3min chapter

This Past Weekend w/ Theo Von cover image

E448 Roseanne

This Past Weekend w/ Theo Von

CHAPTER

Introduction

TheoVon.com slash T-O-U-R for the return of the rat tour. New shows in Tampa, Florida on June 25th and Grantville, PA on July 19th. Roseanne Barr is our guest today at Joe Rogan's Comedy Mothership Live from Austin, Texas.

00:00
Speaker 2
And you don't have to specifically name brand. It's fine. But what technology you have now compared to what you had when you first started maybe some ways that that's facilitated the work that you do. Or I want to say not facilitated the work because the work is still challenging, but maybe it eliminates some of that, the grind of just being lost in Excel and hammering through. I think it
Speaker 1
really comes down to... Well, first and foremost, ETL, getting your hands on any data from any system is much easier now than it was in the past. I think when you and I were doing this back at the turn at the millennium, we were ODBC to to our whatever our financial was at S -Base. Great
Speaker 2
I think. Yeah,
Speaker 1
Great Plains. That was it. Into an access database. And we're sort of learning GUI, right? We're building queries in a GUI accident, Microsoft GUI interface. And we made some powerful tools out of that stuff and changed the way that our company was able to manage. It's, to say the least, complicated capital planning, because when you've got little pieces of capital all over the country, it's difficult. I think we did great works with very little. The difference between then and now is that then, even of entry access to financial data was still sort of good access to financial data. The difference between then and now, the real primary difference in my mind is that it's not just financial data that we have access to now. We have access to sales data, marketing data. We've got access to larger world of big data. We've got access to telecom. Maybe it's switch data. Maybe it's traffic data. We have access to correlating data that enriches the finance data that we've always had access to. And so that is a game changer because now we can start doing... We can start making much powerful guesses about what's happening with our customers, what's happening inside our business on a cost basis, on a sales basis, all of these things. So to me, the big difference between 20 years ago and now is that we are able to join to data that we didn't have access to before. Think about it. I suppose it was probably there, it just wasn't as easy. I suppose we could have, if we really wanted to, figured out a way to get a hold of switch data, engineering data. So the good news is, is that it's easier to get connected to multiple disparate sources of data. The other news is that it moves us closer to IT, which, you know, traditionally, IT has sort of weirdly fallen under the provenance of the CFO, right? You know, back before IT was its own thing, back before CTOs existed, CIOs or whatever you want to call them. I think an opportunity that we all have going forward is trying to find the correct interaction between IT and business intelligence and finance because it is kind of a brave new world, right? IT sees the kinds of things that you and I are doing in business intelligence and they're going, that looks an awful lot like IT. That looks an awful lot like information technology. Why don't we own this? I mean, there's a whole debate around that about where
Speaker 2
should data science live? And then, of course, I'm a hom for finance. And I think, well, to your earlier point, we are the original business analysts. And that's why I'm so mad. How did sales and marketing just like a rocket ship takeoff in using machine learning and finance and accounting has been left in the dust on that? And I think we're getting there more now. And I think now though, you're seeing more machine learning just being built into other tools, other software that we use, which is different ways to do the forecasting. But I kind of was miffed for a long time that I felt like the best machine learning resources were being put to sales and marketing.
Speaker 1
And... You want the real answer? Yeah. Well, actually, I think, I know what you're going to say, but let's hear it. Marketing professionals are like weathermen. Who was using big data first? Meteorologists. Why? Because they don't know which way literally the wind is going to go. And when you're in the business of being close and you don't have, again, in a more serious note, if the benefits of your activity are a little softish, then you're going to start, you're going to employ tools that add a little bit of hardness to the benefits that you are delivering to the business or, you know, suggesting that you're bringing.
Speaker 2
And since we're dogging on people, sales and marketing, I don't trust their pipeline. I've never... I've been frustrated, but never actually angry, ready to punch someone in sales and marketing. IT on the other hand, I just come to a meeting with IT just ready to fight. I'm just ready to go. Yeah.
Speaker 1
To nature of the relationship, I'm not sure why it's that way. The way that IT gets done, dev cycles and sprints and all of the things associated with... IT spends so much time trying to manage throughput for limited resources and managing intake and that kind of stuff that's, frankly, finance professionals, we don't have time to participate in your intake process because we're beholden to the closed timing. We live period to period. If you can't help me this week, then you can't help me, man. Often, that sets up an immediate problem, an expectation versus reality problem when it comes to getting work out of IT. IT sometimes sees finance -centered business intelligence folks, the way that
Speaker 2
finance -centered business intelligence folks see market analysts. If somebody wants to be at the forefront of FP &A starting out their career, what are you telling them that they need to study and focus on right now to be successful?
Speaker 1
I do think you focus on analytics. You focus on, you find the bleeding edge in terms of tools and processes that are being used right now. And to the extent that you can be, be interested in that, be curious about that, and stay in front of that. Because the fact is that you'll get financial literacy obviously is critical, but from a business intelligence standpoint, if you're getting your education, the business intelligence part of the analytical part of your work is going to be more and more as time goes by, more and more centered on your knowledge of the industry, of your particular business. You're not going to get that knowledge until you're in the business. So go study tools. Go study the technology and show up to whatever business you choose, whatever business you get hired onto with a toolkit that you can apply to that business and that business's industry and customers right out of the gate. The days of MFA and that kind of stuff, I don't think that's a useful educational track now. Like I said, if you want to be in the business of business, you need to embrace the tools, first and foremost. And I know that everybody's doing that. Every undergrad is coming out of their business undergrad program with knowledge of the... They've got Power BI, they've got Tableau, they understand the basics of data and database structures and that kind of stuff. And that to me is the bare minimum. And if you want to make yourself, you know, if you want to elevate yourself, you know, get curious about that stuff and get out on the, like I said, out on the bleeding edge of that stuff, because that's where that's what everybody's looking for. I look at it like this, the
Speaker 2
role of the CFO, and then therefore, all the departments under the cfo have changed significantly from just say twenty years ago maybe a little longer but twenty years ago is probably accurate if you're a cfo you probably are a cpa you came up through. And the most important thing for the CFO to do was to be able to close the books and speak to the numbers. And you were, it was very backward looking. It was, we're going to close, everything's going to be ticked and tied, and that's it. So what's happened in the last 20 years, what used to be the CFO is now what the controller does. So the controller has elevated as well. then on the CFO side, you had to lean in a lot more to the FP &A, to the forecasting, to the strategy. And you could... And I used to say this because I bounced around a lot of industries as well. I'd say, I don't care what the widget is, just drop me into finance and accounting. I'll figure it out numbers of numbers. But, so now I think with the evolution of the role, you have to have, you have to have the basic accounting and understanding of just the chart of accounts and how everything works and how the three financial statements flow together. And you've got to have the domain expertise, you know, the training to speak, the finance lingo and understand all the components there. You come in, you have to understand statistics, analytics, BI, and the tools because are you going to do this all in a calculator? Are you going to do it in R? You have to be able to operate in the environment that the company is using. So now you come out of the gates. You say, okay, I have a master's degree in finance to understand all this. Now I have a master's or at least coursework in data science and statistics. And now I know enough. And now I've got to go out and apply that in the world. So it's really, it's a huge ask right now. But one of the things that I think going to make that easier is the whole world of data science used to be blocked off by SQL at a minimum. Hopefully also you write Python or C++, you know, whatever your programming language is. But now what I'm seeing early days of generative AI is natural language becomes the new programming language. So as long as you can get, you know, we're just a matter of a couple years away from this, but all of your databases, all of your big tools where you're handling this data are going to have an interface that is, let me talk to my data and let me pull it this way. So, but you have to just like in, in an example, I always use is if you are to be talking about the income statement, you need to know the difference between net income, EBITDA, all the different points there, otherwise you don't know what you're talking about. Now you're going to have to understand the fundamentals of statistics to understand what you're talking about. But as far as the coding, that's not going to be a barrier. It's if you know and you can interact with data through natural language and you can, you know, you know how to test and see if your seasonal auto regressive integrated moving average forecast is I can't believe I spit that out without stumbling, but to be able to understand how it works and what you've asked the AI to do. I mean, I think maybe that's going to level the playing field a little bit, but I think it asks for the same things that you said that what you should be studying right now. Maybe you don't have to know the coding, but you're going to have to know the rest of it.
Speaker 1
Yeah. You got to chase like, what's the minimum education that you need? What's the minimum toolkit that you need? And like you said, now is SQL, Python. You have to understand the tech, right? You have to understand it. And then, yeah, the next thing is, I don't like to use this because so many people use it these days, but the next step of programming prompt engineering, which I really don't love that phrase because what it suggests to me is that, and maybe this is the case, but what it suggests is that there will be that generative AI and interaction with generative AI will become codified just like any other language.

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