SDS PODCAST EPISODE 16 WITH RICHARD HOPKINS · Richard: PwC. That's correct. Kirill: Yeah, so...

Show Notes: http://www.superdatascience.com/16 1

SDS PODCAST EPISODE 16

WITH RICHARD HOPKINS


Kirill: This is episode number 16, with Director at PricewaterhouseCoopers Richard Hopkins.

(background music plays)

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.


Hello and welcome to this episode of the SuperDataScience podcast. I'm super excited to bring you today my good friend and mentor, Richard Hopkins. Richard and I go way back. We met back in 2012, when we were both working at Deloitte, and we've had a hell of a ride since. We worked on this massive project for months and months and months where we were flying in and out of this remote location, and we really got to bond along the way. And in this episode, we share our experiences about two things. First of all, we talk about data and how Richard, being the director at PricewaterhouseCoopers and running a lot of the business transformation that they do for their clients, and the business operation side of things, how Richard uses data in his day to day role, and what insights he can share from his position with you about how data drives organisations.

Also in this podcast, we talk about mentoring. So Richard has been my mentor for several years now, and I have learned so much from him, and in this podcast, we'll answer some questions about what mentorship is, how to be a


mentor, how to be a mentee, how to look for a mentor, and what this relationship entails.

And finally, there's a special surprise for you in this episode towards the very end, Richard will reveal a position that he's currently recruiting for. So if you're interested in developing a career in data science, this might be your opportunity. Especially if you're located in Sydney or Melbourne in Australia, and you're interested in the space of consulting. So there's a very interesting position, or several positions, that Richard is recruiting for. He's building up this team, and he actually needs people with data science skills. So if you want to jump straight ahead to that before you listen to the podcast, feel free to do that. This is going to happen somewhere around the 57th minute of the podcast. Or feel free to listen to the podcast to understand exactly what we're talking about, understand the background, and definitely check out that opportunity. Richard shares some contact details as well. So if you're in those cities and you're interested, definitely get in touch with Richard. This could be a great opportunity for you.

So all in all, this is a very exciting episode. It was so much fun to catch up with one of my good friends and mentors, and I'm happy that you're going to be part of this conversation. Without further ado, I bring to you Richard Hopkins of PwC.


Welcome everybody to the SuperDataScience podcast. Today I've got my friend and mentor, Richard Hopkins, joining me for the show. Richard, welcome to the show. How are you today?


Richard: I'm well, Kirill. Thanks for having me.

Kirill: Thank you so much. And where are you located right now? I'm in Brisbane, Australia. Where are you?

Richard: I'm in Sydney, Australia, on a very grey, overcast, miserable day.

Kirill: Yeah. Yeah, what's going on with the weather there? It's so sunny here in Brisbane!

Richard: It's sunny one day, and cloudy and cold the next.

Kirill: Yeah, yeah, I saw a few photos online. And just to quickly introduce Richard, we go way back with Richard Hopkins. We worked together at Deloitte, and then kept working together, even when we weren't on the same project, and since then, Richard, you moved to Sydney. What took you down there to Sydney?

Richard: Yeah, so I got offered the national lead position for their Operational Improvement team. So I head that up in Sydney at the moment.

Kirill: At PwC, that's right?

Richard: PwC. That's correct.

Kirill: Yeah, so Richard made the move from Deloitte to PwC, and now he's building a huge team there, in this area. And what exactly do you do, so our listeners can relate a little bit more?

Richard: In layman's terms, I basically make companies more profitable by improving operations. And I tend to work with companies that are in a distressed environment.


Kirill: Yeah, that’s exactly what Richard is best at, taking companies out of a bad place and putting them into a good one. And that actually is a good segue to how we met. Can you elaborate on that a little bit more? Because I was new to Deloitte, so I just joined Deloitte, and I was put on a project with Richard. What were your impressions at the time?

Richard: Yeah, so we worked together on a project where a very large global company had bought another large global company. And about 12 months after the merger things weren’t going so well, so our team got brought in to try and remedy that. And one of the key missing elements of that team was a data scientist. So I requested that we get a data scientist and they put you forward.

Kirill: Yeah, I clearly remember that time. This was back in 2012, everybody. I was looking through the profiles of the people who were working on the project, and specifically of Richard, because I was going to work with him, and I was looking at your photo and I was like “I don’t like this guy.” (laughs) Really, the first impression was like “I don’t want to work with him.” Remember we had that first phone call, because this was a remote location and you were already at that location? We had that phone call and from there we started to get along.

Richard: Yeah, it worked well. 13 months working together and we grew that project and had a really successful outcome, so it was great.

Kirill: Yeah, crazy. And we really got to bond. It was the first time when through a work environment, I actually got to bond with someone. I remember out of those 13 months, for 7 of them, we were flying in and out, like one and a half hour flight on Monday to the location, and one and a half hour


flight back on Friday. And all those stories at the airport, getting there early to sit in the lounge and stuff like that, having a chat – that was so much fun.

Richard: Yeah, it was great.

Kirill: Good times. So we really bonded with Richard and we’ll talk more about the role that mentorship has played in our relationship and in our career growth further down in this podcast. But for now I wanted to start with the obvious question. For those of you who haven’t heard or don’t know Richard that well yet, Richard is very good at what he does in the sense of restructuring companies and bringing them to a good state where they can either be sold or they can continue operating. However, Richard isn’t actually a data scientist. He specifically asked me to say that, to stress that point. That he isn’t a data scientist, but at the same time he knows how to work with data, and he knows how to put the right people in the right places to do that data work for him and then he takes action based on those decisions. I want to get a little bit into that and we’ll slowly get into that. So can you tell me, and for the benefit of our listeners, when did you first become aware of the fact that data is becoming so important globally and in the way that businesses operate that you started to implement or use data in your business decisions?

Richard: Yeah, sure. It was probably that project that we worked on, Kirill. I was reporting to senior management in Australia. I was looking at the financial numbers and the reports that management were producing from within the company, and I could see a very large disconnect between what was being reported and what was actually happening in operations on the ground. And you mentioned working from that remote


location. That gave me the visibility on what was happening on the ground. So I saw a very dire need to get the data correct and in a format that actually resembled the true picture. That was a real catalyst for me to start pursuing that work flow that basically links data and operations together.

Kirill: Yeah, totally. And would you say that had an impact and it was a worthwhile thing to do?

Richard: Absolutely. It changed the dynamics of that project. I mean, it didn’t necessarily change what I wanted to do on the project, but it gave me the support and the ammunition to have robust discussions with management to make change happen. Not having that data previously, I was acting more on hearsay rather than acting on facts and they could always defend their position. But once I had the data that showed the true fact, and in a way that matched up with, or supported, what was happening in operations, that’s when changes could happen.

Kirill: Very, very true. I remember some of those conversations. I sat in on some of the conversations where Richard was actually presenting to global CEOs of this billion dollar company. This is very, very impressive. And a lot of this stuff we can’t mention – the name of the client and so on – but still to this day I remember the very tough conversations because there were so many parties involved, including the acquired company and the acquirer and different executives in the company. So data definitely helped to back those decisions. And would you say, Richard, that at times—because from what I was hearing and seeing, I would think this was the case. But from your position, would you say that at times, what the data was showing was very


controversial to what the executives knew and what they were standing by? And you had to actually convince them that they were wrong. Was that the case sometimes?

Richard: That’s right. It actually was an interesting cycle. The first meeting we had utilizing the data profile, and you probably remember this, the data didn’t quite show the right outcome to what was happening in operations and we had to spend a lot of time changing the data. So at the first meeting we had the stakeholders, certain parties definitely had the upper hand. The second meeting we had with the stakeholders, when we’d had a couple of weeks to structure the data in a way to make it very usable and it actually resembled the true position, the tide turned in terms of the negotiation power. And also, the overall outcome for all stakeholders became a lot better because everyone was a lot more informed. That was a real eye-opener. The data was saying one thing, operations was saying another, and it took quite a bit of a process to get those two to align.

Kirill: Yeah, that’s a great example. And I like that you mentioned that the outcome is better for everybody when you see the truth. Data doesn’t lie. Sometimes consultants go into a company and they make stuff up and they hypothesize and so on. But when you look at the data, the bottom-up approach, it just doesn’t lie to you.

Richard: Absolutely. And I think that’s a critical point and hopefully we get to talk about it more, is how the traditional top-down approach that consulting use and their methodology matched with the data scientists’ bottom-up approach, if used properly, can be extremely powerful.

Kirill: Yeah, we’ll definitely touch on that more. But for now I just wanted to go into a bit more detail into that. I would like you


to give our listeners a generalized example of all of this that we’re talking about, about data and operations, and how that changed the stakeholders’ view. I understand this is all sensitive information, and we cannot disclose a lot of stuff, but at the same time out of the things that you know, maybe you’ve done other projects with other companies, and maybe you can generalize an example for us just to get a feel for what you mean when you say that data really gave you that extra view or other perspective on what’s happening in the operations and it really clicked when you started looking at the data.

Richard: Sure. So one example that comes to mind was a global airline that I worked with for approximately 12 months. They were having some working capital issues. And for the listeners out there, working capital is basically how do you get as much cash as you can in the bank rather than spending it and throwing it out the door. So it’s a combination of debtors, creditors and inventory.

They were having a huge working capital issue and they thought that they had to go out and collect cash faster off of debtors. They thought that was their solution because their debtors were over the standard 30 days. And when I undertook the analysis, it actually wasn’t the fact that debtors were their problem. It was very clear that they were paying their creditors too early. It was also very clear that they were paying their creditors too much. And it was also very clear that they had way too much unnecessary inventory. And the only way that I was able to do that was having the data tell the story. I actually used your colleague – I think you remember Anna – on that project.

Kirill: Oh, yeah.


Richard: So again, I partnered with a data scientist to come up and help me find the solution with that project because what the company thought was the issue was very, very different to what the actual issue was.

Kirill: Fantastic. That’s a fantastic example. And I like that very capitalistic approach or term when you said that they’re paying their creditors too early. You’d think that you’d want to pay your debts back really quickly. But if you have payment term of 60 or whatever days, why pay earlier? That should totally make sense to a lot of people. That’s something to abide by.

That’s a great example, and that’s a good introduction to another question I wanted to ask you. How do you feel about working with data through the power of the data scientist that you’re working with? Obviously, with your background and with your focus on operations, you just cannot afford to sit down and learn all those tools, and learn your Python, R programming, and you don’t necessarily need to. Because you have this access to all of these people who already know these things who can do the job. So what would you say about how you feel about working with data scientists in one team and managing them and getting the most out of their interaction with the data that you supply them?

Richard: Sure. I’ve just hired a data scientist in my team and the reason why I specifically hired this data scientist more than going and working with their data science team within PwC, which there is a whole data team, the key difference and value add that he brought that traditional data scientists didn’t, and I think you learned this as well, Kirill, and this is the skill that you have now, is understanding the operations of the business or the situation that you’re doing the data


analysis for. I think sometimes data scientists tend to go straight into the analysis. They get an answer and they think that’s the answer because the data said it was, and they haven’t relayed it back to the operational environment. And they also can’t explain how to use that data in an operational context. I think that was the best data scientist I’ve worked with are certainly the ones who understand how to use the data and make an operational decision.

Kirill: That’s some valid advice for those of our listeners who are in executive positions or managerial positions similar to Richard’s where you’re not the person crunching the numbers, you’re not the data scientist, but you are working closely with data scientists. I totally side by that advice, that it is very important to make sure that you create an environment where your data scientists or analysts are immersed into that domain and knowledge that the business problem at hand presents. For me, it was a make it or break it on that project we were working with. I wasn’t just crunching numbers. I was actually flying over there for 7 months every week to immerse myself in the operations of that business to understand it better, to uncover those things that sometimes data cannot show you, but they help you deal with the data much better. Thank you very much for that advice. I’m sure a lot of our both data scientist listeners and managerial listeners will find it useful.

Speaking of domain knowledge, how do you personally go about developing domain knowledge? When you’re on a brand new project, you just started with a company, what are your first steps to understand that intrinsic environment in which the business operates?


Richard: It’s a combination of listening to the team and having those stakeholder meetings and stepping through the end-to-end process. Sometimes you look at the whole business, which is generally what I do, but other times you look at very specific aspects of the business. So it’s just getting an end-to-end view of the operations and then it’s going through the systems themselves and seeing what the numbers are telling you. So for me, I very much work with the accounting system, so I look at the profit and loss, balance sheet, cash flow, and try and link up what the system is saying to what the people in those meetings told me about operations. Again, it depends on what aspect of the business you’re looking at.

Kirill: Yeah, solid points there. I think it’s probably a bit different to the way I go about getting domain knowledge because obviously you’re in more of a position where you deal with executives, and as a data scientist I deal with people more. Also executives sometimes, but now that I’ve got some experience and I’ve been leading projects, but when you’re just working on the projects it’s more about sitting down with the people and watching them work it. I remember at one of my previous jobs after Deloitte, I was actually sitting down with people to—you know how sometimes when you call into an organization they say “This call is being recorded and monitored for training purposes,” and stuff like that? So I would sit in on those calls that are being recorded and monitored just to understand how the business operates. You get some very powerful insights out of that.

Richard: One thing that I tend to notice is that new recruits, whether it’s a data scientist or an accountant or consultant, the listening skills of those people tend to be not lacking, but


not to the level that they need to be to really obtain the information that they’re getting from the company or the stakeholder. So I guess one bit of advice when you are starting out on projects and trying to obtain as much information is try and ask as many questions as you can. The ratio should be 70% of them talking, 30% of you talking. That’s just my rough ratio but really, really listening to those people on the ground and trying to help them understand what their issue is by asking the questions because quite often, they’ll do the problem solving themselves on the spot. It’s really crucial, and it’s something that quite often new recruits to the profession lack.

Kirill: Yeah totally, and a very good point there as well. It stands in line with the saying “Those who know don’t speak and those who speak don’t know.” If you know what you’re doing, then you’re not going to be talking. You’re going to take some time to absorb more information rather than just start talking at the time. Oh, yeah, now I remember. (laughs) This brings back memories.

Richard was very politically correct just now, being very vague who he’s talking about but actually I made that mistake one time. We had a call on that project and I was talking to the CFO of that company directly and reporting on something, and I was just blabbering on and on and I was just making these assumptions as I went on and the CFO just went like, “No, that’s wrong.” I was in this situation where I just said too much and I couldn’t take it back, and then afterwards you told me “Everything was good but you should talk less sometimes and listen more.” Do you remember that?

Richard: I certainly do.


Kirill: Yeah, I definitely learned from that a lot. All right, so before we move on to the really cool part of the podcast where I want to talk about mentoring, I wanted to ask you about that point you mentioned, the top-down versus bottom-up approach. Could you tell us a bit more what is this top-down, and what is this bottom-up, and who uses what and how do they compare?

Richard: Yeah, sure. So traditionally consultants use what they call a top-down approach, where they will look at the market data, then they will talk to senior executives, then they tend to go down to the next level of line managers and then they get down to the people on the ground, if they ever do get down to the people on the ground, but very rarely. They scope their projects based on the information available from the top-down approach and then form that opinion.

In my view, what accountants tend to do, what data scientists tend to do is work much more from a bottom-up, where the data and the people on the ground outline what actually is happening day-to-day. Quite often there is a disconnect between those two, the top-down and bottom-up numbers, so where I’ve found that my best success has been is where I do both of those analyses and I link those two together generally in what’s called the driver tree analysis. I won’t go into detail of exactly what a driver tree analysis is, but it’s basically working out what happens with the financials and what levers in the operations are causing those financial results. Again, I won’t go into too much detail about that. But having that top-down and bottom-up approach links the data to operations and gives the full true picture. I try not to go too technical in the operation/accounting space.


Kirill: No, that totally makes sense. I also learned the comparison between the two. After working for some time at Deloitte, and then working with the consultants as well, because consulting was a separate part of the business. It’s actually very interesting for those of you who haven’t encountered any of the big four companies, which are Deloitte, PricewaterhouseCoopers, KPMG and Ernst&Young. It’s very interesting to look at their structure and how they have separate divisions within the company and what they’re in charge of and how they work together. So if you find some time, just look it up online and see what that looks like. And that’s powerful. After working with consulting for some time, I figured out that top-down is a bit different to bottom-up. And from where you are now, Richard, and from all your experience working with both of these approaches, what would you say are the advantages and disadvantages of each one?

Richard: Sure. So the bottom-up approach you can tend to come to a conclusion a lot quicker. So that’s a big advantage as in the numbers tell what happened. However, to drive change, it’s very, very difficult. If you work out something’s wrong, but you just want to go in there with the numbers and say “The numbers say that you guys are selling too many of these widgets because it’s costing you too much money.” Well, the company’s not really going to listen to you because you haven’t collaborated, you haven’t talked. They might sell those widgets for a specific situation where they have to sell those widgets to sell another product. That’s where it’s got an advantage in that it gives you a very quick outcome, and you can tend to make a decision. However, that decision might not be the best decision in the full context.


The top-down approach, though, where you tend to work with stakeholders and get the full overall picture, have workshops, takes a lot longer and it doesn’t necessarily match the data. However, you come to a conclusion with the stakeholders. People have their input, they feel like they’re engaged and you can tend to implement change a lot easier. But it takes too long. So when you’re working in an environment where the company is extremely profitable and they’ve got a long, long time to make a change, such as a really big university, or the likes of Microsoft or one of those sorts of companies, you wouldn’t necessarily need to do a bottom-up or much of a bottom-up approach to get that hard answer because you don’t need to make a change that quickly. However, when you work in a company that’s distressed, you have to make a change very, very quickly. Otherwise the business could go insolvent. So that’s where we merge the two, top-down and bottom-up approaches, to bring stakeholders along so they implement the change but also get to the answer very, very quickly.

Kirill: Okay. Yeah, that’s a good summary. And I’ll just reiterate that again. So the bottom-up approach, which is more the data-driven approach, the data science/accounting type of approach, gets you the numbers and gets you results very quickly so you can tell what’s the problem very quickly but you might not be able to drive change quick enough because you just don’t understand the business well enough.

Whereas the top down approach, which is more of the consulting approach, usually doesn’t get you the answers as quickly, but at the same time it allows you to understand the business better and in the long run drive the change. So the best approach, when you have the resources, is to


combine the two, some people doing top-down and bottom-up. And I have an interesting question then. If you’re doing top-down and bottom-up and let’s say you have two separate teams – one is doing top-down and one is doing bottom-up – and then they come to you with the results and if the results don’t align, who are you going to side by?

Richard: That’s where a good project manager helps both the teams understand where they’re meant to go. It quite often happens that they don’t align the first time, and you might remember it in our own projects, and that’s where the project manager will go to the data scientist and be like, “Okay, this number doesn’t make sense because of XYZ. Can you go and rerun the numbers?” I remember a lot of conversations we had where you went back and reran the numbers. It wasn’t that your numbers were wrong, it was just they were analysed in the wrong context, if that makes sense.

Kirill: Yeah, I will never admit that my numbers were wrong.

Richard: They weren’t wrong. So that’s where the project manager can help shape both of the teams doing the analysis. You know, acting in silos is never a good idea, and that’s the same with project teams.

Kirill: Yeah, totally. All right, that was some very solid conversation and I think we did some rapid fire questions there about data and consulting and restructuring. From here I would like to move on a little bit to our relationship as mentor-mentee. For those of you who are listening to the podcast and don’t know, I have several mentors in my life and Richard is one of them. In fact, Richard is the mentor I’ve had the longest relationship with. It started all back in 2012 when we were flying in and out of that remote location, and


then we managed to keep our relationship going and I’ve learned a lot throughout this experience and I feel like I’ve grown a lot.

So a lot of the time now I get asked “How do you find a mentor?” “How do you approach a mentor?” “What do you get out of this mentor-mentee relationship?” and oftentimes I cannot give answers because it’s case by case for everybody. It’s person-specific. It depends on what you’re after in life, what you’re looking for. But at the same time I thought it would be a good opportunity now that I’ve got Richard on the podcast to go into a little bit more depth into how our relationship was structured, so maybe some of you guys can take away from this and maybe pursue the same thing in your careers. Let’s kick this off and let’s start with, I guess—Richard, we were obviously manager and data analyst at the time. And we could’ve stayed like that. But what do you think triggered that spark in your eye? (laughs) I’m joking. What do you think triggered that start of our relationship as mentor and mentee?

Richard: I think what I certainly got out of our working relationship was that your skillset was so different to mine and that I quickly realized that if I was to get that skillset, I’d have to learn from you. So I’ve actually learned quite a lot from you in that regard and I feel that the roles were reversed as well where my skillsets were very, very different to your skillsets and you wanting to be an entrepreneur and have your own business, you quickly picked up that I had that skillset that I could offer you so you could learn that and apply that to your future endeavours. I think that mutual respect of our skillset plus the fact that we both had very similar interests and the conversation was never dull gave us this outcome.


Kirill: Yeah, totally. Thank you very much. I appreciate you mentioning that because I can see how definitely my skills in operations and business have improved drastically. But I can also see how after we met and connected, your passion for data, or vision for data, in your projects has also taken a bit of a different turn, and you’ve put a bit heavier weight on that, especially now that you’re hiring data scientists into your organization. So I’m glad to hear I had some sort of influence in that area. That’s always good to know.

At the same time, while we were travelling all the time, every week, we actually stayed at this one hotel – obviously in different rooms – and in the morning we’d wake up at 5:00 AM, we’d go to the gym together, and we’d have coffee at the coffeehouse, which I still cannot forget that one time when with my Russian arrogance—the coffee was late, remember that?

Richard: I do.

Kirill: Yeah, the coffee was late and I knocked on the glass and Richard was like, “Please, don’t do that ever again. That is so rude.” Yeah, I learned in that respect as well how Australia is different to where I’m from originally. We were doing a lot of things together outside of work. We quickly became friends. Do you find that friendship helps mentorship or it detracts from it?

Richard: I don’t think it impacts it negatively. I think you need to have a good relationship with your mentor and I think that’s key. And I think the best mentor relationships—I mean, I’m a mentor to a number of people and I also have mentors. There’s always a mutual respect. I think that is naturally aligned to a friendship type of relationship. No, it’s not an absolute necessity, but I can’t see it hurting.


Kirill: Okay. Yeah, that’s good. That’s interesting that you mentioned that you’re a mentor to several people but you also have your own mentors. So it doesn’t stop there, right? It’s not that since you’re a mentor to somebody that means you know everything in the world. You also have your mentors. Can you elaborate a bit on that? How do you feel about having your own mentors and also being a mentor to others?

Richard: What I start with when people ask me to be a mentor—just for the listeners out there, don’t expect everyone to say yes to being a mentor because it’s a lot of responsibility. You are shaping someone’s life, and if you don’t feel like you will be a good enough mentor, you can often say no. So in me determining whether I want to be someone’s mentor, I need to feel like they have got something to contribute to me also. I think if I’m in a relationship where I’m just giving and giving and giving, it’s very one-sided and I don’t look forward to the catch-ups. But if I’ve got a mentor-mentee relationship where yes, I might be giving the majority of the advice, but also every single time I catch up with them I’m like “Oh, geez, what they said about X was really good,” or “Y was great,” or “I really liked how they approached this.” I might be a bit selfish, but if I feel like I can help them a lot, and I feel like they can help me a little, then I will certainly be open to a mentor-mentee relationship. Then on the flipside, when I’m picking who I want my mentors to be, I want to have that connection with them. I also want to feel like I have skills that they will benefit from because that would mean they’re excited to catch up with me. But then at the same time, I want them to be able to add materially to my career. And that’s just the way I approach it.


Kirill: Yeah, that’s fantastic advice. I never thought of it that way but it totally makes sense. It totally makes sense now. It shouldn’t be a one-way, because a lot of people get excited about getting a mentor and they think it’s a one-way relationship where they will just get value out of it all the time. That’s not the case. It should always be a give-and-take. It doesn’t mean that you have to go out and build a certain skillset that you’re going to be able to share with somebody. You already have that. Just based on the person you are, based on the experiences you had, even if they’re not related to your career, even if it’s just playing the piano or being an outgoing person, you already have something that you can share. You just have to find the right person that is interested in learning from you as well.

Also, that’s great advice for people who are in a position where they get to ask to be mentors. It is a big responsibility and you should approach it not just by how are you going to change other person’s life, but are you going to stick around? Are you going to be a mentor long enough to change their life? And for that to happen, you have to get something out of it too. And it’s not being selfish, it’s just fair, I think. Yeah, that’s some very interesting thoughts, especially for people looking for mentors now to digest that and take that on board. And what would you say, in a nutshell, is the essence of mentorship? What is mentorship to you, Richard?

Richard: In a nutshell, one word would be support. What I’ve needed from my mentors over the years is either supporting me and approving the direction that I’m going with my career and the decisions I’m making, or opening my eyes up to the fact that I might not quite be doing the right thing and direct me


in the right way. So I see a mentor as supporting the growth of my career.

Kirill: Yeah. And not just career, right?

Richard: You’re right. It could be anything. It could be personal growth, it could be spiritual growth, it could be—I mean, how many times have we talked about relationships, family, non-family? It can be anything that’s impacting on a person’s life, can be involved in a mentor-mentee relationship.

Kirill: Yeah, I totally agree with that. At least half of the time, we’ve talked about non-career related stuff. I can honestly say that has really supported me through the good times and through the bad times. It’s shown me when I’m on the right path and I’m doing the right thing, and with your life experience, you look at my situation and you’re like, “Oh, yeah. You’re making the right decision.” You probably don’t say that. You say it like “This is why it feels like you’re making the right decision.” Sometimes you guide me and you say, “Hey, maybe you should consider these other things.” I think I’ve told you this before, but one of the things I really like about our catch-ups, flying to this project and working in some harsh conditions we got to—in Australia it’s called “banter”. Is that the right term?

Richard: That’s right, yeah.

Kirill: So banter is like when cricket players, they just — not swear at each other, but they make fun of each other and make rude comments. It’s just like friendly but still kind of on-the-edge type of exchange of comments. You know, we’d get used to throwing that around, but whenever we got to our mentor-mentee catch-up and everything, we’d walk to the


place where we have lunch – we’d usually do it during lunch – and as soon as we would sit down at the table, it’s like “All right, now the catch-up is starting.” I could see your face change. Instantly, you were no longer that Richard who’s a friend, who’s “we’re just having fun” and just making jokes all the time, but you were Richard who’s actually listening to me. I could see how you instantly turned into this person who is ready to listen to what I have to say. And I really appreciated it that time. So thank you so much for being able to switch over into this mode of listening and understanding what is bothering me and giving me some of your thoughts on that.

Richard: No worries. It’s been good.

Kirill: Yeah, it’s been fantastic. Unfortunately, now that you’re in Sydney, it’s a bit harder to catch up for us like that. But still, whenever we can. Last time we caught up was in May, right?

Richard: Yeah, last time it was in Sydney, that’s right.

Kirill: It’s your turn now to come back to Brisbane. (laughs) And we even have this tradition that when we catch up we catch up for lunch, and if one time I pay for lunch, next time Richard pays, then I pay, then Richard pays. So it is definitely a give-and-take type of relationship. All right, so hopefully that was useful for those of you who are in a position of a mentor or looking for a mentor. Yeah, some very interesting thoughts there.

Now I would like to move on to back a bit to data science, or data in operations, and just a bit of a rapid fire of questions. As a manager or leader in operations, what would you say


has been the biggest challenge you’ve ever had that had something to do with data?

Richard: In terms of the technical aspect or in terms of how I’ve used data?

Kirill: I was actually going for how you’ve used data, but technical aspect is also a pretty interesting question as well. If you can answer both of them, that would be great.

Richard: Sure. One probably leads the other. In terms of the technical aspect, quite often we come into companies that don’t have sophisticated IT managers or data people involved and so the systems are a shamozzle, and they are a bolt-on, or there’s a million spreadsheets that don’t talk to each other. And the biggest challenge I think I’ve ever faced was an Australian company turning over close to $10 billion. To put that in perspective, that’s probably 18,000-20,000 employees, and they still used Excels for everything. Everything.

Kirill: No way.

Richard: Yeah. And every department had a different Excel. And every department reported based on this Excel. There was nothing to pull it all together. This was just an ultimate, siloed, horrible business. Trying to come in, obviously, with the way the commodities had taken a turn for the worse – things changed very quickly for this company and they needed answers but it was almost impossible to find the answers in the timeframe we needed, basically. So that went from being 8 operational people and 2 data scientists to me being the only operations person and everyone else a data scientist.

Kirill: So you went from 2 data scientists to 7 data scientists.


Richard: I think it ended up being 7 to 10. It was closer to 10 towards the end of it because we needed one on every side and we needed to get down to the raw data entry people and it was just – it was hard. However, in that time—that took 6 weeks. It normally takes 2 weeks, but it took 6 weeks to come out with an output that allowed me to make a decision. But what was really interesting is because the people running the business were all operational people and never had to understand the data, trying to convince them to make the decision because the data said so was so hard. They were such instinctive executives that having to go through the process of data, me and my traditional operational selling style, which is not very detailed – it’s got a little bit of detail but not a lot – they couldn’t understand it. When I got a data science partner to come in and talk through every single step that they did along the path to come to the answer, they started to get it.

So that’s where the balance of using data to help drive decisions was really, really crucial, and where partnering with the data scientists just made the biggest difference with that engagement. If it was me doing it the traditional way, I wouldn’t have gotten anywhere.

Kirill: Classic! That’s a very, very peculiar or very traditional example of executives not getting it. But that’s crazy! Everything was in spreadsheets! I just can’t even fathom that.

Richard: Well, I think you remember one company we worked with where most of their stuff was in spreadsheets or across four different accounting systems.

Kirill: Yeah.


Richard: It is bizarre how some companies operate.

Kirill: Yeah, totally. All right, that was a great example. Next question I had, and I don’t know if the answer will be different, because that seems like a huge win for you—it was exactly that. What is the biggest win in your career that you’ve ever had where you’ve actually used data to your advantage? Would you say that was the win, or do you have another example for us?

Richard: Actually, the single biggest win where data completely turned the whole engagement was—I don’t know if you remember, but you, me and Keith had to prepare a board report for a pretty hostile meeting. We had about a day and a half to do it.

Kirill: Oh, yeah.

Richard: And the national CEO and the global COO from our side and then the equivalent on the other company’s side, which was also a multibillion dollar company, were going to be in the meeting. Fortunately, we had worked very hard for them a couple of months earlier to pull all the data together and have really nice spreadsheets and analyses and databases and everything automated so that we could pull analyses very, very quickly and we knew it was accurate. So this meeting happened and my equivalent on the other team’s side came out blasting, saying how much money we owed them. I think they were saying we owed them $40 million in the next week and saying how terrible we were. It was just so one-sided. And then the CEO of the company we were working with got out a report and said “Well, no, that doesn’t sound right,” put our numbers in front of them, and that number turned out to be that they actually owed $5 million


to our company. So every meeting after that, we automatically had the respect of everyone in that room.

Kirill: I remember that. I don’t remember the exact details because it was obviously a much bigger feat for you. Your career was probably on the line in that meeting!

Richard: Correct.

Kirill: Yeah, totally. That was insane. You know, looking back on those days, gives me so much—what’s the word? When you reminisce something, you get nostalgic. It was so much fun. It was like a game of chess, you know? Like, this company, and this company, and then they’re sorting out their business, and we’re on one side, and there’s the equivalent of you on the other side, who is going to get the facts right and so on. Yeah, it was so much fun. Do you miss that time or do you still have that—?

Richard: I’m still doing it. I’m still having that much fun. I love it.

Kirill: Yeah, I can imagine. You’re still in that environment. You just made me realize now that I actually miss it a little bit, but what I don’t miss is the hours. Stepping aside a bit from the rapid fire questions, you’re still working. You’ve been in the consulting space for how long now?

Richard: About 12 years.

Kirill: 12 years! That’s probably the longest now out of all the guests that I’ve had on this podcast that have been in consulting. You’ve been working 12-hour days, weekends, at nights, in the morning, all over the place, on holidays, and you’re still doing it. What keeps you going? How come you don’t get tired of this?


Richard: The biggest thing for me is I learn. Pretty much every job I learn something new. I’ve still got a lot to learn in my career. I mean, I’m quite young, I’m only 32 and I started this when I was at university, so that’s why I’ve got the 12 years under my belt. Every single engagement, I learn something new. And every engagement then allows me to create something new. So my offering is very new and it’s the only one in the market. And the only reason I’ve been able to do that is because I keep learning. So the day I stop learning is the day I’ll stop doing it and I’ll do something else.

Kirill: Good answer. It’s definitely worth the trouble you go through to learn all the things that you’re learning right now. Okay, next question: What is your most favourite thing about being able to use data in your business decisions?

Richard: It saves a lot of time. A huge amount of time. I could not do what I do if I didn’t have the data scientists working with me.

Kirill: Great answer. I think one of the best answers I’ve had to that question. Okay, so from where you’re standing at and what you’ve seen about data and how you use data in operations and how you’ve seen it being used in businesses, where do you think the field of data science is going, or the influence of data in business, and the part that data plays, the role that data plays, in businesses? What should our listeners look out for to prepare for the future and to adjust their careers accordingly?

Richard: I’ve actually thought about this a fair bit over the last few years, Kirill, so I might have an answer that’s a little bit weird. So one of the things I’ve always wanted to do, and there’s a few people doing it, is to have a problem-solving tool that links data and the ERP systems to operations more


effectively. So what I mean by that is at the moment, if you want to install an ERP system with a large company, you have to spend 10, 20 million dollars, whatever it is. You have loads of consultants in there, it takes up all this time, and at the end of the day, it actually doesn’t align to the operations and what operations actually need in the business. You know, one of the reasons Oracle was such a success is because they were the first ones to sort of align the ERP system or the data with operations. So what I would love to be able to do is to develop a problem-solving tool that helps very automotively, or whatever the word is, work with companies and the ERP systems and have the two line up without having to spend the $20 million+ to implement it.

Kirill: Sorry for interrupting you. What is an ERP system for those of us who don’t—?

Richard: Enterprise Resource Planning tool. So it’s basically your HR, to manage payroll, to manage paying accounts, to manage your accounting, it manages projects. It’s basically any IT system that you have in your company. It’s the systems that produce the data that you guys analyse.

Kirill: Okay. So you think one of the things that’s going to change how data is going to impact large organizations in the future is that these ERP systems—there’s hopefully going to be a way to implement them much faster than spending $20 million on consulting fees and so on.

Richard: That’s just the tip of the iceberg. I also think these systems are actually going to do the jobs of a lot of the people who use it today. For instance, there’s no longer going to be 3 people doing payroll or people doing accounts payable; it will be a robot analysing the data and determining if it’s right or wrong based on other operational data that it’s learned from


its other system in the company. I think the technology is going to get so much smarter and the coding is going to get so much better that these things — within 99% accuracy — will be able to do the job of basic admin people in that role at the moment, which is—at the end of the day, who does it. That’s just one example of where I think data is going to go. It’s just eliminating those areas, time constraints etc. in business.

Kirill: Very interesting advice. So for those of you listening out there, I just want to put this in perspective. This is advice, or an opinion, coming from a person who’s been 12 years in business operations, who’s turned around dozens, probably hundreds of businesses, and who’s seen how they work inside. So Richard right now is outlining a very evident (to him) pain point of these companies. That means that one day, somebody’s going to address it sooner or later. It might be you, somebody who is listening to this podcast. The fact of the matter is that one day, there will be a company that solves this problem and it will bring efficiency into the businesses, it will bring cost-savings, but at the same time it will also bring a lot of redundancy, so a lot of these people that are actually fulfilling these roles will become redundant. That is unfortunate, but that is the way of progress. That is something to think about, whether you’re in one of those roles and you might want to start looking into ways you can adjust your skillset to get a promotion or a different job or somehow still be valuable to your company. Or maybe you’re going to be one of those people that is going to be implementing those things in the business that increase efficiency. So it is a very valuable insight from somebody who’s been working in this space for such a long time.


Richard: I was just going to say anyone who works out how to fully automate the system is going to be a very, very successful person.

Kirill: Yeah, totally. Okay, thank you so much for that. And before we wrap up this podcast, you had something you wanted to share with our listeners. What was it?

Richard: Sure. I’m actually aggressively hiring to build my team in Sydney and Melbourne at the moment, and one of the key skillsets we’re looking for are data science skills, so if there’s any listeners out there that have basic accounting knowledge but also a data science background or skillset, please get in contact with me because we’d love to chat to you.

Kirill: Fantastic! So there you go, guys. Right on this show, right on the podcast, Richard is sharing with you some opportunities for your career, especially if you’re in Australia, in Sydney or Melbourne. If you have a background in data science and some experience and a bit of accounting knowledge, get in touch with Richard. You’ll be able to get his LinkedIn and maybe any other contact details he shares on the show page and we’ll share that at the end of the podcast. So make sure to check that out. This might be your golden ticket into the world of a beautiful and fantastic career. Thank you so much, Richard. How can our listeners contact you, follow you or find you if they want to get in touch about this opportunity or just look at how your career progresses throughout the years?

Richard: Sure. They can send me an email. It’s pretty easy, it’s [email protected], or they can shoot me a note on LinkedIn. They can find me through your profile or they can just look me up.


Kirill: Awesome! And we’ll definitely leave those details on the show notes page. Get ready, prepare yourself to be bombarded with emails. And one final question: What is your one favourite book that you can recommend to our listeners for them to become better data scientists or just improve their careers or even personal life?

Richard: Well, it’s interesting because I was thinking I was going to recommend some really cool “Security Analysis” book by Benjamin Graham or some data science book that I read trying to get my skills up, but I realized you guys are probably a lot smarter than I am in that space. But it goes back to that listening skill that I think you learn as you go through the professional world. So the best listening book I’ve ever read is “How To Win Friends and Influence People” by Dale Carnegie. I think if there’s anything that can help with life, not data science specific, but it will also help with that, that book probably had the greatest impact on me. So I recommend getting that.

Kirill: Thank you so much. I’ve actually been recommended that book, I don’t think on this podcast but by other people as well. It’s on my list. So “How To Win Friends and Influence People” by Dale Carnegie. All of you guys listening to this podcast, definitely check it out. Thank you so much, Richard. I really appreciate you taking the time out of your busy day to join us on this podcast.

Richard: Not a problem. Thanks, Kirill.

Kirill: So there you have it. I hope you enjoyed this episode. And if you’re in Sydney or Melbourne, Australia, or if you’re planning on relocating to those cities, then definitely get in touch with Richard. This could be such a great opportunity for your career. For me personally, working with Richard


was a very mind-opening experience and I learned a lot. It was a great opportunity for growth, both professionally and personally. And for takeaways from this episode, it’s really hard to decide, right? We talked about data science on one hand, then we talked about the top-down and the bottom-up approach, and all those other things, and at the same time we also talked about mentorship.

So I’m going to highlight two takeaways from this episode. First one is probably the difference between the top-down and bottom-up, and how they can be used in combination, and the advantages and disadvantages of either approach. And in terms of the mentorship side of things, definitely a great takeaway for me was something I didn’t think about before – is that a mentor-mentee relationship has to be a two-way relationship, so you can’t just expect your mentor to constantly be giving you value and adding value to your career and to your growth, you have to be giving something back. Even if it’s not as much, it still should be something that you have to offer your mentor. So there we go. That was the episode with Richard Hopkins.

If you’d like to get the show notes, transcript for this episode and all the other materials mentioned in this show, then go to www.superdatascience.com/16. There you will find everything, including the contact details for Richard, where you can get in touch with him and follow his career. And on that note, I’m going to wrap up today. Make sure to follow Richard on LinkedIn. I’ll see you next time. Until then, happy analyzing.

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SDS PODCAST EPISODE 16 WITH RICHARD HOPKINS · Richard: PwC. That's correct. Kirill: Yeah, so...

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