The Balance Between Soft Skills and Data Mastery with Robert Bonovich

This is Making Better Decisions. I’m your host, Ryan Sullivan. Decisions are where rubber meets the road for organizations. Each week, we’ll be learning from people who are on the front lines of turning raw data into better outcomes for their organizations. This show is sponsored by Canopy Analytic, helping companies make better decisions using data.

Ryan: Hey, everybody. Welcome to another episode of the Making Better Decisions podcast. Today’s guest got his bachelor’s from Santa Clara University with a major in econ and minors in computer science and mathematics, which I’m sure we’ll be talking about in a little bit, uh, has quite a bit of experience in sales. Um, but most recently moved into the analytics world under the umbrella of sales operations, has worked for a handful of big name companies like Coupa and Cisco. Please welcome to the show, Senior Business Analyst at [00:01:00] Cisco, Rob Bonavich. Welcome.

Robert: Perfect. Thank you so much, Ryan. Excited to be

Ryan: Glad to have you on. So we like to start everybody with the same question and take it in different directions from there. So I’ll hit you with what is one thing Do you wish more people knew about using data to make better decisions?

Robert: Yeah, Ryan, that’s a great question. And I think my answer is, I think one thing more people should know about leveraging data to make better decisions is to never underestimate the soft skills.

Ryan: Hmm.

Robert: can put, you can put in work when it comes to Python, you can do some Java, you can do whatever kind of language you want, do the most insane science project and experiment of an analysis.

But if you can’t communicate that to stakeholders, and if you can’t present that in front of executives, I think it’s just lost its value. So don’t underestimate

the need and the balance that you should have as a data analyst between [00:02:00] those hard technical skills and those soft skills.

Ryan: Yeah, I love that. I mean, we have obviously kind of worn our heart in our sleeve by naming the podcast Making Better Decisions, but it’s not like Making Better Data Models, like where obviously employing people like us costs money, and it takes time. And then the stuff you build, you got to pay for. It only makes sense to spend that money if there’s some sort of like business return on the far end of that so In our experience that usually comes down to like either making a decision you couldn’t make before Or making a better quality decision than the one that you would have made before.

Maybe, you know, faster, easier, whatever. One of the things that I think is really cool about your experience, um, [00:03:00] really about having all of these kind of different people from, with different backgrounds come onto the podcast. So your experience in sales is really interesting to me. It’s actually an area that I’ve spent quite a bit of time building analytics for myself. Talk to me a little bit. about what are some of the decisions that are getting made in a sales department that you’re trying to help them make more quickly or more easily? You know, what are some of those things that really help a sales organization close the most deals for the most money? Hmm.

Robert: and once again, you know, that’s exactly what I get hired to do, right? Answer the billion dollar question. What is it that we can use to Leverage data for, to then go out and execute as best we can in the fields. I think that I have actually a great example of this, something I did while I was working in sales and something that I did is we had as a member of sales in, at a SAS company.[00:04:00]

You have so many tools at your disposal that you’re trying to leverage to go after your leads and these prospects, correct? There’s LinkedIn, there’s Zoom Info, you know, there’s Apollo. io, there’s even more tools being developed as we speak that aren’t even in the market yet, right? And one of the important aspects of being able to leverage data in that sense is being able to take all these different tools, consolidate them into your own database.

And this is what I did is I actually took all these tools. Brought them into my own database, created my own data set, and I looked at some specific insights that mattered to me. So at the time I was doing sales at Coupa Software. It’s a SaaS procurement company, right? So our target prospect was more likely in the healthcare industry, more likely something like a startup biotech company that has a lot of funding to go out there and create some sort [00:05:00] of drug or solution for a pharmacy.

And so all that I did is I took all these different data sets, combine them together. And I said, okay, from LinkedIn, from LinkedIn sales navigator, these are the key. metrics I want to look at, whether that’s year over year growth at a company, how many individuals actually work at that company. And I said, okay, this is kind of like my strike zone, if you will, of what I want to see when I go out to reach out to a prospect.

I want to see someone who’s leveraged Coupa before. I want to see someone who’s at a high growth healthcare startup. And so what I did is I was actually able to take all this data together and I came forward and I had exactly, I think 10 prospects on my list, and these were accounts that had never been reached out to by anyone within Coupa, right?

I was an ADR, so that was my job, trying to go outbound call. Trying to find what are the best prospects I can bring to an [00:06:00] actual accountant executive for them to go and close this deal. And two of those that I called in were immediately like, Hey, I’ve actually used Coupa before. That’s hilarious. I know exactly what Coupa is.

Yeah, I would love to have a discussion with Coupa. You know, we’re in a startup growth stage, so we need to hire one other person to handle our procurement from the finance side. Just because it’s a smaller company, right? They didn’t build up their procurement, um, department quite yet. Yeah. And so as soon as we hire this person, it makes sense to have this conversation.

And so two of those actually were closed one by a sales executive and quite literally a third of a sales cycle, just because I

was

able to put together this data set and look at the exact criteria of my perfect strike zone of what a prospect is.

Ryan: Yeah. I love this. I mean, obviously whenever you’re talking about data and In a modern workplace, [00:07:00] talking about silos and like, Hey, we have all these different systems. We have information from all these different places. This is one of the key pain points that everybody experiences, especially, uh, you know, maybe back in the nineties or eighties or whatever, right.

It’s like, Oh, you know, like we have, you know, the one provider and they do, they do our computer stuff, you know, now it’s, it’s. There’s a different tool for every single thing. Sometimes we have three tools for like one thing and they work really, really well, but they may not talk to one another. Um, in my experience, this has been exacerbated with sales.

Like there are just so many things that can help with different portions of the sales process, but getting them all to talk to one another is so difficult because there’s so many of them that. Exactly what you talked about. Is, is just kind of like the dream use case for sales. Like, Hey, can I make a [00:08:00] list of the 10 people I should call today? Like imagine a sales organization where every single person has the highest quality, 10 people to call each day. Like, tell me that company’s not going to the moon. Um, So I think that you make a really great point and it’s something that’s not just applicable to sales. Obviously it’s kind of extra applicable to sales because of the number of tools and the diverse approaches and methodologies that people use, um, for sales and for prospecting and all that stuff. But that’s something that I think everybody can take away from it. One of the other really cool things That I heard. And this is something we haven’t gotten the chance to talk about on the podcast yet. So I’m excited to dig in a little bit into, this is just kind of a personal pet theory of mine, but people that have the data gene. And it is like, it’s like a thing and it’s, it’s [00:09:00] found everywhere, right? Like no matter what part of the business or what type of background or like mentality or anything that a person has, there’s just like a certain percentage of every one of those populations where you just find your data nerds sprinkled through. And for me, the best way to find these data nerds is to find the ones that have done like personal. So for me, like any part of my life, whether it’s like, you know, taking care of personal finances or, you know, trying to beat like a new, like, you know, weightlifting record or cycling, you know, like whatever, like whatever it is for me personally, like I’m always building like a little model. to try to understand it and try and like hack myself into doing better. And so hearing the same thing from you, I think kind of ratifies it. It’s like, okay, like these people with the data gene, they just recognize that if we can pull all of this information together and have your own perspective on it, it can really drastically drive results. [00:10:00] Now, the thing that’s, Interesting about that is obviously like, I have that in my own life, but then I go out and I try to do something different. for clients. Now, whether that’s like, I have consulting clients, you have, you know, your internal, like whoever the reps are, like managers or whoever that you’re kind of building some of these reports to support. Tell me a little bit about some of the growing pains as you go from, Hey, I’m just building something for myself. It can look and feel however I want to now I might be building for a large team of reps and managers that are out there trying to actually use it.

Robert: Yeah, absolutely. And that’s a great question. What I have to say to that, Ryan, is I think, and maybe this is something you can relate to, and this is something I feel like all data professionals can relate to, and it comes to imposter syndrome. When you’re working for yourself and doing a project for yourself, there’s no one that you have to, [00:11:00] there’s no deliverable for anybody else.

It’s just you creating something for you. You’re passionate about a question you want to answer, a problem you want to solve. And that makes it so much easier, right? That’s something I believe we’ve discussed this before, which is, it’s something that both of us have done as, uh, individuals who love fantasy sports.

We’ve both gone out and, right, we’ve taken together all this fantasy football data to create our own models to say what is the best way to go take all my friends money and win my fantasy football league, right?

Ryan: That’s right. That’s right. So for anybody that is a listener right now, that’s the actual value proposition of the Making Better Decisions podcast is taking your friend’s money in a fantasy football league. This is, you know, I know that we’ve sounded very professional so far, but this is really it. We’ve been working up to this.

Robert: We’re gonna publish a GitHub together, right, Ryan, after

Ryan: That’s, that’s right. That’s right. 10. 99 a month. See me after baby. [00:12:00] That’s pretty funny. Yeah. Yeah, absolutely. I, and in fairness, the proof was in the pudding with that. Like I was, I loved playing with my buddies. It was mostly just kind of like the smack talk and, you know, an excuse to stay in touch with my friends after school.

And, you know, so it was all good stuff, but I wasn’t good. Right, like I was kind of bottom of the barrel, like my goal each year was to not catch my league’s punishment, like whatever it was for that year, that’s what I wanted to not happen. And as I started getting further and further into analytics, like eventually it dawned on me, it’s like, Oh, okay.

Well, this is a data game, not really a sports game. And starting with the year that I implemented the model, I was in four leagues at that time. I went from being like bottom of the barrel and all of those leagues to winning one league and making it to the, at least a quarterfinals in every league. And it just like [00:13:00] continued like that.

It was like a light switch. Um, one of the things that is really, really neat. Yeah. about these types of personal projects is that they teach you what is really important about The subject material, because as you dig in, okay, you know, so for example, right? Like I’m a person that like, you know, I followed and watched a lot of sports, but you don’t really dig in and understand, okay, well this is actually like way more valuable than this, or this is actually like, you learn new things as you’re going.

And then based on that question, then you ask another question and then you find out another thing and ask out another question. So. Talk to me a little bit as you’ve dug into like going from being a sales rep into being someone who’s really digging into the data and trying to understand that. What are some like nuggets of truth that you’ve figured out about how sales works from being in the data?

Robert: [00:14:00] Oh, that’s a great question, Ryan. I would say one of the nuggets And something I look at when I’m asking myself questions while doing my data analysis, looking at sales. It’s the same, like, relating this to fantasy football, it’s kind of like the same as when you watch a football game. There’s what people always call the eye test, right?

Which is just individuals who can just look at the game, And just without even looking at data, they have just watched so much football and they’ve been playing around with their own hypothesis and their own theories in their head, right, about what they think is going to happen on the next play, who they think is

going to score, you know, based on a couple of different factors.

For example, you see first and ten, or first and five at the goal line. In a football game, you’re like, they’re going to run the ball for a touchdown. You just did some pretty analytics in your head. Right? And the same

goes for sales, which is it’s constantly getting this data in front of [00:15:00] your, in front of your face, having that eye test, but the eye test is really just asking the little questions about what’s different.

What am I seeing consistently over and over again, that I should start asking that next business question. Right? Is it,

you know, I’ve seen a lot of close one data, or I’ve seen these really high win rates I’ve been calculating, and it seems, I keep saying, I keep seeing this word health care and life sciences.

What’s going on with that, right? And it’s going into that and saying, if I’m seeing all these win rates, what other metrics can I look at? What’s going on with conversion rates there, right? What’s going on with, are there campaigns that are being run that have to do with healthcare and life sciences?

What’s going on with the calls, the percentage of calls that are being answered, the number of emails being answered? The likelihood of a, of a prospect reaching out, that’s a part of this industry, what’s their title. And it’s just, that’s

what the eye test becomes is what am I constantly seeing? And it’s just, that’s something that I don’t think you can really [00:16:00] teach.

It’s just something that everyone, I really truly believe they do have it, whether or not they know they do. Which is just being able

to look at something. And if you start seeing something over and over again, ask yourself the question, is this a trend? Is this a trend?

And this is the trend that as I dig into this data, I can start being able to understand what the trend is, summarize it.

And the next big question becomes something we’ve discussed, right? How do I present this trend? How do I go about creating, whether that’s the visualization, maybe just some bullet points in a PowerPoint slide, right? But being able to then take this trend, communicate that effectively to someone else who can go make a change, right?

Hey, we’re really not getting a lot of, a lot of outreach that’s been successful. Could this be a sales tool problem? Is that something you’d bring to your sales ops individual, right? An executive in sales operations, you’d say, Hey, this looks like it could have something to do with their sales tool. Or [00:17:00] maybe it’s, maybe it’s another question that you need to ask a different executive in a different individual.

Ryan: As you were answering that question, I realized how hard of a question it was.

Robert: really did me dirty

there. I was like, Oh my God.

Ryan: well, honestly, I think that you kind of crushed it. And so here’s, this is, this is obviously like not, you know, some sort of like research back thing. This is just based on my personal experience, particularly in marketing and sales. The very fact that something worked. means that it won’t work forever. So for example, right? Like I have some sort of like leading question or, you know, particular cold call outreach, and maybe it works really well, but then what happens is everybody starts using it. And then everybody on the receiving side is like, Oh, well, I’ve seen that a million times.

And then it stops working. Right. Cause [00:18:00] we just kind of like tune it out. And In that regard, I think that sales and marketing has like these like cycles and like we, you know, the market kind of prices in new information very similar to, you know, like a securities market, like the stock exchange or something like that.

Like it’s really, it’s more about the edge. Versus the method. And so the sales landscape is constantly changing. And I think that was really reflected in your question that it’s, it’s less about like, Hey, here’s this one thing that I figured out that everybody else will be able to go and use. It’s more about. wherever I stand, wherever the market is currently shifting, how do I ask the right questions to find that edge so that I’m saying the thing that’s useful? I’m differentiating. I’m somehow making a more personal, less kind of jaded outreach to a client so that we can start a meaningful conversation.

Robert: Absolutely. Absolutely. And I think that also comes down to hate to do another sports [00:19:00] analogy to you, but I’m a big sports guy. So going to, um, I think like with baseball, you can go in there and work really hard, create some science project to try to hit a home run and find that one trend that is going to transform the business.

And I’ve seen that happen. That happened. Um, You know, I’m part of the Splunk acquisition at Cisco. That’s what happened with my team. My team actually went in there and figured out we’re getting crazy win rates when our talk track has to do with cloud migration. customers from on prem to the cloud, it’s killing it.

That is what’s, that’s exactly what’s responding to the market right now. And so, That’s the charge they started leading, and oh my gosh, did it work. Like it, that trend was a home run. And it, it really helped Splunk a lot. I can’t say the dollars. I almost did there. I can’t say that, you know, that’s, that’s information I can’t relay over, but I’m just going to say that that trend that they found, it was a home run hit.

And I think that the problem with home run hits and [00:20:00] what people forget, especially when it comes to baseball, if home run hits were that easy, baseball would be pretty easy, right? It doesn’t happen. It’s always about, and what I think a lot of people get carried away with, everyone wants to hit the home run.

But if you’re constantly just making little data insights that can create singles or doubles, You’re getting on base, you’re still creating value, right? You’re still influencing others to say, Hey, I know this isn’t the biggest thing, but guess what? The data’s saying when we’re reaching out, we should reach out on LinkedIn.

It’s more personalized rather than sending an email. People don’t want to read their emails right now. That’s what’s happened this last month. Let’s go to LinkedIn. And what happens if that just increases win rate, conversion rate 5%? You’re talking about results. That’s still a lot of money. And it’s just that little insight that some might say, Oh, you know, like, Oh yeah, I guess that’s like a pretty good insight.

It’s not a home run, but you’re still creating [00:21:00] value. And if you’re constantly stepping up to the plate at work and creating that little bit of value, Right? Isn’t there that, um, what’s that saying? Like if you just put in like 1 percent a day, you’re getting 365

percent better every year? It’s just the same thing with data.

Ryan: Yeah. Yeah, absolutely. I, I’m totally with that. I think particularly. We haven’t used this analogy before, but if you can’t tell, I’m a bit of a baseball fan here. So I’m gonna, I’m gonna take what you’re giving me. Um, we talk about this all the time, particularly with like doing a larger initiative, right? So if I’m Just making, you know, some dashboard for somebody.

It’s pretty much just like they asked for the dashboard, build the dashboard, hand it over. However, sometimes folks need help with strategy or with executing like a large project, maybe building the entirety of a database and taking, you know, 35 different systems, dumping it all in there, building, you know, all of that stuff.

That’s a [00:22:00] much larger undertaking. And what we see with a lot of those is this drive to just slam Like everyone wants to be Otani and just like go yard four times in a game, right? It’s like, okay, you know, I love that. Love that energy, but like, let’s focus on trying to have a team of people that can comfortably hit contact hits just over the infield, right?

And if you do that, right, you’re still going to win ball games. So figuring out. Like based on the return on investment, like what are the business goals that you’re trying to achieve? Taking that, you know, iterative approach, seeing a return on investment, gets everybody on board, actually puts the money in the bank account. And then you can keep snowballing that as opposed to saying, Hey, we’re going to send our best player to batting practice for the first half of the season. And then when he comes back, he’s going to be great. Right? It’s like, no, like you, you want them on the pitch. Field for the first half of the [00:23:00] season, you know, so that, that iterative approach is something that we are big believers in.

I think that’s a great point. Now, if I can, I want to transition us a little bit. So I said in your intro that we were going to talk a bit about your background. So, My wife was was an econ major. I’m a big Freakonomics fan. I suppose that that qualifies me the same as having gone to school for economics, right? I don’t think so. I’ve mentioned it on on the podcast before, but if either of the Steve’s from Freakonomics are listening, we’re big fans. We love you. We’d love to come on or have you on anytime you want. But the thing that’s actually most interesting to me about your background is like, you know, econ makes a lot of sense.

You’re going into the business world. It’s like a pretty rigorous way of approaching, understanding business, understanding incentives, how people interact with markets. The computer science and the math is, [00:24:00] On the pure side, you know, now, obviously I kind of followed the same path. So I went to school for, uh, mathematics, which my high school self would never have believed if you told him. Uh, but I ended up really liking it and it was a very natural transition into the business world and I kind of felt. A little bit like I had the secret handbook to some ideas. Some ideas were hard, right? Like business is hard. There’s a lot to learn. I’m still at 5 percent I think of understanding everything that someone could understand there, but talk to me a little bit about your background in computer science and mathematics and how those have helped you in business at large, but also in sales.

Robert: Absolutely, absolutely. So I guess to start off with computer science When I was going to be a junior in high school, I’m from the South Bay over here in San Francisco Bay Area. I actually, over summer, I snuck into [00:25:00] an introductory to computer science course at my local community college. My sister, uh, I have an older sister, and she

Ryan: All right, good will hunting. Yep. this is actually my life, didn’t just, I didn’t just copy paste the plot, no, but I uh, so I, I did, I snuck into this course, and um, just cause my parents were like hey, your sister has to take this, she’s just trying to get some credits to like, not to take some courses, going off to college, um, you may as well just like try it and see if you like it.

And I had never coded in my life and I was just kind of like, okay, sure. I’ll just try it out. And I absolutely loved it. My professor was awesome. And, um, I ended up getting like an A in the course, absolutely loved it. And that’s actually when. I learned Java and that’s when I went back and actually created my own fantasy football model.

That’s when it started and I was [00:26:00] like, oh man, like the ROI on this is crazy, right? In my fantasy football, you know, high school days where you win, uh, 20, right? When, when you win the league. But, uh, but that was my first experience. And after that, I ended up, I went to Bellarmine College Prep in San Jose area.

And so I ended up taking AP Computer Science and going into C from Java. Um, working with those more complex languages than what I do now. And, um, and I actually did robotics at Bellarmine. And through that journey, what I realized is I really liked being able to have, it felt like a superpower, right? It’s kind of like, it’s kind of like law in a way, where it’s like you do have this power language at your disposal that you know that does create value.

And As soon as I figured that out, I actually ended up doing an iOS development course. So I got into like [00:27:00] Swift and um, I went to Santa Clara University and that’s where I originally wanted to start was going down this computer science route. And as I was there, I was so stuck on the idea of, I’m going to do computer science because I’m going to become a software engineer.

And I started taking courses in computer science that I was just not passionate about. And It really threw me through, honestly, an identity crisis. Because I was like, this is something I’ve been doing for a few years now. I’ve been trying a few different languages and I love this stuff, but I’m also, you know, I came on here talking about this stuff because I’m someone who’s a lot more outgoing.

Um, I’m more extroverted. I just don’t, I was, I was freaking out about the idea of, I’m going to sit in the back of a cubicle and just be typing away at my computer every day. And you know, maybe say hi to people at the coffee machine. I was like, I just don’t, that’s not the future I envision myself. And, um, so from there, I, [00:28:00] part of my computer science curriculum is I did have to take a lot of higher math courses as well.

And, I was loving my math courses, right? Like, I was doing multivariable calculus, and I’m like, oh my god, I can’t believe, like, this problem, this problem solving is insane, and it’s so difficult. And, um, you know, differential equations, all, all those good classes. And I was doing really well and enjoying those, and I was like, I had to sit down and be honest with myself and say, hey, this is something I’ve just been way more passionate about.

I don’t know where this is going to lead, but it’s, I don’t think it’s going to be a software engineer in a cubicle saying hi to people at the, at the coffee machine, once a day. And, um, I think this is the route I want to go. So it helped me narrow my view of I do like to solve problems. What I did learn in math is I don’t like to really solve abstract problems.

I wanted to solve real problems. But to me of like, what do I mean by real problems versus I mean, you know, I get it. Like obviously you’re throwing these things in the calculator and you’re just kind of like, what the heck am I I hope [00:29:00] this is right, I don’t know. Versus something like physics where there’s a little more, okay, hey, uh, so and so threw a baseball at, you know, this velocity and, and where’s the baseball going to end up or what height was it?

It’s like, okay, well, there’s some realism in that, right? And that’s when I, I actually took a few economics courses and, uh, I actually was doing this course called Econometrics, and we actually leveraged R in my class, R programming language. And so that was my first taste of data analytics, was getting actual problems saying, hey, here’s the gravity theory, prove it out.

Here’s the formula. Here’s the data. Leverage R to figure out which companies are increasing their GDP based on the gravity theory. Basically, that they’re located closer to another country that has higher GDP. A very high GDP, it’s going to influence those other close countries. And that was the first project where I said, whoa, I’m going back to programming, which I never saw myself doing again.

I [00:30:00] was like, what am I, I’m coding again and I’m loving it. like, okay, there’s something about coding and, and problem solving that fits in, in what I see myself doing in the future. But now there’s this other aspect of actually solving these business questions and these questions I’m getting in economics.

And I ended up, um, at the time while I was finishing these courses, I unfortunately was at school during. The COVID pandemic. And, um, and so I actually, after I finished these courses, I was going to Coupa Software to do an internship. I had just interned the summer before doing a deal desk internship.

Really loved it. Had a great time, an amazing boss and everything. Knew I wanted to go back to Coupa Software, but this time, at the time I was considering getting into sales. Just something I always want to do. Once again, I was like, I don’t see myself being in the back of the cubicle. I’m very outgoing. I want to be able to have a job where I can connect with individuals.

[00:31:00] And, um, I like the idea of learning how to sell, if anything, to sell myself. And with COVID happening, unfortunately that internship did not exist anymore for the sales org. And so I ended up taking another internship in alliances and partnership where I had someone tell me, Hey, you should try this department called business value engineering.

They do some really cool work that aligns with. What you’ve been doing in your class is using some sort of coding language to go and solve real business problems. And what our business value engineers were doing is they were actually taking spend data from prospects. and building ROI cases for them to purchase Coupa.

Saying, hey, I see you’re spending a lot of money, you know, with X, Y, and Z vendors. Here at Coupa, we’ve actually already pre negotiated contracts with, you know, 30 different vendors across all these different verticals. If you were to switch to our [00:32:00] contracts, we can actually do the math. To tell you how much you would have saved in the last year, two, three years.

And that was one of our biggest sells in terms of ROI was saying,

this is what we, especially, I mean, imagine you’re a small startup, you don’t have the bandwidth to go out there and to be negotiating these.

Robert: These rates.

Ryan: stuff, pay less money.

Robert: Exactly. Exactly. Get something a little bit similar. You got a coffee machine from CoffeeMaid instead of, I don’t know, Nespresso.

Guess what? You save like 10, 20 bucks. Right? Something like that. And so, as I was doing this, that’s when I first dipped my toes into Tableau, into Python, um, and more of the coding languages I leverage today as a data analyst. And that’s when it really hit me, where I was like, I am leveraging programming to gather insights, to go out and create ROI for a prospect, to support individuals that, you know, they’re go they have these big dreams, and I want to support them [00:33:00] with my analysis saying, hey, we could save you this much money if you come and use our product, and you can actually focus on the other things that you need to focus on to achieve those big dreams. And I love that. And that’s exactly what I wanted to do. And that’s exactly how my background rolled from computer science to, to data analytics. And the one thing I would add to was being able to then communicate this to a sales rep, right? Cause you could, yeah, sure. Just send them the document. Here’s the spreadsheet.

To a sales rep, you know, like sales reps don’t want to work in spreadsheets. They’re people that they want to talk to people, right? And so that was the next step was getting online and saying, Hey, these are like the top three insights I have for you, where you can actually create ROI with this prospect.

And this is what I would communicate when you talk to them. And being able to actually present that data was the biggest selling point for me. Because that was the point where I said, I’m using my soft skills. I’m using my technical skills. I’m [00:34:00] learning a lot. I’m helping people. And I am solving very complex problems, just like math.

And everything came together where I said, this is exactly where I want to be.

Ryan: yeah. I love that. I mean, there are some, some big similarities and differences between our stories, but. One of the things, cause I thought a lot about this and seeing as how you have kind of like the unique situation of having had a similar experience along with me, but then also the econ background, I’m going to steal a little, you know, econ terminology. So one of the things that I realized was like within math. Right. I was, I was good at math, but not great at math. And when I say that, I mean, like I could get A’s in college level math courses, which is not nothing, right? That’s

Robert: No, that’s very impressive.

Ryan: however, right? I knew that I was never gonna be one of those [00:35:00] moonshot type, you know, like crazy math people, right?

Like I knew that I was not someone who was gonna like, you know, land like a named chair at MIT, or you know, like one of these like big. Prestigious things, right? Like one of the things that being a math student taught me was that there are levels of intelligence on this planet that are really unfathomable, right?

And you don’t know them because they don’t talk to anybody, right? They’re like hiding in a place doing math or what, you know, whatever else it is, right? And I’d meet these people and just be like, you know, so for me, Yeah. In those, you know, highly technical spaces, there are unbelievable talents. But what I found was that different spaces have different scarcity levels and hence demand for different skills. [00:36:00] So if I’m a math professor and I do one unit math, of math thinking that has a pretty comparatively low level, right? Everyone’s kind of like, whatever, nerd, like you were over in your office, like you’re doing your thing. We kind of expected you to do it. No one really gets what you do anyway. So thumbs up.

Keep publishing. If I take that same unit of math thinking and I now do it on an FP& A team, people are like, Oh my gosh, this could change the business, right? Like, like you said, right? Like we’re closing deals in a third of our cycle time, right? We’re, you know, whatever it is, right? There’s that avenue for the impact to be realized.

And it’s just that type of thinking is scarce. And what I found was, you know, you can’t go too far. Like there are some fields where like I could do a bunch of mathematical thinking. So what, what was interesting was one of the guys that was [00:37:00] in my math program with me had a landscaping business. And so one of the things that he did was like build rock walls, which is very difficult.

I don’t know if you’ve ever tried to build a rock wall, but it’s hard. I tried once. It was awful. I was terrible. It fell apart. He would talk about how he’d just be like, Yeah, like, obviously, like, I’m not gonna keep doing the same business with this degree, right? Like, cause it’s just, the amount of, like, value add from, like, high level mathematics that I can do in this line of work, like, sure, could I build, like, a big, you know, program or thing for, like, rock walls, but it’s, like, There’s no benefit.

Like people just need somebody to come out and build a rock wall and then leave. They don’t need like a lot of, you know, math thinking. So I’ve kind of found the same thing. Like I had to find that right avenue where that type of math and computer science and problem solving mentality. was scarce enough that I could provide value, but was also expected enough that there was like an avenue [00:38:00] to do something with it. So I needed to be in a place where if I, you know, made a new report or came up with some idea, the organization was ready and capable of digesting that idea and doing something that made it more money. Um, so that, that was kind of my experience, which it sounds like was really similar to yours. I want to ask you, right, we’ve talked a lot about like, you know, some successes and things that have gone well. Tell me a little bit about some of your challenges. What are some of the things that have been hardest from a data perspective? You know, we talked about siloing, maybe there are some others and then what are you currently doing to try and attack those challenges?

Robert: Right, right. And that’s a great, that’s a great question as well. Um, I would say I, I, I could speak it to this at three different levels. There’s one level of what has, you know, me personally, what has been [00:39:00] Hardest thing in my data career, I guess. Um, I could speak to what I’ve seen and then an instance of like a use case.

Where I’ve seen things go very, very wrong. And,

um, I can also discuss something that I have personally done, where I have made mistakes with my actual data work. So, honestly, if you go down any of those three avenues, which one would you prefer we explore?

Ryan: Hit me with the first two.

Robert: Okay, okay. So for the first one, the toughest thing in my data career that ever happened.

Was I told you that I was an intern at, for the business value engineering team at Coupa Software. If you go on my LinkedIn, if you go on my resume, I wasn’t there full time. And what ended up happening was, I, it had nothing to do with, with my performance. Um, if anything, I actually, I had this one, one of my biggest tasks as, as the intern was, I needed [00:40:00] to create the, um, a customer insights analytics dashboard.

had worked for, I’m not even joking, a month. This is all I did, was putting, cleaning all this data in Python, throwing it all into a Tableau and then a Google Sheet view, because You know, my full time employment, this was my make or break, right? There’s a lot of pressure in creating a very good dashboard.

Ryan: Yeah. Yeah.

Yeah.

Robert: I had created these 3D crazy coded models and VBA scripts and everything in Excel and in Tableau. And I had these two different views. And I remember I came to my boss at this time. Um, and he was, he’s, he’s a very smart guy. He’s awesome. He’s like, has his own startup in like the AI world now. He’s, he’s a genius.

And, And, he came to me and he’s just like, ah, we should have checked in on this like a long time ago, because this is just [00:41:00] not my view. I want it to be something plain white, like an Apple marketing view. And I want it to look like this. And I’m, and, and I’m like, you know, this is Thursday. My internship ends.

So I’m like, okay, so should we like meet next week or in two weeks? And then I could show you what I have then. And he’s like, Oh no, I need to get like a first draft out by Wednesday. We have to meet Tuesday. I remember work the rest of Thursday up until, you know, 11 PM grinding away at this next day, 5 AM to 11 PM work in the weekend.

Everything finished this dashboard five minutes before I was supposed to meet with him. So proud of it. So excited. He looks and he’s like, this is exactly what I want to see. I can tell you put, I’m so sorry. You had to completely redo everything basically and put together this type of view that I had in mind.

But this is the type of work that we expect here at Coupa Software. I’ve been really [00:42:00] impressed and you know, we’re going to give you an offer. And I remember being like that, let’s go. Like, this is amazing. I’ve been working so hard to break into this and now like, you know, especially like with the, the way the job market was, you know, during the pandemic, I was like, this is just the most amazing thing to hear.

And unfortunately when I’m happening is, you know, I got the verbal offer. And a week later, we, Coupa had the largest acquisition to date with this company called Llamasoft for supply chain management solutions. And they had an onboard, like, you know, dozens of data analysts and data scientists. And so they all fell under.

The business value engineering department, no more headcount. And all of a sudden I had graduated Santa Clara and my verbal offer fell through. I didn’t apply anywhere else. You know, I thought this is where I was going to be. And that was the moment where I was kind of like, will I actually become a data analyst?[00:43:00]

And. It was really difficult. It was really tough. There’s so much doubt, especially when you feel like you really earned something, and you got the validation and confirmation, to then have it all just be ripped away from you. And that’s just something That I would advise to new grads, if there are any new grads that listen to this, that’s something you have to be prepared for.

Looking back, God, I wish I asked for this to be on paper, signed and agreed and everything, but you know, as a 22 year old, bright eyed thinking, I did it. It’s done. I got, I got the yes. You know, that was definitely the toughest thing that has ever happened to me in my data career. But, you know, the only thing you can really do when you.

Hit a bottom like that is find a way. You got to persevere and you got to just put your head down and you have to work. And so what I ended up doing is I found this data science and analytics boot camp from a UC Berkeley, a [00:44:00] UC Berkeley extension, and I signed up for it. It was a about seven, eight month curriculum that took you from the basis of Excel to, you know, neural networks, like very intense, um, work.

Because I said, Hey, if I’m going to do this. I need to have a background where I’ve earned this position. I could be better at Python. I could get better at Tableau. And I ended up taking another role at Coupa where I actually dove into sales. I always wanted to get into sales. I always thought that those skills would be something that you could just have for the rest of your life.

Being able to connect with individuals, um, being able to sell yourself. Right. I realized I really needed to do that because, you know, I wasn’t able to do it before. So it became an opp I turned that from a failure to an opportunity and an area of growth to say how can I get better and position myself and have not only the best hard skills but the best [00:45:00] soft skills?

And I figured who has better soft skills than someone who can sell? And who has better hard skills than a data analyst who can do data crazy neural networks and data science? And so it was nine months of every night working in data, having homework, having assignments, and every day of, you know, 50 to 75 cold calls.

And it was an absolute grind. But, you know, that’s what it, that’s what it took for me to be able to get to the spot I wanted to get to. And to become a data analyst at Splunk, that’s what it required. Now a data analyst at Cisco, that’s what it required. It required saying, this sucks, let’s go, let’s go, what can I change about it?

You can’t change what people think about you. You can’t change what your past effort was. But we can change is how am I going to get better? And that was one of the toughest lessons I had to learn. That was the best [00:46:00] lesson I could have learned. I’m so grateful for it. And if it wasn’t for that, you know, I would not have the technical toolkit that I have today.

I would not be able to sell myself today. I wouldn’t be able to, to present with confidence to executives, right? Cause if you could just talk to someone random on the phone and get them to buy time from you. And if you can do neural networks in Python, you can be a great data analyst.

Ryan: I love kind of the, the outcome there. And I think you’re right. I think analytics has become, you know, a very exciting and sought after type of role. It’s one of the fastest areas, you know, uh, you know, in business of, of growth and value generation. And I similarly talk to a lot of people have, you know, mentored a lot of people or, you know, people that I’ve hired over the years, that’s kind of always, [00:47:00] It for me is like, and, and you, you said it perfectly, right? Like these, these two things, like one, just make yourself the most valuable advisor you can be, and then make sure that when you’re advising somebody. They like it, you know, and if you, if you just keep focusing on constantly developing those two things for your whole life, just over time, the rest of the stuff just kind of sorts itself out. And you’re, I mean, you’re right. Like there’s, there’s definitely some, some portions of, of real grind. You know, as you’re making a step change.

So, you know, I love, I love the story. I love the outcome. Congrats on making it work. And, um, you know, also, you know, kudos on the, the vulnerability on the podcast, like getting to talk about one of those challenges. I, I love it. Now, one other thing that we talked about before that I want to make sure we don’t wrap up. Without talking about is you have another, in addition to your data and analytics career, you have another [00:48:00] exciting side hustle. Why don’t you tell everybody about that?

Robert: Absolutely. Absolutely. So I am, uh, a founding investor of a new bar opening up in San Francisco called Studio Golf, and we’re going to be opening up. The goal is January. We’re going to see what happens. The goal is January. We’re going to be opening up in the East Salesforce Tower, and we’re going to have three.

Golf simulations. Um, and we’re going to have, you know, a bar, a restaurant with, um, with some good food. We’re going to have a bunch of chefs over there and everything. And, uh, and we’re all, you know, very excited to, to get this thing off the ground and get it going.

Ryan: That’s right. I love it. So if you ever want to, you know, grab a burger or a beer, you know, hit some balls, hopefully long and straight, um, and maybe talk data and analytics. Got to check it out. I love it. So, you know, again, congrats on the success, but [00:49:00] also good luck with the new venture now

that you’re welcome yet.

Now that was kind of a little bit of a transition. What I like to make sure we do is obviously like the nitty gritty, I think is a lot of what people come for, but getting the opportunity, as you mentioned, right, there’s been a bunch of people that have come on and talked about the importance of soft skills and the importance of interpersonal relationships.

And, you know, I. Think that the same thing applies to the podcast. Like if someone that’s listening can connect with you and understand you a little bit better, just kind of like really brings home everything that you’ve talked about. So tell us a little bit about, you know, when you’re not off, you know, grinding and educating yourself and working and selling and starting new businesses and doing all this other fun stuff. Um, you know, what do you like to do for fun? What are some of your interests? I know we’ve talked about stealing all your college buddies money in the fantasy league. Anything else?

Robert: Well, I mean, that’s definitely my main interest. So, you know, if any of them are listening. Just know I’m coming after you. I’m coming

Ryan: Yeah, but set that [00:50:00] aside, baby. I’m taking it.

Robert: But uh, I would say some of my other interests, you know, I, I, I feel so bad because I’ve been talking about like, you know, one of the hardest grinds of my life. Actually like that’s, you know, my day to day and everything. And yeah, you know, I do work hard. I do work hard. But you know, I think it’s something that I learned at Santa Clara, which is you got to, you know, if you want to have both skills, you got to work hard and play hard, right?

If you want to get the soft skills going, you got to connect with people. You got to get off the laptop. You got to go out and and go embrace the day. And so one of my things that I do when, because you know, there are days where it’s like, I just don’t have the social battery. I don’t want to go out and do something today.

And so what I do with some of my buddies is, and you see this headset, play a little Call of Duty. Right? And so this, this is

Ryan: Yeah,

Robert: And, uh, don’t look at my numbers. I’m not giving anyone my gamer tag because I don’t want them to validate what those numbers look like.

Um, but, uh, but yeah, I do do [00:51:00] that with some of my buddies and, uh, love, love doing that. So it’s a lot of fun.

Ryan: I, uh, I actually do the same. Like I, uh, I am horrific at video games, but it’s just like, how often do you get to just like, Have a good excuse to get like three or four of your buddies together and trash talk for, you know, an hour and play a game and have some fun. It’s like really a great way to stay socially, uh, you know, in touch with friends.

So I, I love that. I do do a bit of the same. Now, if anybody liked anything that you had to say, once like hear more about anything, check it, you know, learn more about one of the projects you worked on or, you know, the, the golf bar or anything like that. What are the best places for folks to reach out and connect with you on?

Robert: Yeah, absolutely. I would definitely say start with LinkedIn, you know, add me on LinkedIn. My name is, it’s Robert Bonovich on LinkedIn. I’m super formal about it. Um, but, but you could call me Rob if you want to, if you want to add me and send me a DM on, on, you know, send a little note. You could [00:52:00] just call me Rob.

So, so that’s that’s probably

the best way. Yeah.

Ryan: I love it. Perfect. Well, uh, Robert, uh, I want to thank you so much for taking the time to come on and like share your experience. As I mentioned, you were super vulnerable and share a lot of personal experiences, shared a lot of really, really useful tips as far as analytics and personal growth and sales and operations. It was truly a pleasure getting to have you on. I just can’t thank you enough for the time and the experience. so much.

Robert: Of course, of course. Thanks so much for having me, Ryan. You know, there’s no reason to have a podcast if it’s not to help others learn. And so I just want to, you know, say to you, I’m really thankful that you had me on here. Really glad I could share those experiences and, you know, really hope that those experiences help shape someone’s future, help someone make better decisions.

Maybe it’s not data, but maybe this podcast is a good way to get others to just You know, think about a problem differently or, uh, maybe something about, you know, [00:53:00] personal life where they’re saying, Hey, I need, I need to do something different. And tomorrow’s going to be the day I get started. So, you know, I really appreciate you having me on here.

It speaks a lot of volumes to your character that, you know, if you wanted this podcast to be all about you, you’d be the only one on it. So I appreciate you extending the invite out to others and letting others, you know, just talk a little bit about what their experience is. It’s, it’s a hard world and data.

It’s hard to break into. Thank you. And I think as long as we can all leverage the community and help each other, we’re all going to be, find ways to be more and more successful.

Ryan: Yeah. I love that. Um, that, that scared me a little bit. Like the concept of me having to somehow hold the air for a solo podcast was just instantly terrifying to me. I don’t think that’ll ever happen. Um, yeah, we’ve done like one solo episode. We’ll probably do a couple more. Like sometimes they’re cool.

Like you get to answer like a bunch of questions that come into us all at once, but uh, it’s nowhere near as much fun as like getting to learn from someone else. You’re right. It’s kind of like sharing ideas. That’s, that’s the, Anyways, I also want to make sure to thank the audience. If [00:54:00] you have listened, especially if you’ve made it this far, thank you so much for sticking with us. Hopefully, if you’re one of those people, go on and give us a thumbs up, you know, like, subscribe, give us a rating, hopefully a good one. Tell one of your friends about the Making Better Decisions podcast. It really helps us out. Rob, Robert, thank you again so much. And this has been another exciting episode of the Making Better Decisions podcast.

Thanks for listening.

Outro: That’s a wrap for today’s episode of making better decisions for show notes and more visit, making better decisions dot live a special thank you to our sponsor canopy analytic canopy. Analytic is a boutique consultancy focused on business intelligence and data engineering. They help companies make better decisions using data for more information, visit canopy analytic.

com. There’s a better way. Let’s find it together and make better decisions. Thank you so much for listening. We’ll catch you next [00:55:00] week.