a great feeling. It’s worth the fear and the anxiety of getting up in front of people for that.

Kirill: Yeah. And would you say the fear is slowly becoming less with time as you do this more and more?

Emma: Yeah, definitely. I think it’s just that those first few classes are the worst ones and then it just gets easier.

Kirill: Okay. They’re the worst ones in terms of fear, but at the end of the two days, did you get terrible feedback from the people who attended or was it actually pretty okay at the end of the day?

Emma: Yeah, it was good feedback.

Kirill: So to those listening out there, if you’re thinking of doing something, but you’re stopped by this fear of the first day or the first time you do it, it actually turns out very well or it turns out much better than you think it will even for the first time. So there’s nothing to be afraid of, you just go and get started and do it. That’s very inspiring. Where can our listeners sign up for these classes? Are you doing them in London?

Emma: Yes, I do them for our customers, but I also sometimes do some of the Tableau Public sessions. So if you ever sign up for the public sessions of Tableau training, then you might get one of us as a teacher as well.

Kirill: It’s too bad you’re just doing it for clients. You should start doing this for just people who want to attend from around the area. I think that would be very cool. Maybe that’s in the future at some point.

Emma: Yeah.

Kirill: That would be very cool. Okay, so that’s definitely a big breakthrough in terms of what you’ve been doing. And what would you say is your one most favourite thing about being a data scientist and being in this space?

Emma: I think for me, it’s the constant new challenges. This industry is just changing all the time, the software gets updated all the time, you can do new things with it, so you’re just constantly learning. I think that’s what gets me going. I’d hate to be in a job where I did the same thing every day for years and years and years. This one just keeps you interested.

Kirill: Yeah, I totally agree. And I have an interesting question for you. We’ve been hearing more and more about machine learning and artificial intelligence. I’ve actually spoken to a couple of guests about this. That’s the future of where data science is going and where ultimately these data driven businesses will end up with artificial intelligence. On the other hand, what you’re doing is in a different space of data science, it’s more visualization and analytics. What do you think about that? Is artificial intelligence something that you think is going to replace Tableau and replace all analytics functions in businesses, or do you think they can exist side by side?

Emma: I think it will probably end up existing side by side. I think there still needs to be a very human element in understanding what’s going on, asking the right questions, knowing context as well. I think context is a big thing. So, yeah, I think they’ll exist side by side.

Kirill: Okay. That’s definitely some great feedback, because sometimes when you hear all these things are going on in AI, you start to think even data scientists at some point are going to be edged out. But I agree with you, I think they can exist side by side, and even if AI is going to start taking over, it’s not going to happen anytime soon, it’s going to happen decades from now. So this is definitely an exciting space to be in and there’s lots of different types of data science that you can work into. And speaking of that, you mentioned that you like to learn. Is there anything that you’re looking forward to learning in the near future?

Emma: Yeah, I’d love to learn more about predictive modelling and using data to come up with predictions for things. That’d be really interesting.

Kirill: Is there any particular model that you want to start with?

Emma: No. I mean, I work with Alteryx quite a lot and they have some predictive models in there, so I’d probably start with those and then see where I end up.

Kirill: Okay. That’s really cool. And have you used R for anything or Python?

Emma: I’ve dabbled a little bit in Python, but I’ve not looked at R yet. So they would be things I would need to learn.

Kirill: Okay. Are you excited about that, to get into a bit of programming?

Emma: Yeah. I’ve kind of learned a little bit of programming before, again self-taught, using Code Academy and things like that, and it would be really interesting to learn some more.

Kirill: Okay. That’s really cool. All right. And I’ve got an interesting question, a philosophical one. From what you’ve seen, especially in your work with companies about data-driven culture and about data-driven business decisions, where do you feel this whole field of data science is going and what do you think our listeners should prepare for to be ready for what’s coming in the future?

Emma: Interestingly, Tableau made an acquisition recently to deal with natural language processing. And I can see it coming one day a bit like Siri. You can ask Siri what’s the weather going to be like today and it will give you a reply. But I kind of hope that you’ll be able to do that with business questions of your data as well. So if you’re in a business you can go, “Are my sales better this year than they were last year?” and you’d just be given an answer.

Kirill: That’s really cool. So do you think that’s actually possible? Because you mentioned that context is very important to be able to ask these questions. Sometimes you need to know what’s going on in the business, you need to have this domain knowledge. How is that going to be incorporated? What do you think about that?

Emma: Yeah, I think the results to those sorts of questions are hopefully going to be useful, but I think, especially to start with, the kind of questions that you’ll be able to ask might be a bit limited to just kind of black and white answers. So you want a number, you want a specific value, you know, it’ll return that for you. But if your question gets a bit more complicated, then that’s probably where you’re going to need some human intervention.

Kirill: Okay. Yeah, and with time it will probably get better and better. And then we can just be creating dashboards just by saying stuff instead of actually creating them ourselves. That would be pretty cool. Okay, we’re slowly coming to the end. What would you say is your biggest suggestion or inspirational message to those listening who are starting out into the space of data science, or maybe are already in the space of data science, but want to expand into this area of analytics, data visualization, Tableau? What would you say is your biggest message that you want to share with them?

Emma: I think from my experience, it’s all about doing this as a hobby as well. If you’re learning something new in that space, if you’re learning about analytics, then do it with something that you enjoy. Find a hobby that you can maybe collect some data about and start analysing that, practice your skills in that. And also really get involved with the communities that are out there as well. I don’t think I would have learned half as much as I know without just reaching out and asking people, especially on Twitter and things like that. There are so many people who are so willing to lend some of their time to have a phone call, to answer some questions. So if you do have questions, don’t be afraid to reach out to other people and ask them.

Kirill: Fantastic. I love that advice. So, hobby and community – guys, get involved in data science with not just work, but also your own side projects and things that you’re interested in, and also don’t be afraid to ask questions on the community. Thanks a lot for sharing that. How can our listeners contact you, follow you, or find you if they would like to see how your career progresses from here?

Emma: The best way to contact me or to follow me is on Twitter. You said before, @EmmaWhyte, I’m always on Twitter so you can always reach me that way.

Kirill: Fantastic. And can our listeners also connect with you on LinkedIn?

Emma: Yes. Yeah, I’m on LinkedIn as well.

Kirill: And you mentioned you have a blog. What’s the blog address?

Emma: It’s www.womanindata.co.uk.

Kirill: Okay, that’s really cool. And what do you talk about on the blog?

Emma: It’s mainly Tableau. I do a weekly “Workout Wednesday” with Andy Kriebel from The Information Lab as well. It’s every Wednesday on mine or Andy’s blog, we’ll set you a challenge and you have to try and remake what we send out in Tableau.

Kirill: Okay, fantastic. Guys, make sure to follow Emma on Twitter, it’s Emma Whyte with Y-T-E on the end. Thanks a lot, Emma. And one more question for you today. What is one book that you can recommend to our listeners to help them become better at what they do?

Emma: I would recommend “Signal” by Stephen Few.

Kirill: Why is that?

Emma: It’s all about how to find those signals from noise in data, so going on that journey of uncovering some really interesting facts in a dataset.

Kirill: Interesting. I haven’t heard of that book before. Is it a recent book?

Emma: It is quite recent. It came out in 2015.

Kirill: Okay, cool. “Signal” by Stephen Few. All right, guys, check it out. Once again, Emma, thank you for coming on the show and sharing your story and some insights with us. It was a great pleasure having you on.

Emma: It’s great to be on. Thanks very much.

Kirill: So there you have it. That was Emma Whyte from The Information Lab. I hope you enjoyed today’s podcast. Quite a lot of interesting insights. Personally my favourite was the way that Emma leveraged her prior experience in what she does now. As Emma described, she came from a background in History and Politics and she never actually studied anything technical. Nevertheless, she found a way to leverage her education, her degrees in what she does now, and it wasn’t about what she studied, it was about how she studied. She focused on her passion for getting to the bottom of things, for being analytical, for understanding the truth behind things. And that’s what helps her be successful in data science.

So think about your own background, whether it’s technical or non-technical. Maybe there’s something else that you’re missing, something that you were always good at or were always passionate about that you are not yet leveraging in your career in data science and maybe that can give you your next push forward.

And of course, make sure to follow Emma on her Twitter, it’s @EmmaWhyte – be careful there, it’s a Y, not an I – make sure to follow Emma there, connect with her on LinkedIn and check out her blog, it’s womanindata.co.uk. And of course, you can get all these links and the show notes and the transcript at www.www.superdatascience.com/83. And on that note, we’re going to wrap up today, very excited for having you here, thank you so much. And I look forward to seeing you next time. Until then, happy analyzing.