Kirill Eremenko: 00:00
This is episode number 387 with Data Leadership Coach, Lillian Pierson.
Kirill Eremenko: 00:12
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.
Kirill Eremenko: 00:44
Welcome back to the SuperDataScience podcast everybody. Super excited to have you back here on the show. Today’s episode is with a special guest, Lillian Pierson. So Lillian Pierson is a data science trainer, and she has helped lots of people to improve their careers in data science. In today’s episode, we spoke mostly about data leadership and what that means. And this episode is specifically for you if you want to add data leadership to your data science career. So some people prefer to improve, constantly improve their coding excellence, and that’s totally, absolutely a great career path, but others want to become leaders and want to lead organizations, want to lead people and want to add that leadership element to their career. So if you’re in that second group, then this podcast is for you.
Kirill Eremenko: 01:44
In today’s conversation, we discussed some interesting things. So for example, you’ll find out the four superpowers of a great data leader, what those superpowers are, why, what they bring to the table and what that means for you. Why becoming a data leader is important in today’s world and what it means for your career growth and what it also means for your organization. What kind of challenges you’ll be addressing. And another thing we talked about is how COVID is impacting the data landscape. Lillian has identified four ways that COVID is impacting data landscape and what you can do with your data science career, depending on which part of the world you’re located in.
Kirill Eremenko: 02:34
So there we go. That’s the main parts of this podcast. Of course, we’ll talk about lots of other things along the way, and I hope you find this conversation exciting and interesting. Data leadership is one of those things that can boost your career in data science for sure. So off we go, without further ado, I bring to you data science trainer, Lillian Pierson.
Kirill Eremenko: 03:02
Welcome to the SuperDataScience podcast everybody. Super pumped to have you back here on the show. And today we’ve got a very special guest, Lillian Pierson, calling in from Koh Samui, Thailand. Lillian, welcome. How are you?
Lillian Pierson: 03:17
I am doing wonderful. Thank you for having me on the show, Kirill.
Kirill Eremenko: 03:21
It’s a pleasure. Tell us a bit about what you do. What is your business about, when did you get started?
Lillian Pierson: 03:27
Okay. So I started my side hustle back in 2012, which was called Big Data Gal. And then I quit my day job in 2013, and I kind of didn’t know what I was doing. I just kind of knew it was in the data space. And then, so I just worked, I wrote books for Wiley and I did courses for LinkedIn Learning and I got a lot of work just from my social media stuff. It would just attract B2B clients, which was great. But then I guess about three years ago, maybe four years ago, I kind of started going into the B2C space with my business and helping individuals. And now I’m pretty much, I’m firmly rooted in that. So I get B2B deals from just organically, but my main focus is on helping data professionals become data leaders, be it through becoming data leaders in their organization and a company where they work or actually stepping out into their own, in their own data business.
Kirill Eremenko: 04:45
Okay. Very interesting. And I want to talk about that in a second, but let’s rewind back a little bit. So where did you get the skills? What’s your background? You’re a data coach now, you help people become data leaders. Where did you get the skills for that? I’m just curious what you did as a data scientist or in the data space before your business, do you mind just giving us a quick snapshot so we paint a better picture.
Lillian Pierson: 05:11
Yeah. Okay. So I’m a professional engineer and I have an environmental engineering degree. So I did a lot of like hydraulic modeling and then I just ended up moving into analytics and spatial data science and Python. And it was back in 2012. And I just realized that data was my calling when I had that opportunity. So-
Kirill Eremenko: 05:35
Awesome. Awesome. I had a similar story. For me it’s, I mean, timeline. I think 2012, I started my work at Deloitte, but then about 2014, 15, I realized like what you realize, data is my calling. So that’s very cool. Let’s talk a bit about your recent Winning With Data, so you have quite a lot of things that you have created assets and kind of like giveaways free things and paid things that help people in different roles in data science. Let’s talk a bit about Winning With Data. This is, I think is becoming your flagship product. What is it all about?
Lillian Pierson: 06:16
It is about helping data professionals transform into data leaders. So that well, one, so that they can feel more satisfied with the work that they’re doing, knowing that the work they do is actually creating value in the organizations they lead. Also, help them earn more money, which everyone enjoys making more money as it raise quality of life.
Kirill Eremenko: 06:39
I like the pre-emphasis of it. Could you tell me a bit about like, where did that idea come from? I was reading through it and at some point you say that your organization needs a data leader and nobody else, and you’re the right person to fit the bill, right? They’re not going to go and find a data leader unless, they already have is chief data science, is a chief data officer, very likely it is the data scientist role to be that voice, to be that data leader. So I found that a very insightful idea and that way every single person who’s doing data analytics work right now should be considering themselves as the data leader.
Kirill Eremenko: 07:19
It’s hard to kind of imagine or how to get that perspective, but nobody else in the organization knows data and data science as good as you, right? You are the expert, subject matter experts. So you need to be the leader as well. How did you come up with that idea and then let’s talk about your four superpowers of great data leaders. I love that and I’d love to get your thoughts on it.
Lillian Pierson: 07:42
So I am on a mission to help data professionals become data leaders and equip them at a low price point with the skills they need to start managing the profitability of the data projects that they’re leading and in start managing the relationships with the executives and be able to do that in-house. So you don’t have to hire a million-dollar per year consulting company to come in and do a half-ass job, you know? Okay. So I’ve worked, I’ve done data strategy, work behind some of those companies and I’ve seen what they deliver and I know the price tag associated with that. So that’s the whole thing behind why I created Winning With Data and why I got into this. So I guess you could say democratize data leadership.
Kirill Eremenko: 08:28
That’s a very good point. Yeah. So what is it that you offer? What is data strategy, data leadership to you? When a client comes to you, what normally is the problem that they have, the pain that you help them solve?
Lillian Pierson: 08:46
Okay. So when I do B2B, I’m usually working with either data leaders or business leaders. So for business leaders, a lot of times they need that data literacy training in addition to process. So I would educate them on the different tools, technologies, people, and processes that are used to create value, business value from data. And then we would look at creating a plan we would start working through. Okay, what is the most efficient use case for your organization, given your current setup and help them plot out, okay, what are next steps? How do we take what you’ve currently got and synthesize it together and so that you can implement this use case and start generating profits with your existing resources?
Lillian Pierson: 09:45
You see, you do need to understand how data, these data science models work, how data engineering works, what are the caveats, how everything fits together in terms of the technologies and the tools, but you also have to understand consulting and business and project management and executive relationships and stakeholder management and all of these things that kind of go into turning that raw technology into something that actually produces value profit.
Kirill Eremenko: 10:27
Okay. Okay. I understand. I understand. Okay. That’s valuable. And so how can individual people, so you have these four superpowers of a great data leader. Maybe if you could talk a bit about that, what are those strengths or focus areas that an individual data scientist needs to develop to become a data leader?
Lillian Pierson: 10:50
Sure, absolutely. And before going into that, I wanted to mention, because before our call, you mentioned about coders, about data scientists that they do just want, they like to code. My husband, that’s what he wants to do. He doesn’t want to mess with any of them-
Kirill Eremenko: 11:08
In some cases, people don’t want to become leaders, they want to just continue developing the technical excellence.
Lillian Pierson: 11:14
Yeah. And that’s great. We need that. People want to feel that they’re making a positive impact on the world, right? However, some people they want to make that impact using technology. That’s just what they want to do. They want to code. They want to just work with the technology part of it. They don’t want to deal with the people. Other people want to scale the impact they’re making with data across people, right? So this Winning With Data program is actually it’s for data professionals who want to scale their impact across people.
Kirill Eremenko: 11:53
Ok.
Lillian Pierson: 11:53
And so the four are, going back to your question, the four superpowers are data strategy, project management, thought leadership, and organizational leadership.
Kirill Eremenko: 12:05
Okay. Why did you identify those four specifically? Just in a nutshell, each one of them, what does each one of them bring to the table?
Lillian Pierson: 12:14
Okay. Well, why I actually broke it down that way actually was because I just, like I do most things I reversed, I backed into it. So I went and I looked at chief data officers and what they’re looking for when they hire a chief data officer and I made a long list of skills, competencies and all that. And I categorized them. And when I did that, it was very easy to break it into those four main superpowers. So data strategy is of course, building a technical plan for your data projects to manage, to make sure they’re profitable. And then project management is actually managing the implementation of that data project and managing, in terms of managing the people and the stakeholders and everything to do with project management.
Lillian Pierson: 13:07
Thought leadership is also a major expectation for data leaders, for chief data officers, for head of data science, all of these things, because they are looking, organizations are looking for people who can inspire and motivate and enhance the culture across the organization because without that data, that passion for data baked into the corporate culture, what you find is you could build tools, but people don’t want to use them. So that is… Okay. So there’s thought leadership and thought leadership, the way I structured it in Winning With Data, there’s two ways. So one is in-house where you’re developing that culture, that thought leadership within the company that you work at. And then the other is on LinkedIn, because you can get tons of opportunity.
Lillian Pierson: 14:05
Like my client, I just did a call with them, one of my clients. And he said, he got from my coaching program, he got a $96,000 contract straight off of LinkedIn. And I’ve made hundreds of thousands of dollars off of LinkedIn, just because just from showing up there as a thought leader, it drives opportunities. So maybe there’s too much, too many politics where you work right now and they don’t want to elevate you into a data leadership position. That’s fine because if you show up on LinkedIn, you can probably attract that opportunity from elsewhere so long as you show up as a data leader.
Lillian Pierson: 14:47
So this program takes data professionals who are doing implementation work, or maybe data leaders, but they want to become better data leaders. And it helps them start showing up both inside their organization and online as a leader to start attracting these conversations, to start getting them invited to the leadership table in their company, all of these things, and then gives them the skills that they need to get started with project management and also data strategy.
Lillian Pierson: 15:21
And then the last one is leadership, which is then more about culture, about relationship management and community, professional organizations, because companies also want to see that you understand how to support business leaders and create that culture. And that you are a presence that someone that you can be the kind of, what do they call it, the champion or the poster child of we are data we’re heading the data space for that whole company. They want you to be able to show up for their company and basically be… Evangelize, I guess they say. Evangelize like our cultures, our value, and start attracting more and more good talent and also start cultivating more and more good fellowship within the ranks of the organization.
Kirill Eremenko: 16:21
Okay. Wow. Fantastic. So just to recap, we got data strategy skills to be able to come up with a strategy or what tools are we going to use, where are we going to focus on, how are we going to grow the team, how are we going to integrate data across the business. Project management skills to put all that strategy into action, supervise, make sure that nothing falls through. Thought leadership skills to spread data literacy around that, within the organization to spread data literacy, to help other people understand, not be afraid of data to create a data-driven culture, to show people that data is here to help. And then on the other hand, thought leadership outside in order to create additional opportunities for yourself in case the ones inside the company are not sufficient. And finally leadership, or you also call it organizational leadership. That’s in order to have those communications with important stakeholders to lead teams, and basically that’s a necessary component of any leadership role.
Kirill Eremenko: 17:20
Great. So that’s very important four components, four superpowers, as you call them. On your website for Winning With Data, you give a great illustration of what happens when somebody develops those four. And you basically say that allows you to move from an analyst or senior analyst level, which includes roles like data scientists, BI specialists, data analyst, data engineer, business analyst, and so on with a salary of between 68,000-113,000, that’s the average salary range. And that allows you to move to roles like data leadership level roles like chief data officer, head of data science, VP of analytics, and the average salary range there is from $190,000-$236,000. That’s huge. That’s double, twice, you can double your salary.
Kirill Eremenko: 18:10
Can you tell me, please, you gave us an example of a client that who had, I think you said $96,000 contract. Do you have any examples of successful clients that you help move from that $68,000-$113,000 range to the 190-236 salary range by helping them not with more coding skills but with this data leadership piece?
Lillian Pierson: 18:36
I have worked in career coaching for data professionals and help them get promotions to leadership positions. And one of my coaching clients for business coaching, actually within the first module, she got a 30% raise all the way up to past six figures just by doing the market research portion. So I have helped people definitely increase their earnings, but this product, Winning With Data, has actually just came out last month and then I’m still doing upgrades. So I have 21 Data Career Quick Wins, I call them. But this week I’m actually kind of putting the icing on the cake here and I’m transforming Winning With Data into a 30-day challenge with 30 quick win challenges.
Kirill Eremenko: 19:33
Got you. So can you tell us an example of one of those challenges? That sounds like a really fun thing to do. I love those 30-day challenges approach.
Lillian Pierson: 19:41
Well, okay. So one that’s super easy is to take the tutorial for, I just teach people how I 47 times increased the number of search appearances like at on LinkedIn. So how do you optimize your LinkedIn profile for search? I’m giving out captions like LinkedIn caption templates, but then you go through the case collections. Cases, like case studies, use cases. And you pick one of the cases that you feel is most powerful and you just fill in the blanks within the caption of like, you just fill in the details from that project and you share it as a LinkedIn post and you start getting that traction. So you start showing up as a data leader on LinkedIn, but you don’t have to figure all that out. I give you the cases, I give you the caption, you just kind of plug and chug, put it out there and start and just continue. So you can just reuse those. Yeah.
Lillian Pierson: 20:43
So one of the challenges is I teach people how to create a board, a project management board, where you have all of your responsibilities mapped out and you actually put it up in your cubicle or on your wall with Trello cards. Once you can better organize the work you’re doing, but also, so you can then map out for everyone who walks by that you actually have a management plan for those, you have milestones and you know the targets and you’re actually tracking all of the bigger picture things.
Kirill Eremenko: 21:17
Okay. Okay. That’s really useful. So 30-day challenge and it’s kind of like a self-paced challenge, right? You take it on and you do the 30 days and what can somebody expect after 30 days?
Lillian Pierson: 21:33
Yeah. So you can do it in 30 days. That’s the challenge, but you could just reuse all of this stuff. There’s no time limits. It’s all reusable. So in 30 days you definitely should have people coming to you asking for your help, asking for your input.
Kirill Eremenko: 21:49
Very cool. Very cool. Awesome. It sounds like a great addition to somebody’s portfolio of skills. Like if somebody’s being focusing a lot on just technical all the time, and then they want to, as you said, leverage the power of making an impact by helping people, by influencing people. I think this could be an interesting addition to look into and thank you for sharing some of the tips that I think that could be valuable. Another thing we wanted to talk about was impacts of COVID on the data industry. So you mentioned you had some ideas around that. Do you mind sharing them?
Lillian Pierson: 22:38
Yes. Okay. So I have this kind of broken down into four ways that COVID has impacted the data field and how you can pivot your career based on basically what you specialize in and also what market you’re in. So Eastern market versus Western market, because COVID has changed everything. In terms of the impacts, one is that small and medium-sized businesses are going out of business or have gone out of business because they lost all their contracts. So when the demand does come back for data implementation people for work, there’s going to be a lot of it, but then there’s not going to be as much supply because the small to medium-sized businesses had to lay everyone off. So the small to medium-sized businesses are getting less client work. These are just like the things that have happened.
Lillian Pierson: 23:38
So if you are a data professional and you think, okay, I lost my job. So I’m going to start an online course. This is a bad time to do that because most companies, most small to medium-sized businesses don’t have enough work, but they have an audience that they’ve developed a rapport with. So they’re going gangbusters with B2C offers. So it’s going to be really hard to get traction and particularly if you don’t have that sort of experience to like get traction for a course right now with everyone else on the market, that was a small to medium-sized business, not having B2B clients. Now they’re flooding the market with B2C offers. You see what I’m saying?
Kirill Eremenko: 24:23
Uh-huh (affirmative). So like, it’s not a good time to create an online course.
Lillian Pierson: 24:27
If you don’t have an audience. Number three is almost all knowledge businesses have moved online, perhaps permanently. Okay. And so this is going to affect people differently, depending on if they’re in Western society or Eastern societies. And number four is data implementation work is super easy to outsource online. Okay? So what COVID really did was it put a lot of smaller businesses out of business. The bigger ones moved online and that removes a huge barrier of entry for outsourcing, right? Like why hire someone for $200 an hour if you can hire someone that can get it done for $30? Right? So the barrier of entry was where we do everything locally, but that’s gone away now.
Kirill Eremenko: 25:20
Okay. Fair enough.
Lillian Pierson: 25:22
Okay. So how to pivot in response to these? So the good news is that all of these changes represent opportunities for all of us, no matter what. So it’s not like it’s just a matter of being prepared, which is why I had put this together, this outline together in the first place. If you’re from an Eastern country, I would be expecting a lot more opportunity in terms of data implementation work coming from Western countries, because now they’ve moved online. So the thing to do now would be to work on how can you secure those jobs. One suggestion is make sure your English is really, really good. Work on the branding so that your branding is representative of expectations of a Western type clients. Also, anything you can do to market and upsell your security, your emphasis on security, because that’s going to be a barrier of entry. These are three things you could work on to secure more contracts from Western businesses.
Lillian Pierson: 26:32
And then if you are a Western, if you’re a data professional and an employee in the Western economy, there is a lot you can do. So you can double down in your area of expertise. So instead of being like the jack of all trades, I would suggest getting really, really niched and specialized. Also, you want to choose. And if you’re a data professional, choose a niche that isn’t easily outsourced. So I came up with two ideas for people. There is, of course, I’m going to say data leadership, data strategy, because my program is Winning with data. But strategy, in most cases, if you’re hiring someone from strategy, you’re not going to low-ball that, you’re not going to try and go get them. You’re not going to like, you really, really want to make sure you hire the right person.
Kirill Eremenko: 27:24
Bite the bullet, get the right person. Even if it costs you more, costs a lot because in then it’ll cost you more if you hire the wrong person.
Lillian Pierson: 27:34
Say you’re a data professional and you specialize in managing remote teams for your data projects. That’s something that is very useful for Western because they can then trust you. Okay. You can manage all of this outsourced work and we will retain you in-house. Right? So that was something that I thought would be helpful to people who are data professionals right now.
Kirill Eremenko: 28:01
Depends on which economy you’re in or which part of the world you’re in as you would focus on different things. That’s a very cool breakdown. Awesome. Okay. And as we’re slowly coming to the end, I wanted to ask you from the training that you’ve done and how you have worked with different companies, different people, you’ve seen this industry, where do you think the whole industry is going, especially with like, with this COVID in mind, what do you think the data science landscape will look like in about maybe two or three years from now?
Lillian Pierson: 28:36
That’s a really interesting question. Five, 10 years, a long time ago, it was all like, you have to know this most advanced, latest breaking machine learning thing, or else you’re not really worth anything, which was why your courses were so great because it made it easy for people to learn these things that they needed to know in order to feel, to be validated in the field. There was this tremendous pressure of like, you have to keep on the latest, greatest thing. But now I see a lot of conversations about that’s kind of gone away where people are like, yeah, if you don’t want to be doing deep learning, training deep learning models for the rest of your life, that doesn’t mean you don’t have value to add. We’re starting to have, it’s not just me basically that’s saying, hey, there’s other ways we need to be adding value in creating in our data work.
Lillian Pierson: 29:35
So I think that that’s just going to continue on in that, it’s just going to be, especially also with AI ethics and data privacy, and how do we bake AI ethics and transparency into the data applications we’re building now, particularly with the contact tracing.
Lillian Pierson: 29:54
And gosh, I’ve seen so many grassroots movements in terms of data literacy now in data strategy, in data ethics and not just like legal people talking this stuff, but actually developers. And now there are people, coders and application developers, data professionals of all skill level coming together to have these conversations themselves and decide for themselves like, how do we address this? Do we self-organize into grassroots movements? Do we assign power to the UN? And I love that because this is our lives. There’s seven billion people and there shouldn’t be 10 or 20 people on a special council deciding the fate of how these data technologies impact humanity. It needs to be much, a wide forum where we can all contribute and be heard. So-
Kirill Eremenko: 30:54
Awesome. Awesome. Thank you. That’s a great description and hopefully, yeah, we will all be able to participate more on this. And I liked your idea about the diversification of different skillsets or people diversifying their skill sets across the data. That’s I think is also a very useful thing to keep in mind for people for the future. I want to say thank you for coming on the show and it’s been a pleasure chatting. Before I let you go, what’s the best places for people to connect with you and get in touch?
Lillian Pierson: 31:30
LinkedIn. So I’m Lillian Pierson on LinkedIn, and then I do a little behind the scenes in Instagram stories, but mostly my big hangout is LinkedIn.
Kirill Eremenko: 31:40
Got you. And the product we were talking about is winning-with-data.com.
Lillian Pierson: 31:48
Winning-with-data.com.
Kirill Eremenko: 31:52
Okay. Awesome. So there we have it everybody. Hope you enjoyed this podcast and got some valuable takeaways. My personal favorite part was when we spoke about data leadership and why it’s important to position yourself as a data leader and what huge impact you can have in your career. Indeed, who else is going to take on these leadership roles? Who else is better positioned in the organization than the data scientists and members of the data team. And I liked Lillian’s advice of positioning yourself as a thought leader in data, both internally within your organization and externally outside of your organization.
Kirill Eremenko: 32:35
So yeah, I’ve heard that thought several times now. This is something we talked about with Kate Strachnyi on her LinkedIn Live a few weeks ago. It’s definitely a recurring ideas. So something to keep in mind about positioning yourself as a thought leader within the organization and outside the organization.
Kirill Eremenko: 32:53
As usual, you can find the show notes at SuperDataScience.com/387, that’s SuperDataScience.com/387. There, you can find all the materials that we mentioned on this episode, including links such as the URL to Lillian’s LinkedIn, make sure to connect with Lillian. If you’re interested in her product, Winning With Data, you can find it at winning-with-data.com or just find the link in the show notes as well. On that note, I hope you enjoyed today’s podcast. And I look forward to seeing you next time. Until then, happy analyzing.
