If you would like to earn CPE credit for listening to the show,
visit earmarkcpe.com slash FPA.
Download the app, take a short quiz, and get your CPE certificate.
Finally, if you enjoy listening to FPNA today,
please go to your podcast platform of choice,
click the subscribe button, and leave a rating and review of the show.
And now, onto the show.
From Data Reels, this is FPNA today.
Welcome to FPNA today. I'm your host, Glenn Hopper.
Our guest today is the head of finance for Google Cloud Consulting in Europe,
Middle East, and Africa. He previously served in finance leadership roles at Microsoft and GE.
Antonio Reza is a seasoned finance and technology professional
with a remarkably diverse background spending multiple industries and countries.
With more than a decade of experience leading projects and areas such as financial audits,
supply chain excellence, mergers and acquisitions, integration, and commercial effectiveness,
he has honed his expertise across sectors including oil and gas,
healthcare, financial services, and software.
Antonio has a true global perspective on business and finance,
having worked in eight different countries and becoming fluent in an impressive four languages.
Join me in welcoming Antonio Reza to the program.
Antonio, it's great to have you.
Hey, thanks for inviting me. It's a pleasure to be here.
Yeah, I love your background. Very diverse and some big logos in there too.
So maybe tell me a little bit about your background.
Take me through your career and how you got to some of your recent roles,
including Google and Microsoft.
Sure. I studied finance and accounting in university.
And then I graduated in 2008 when all the financial crisis hit.
We couldn't find any jobs.
And what I ended up doing was starting a company with my mother,
who was an entrepreneur at heart.
And we started a small, hard manufacturing company.
And we operated it for about 18 months.
And we sold it after 18 months for a small profit to a bigger manufacturer.
So I doubled my fee a little bit on the entrepreneurial world.
After that, I always wanted to work in a multinational company.
And at the moment, GE was the poster child of excellence in terms of finance training.
And I moved to Mexico City. I'm actually Mexican out of origin,
foreign and raised.
And I got a job at this rotational program called the FMP Financial Management Program.
And I did two years there where you rotate every six months in a different business unit.
And my fourth rotation, I got to go to Brazil.
And I worked in the oil and gas industry.
And that was a lot of fun.
And then I moved into the internal audit group,
which was in reality, it was an internal audit group and a consulting group.
So it was a bit of a mix between what you would do typically on a KPMG
and McKinsey, for example, because we actually had to find the issues and then help the business solve it.
So that was very interesting from a finance skill set perspective.
I stayed three years there.
And that's where I got to go to about 10 countries on the world,
different business units.
I met my wife there, who's French. And I ended up transferring to Paris.
And got more roles in the commercial space, commercial finance.
And then eventually a little bit of integration.
I'm an integration because G bought a company called Alston,
which was the French jewel.
And that was a lot of work on synergies and how do you materialize those synergies?
And then I ended up moving to another business, which was renewables.
And that was my last role at GE.
I am a pretty geek at heart.
I built my computers.
I've always liked technology.
So then my prior boss at GE became the CFO of Microsoft for the French subsidiary.
And she invited me over to take a role with her team.
And I worked on the Azure space to try and grow market, gain market in the French market
from AWS, who's the main competitor.
And then after that Google came calling up to two years of that.
And then I've been there for about almost three years now at Google Cloud.
Yeah, that's great. And that's really new host here.
So that's one of my favorite things about hosting this podcast is
I'm talking to so many like-minded people.
And I love hearing Geek at Heart building your own computers.
I mean, I did the same.
And back in the day, thinking about programming and basic.
I'm dating myself here.
This would have been in the 80s sitting in front of my Commodore Victa 20.
And it's funny, though, all these tech people who end up going in finance.
There's like some kind of correlation there.
I'm not sure what it is.
I think it's the numbers.
Because I think you like detail.
And I compare to think about it.
You have to understand when you put together,
even if it's for gaming, not to be here a little bit.
But you have to think about the CPU and what's the clock rate
and how much RAM and how much the GPU you choose Nvidia.
Like you like details, which I think translate very well into
spreadsheets and cost levers and profit levers and things like that.
So I think there's some sort of a synergy there.
If you don't go to engineering, right?
Yeah, yeah. And actually I started in mechanical engineering.
And the calculus was too much for me.
I prefer business calculus to engineering calculus.
So that's great.
And that's, I'm saying a common theme here.
And I think it's that there's no gray area.
You know, it's with numbers.
You just it's black and white.
It's binary.
It's just and it's kind of nice to have that sort of resolution.
Whether it's a, you know, a program works or it doesn't.
Hardware build works or it doesn't.
And the financials, you know, the trial balance balances or it doesn't.
And thinking about, you know, just you've moved so much in your career and you've done
finance and accounting work in so many different countries.
I'm assuming your IFRS and GAP kind of going back and forth a little bit.
Does it, how did, how did all them moving in the different exposures working in different,
we'll get to the industries later.
But thinking about just country specific differences.
How is that shaped your sort of approach to finance and accounting if at all?
So we always operate it in US GAP.
But you always have this statutory requirement.
And sometimes you had like, I want to say like two ledgers.
But you would have, you know, you have different set of accounts when you do with US GAP.
And it's a different set of accounts for statutory.
So I never doubted in that.
I think more on the perspective from working in different countries,
it's the culture of how people treat certain things.
Sense of urgency, attention to detail.
A lot of the advanced or more advanced finance working by.
It's like if you work in the US or you're working in the UK or Europe,
it's more discipline.
There's more substantiation for completeness, accuracy of accounts, things like that.
When you went, when I got to go to Angola or Brazil or in Latin America,
that was different.
It was more of a, the bare minimum to substantially the accounts.
It was, it was difficult to get a sense of how do you use this,
like what do you use this account for?
How do you even do business in this type of thing?
How do we grant concessions?
Like all that type of stuff is just changes from country to country.
And you have to understand those cultural exceptions that happen.
Because not even though it's an American company,
we don't operate the American way in a Brazilian market or in a Angola market.
There's different things there.
I got to see a JV in Angola, which also you see the pressure from the partner.
That was interesting. I think gives you more perspective on how business should be.
And a global view of what are some of the opportunities or what are some of the disadvantages?
And what can each market learn from each other?
You've worked for such big international companies at GE,
Microsoft, and now Google.
And I'm an AI geek, so I'm immediately going to go straight into Google and where you are right now.
And I'd love to hear about just sort of the mood and the focus and the environment at Google,
just in the midst of all the whirlwind around AI right now.
I mean, what's for you guys?
I mean, I know you're not writing machine learning algorithms,
but just across the company, what do you feel is the what's the sense of the mood
and the focus of everyone there right now?
You feel a little bit of prioritization on using these tools.
At least getting your feet wet on understanding how to prompt, how to ask a question.
Because there is a learning curve on you can ask a question to Gemini or a charge of your perplexity.
Whatever to use.
But it might actually not be insightful.
It might be a repository of knowledge,
but it's not insightful. It doesn't generate insights yet.
I think in terms of machine learning, I got to see that more closely when I was at Microsoft.
When the hype wasn't that big yet, we actually used it for our commercial forecasting process,
where revenue we used to do it, you know, in an Excel sheet based out of the inputs from the commercial team,
things like that.
And Microsoft actually figured out how to use statistical analysis through machine learning
and things like that.
We're in the context of a SaaS business where you have new deals, renewals,
and then this concept of recurring revenue.
So it's very statistical.
So they used all of that data across the world, across all the products,
to forecast what the revenue was going to be.
And to me, that was fantastic because it just added another layer of evidence
of what the forecast should be, rather than relying on the commercial person to tell you,
hey, this is what's going to land.
And you said, why?
Because historically, your close rates have been X.
And there are renewal rates.
There were coming, and the amount of deals that were coming is X.
The discounting historically has been X.
You know, it just gave you so much more data to challenge assumptions.
And I really liked that.
And that was used by the whole finance team back in the day in Microsoft,
which wasn't that long ago.
It was like four years ago.
I think now we're entering more in the era of how do you use generative AI to create insights.
And I think that's still in finance a little bit on the long way.
I think I feel it's more about automation still more than anything that's generating insights.
I think the only function that I think can use that more in TANs is, for example,
investor relations to think of very difficult questions that analysts can get.
Because you can ingest all those questions into a model and then ask you,
ask me a question similar to this.
Whereas in an SBA role, it's a little harder because then you would have to ingest data on
commercial deals on these counts.
And all of a sudden, it's still not there because there's not an enterprise solution.
Yet, right?
And I think that's one, but it's still very early days.
Yeah.
And that's, you know, I think about sort of the black box nature.
And in generative AI right now, there are so many issues that you have to address for,
you know, hallucinations and results that are not repeatable.
And like we said earlier, numbers are binary.
They're either right or wrong.
There's not not a gray area in there.
And I think about, you know, if I'm a public company,
CFO or head of finance, I'm not going to sign off on something
that I just threw into a black box and got the results.
I mean, we're at, I say we're at trust, but verify,
but that trust is still kind of low.
I mean, it's funny for me to say that because I'm like, you know,
I'm a huge evangelist for AI and for the potential of it.
But we just have to know where we are in the stage of it right now.
And I guess, you know, to that end,
then I know you can't, you know, speak on behalf of Google.
And there's probably a lot that you can't talk about it there.
So I'm going to throw this question out in two ways.
We could look at it as how AI is being approached.
And I think you've maybe kind of already addressed this.
But how it's being approached by the finance department at Google
and generative and machine learning.
Or in general, like what are some potential applications
you could see where we could use generative AI, you know,
if not immediately today in the near future.
Yeah, yeah.
I think, and a lot of this stuff is public red.
But I think like I said, automation is a big one.
How can you reduce a lot of the, and I go back to this microsoft example, right?
Which is also something that, you know,
I think there was a Wallsper Journal article,
one of the VPs of finance at Google explained that.
But the whole automation process of how can you take the work
that is very manual and done by a lot of people
and takes a lot of time, like close or even a forecast
and make it so much shorter and with less resource.
That's probably the best application
because it just frees up a lot of time.
And if you think about it from a full time and equivalent perspective,
it frees so much people time to focus on more value out of things, right?
To be in those meetings with the commercial people
or the suppliers, the purchasing department,
just to understand more things of how the business actually operates
instead of you spending so much time on the closing process.
So I think that's one.
I think another one that I really liked
and that was from my time at GE is RP,
sort of robotic process automation.
So back then, we used to work with UI Path.
There was a lot of G alumni that went to that company
from the IT side and the finance side.
So a lot of work was on automating the closing process,
the cash reconciliation, and things like that.
I think those are great use cases.
I think on machine learning is going to facilitate more things
like how do you forecast more accurately
again using statistics, things like that.
I do see a world where some of the finance people
are walking that line or certain with that line of
do you learn SQL?
I'd go, do you do learn SQL?
What Amazon, do you do learn SQL?
And then you go a little bit to the right
and then do you learn a little bit of Python, right?
Because if you learn Python SQL and then you know
which is a little bit of the main core languages
for ML and AI, you got a very powerful finance person.
The other thing I've seen is there's some noise around
coal pilot already with some of the CFOs on the Microsoft side.
I think that's generating a, something
I say about generating a pivot, a chart that
goes into a presentation, that's just like saves
a ton of time by this.
Especially finance people at a certain CDR level.
They just spend so much time on slides
on the commentary and things like that.
So the commentary, for example,
if you are looking especially historically,
if the narrative is good and you have this personality
of the AI, because you can prompt that personality
to be a sound like an inverse relation page for a lot,
that's also super useful, I think.
So those are some of the use cases that I've seen
or I think they're going to be.
Yeah, and that's, you know, it's funny because even a year ago,
if somebody were coming into FPNA and they said,
okay, I've got my masters in finance and I've done,
you know, this is my background.
What do I need to do to supercharge my career?
I immediately would have said SQL and Python and data science.
And now, though, I still think data science is very important
because it's, you know, the extension of, of BI.
What I've been amazed with with generative AI is,
I call it, it's, so we've been talking about democratization
of data for a while, but with generative AI,
you could, it's almost democratization of data science
because now the barrier to entry where if you can write SQL prompts
in natural language and you don't have to know Python
because your generative AI tool is writing it under the hood,
it's like you can play with the same tools,
but it's dangerous just like if somebody were coming into FPNA
and didn't know, you know, the difference between cost of good,
sold and expenses or whatever, you have to have that domain expertise.
So I think about how you're going to add more value
is just like not every data scientist is a coder.
Certainly they have a fundamental, certainly a right SQL commands,
but you have to understand the models and the algorithms
and, you know, what you're looking for that this is classification,
this is prediction, whatever the, the case is,
but it feels like now you get the domain expertise
and finance and accounting, you understand the principles
of data science so you know what questions to ask,
but you don't have to be a great coder anymore.
It's going to be, I mean, maybe we're not 100% there,
but you and I could go on Gemini right now
and you know, have it right code to build a really cool little Python app
that, you know, reviewed financial statements or something.
Yeah, I think that one is one that I've been struggling
and debating a lot with friends because even the NVIDIA CEO came out
saying that people should not learn to code.
I don't know if you saw it, but he was like,
Hey, people should not learn to code anymore.
Even though it seems like the push from schools and parents
and everybody's like, you learn to code and he was actually saying,
you shouldn't.
I still think you should because a lot of the things,
and again, if you like that type of thing,
right, if you don't like it then don't.
But for example, I do SQL scripts on my job right now, right?
And it's something that you cannot ask a model to write for you
because you need to understand how a data set works,
how does it look in the table, even something so deep in knowledge
as which table do you need to use to retrieve the data, right?
And then I think that coding helps you, at least it has helped me
understand how to think, right?
Because you're thinking in this sort of algorithmic way
or decision tree and you don't need to know SQL to do that.
You can even do it in Excel.
A lot of people do in Excel or she's from a new statement.
That's already another rhythm.
So I think it's important to understand that.
And then yeah, no data science principles,
but I don't think that the finance teams
or at least all of the members of the finance team should do that.
Because I think it's more important to understand
what does an income statement do or what are the accounts in there,
what are the cost of the profilers?
And then you're talking about the analyst level, right?
The data scientist, another thing I remember
when I was working with data scientists at GE,
50% of not more of their time is actually
spent on cleaning the data.
So you know, depends on the people.
But like, do you want to really spend,
if you work five days a week,
when you have days, just cleaning data to then figure out the model
and then you're going to get more feedback,
you're going to have to ingest more data, clean it up.
So I think the conversation is a little bit obsessed right now
with, ah, you know, you know, learn how to code
and finance professionals.
And I'm like, yeah, at an analyst level,
I see that to get a job and become more competitive.
But then once you hit manager, let alone the director,
that stuff doesn't matter anymore, right?
Because you're no longer producing that.
You're more taking care of what is the team saying,
what is the right message that I need to tell my,
you know, manager and director investors, things like that.
And then a lot of influencing, right?
I think the role of the finance person
is a little bit like the referee of a match, you know,
sales wants this, but then, you know,
purchasing wants that.
And then it's an economy, you have a trade off.
So then that skill of negotiating
and influencing people and being a referee,
I think it's more of an important skill set
that they're going in their rabbit hole
and learning Python and SQL and all that stuff.
Like, yeah, that's my opinion.
Yeah, no, that makes sense.
And that actually brings up the question is,
it's interesting because I talked to so many
different companies of all sizes.
I'm always interested in hearing what the makeup
of kind of the FPNA team is at various companies
because when you talk about a manager and a director
and what you're doing, you know, at a much smaller company,
you may have someone, they may have a CFO title,
but they're really, maybe they're still doing bank rags.
I don't, you know, probably not, hopefully not.
If they got that title, but it's just so the different size
and scope.
And so thinking about a company as big as Google,
can you tell me how your FPNA team is set up?
So I think this is a place to probably most of the tech companies
are that, but I think you would have,
if you think about the CFO office,
you'll have like the investor relations department,
the Treasury department, right?
So that's very niche, you could say.
Then the FPNA team typically, what I've seen is
you always have a product team,
somebody who's really planning, forecasting,
and analyzing the product.
And whether you're thinking about someone like Amazon
or Google or Microsoft, they have different products
from cloud to workspace or M365 or retail
in the case of Amazon.
Somebody's always analyzing forecasting and planning
for the products.
How much money are we going to get from this product
and how much cost are we going to do for this product?
Those I think personally, those are one of the sexiest jobs,
even though sometimes they tend to be more of a cost analysis
rather than a revenue type of commercial financial.
And I think those roles typically sit at the HQ level,
typically in the States,
but they have a lot of power and a lot of weight
because they dictate a lot of the,
where the trajectory of the company is going to go.
Then you would have like a go to market team,
which typically me sitting in France,
that's what I saw when I was sitting in Azure
for Microsoft friends and now at Google Cloud,
you have this business partner, right?
That's the new term that goes with the sales person
or the sales director, works a lot with marketing,
where they are just trying to get as much volume as possible.
Those jobs are fun because you get to see
a lot of the commercial process, you get commercial acumen,
and if you really, really, really like it,
you start understanding things like, you know,
the economics and you know,
should we give out these concessions, payment plans,
investment programs, there's a lot of stuff
that of, for example, because we want them to,
and technology typically want them to adopt your product.
But the customer might be red soon to do that.
You have this investment program where you give them money
or credits in exchange of signing a bigger deal, right?
So you would, you would typically have that go to market team.
And then there's other more like shirt services, right?
That help you out with the closing and things,
and then you have the control issue team
that's more on the clothes and the statutory reporting,
things like that.
I would say those are the two main verticals product
in go to market, and then you have what we,
you would call the central finance team
that is more of, you know, dealing with analytics
and the portfolio value, more like creating the whole package
of the whole company level, and then you would slice and dice.
But I would say those are the main things
that I've seen from Microsoft, Google,
and then from some friends that I have at Amazon,
that's more or less how the big three work.
And probably, man, I work the same way.
And that's, I so love business partnering now
because I think I'm old.
So when I started in, in finance, it was,
it was almost, you know, finance and accounting
were in this ivory tower where it was just,
we don't care what the widgets are,
we're just counting them and giving our reports
and we defined our KPIs two years ago
or whatever the case is, and we're just running with these
and kind of operating outside of the business.
But as, I think probably as data's got more available
and as the sort of expectations of real time information,
the whole emergence of business partnering,
first off, it's huge for the business
because you can get so many more insights
and you may find data that will inform
whether it's your forecasts or your analysis,
or whatever, but by being embedded with those teams.
So it's great for the business,
but I also think it's huge for development
when you can actually get out of that ivory tower
of finance and accounting and see how the actual process
has worked.
I mean, do you, has that been a part of your career?
Have you been, you know, more broadly involved
in understanding, or I guess the easiest way to say that
was, has the bulk of your career been
in this kind of business partnering mode
or have you been in places also where it's just,
we're just in finance and accounting,
we're doing our thing?
No, to be honest, I haven't had like an HQ role
that you just sit there.
So a G, they really pushed you and even forced you
to go walk the shop because it was a huge industrial business,
right?
Except for financial services when they have G capital,
and even then we always, always, always had to sit
as close as possible to the operations.
So for you to plan something right,
you have to understand what the shop was doing
and the flow of product, right?
So the, and back then when I started,
G was more on the cost side, right?
Cost accounting, things like that.
And I think cost accounting is like a huge deal,
but not a six, not the sexiest job to have,
but then you go through that and it's great
because you see, you know, raw materials,
you have to understand purchasing,
you need to understand logistics,
you need to understand reception and warehousing.
And that's walking the shop, then whip,
we're in process, you have to understand
how are they booking hours on costs,
how are the materials being booked, how are you scrapping?
How's the, well, all that stuff you need to understand,
I remember all the white boards in the factory,
I understand lead time, all those things,
so you could plan, you know,
where am I gonna have a shortage of materials
so that I can, I can forecast that.
And then finally, you know, finish goods,
I remember like, how do we forecast finish goods?
How much is gonna get out the door?
You have to learn about income terms,
so understand the, you know, the transfer of risks
so that you could recognize revenue.
So all my roles at G were like that,
like very close to the shops and factories,
so I always, you know,
we didn't use to call it business partner bag, man,
but and then the later stages of my career,
that has been more on the commercial side,
because that's, I think that's where the action is,
or you feel more of an understanding
what the market is saying or what,
you can pulse the market better, right?
You can do it, you can hear through the voice
of the sales guy who is always optimistic, right?
They always just wanna sell,
but then again, one of the big learnings from GE was that,
you have to have this external focus team
where, you know, I hear something from the sales guy,
but if you put your ear a little bit to the ground
and you start hearing what are the investor calls saying
for each of the customers that you have in the company,
and then you can triangulate that to see,
hey, I don't think it makes sense,
I think this is the help
because these guys are probably gonna go bankrupt
based on what the analyst question were, right?
And sometimes that's context of the sale there doesn't have.
So I think that type of role I also enjoyed,
I've never been to be honest with you on a, maybe one,
but on a role that has been purely, you know,
planning and analysis far away from operations.
I typically don't like those roles
because I think like you said, it's very siloed.
However, the irony of those roles is that you're closer
to the chain of command.
So you're more visible,
therefore, promotions tend to happen in that space.
So it's a bit of a trade-off of what you wanna do
with your career.
FPNA today is brought to you by Data Rails,
the world's number one FPNA solution.
Data Rails is the artificial intelligence-powered
financial planning and analysis platform
built for Excel users.
That's right, you can stay in Excel,
but instead of facing hell for every budget,
month and close or forecast,
you can enjoy a paradise of data consolidation,
advanced visualization, reporting, and AI capabilities.
Plus, game-changing insights giving you instant answers
and your story created in seconds.
Find out why more than 1,000 finance teams use Data Rails
to uncover their company's real story.
Don't replace Excel, embrace Excel.
Learn more at datarails.com.
I think about how much FPNA has changed
just over the last, I don't know, say 10 to 15 years,
just because there's so much more data right now,
but there's also more and more of an expectation
of real-time information for everything.
Shifting from that rearview mirror look,
which I know we've been there,
especially at the biggest companies in the world,
where there is a lot more real-time data,
but it's also like the expectations are higher as well.
So what do you see as maybe the challenges and opportunities
for FPNA right now?
Either at an individual employee level
as far as their training and development
and where they should be focused right now,
or even as a field in general or at companies?
Yeah, I think that's never going to change,
but storytelling is something that they don't harp on
when you start your career.
They did with me, but I think it's because, again,
G's are very unique school or was very unique school.
They used to tell us everything's a story
and you had to do all these, like, you know,
produce a massive amount of slide,
similar to like McKinsey, right?
And even though I hate slides,
I think it's a great way to understand how do you tell a story.
When you couple that with an understanding of what the audience wants,
by that, I mean, we're talking to an executive,
like he's not going to go through the whole spiel
of I did X and Y and C, therefore this.
They just want to know this is a result.
And they have time, they go, oh, I know that that's the result
because of X. I did X in the Y and Z.
Right? So it's super counterintuitive
to the scientific method that they teach you at school.
So you come out of school and then you know,
the formulas, everything and how to, you know,
do a financial analysis and black schools model, whatever,
when you don't use that in real life, they're rarely.
And you're used to telling stories like that.
I did this because that's how your program to do it.
And I remember one of my executive managers
when I showed me this book and it's like the Bible
for the consultant world, which is called the Pyramid Principle
by Barbara Minto.
And it just made it so much easier and you understand,
oh, so this is how you need to communicate in the corporate world.
Fast, structured, but foundations at every layer.
I think that's one that's never going to change.
And I think it's something that the earlier
you can do in your career, the better off you are.
I think the other one is how to influence people,
it's crazy.
How do you convince them to do something?
I wish I could have read more books like,
never split the difference.
That they teach you how, you know, the sky,
who's a, I think, if the IOCIA guy,
that dealing with really high-stakes situations
and how do you control the emotions and then how do you convince people?
Right? Because for example, there's a lot of tensions
and budgets and planning and things like that.
People, it's their money.
How can I spend my money?
And sometimes you need to negotiate, guys,
like we only have this amount of capital.
This is how it's allocated.
And yes, there's a stretch here and this is stretch there.
And you cannot travel, whatever.
That's always going to happen.
So how do you deal with those types of situations?
I think that is way more important than understanding,
you know, super deep expertise in accounting
or I, for our ass, or you're scapticking.
I think that's all we, that's never going to change the stu for in my opinion.
And I think it's going to require more,
like just being in general, more understanding of,
you could save data and things like that.
But I'm still not quite sure because there's a lot of data.
The question is, the people really use it.
I have no idea how many times there's all this,
a new dashboard and like you said,
it's live data, whatever.
I don't know if I believe in the live data thing to be honest
because do you think an executive is going to be the whole day
looking at the dashboard and refreshing thing?
No, they should be spending time with customers,
spending time with their teams, spending time trapped in a proposal.
So in reality, snapshots work better, right?
And then you know that role that you say when you talk to someone
and you leave with the last impression of the last, you know,
there's only like so much thing that you can fit in your head.
So you fit either the six minute worth of content or an hour call.
So that executive is going to be overwhelmed with the amount of detail,
especially if this dashboard has like, you know, charts and graphs and whatever.
So I think very snapshot, relevant content of like,
these are the three metrics that you need to know of.
This is where we're doing to move the needle.
And that's it.
And then the executive's, and then he can do his thing and then do actions
that will drive those three metrics, right?
So I'm not entirely sure if a new dashboard all the time is the best answer.
And I think that if PINA has become obsessed with dashboards and data and all that stuff.
And I think it's, it's good to some extent,
but we could be doing something else.
The problem with corporations when they do that is that sometimes that's a mandate
that comes for very high above.
So the people that are analysts or managers don't have a say on whether
the dashboard doesn't get built or not.
You know, it's interesting as you were walking through that,
it really reminded me of so earlier,
we're talking about how finance and technology that it's sort of the same mindset
is drawn to both of them.
But now so we're in kind of a right brain, left brain situation,
where you have to be have the very logical mind that can, you know,
construct all these numbers and put everything together and make sure everything's correct.
But then the ask is also to be able to communicate it and to be able to tell that story and everything.
So I think, and I think data science and FPNA are very similar in this way is,
like you said, you've got all this data.
It's to keep it from becoming like billboards you drive by on the interstate.
It's refining that data into something that's meaningful.
But then you're also kind of an editor in that, you know,
you know the story you want to tell and you can kind of get into that,
what lies, damn lies and statistics mode,
where you can make the numbers, it's sort of the Malcolm Gladwell thing.
You can make the numbers say whatever you want them to.
So having that sort of editorial mind to know your audience,
but also to be reporting the truth, whatever it is,
but to be able to convey it in a way that's, you know,
different ways to deliver good news and bad news or whatever.
But you should not try to cover anything up,
but that you've got to, depending on, you know,
whether it's investor relations or management or whichever group
you're talking to that they understand it and that you're accurately representing it.
So it's really kind of a cool marriage of both sides of the brain, I guess, in what we do.
Yeah, we used to have this hack.
His name was Jeff Borsing.
He was a CFLG at some point and he was saying,
always ask why five times.
That's the way to tell a story.
Okay, like we grew 10% why?
Okay, we got more volume and customers.
Okay, why?
Because there was a one off event with one customer and they paid a bunch of money.
Okay, and why didn't we have a one off?
Because we gave them a discount, why did we give them a discount?
Because there's no controls in place to control it.
So then you realize that the problem was not the growth.
It was that you lack a control having salespeople, you know,
rein down and giving discounts.
And, you know, I think that's a lost art and people need to do that more often.
Whether you're in finance or not, but the other day my wife just started a new job
and she said, fine, it's true.
And she was saying, I the culture is very different from G.E.
She actually stayed longer than me and then G.
And she was saying, yeah, people sometimes write, you know, there was a 600k variance in
control.
And the commentary was very up because of ex movement and the entity.
When you need to pitch that to someone on a need be P or SVP level context and storytelling,
like human being type of thing is that's what matters and that's what you remember.
Right.
I'm really loving talking to you because I can sense your sort of your passion and interest
in not just doing the basics of F.P. and A, but your deeper focus on it and realizing
the storytelling and how to get down to the answers.
And clearly you read a lot on it.
What's kind of top of mind for you right now?
What are you trying to learn it or master anything new or where's your focus right now
for development?
So, be honest with you, just more economics, more than something finance or any sort of
anything like that.
I think that back to that storytelling point, there's a lot of context that needs to
be given because of what happened with COVID, because of what's happening where they are.
How are we losing jobs?
And when you look at it from an enterprise level, a single enterprise level, it's very
hard to miss what's going on in the outside.
And typically one thing I've seen is that finance people are very inward looking, right?
The data that you use is probably 80% or 90% of the time is internal.
Your customer data, your cost data, your revenue data.
But what's happening in the outside is something that either you don't have access to because
it's expensive, you need to buy an analyst report from Goldman Sachs, whatever.
But you can have these signals from the news or from studies or something like that.
In the case of big companies, they probably have an economist team and get their resources
from that.
But really understanding economics in a deeper level, I think is a good toolkit to have,
like do you understand what drives prices.
If we think about it, I feel like a company is an economy in itself.
It's a bunch of capital that needs to be allocated, but that capital has an opportunity
cost, right?
It can be used in different ways.
So how do you make sure that you allocate it in the right way?
I think that's a good skill to have and to master and we don't pay enough attention to
it.
So how is inflation going to impact the product, right?
The manufacturing cost.
And you can know that like very high level, well, price of rising, heavily deeply, what
doesn't mean, right?
The whole economic context, the job losses, upskilling, right?
I remember that I heard this crazy statistic once that suffer engineers because I work in
tech.
Their skill set lifespan is 18 months.
So they need to constantly upskill themselves because in 18 months, there's something new.
And if they don't know how to use it, they're already absolutely.
So that to me was like, wow, and why?
And then you see that, you know, so many tools, or if you think about how many programming
languages there used to be, and then I have plenty of it.
You have Python, SQL, C++, Ruby, or Rails, JavaScript.
It's the same.
And then now they have to learn whatever to do AI in a mail.
So the upskilling thing is this great.
How is the economy being affected because of COVID?
Like I said, that was a huge thing, right?
How is capital flowing through that?
I think that's a great skill to have to have that context for storytelling, especially
when you work in a global business and you have to explain why did France do better than
Germany or the US?
Or why are things, again, back to the US-centric culture corporations?
Why is the strategy that's working the US, not working in France?
And I can tell you, in many places, G, Marx, and then Google, sometimes people didn't
understand that, hey, France has very strictly rule, so you cannot fire people here.
You have to give a three-month notice for you to quit your job.
So even if you do a restructuring plan, you cannot fire people into it.
You have to wait.
So there's all these nuances, cultural nuances, and economic nuances that you need to understand,
I think, for France.
So that's a priority for me.
And the other way, yeah, they say, I think, right?
How do you understand the impact that it's going to have?
I've bought recently this book from...
I mean, it's the last thing.
I should remember this first time it's most stuff up, but he's the new AI leader for Microsoft.
He came up with a book called The Coming Wave, and he explains how this is like the fourth
big wave of technology, right?
And he tells the story of how the Industrial Revolution was won, the printing press, like
all those things, and then now, how is AI going to create this new wave?
And how dangerous it is to become a tsunami, because of what it means.
Because it needs to be regulated, or actually, the word he uses is contained.
So that is critical for anybody who's in tech, and even anybody who just is interested
in AI, I think that's going to be something that's going to impact all of us.
It's a good idea to learn about it.
What does it mean really for everything, right?
Even for your kids, like, what should they study in the future?
That's great.
And I think I love the economics part.
I feel like, you know, in FPNA, you're always looking for what levers to pull, and what
things are trying to find these correlations, and I know during COVID, I was addicted to
the Federal Reserve, the Fred site, where you can get all the economic data, and they've
even got the Excel plug-in.
And I was just trying to find correlations, you know, to the historical to try to figure
out what was going, because it was very hard to forecast through COVID.
I don't know what anybody's doing anywhere, and trying to find something that you could
grab onto.
I wasn't super successful in that, but I don't think anybody was.
I do want to go one more before we get to lightning round, just because I always, I feel like
we always learn so much from this.
But, you know, it's the hardship.
If we survive, we come out stronger on the other side.
So I always like to ask, what's kind of the worst or most challenging FPNA experience
in your career, or maybe a big mistake you've made in what you learned from it?
I mean, I wouldn't say it was FPNA related, but I think it was my transition from individual
contributor to manager is always hard.
And that happened to me in my first promotion.
I couldn't delegate number one.
And then even the stuff I delegated was wrong to delegate.
I think you mentioned this before, right?
But trust and verify, that was another thing that you just, either you just trust it.
And also like that's when you get into these, I had a very bad experience of not being
able to manage my team.
And things got out of hand.
And, you know, a long story short, we ended up, it was an audit.
We were late with closing the audit.
And we were actually auditing a site in Venezuela.
We were late, the Internet of Venezuela is not right.
So we actually had to fly back to Houston and we stayed at this office and we didn't
sleep.
It was five of us.
We didn't sleep for 56 hours.
We actually didn't go home.
We just stayed in the office for 56 hours, trying to close the test work files, the audit,
the pages, everything.
It was exhausting.
And I don't recommend that, but it was a painful experience that taught me so much about
reading people who has good skills, who, I don't like to say bad skills, but like, who
has opportunity to grow needs a little bit more babysitting, right?
That doesn't mean that they're bad.
And how do you delegate more effectively to get stuff done?
I think that's, those are the big lessons.
Yeah, otherwise just how it impacts your health, right?
Like back then, I was 25, 27.
So yeah, not sleeping didn't do any damage whatsoever.
If I tried to do that now, it would destroy me, right?
So it's also teachers, you know, a little bit about, I think most, there's a trend lately
I've seen on LinkedIn that I agree with it that you use your 20s to hustle as much as
you can to climb up and have a successful career.
So your 30s, you don't have to be spending nights, working, etc.
So, all right.
Now we really have to go lightning rounds.
Let's see, I don't want to influence your answer here, but one thing we always like to
ask is what's something that not many people know about you.
And I did see that you and I both share an interest in filmmaking and trash lines.
So not saying that's what you have to talk about, but what's something that people might
not know about you that they couldn't find online?
They couldn't find online.
So I used to do CrossFit training intensely for really a good five, five, six years.
I loved it.
I was literally the guy who woke up at 5 AM and went to training sessions, lifted heavy,
did a snatch of 120 kilos at some point.
That probably most people wouldn't know at this point.
I stopped because I had two kids.
It's just now, it's harder for me to snatch that volume, but that's probably one.
And then I did the occasional trash line sometimes.
I'm trying to get back on it, but it's hard.
That sport just requires so much time.
But it needs to be hyper organized to get the swimming in, the bike in and the running
in.
Agreed.
Yeah.
And with young kids too, man, just no life outside of work, kids and trash line training.
That's it.
Yeah.
Maybe not prioritized in that order.
Here's kind of a bonus question too.
So you've worked at Microsoft.
Now you're at Google.
Where do you go?
Excel or Google Sheets?
Excel, 100%.
I kid you not.
I think Google Sheets makes you lose 50% product TV.
If you're like an Excel power user and you're used to spreadsheets and formulas and, you
know, just to give you a perfect example, there's a keyword shortcut for you to paste the
formula from one cell into multiple cells in Excel.
And most people will notice you cannot do that on Google Sheets.
You actually have to write, select the cells, right click, go to options, pay special and
then paste formula.
You cannot do that through shortcut.
Right there.
It's just a waste.
You know, to be fair, Sheets was not designed for the finance power user, right?
It was more designed for, you know, regular users.
It's just simple spreadsheet budgeting, things like that, right?
But yeah, so it's, you can't beat Excel.
That leads us right to our favorite question.
What is your favorite Excel function and why?
I mean, people are going to criticize this.
But I think what the moment that I figured out, Intix Match, I felt like a champion that
I didn't have to use to look up and now probably people are going to say, I need Excel
look up is better.
I'm sure.
But it looks cooler if you have an Intix Match, I think.
So yeah, yeah, perfect.
Tell me how, for our listeners, how can people get in touch with you?
You can follow me on Twitter at the Antonio Reza, LinkedIn as well, same thing at the
Antonio Reza.
That's where it's typically right.
I also have a newsletter called Money and Robots, which is I talk about AI and finance
and things like that.
And I try to send that every now and then.
So yeah, those are probably the three main places you can find.
Alright, Antonio, well, I really appreciate you coming on the show and all your insights
and look forward to talking to you again soon.
Likewise, thanks for having me.
It was great.
