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And now, onto the show. From Data Reels, this is FPNA Today.
Welcome to FPNA Today. I'm your host, Glen Hopper.
Our guest today is Nicholas Mann, founder and CEO of Stratos Consulting and deleting expert on data and analytics.
With over a decade of experience as a technology consultant, Nico is helped FPNA IT and data teams across industries.
He's worked in fields including life sciences, manufacturing, retail, and financial services.
He previously served as enterprise applications architect at Akebia Technologies, where he honed his deep technical skills, integrating diverse data sources into cohesive platforms.
Nico is a passionate advocate for the snowflake ecosystem and excels at helping organizations maximize the value of their data infrastructure.
Please join me in welcoming Nico, who will share his insights on the modern data stack and how companies can harness the power of their data.
Welcome to the show, Nico.
Thanks, Glen. Happy to be here.
Excited to have a good discussion with you today on data and analytics.
Yeah, really looking forward to this.
And for our listeners, you and I were introduced from another data geek who I just put in the same fraternity that you and I are in, Nathan Bell, who I'm going to say I'm going to throw a challenge out to you.
Nathan's episode is the most popular episode since I started hosting the podcast.
So our goal is to try to beat that one and put, take Nathan down a, a not your two, you know, challenge accepted Nathan.
It's on. So, uh, well, let's, I've got a million questions for you and these always go so fast.
So let's, let's go ahead and just dive in.
So first off, tell me about your journey and kind of what inspired you to start Stratos consulting because I understand you initially focused on the office of the CFO and now you've expanded that a little bit.
So tell me a little bit about your journey and what you're doing now.
Yeah, definitely. So I was very fortunate to come out of college in a field that I was actually good at, which was very surprising.
So I came out of college with a degree in finance and information systems and I knew I was never going to be this investment banker investor.
And on the other side, I was not a deep coder.
I actually failed my visual basic testing college.
So, which is much to my dismay, but it was one of those things where I said, okay, well, how do I blend these two degrees together?
And so I was very fortunate to get my first job within technology consulting that did exactly that.
So I worked for a company called Peloton Consulting Group where their focus was with the office of the CFO and deploying financial applications for enterprises to do their budgeting and forecasting.
So right out of school, I was open up to this new era of, oh, there's data involved in companies, especially within the finance teams that I knew nothing about.
That was never taught in college.
It wasn't something people said, here's a career trajectory you go.
It was always, oh, big fork and soul thing or you go to these banks.
But never this very niche industry to say, hey, people are using data and analytics at these companies in general.
And so I really embraced that and had an opportunity to work with a lot of different industries.
And then I started to get more into business intelligence BI was becoming a big thing at the time.
A lot of these finance teams said, hey, why have all this data in my enterprise performance management, financial planning software?
How can they do more things with it?
How can I blend it with other data across the organization?
And so certainly got a lot more into that.
Got to see what some other companies were doing and help other companies get there along their data journey.
Quite frankly, I got to the point where I was burned out.
Consulting is a very intense culture.
And as much as I learned from over those first five years, I wouldn't be here today without them.
And I made some incredible relationships.
It was incredibly time-consuming, mentally, physically, everything associated there.
And then I had the fortune opportunity, one of my past clients, he actually moved and started a new job at a place at the time was Carrick's bio pharmaceuticals and eventually was merged with a kebia therapeutics.
They said, hey, Niko, we're looking for a technical person.
You can kind of invent this role however you wanted to be.
I figured you could help the various teams here from finance to commercial R&D across the organization and really help them with their data needs.
You want to have conversation.
And of course, really love the guy.
He's still a good friend today.
And so had that opportunity then to meet with his boss, get involved in that culture.
And I really, really liked it and what their vision that they were trying to do there.
And yes, I had the opportunity to start to work with a lot of the latest data and analytics cloud technologies.
So you think about it at the time.
Now this is seven or eight years ago.
Not all companies were bought into the cloud and that's still the way it is today.
So I got to now work with some of the latest data integration technologies, the latest reporting, financial planning applications in the cloud.
And so I got an amazing experience and of course, growing business acumen within the pharmaceutical industry from an internal perspective.
Not just the outside, not just being a consultant.
So I got to see the true pains that consultants see a lot of the time, but they don't necessarily see when they're not in those internal conversations.
And so from there, had an opportunity to make a lot of really incredible relationships there and help out in many ways that I could and doing things like integration work, managing the commercial data warehouse, helping them with at the time they're on host analytics for a financial planning platform.
So being the admin of that and really just bringing data across the organization and automating it as many ways as possible.
Because as a lot of you probably know, there are a lot of manual processes that get overlooked, especially within finance and FPNA as well as just other parts of the organization.
And so that's a long way of saying, hey, I went from consulting to the corporate world, but I found that the corporate world was a little too slow for me.
People had all these visions and ideas, but nobody wanted to execute against it.
And it was just I'm somebody who likes to move very quick and bring value and learn and grow and work with amazing people.
And that was great for a while, but then it got to the point where that was no lot of the focus.
And our head of FPNA at the time, he said, hey, Nico, I'm going to another company.
I'm going to be a VP of FPNA over there.
I just want to let you know this is my last day.
And then the week after that, he said, hey, I'm looking for a financial planning tool.
Can you give me a recommendation?
And he's like, well, this is all great.
But can you actually implement it for me?
And I think I don't know if he was joking or not or if he was being sarcastic.
And so I thought about it.
And I said, yeah, I could probably do that for you.
And so it was that it's funny how timing is everything where I was kind of at this point in my career where I said, hey, I kind of want to go back to something else or back to consulting or something new that's fast paste.
And this opportunity popped up.
And so I formed an LLC went through the processes of the contract.
Everything there had my first project with them.
Signed that and then gave my notice to carrots at the time and then kept them as a client.
Because of course they wanted to still use my services to support their environment.
I've made a lot of great relationships.
And really the biggest thing was, hey, I had learned a lot in consulting.
And then I'd seen the pains of the corporate environment.
Now I wanted to bring this back to consulting where I could say, hey, let's do consulting right again.
This isn't about hyper growth.
I don't need to double our revenue every year, bring all this head count, bring all this notoriety to myself or the business.
I was never at the intent by any means.
It was, let's work with as many clients as feasible.
We can grow organically.
We don't have investors.
And then that way we can focus on the culture that we want to build here at Stratos, which is around, hey, people are excited to come to work.
They enjoy the people they work with.
They don't feel like they're burning out.
And of course the ultimate thing, they're bringing value to the clients and they enjoy working with these clients.
And I'm fortunate to say we've had an incredible team.
We've grown over the last six years or so and certainly made some transitions.
And it's been quite the journey.
And so there's a little more to the story there, though, is that when I first started Stratos, as I said, the focus was on the office of the CFO and FPNA and doing financial systems and data, just specifically for those functions, even though I had more experience across the enterprise as it related to care ex-pharmaceuticals.
So within called the last three years, we've made the transition to focus more on the data and analytic space across enterprises because there's so much value you can bring in finance, but finance needs data from other parts of the business and other parts of the business need finance data.
So we wanted to focus on how can we focus more on the enterprise label for level for data and analytics and bring that value to different stakeholders in each of those functions.
So you can see this progression over time to now get where we are today.
Your story is so interesting because there are so many parallels and I look at what's happening in finance and FPNA today.
And I think about when I started.
So I have my first finance gig.
I was fresh out of business school.
So I was a good generalist, but I wasn't a finance expert back then.
And my first role, I actually didn't roll up to the CFO.
I rolled up to the COO. Basically, he needed someone to translate financial speak into operational speak and vice versa.
So I was in this hybrid role where I was gathering all these operational metrics, but I was using them to forecast our purchasing needs and our inventory levels and maintain all the things that a COO looks at.
And so my evolution through finance came up really where because I spent so many years at the start on the operations side, it was realizing I knew more about the data, the metrics were tracking on the upside.
And then I was just getting what finance was feeding me.
But I think that informed my whole career because I was used to working with all this data that became, I started to see firsthand what the levers are and how how these metrics impact the financials.
So and I actually in that role ended up running the company's first BI team.
This I'm dating myself a bit here, but this is the early 2000s when this was happening.
But that is informed my whole finance career.
And as time has gone on, because I've been so adamant about systems and technology and data, like the amount of time I spend today on actual finance work is a lot different than I mean, it's a lot less than what I spend on systems, technologies, data, KPIs and just in organizing all of it.
And I really think, and this is just my view, but the you know, 15 years as a CFO and going through multiple companies, this was my approach to it there and going through multiple ERP and multiple systems to try to gain data.
I think finance and operations and all this data are hand in hand.
I would love it. If all finance people had this deeper understanding of BI and even data science, it's just that's what's worked for me.
Now there are still needs obviously for the straight accounting and the true FPNA, but I think there's a marriage between those now and I see the integration there.
And so I'm an advocate for why FPNA pros really need to know the BI and data science.
And with yours, I mean, I know you're more heavily leaned into the data side, especially now that you're not just the offices CFO that you're looking across the company, but what are your thoughts on the role of BI and data science for FPNA professionals?
Yeah, it's one of those things that I think for a long time, FPNA people, finance people, they stayed very closely within their domain and said, hey, I'm really good at figuring out how to do the budget.
I probably have all my spreadsheets to pull all these things together.
And then I'm just going to follow a good process, build relationships, and grow from there.
And I think that worked for a long time before BI data science analytics became more enterprise level.
Call it 10, 15 years ago.
Well, finance never thought about, oh, why can have my own person who understands analytics or understands the data components in my space specifically?
That's an IT function. And I have to rely on IT and they're paying to work with, I was an IT CEO, I can say that.
That's true. But it's one of those things where now that the technology exists, I think for people to grow, they need to embrace exactly as you described in your career, embrace some of these other data components that actually drive and help run the business.
Because if you stay in your lane and you're just focused on FPNA and just doing that, that's great.
You're probably going to go really far because people know those need those roles.
But I think a lot of ambitious people have this vision for, oh, I want to be a CFO of one day.
And that's amazing. And the best CFO is I've ever worked with.
Well, they have a broader understanding of the business needs, not just at a high level on how to do that from a budget, but they've been there.
They've taken on responsibilities and seen those inner workings of how data is impacting the rest of the business and the pain that people have to pull some of that data together for some of the most, well, you would think are rudimentary questions.
Like, what are my sales numbers for the upcoming forecast?
What is my revenue for the last quarter?
Oh, does that in fact, in all of these management adjustments or other elements that will come into this normally?
Oh, well, no, I have to go pull these from different places.
So until you've actually lived that and done that, it's hard to get a full appreciation for how valuable that skill set can be and will be long term from a data perspective for for FPNA.
But I would say that applies to any other leaders within different functions.
I mean, we're in the era where generalist plus, if you think of that team model, okay, I'm deep in something, but I'm broad a lot in a lot of other ways.
That's going to take you so far in a finance or FPNA role because you're not someone who just says, oh, that's not my job.
You say, you know, I probably have a pretty good idea of how to do that.
Even if I haven't done it before, let me give it a shot, see if I can help you out.
Let me go get the data you need.
And then people are going to see you as an advocate and want to advocate for you.
Like, wow, they were really helpful.
And that takes you farther and you're career than so many things as well too.
Yeah, and that's such a great point because when I started my career in FPNA back in the Stone Age, there were very much these data silos and everybody was protective of their information.
Like, like I said, I was worked for the COO.
So I had to fight tooth and nail to get access to the financial data that I needed from the controller who was just, you know, he was hanging onto it.
But since then, you've seen the whole concept of business partnering where you have embedded data scientists or embedded FPNA people within departments and within different organizations.
And really, I think the evolution of the role of the CFO has gone from you used to be able to stay in that ivory tower of finance and accounting.
And you would say, you know, I am a mile deep on, I know every gap rule.
I know every SEC, you know, I know all the components that I need to of finance.
I don't care what we're selling.
I don't care what the widget is.
Just I'm reporting on it.
But the nature of, and I think because there's so much more data and the people realizing the power of it, you've had to expand.
You couldn't just be that, you know, super deep domain expert.
You had to understand the business.
So I think, you know, it sounds like you and I have seen the sort of same evolution in the profession.
And it's, I think it's making it a more powerful and a more understood profession too.
And the more we can embed ourselves across functions in an organization, the more we move away from being seen as that cost center, you know, IT and finance both get labeled with that of what value to, you know, you're, you're just a necessary expense.
But we're showing, and as we get better and better at using our data, we're showing that we have increased value and decision making and prediction and, and, and explanation of, you know, why we hit or missed our numbers and what we need to, to do to drive them.
And I want to add something else to that point too, because data as a whole, we use the data terms data analytics.
Wow, there's a lot of things that can go into that.
And from a business perspective to how you analyze the information, I think the value related specifically to F PNA is you need to focus on, well, where, where's my, I hate the term low hanging fruit, but I'm going to use it here.
Where my quick wins in the sense, because you don't necessarily need to say, oh, I need to do go do all these things with AI, because that's cutting edge.
You need to look and say, well, where am I spending my time on data tasks that could otherwise be automated?
And I'll give you a perfect example.
And one of the companies I was working with, there was a head of F PNA, and it was an F PNA team of four, it wasn't huge.
He told me he was manually downloading data from their Oracle ERP.
He was then putting it into Excel.
He was doing some pivots to make sure to line.
He would cleanse the data a little bit, maybe enrich it with some additional attributes, and then load it into their financial planning system to do their budget forecast.
So I immediately was, what are we doing here?
This is something we can automate within a week or less, just so you're not doing this on a regular basis.
And so that's a long way to say, data doesn't need to be this, wow, pie in the sky vision.
I need to accomplish all of these things.
Look for the things where you're spending the most time as an F PNA professional and start questioning why.
I think so many times people are brought into a company and maybe they're junior mid-level and they're, it's like, hey, here are your tasks.
You need to go do these things.
Okay, cool. Do them diligently.
You get faster at doing them, faster at doing them, but you need to take a step back and say, and ask, why am I doing these in the first place?
And not only why, is there a better way to do it?
And then, oh, let me do some research to see if there's a better way to do it because maybe you don't have the internal stakeholders to support you in that journey.
But when you start bringing that kind of value as little as you might think it is, those are the game changers because when organizations happens all the time, they go through transformations where they go through layoffs, they go through reorges.
And when they're choosing people who to say, who to keep and who to stay, if you're someone who's grown their business and have saved them money so they don't need to hire additional headcount, you're a keeper.
And that's not going to change.
Anyone else who's just following tasks and does the same old thing, I hate to say it, but they're going to be looking for other people to stay because they want people who are innovative and innovation does not mean cutting edge.
Innovation means how do we do things better using the tools that we have?
Again, I love hearing you say that because I think that the success that I've had in my career, I can attribute to, there's a quote that I think it's often attributed to different people incorrectly to Steve Jobs and others, but it is, if you want to find the most efficient way to do something, give the job to a lazy person.
And so as you're talking about all the ways that we can move forward, for me, it was if I had to spend two hours a day or two hours a week doing the same task over and over, this isn't how I'm going to spend my time.
I'll spend 100 hours trying to automate it, but then my ROI is I get those two hours back every day or every week or whatever, and then I'm off doing something else.
And I think that's the way we need to think about our processes.
And as AI makes more AI and just technology in general, it allows us to automate more and more of these kind of menial tasks.
The tasks that led our group to be considered a cost center, where we're just doing data entry and not adding a lot of value.
We need to shift away from that and get to where we can add value.
And I think a big, you know, I think data maturity and automation kind of go hand in hand because it's, you know, the data is the artifact of the automation or the or the or it's seen the other way data is the fuel of the automation.
So it's all, you know, linked systems capturing data points along the way.
And I think, you know, you and I were talking before the show, regardless of the size of a company, if you look at where that company is on a data maturity level, one, it's going to tell you how much automation you have and two, how much is ad hoc and how much is systematized and how much you have sort of a data lexicon and understanding of KPIs and all that.
But I think a lot of businesses don't really kind of understand where they are in that data maturity scale, the whatever, I think I've seen different versions.
There's either three, four or five levels of data maturity.
But we were talking before the show about your FPNA data maturity assessment.
And I think for our audience, that would be, and I'd love to actually link it in the show notes if we could as well.
But can you tell me a little bit more about that tool?
Yeah, yeah, absolutely.
So one of the things, I mean, you hear about these companies out there, BCG, McKinsey, I mean, people spend hundreds of thousands, millions of dollars on these companies, these strategy companies to come in there and tell you five things that you probably already needed, you knew, but now you're paying the consultants to tell you that.
So it's kind of like a, a COA situation.
But in this particular case, well, we found, and we've used this time and time again with different clients with prospects is this short survey that essentially is a quiz of testing you on some of the things that you are doing today and how you're doing them.
And what we found with very, very close consistency is that people are actually bucket it into certain categories that make perfect sense for them that they could have spent weeks, months, beyond to actually figure this out, working with some extensive high-price strategy consultant.
And what this has meant to tell you is that, hey, you fall into one of these four buckets.
Here's where you fall in based on the criteria that you provided.
And here's what your focus should be.
Yes, for example, some people might want to focus on AI because that's all the buzz, but then you start answering things like, oh, I'm spending 70% of my time manipulating data to get it into Excel models, or we're not even on a financial planning platform.
So I would challenge people and say, how can you even possibly consider AI as being an important project when you're paying very high salary individuals to do all of this manual work that could otherwise be solved by automation?
And that's just one example.
But if you go ahead and follow this quiz and take a survey, if it's free, it's more of a self-assessment for yourself to see, how do I compare to other companies out there?
And what should I be focusing on given my current scenario and situations going on today?
It's important to do that self-assessment and understand where you are.
But then, a lot of people I talk to, especially if I'm coming in on a consulting assignment, it's still that mindset that's hard to let go of, well, really that's IT, or really the data guys roll up under this group, or they're not going to give us access.
And so for me, it was always, well, I need to figure this out so that if I can't get outside, you know, we're going to do citizen development here.
We're going to run Python on our own laptops or whatever, and you know, running our own SQL tables and all that.
But it is a common situation where FPNA people feel like they're kind of at the mercy of these other groups.
And I'm one of the things we're seeing it more and more, but there still are people who are fighting this battle.
So what do you think? I mean, how could finance and analytics be integrated to create, how can you get over that data silo and get access and integrate analytics, BI, finance all together to create, you know, to add power to the FPNA function?
Yeah, and I think it really depends on the size of the organization for companies that tend to have, say, smaller FPNA teams, call it one to five to six to seven.
There's going to be opportunities for individuals to stand out and kind of like what we were talking about earlier is go take on additional responsibilities outside of the function, just to learn the business and understand the data needs associated with them.
Those people may not go and actually solve them right away, but they can then at least partner together and say, okay, are there some quick wins that we can solve together?
Can IT solve them? Or do we need to figure out a roadmap to actually solve these together by bringing an external partner or once they grow to that scale to be able to afford these things, they can then go in and do that and support those initiatives.
Now, as you start growing though, and you begin a team, you probably call it over a hundred million, you're maybe a couple billion anywhere in between your needs of your organization grow.
And you think of FPNA teams growing where, okay, well, now they're doing very granular modeling across the organization probably for multiple products.
So you're doing revenue planning, really granular product costing to figure out the right price volume mix, you're going to do long range planning, and of course the cost side of all of this.
So your op-X, any type of product allocations and complexities that go along with these growing organizations, especially ones that do acquisitions and other things there, there's so much data that now is coming up.
Now, these companies, and we've seen it, they'll try to have an enterprise call it a data center of excellence to own data across the organization.
So not just for finance or FPNA, you're talking about manufacturing, commercial, marketing, everything in between.
Now, it sounds good in theory, and I don't think there's anything wrong with trying to take that approach.
I think the execution has been inconsistent at best at a lot of these large enterprises that we've seen over the last five or 10 years, depending on where they're at in their journey.
And so what we end up seeing is to this transition where we are today, you're starting to see this focus on data.
And what that means is yes, you might still have standard governance data architecture, infrastructure within your organization.
So everyone's on the same platform.
You can have consistent training.
You have governance frameworks to support everything, but you then have data experts, domain experts sitting within each of these teams.
You might also hear the term data steward.
And we saw this very successfully, one of our clients, their multi-billion dollar pharmaceutical company.
And so one of the things they did is yes, that company had a center of excellence, but finance was frustrated.
FPNA was frustrated. Counting was frustrated because they need to close their books best.
And this is a global billion dollar company.
They're closing their books in four days.
So you know what that means?
This better be a well-loiled machine or someone is going to have to go up to the executives and say, here's why we missed our three and a half four day closed.
And so what they found is because there was so much reliance on IT for management of their databases, for their data, for the reporting, they said, this isn't working.
We can't scale. We can't go through all these acquisitions and expect to have the same results because we're not getting the support we need from this quote unquote center of excellence.
So what they decided to do, which is where we came into play as part of this, is create a finance FPNA center of excellence.
And so we said, first of all, honing in, again, going back to all the pain points, where are you spending your time, where are their bottlenecks today?
So you think about performance bottlenecks?
Let's use a perfect example.
Their incremental data load process from all their ERPs took around two hours.
Now that's not that bad if you're like, okay, we only have to run that one time.
But how often do they only have to run that one time during a closed crisis?
And especially if an accountant said, oh, I forgot to put in that major adjustment that we usually do at the end of the month.
And I need to go do that. Well, if you need to rerun that and you're you're on the clock, you're waiting for this to go.
You're going to be very anxious because someone's going to be calling you saying, well, where are those reports?
And so these are where we look for opportunities to say, hey, let's fix some of these performance issues.
So we got that incremental process down from two hours to, I think it was 20 or 25 minutes.
So now they're able to run this consistently.
Didn't matter if somebody forgot, of course, they want to stay on that schedule and everyone's really focused and getting things done.
But nobody's perfect. Things happen.
And that way you can adapt and focus on that fix when the time comes.
Now, the reason they're able to be successful with this as well, if you think about reporting.
So if it owns all the reporting and any kind of change needs to go through extensive version control, it needs to go through SDLC software development lifecycle, that could be a three to five week migration process three to five weeks here in another month.
You're done to the next close.
That's impossible. So what we said is well, within tablo, let's actually build out the things that require the most sensitivity or adjustments on a regular basis.
And that way when you focus on you hear the term last mile reporting that people in FPNA using their data store could actually make those adjustments.
And there was, don't get me wrong, there was quality and governance and checking around all of this.
But it could work at the speed of FPNA, which is a lot faster than IT or the rest of the business when it comes to their data needs.
And so they saw so much success with this.
And unfortunately, this company over the last few years, they went through multiple reorgs where they actually had to do a lot of layoffs, their drug loss, exclusivity happens all the time in the pharmaceutical industry.
And so because they had all this process in place, they had the center of excellence, they had automated integration processes, their reports were automated, a lot of what they used to do in PowerPoint and Excel, they weren't doing because it was all in tablo.
They actually were able to run a significantly leaner team, call it there at a team of 25, 30 FPNA people, and now they're down to just a handful of people running the most business critical processes because all of the data is automated.
And so that's a long way of saying data doesn't have to be AI, it doesn't have to be cutting it.
It can be what helps makes people's jobs easier so they can actually focus on the value that they're bringing to the company.
Because now they're in steady state mode and they don't need all those people for growth because they're not growing, but they can run their business critical processes.
So focusing on functional domains around the idea of an enterprise sector of excellence, it was a long, long way for productivity.
Yeah, and you mentioned a couple of F tools out there, and I'm always, I've worked with a lot of software providers and have used a lot of these software tools and I think they all have their place.
So I want to be, like I don't ever shill for one software over another, but whether somebody's using tablo or Power BI or another reporting tool, I mean, I think that there's the foundation level of the data and whether it's a data lake, data warehouse, however you haven't organized, understanding what it is, which data to use, what our source of truth is and all that.
As an example, I know you're a big snowflake guy, but the modern tools and as people progress through their levels of data maturity, talk a little bit about how these modern tools and so the reporting layer and then the structure of the data and the sort of cloud architecture and all that, how do these new tools help finance teams advance their capabilities?
Like, so the point you just referenced where they went for 25, 30 to many fewer FP and A people, but I'm probably getting at least as good if not better results because of the tools.
Walk me through this a little bit.
Yeah, the technology is very interesting because the barrier entry to the data, call it data infrastructure in general for teams even five years ago.
Well, let's go back 10 years.
It was very difficult because most companies call it maybe they're on SAP ERP or Oracle or something else.
They would owe or Microsoft, they would always be that shop.
They'd be like, I'm an Oracle shop, I'm a Microsoft shop, I'm SAP.
So that means I have their ERPs and then I'm also going to have their data integration tools.
I'm going to have their analytics platforms.
Now, as a lot of you know, not all of these companies are perfect at everything because if they were, all these other companies wouldn't exist.
But the point is in that these were very challenging environments to stand up.
Let's say you were somebody in FP and A and said, oh, I just want to source data out of my ERP because I want to do exploration on this data to see how we can do additional reporting.
I don't know where we're at yet.
So you think of almost like a mini data lake and then you want to say, okay, well now let's model the data into a data warehouse, the data mart to see how we can use that.
That was a no go because IT would be well, first of all, we own the databases, how dare you tell us to do what to do with the databases.
And then second of all, they say, well, that's going to take us a long time.
We don't have the resources.
It's too expensive to get servers, the memory, we're very tight on our budget and you guys are fine.
Like just download it to excel.
You guys are excel. Right.
That's definitely. So fast forward to modern day.
So much of that barrier to entry has been lowered by using tools like snowflake.
But snowflake is the data platform and it's an incredible platform because of its capabilities in the cloud.
There's no reliance on DBAs anymore.
It auto scales. It truly is a very low maintenance data infrastructure.
But that's only one part of it and that's the value of something like the snowflake ecosystem is these other tools that make it easy to get data into this platform.
So you think about in the past, when you wanted to ingest data or source data out of your ERP, CRM, third party API applications, any external data done in Bradstreet, any of these other ones that are out there, it was very challenging.
You have to go hire someone or have someone in IT write a custom logic against an API and say, here's how we're going to get this data or they would download files to an SFTP, transform them, upload them again, a very time consuming process to do.
Now with the tool like five-tran and some of the other ones out there, they manage all the connectors.
You plug into these data sources and it'll extract the data you want into your snowflake platform.
And then you have added.
You can put a recording tool on that.
You can query it and you can do that without IT.
It's truly can be owned within the function if you don't have that support across the enterprise.
So that barrier to entry is so much lower and people say to me, all the time, oh, Nico, I could just custom code that.
Or let me go hire someone to custom code that because that'll be cheaper.
Okay, well you did that that one time.
What about the next 20 times when you need to go add in new connectors or you need to fix something, are you going to go hire this high priced consultant to do this every time?
No, and I'm a consultant.
So I'm telling you not to do that.
I'm telling you go go use these tools because the maintenance is so easy and the barrier to entry is so low that you can start using consultants for the actual value add activities.
The consultants should bring like how do we architect our data?
What are our date? What is good data modeling?
Look how do we build out our data warehouse?
And let's execute this in a very quick manner and deliver it because we don't have the internal people to do it.
Instead of hiring consultants to custom code connectors or spend all this time standing up things that software could otherwise do at a nominal fee.
So you have to look at that true total cost of ownership and these tools have made it so much easier to bring FPNA data, finance data, accounting data, really enterprise data, but I know we're talking at PNA today.
So into this data platform makes a world of a difference.
And I'll say I mean even a few years ago we weren't having these conversations with FPNA.
Now a lot of people I'm talking to RFPNA because they're excited.
They see the value of oh we can just stand this up and it fits into our architecture.
We can get access to that data a lot quicker than we used to be able to.
Whereas a lot of it was oh well sales always got the priority because that's where they're driving revenue so they can support their needs.
But no other people have data needs today and using these modern technologies can get you there.
So I would say watch out for consultants or anyone out there who says talking about processing the old way like downloading files, moving them over here, relying on all of these outdated movement processes.
Start to question that.
Look for people who are working with the latest tech to get you where you need to go a lot faster.
Yeah I think I have to apologize at some point in there I might have blacked out because I went into like a Hadoop PTSD moment.
Yeah I was just thinking back to you know just dealing with data in the early days and just waiting on the overnight uploads and the long reports and the crime jobs and all the stuff that we were doing that we were waiting on data.
So yeah wow okay I'm back it's modern day we're good.
Wait did you say Hadoop?
Yeah yeah. Oh let me let me talk about Hadoop since we're on the topic.
So I actually wrote a post about this on LinkedIn the other day which was one a very high engaged post but I guess Hadoop rubs people the wrong way a lot.
So one of the things in the big data era and this is why I equate AI and big data in a lot of ways that they are very similar because call it 10 years ago maybe even six years ago people were saying oh big data should I be doing that who's doing that like let's go do it let's go get a Hadoop cluster and I even had this conversation for one of the companies where they said Nico should we
get a Hadoop cluster and I'm like why because they said well because other people are talking about and it's all that I'm like well first of all that's a file management system more for a data lay what you guys need in finance and F P and A is structured modeled accurate data you need a finance data warehouse and so we talk more about that and then we ultimately ended up
that's what we deployed was an F P and A financial data warehouse for them to automate a lot of their data and management processes have a lot of their reports automated out of tablo and I mean that's was five years ago and we've seen the value with thousands of hours these teams continue to save every year because of this but I'm just thinking they had done Hadoop where would they
be today with the frustrations you just they probably be black and out too because they're like oh my gosh the Hadoop again is not this isn't what I needed but when I was back at Peloton and so Hadoop was kind of on the early days this was seven or eight nine years ago and we were as a technical analytics team we were getting into it this isn't that user friendly like that's
kind of complicated and we're smart like we're going to figure this out but we decided strategically not to sell that to anyone because we're like if we can't figure this out we figure it out but if we have actually maintain this or teach clients to maintain this there's no value for them here I mean there's got to be something better and then of course Snowflake came
out a few years after that you have AWS, Google following they have all similar technologies now in the cloud but a lot of companies are still using Hadoop but I think they're still facing those frustrations as well today.
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You know if you it's the devil you know and you've gone to all the effort to set up a Hadoop cluster or whatever you know whatever your current data lake environment is so I think it's it changes hard and it's disruptive it's just like putting in a new ERP or any kind of big software thing it's very hard if people know a system and it's working and it's clunky and it's not modern
it can be very hard to shift from this is working I don't want to rock the boat you know how do I get to this sort of cloud first modern data architecture and then there's also just in that there's challenges and issues around just the data and analytics environment in general so I guess you know my question is maybe two part it's one what you recommend for somebody getting
started maybe on sort of the more technical side but then you have to have this data driven culture and you know understand the value of analytics and understand the value of automation like it's intimidating to try to change something that is maybe not optimal but you've kind of figured out how to work in this system so if you come into a company that is just you know
really maxed out on what this old system is and they're trying to move into this new modern architecture well I mean what are your steps there how do you help them?
And you're talking about one of my favorite topics which is people in data which I'm sure we could do a whole another podcast on so let's start there though so technology has always been incredibly successful within finance within IT across the organizations but the adoption the change management behind it has been one of the most challenging things that companies
have wasted so much money on this tech only to find that nobody's using it because they didn't go through those proper channels to see our people actually going to get value out of this and so whenever we go into an organization usually it is somebody who there's probably a project sponsor or stakeholder they may be director of VPC level depending on how large the organization is but we
like to learn what are some other things that are going to be other people departments that are going to be directly impacted by this change so those are some of the areas that we start because a lot of time people say oh yeah no this is just for us this is just for us and then as you progress into this larger project you find it's not just for them and there are a lot of people
who get really frustrated because you say oh well you didn't include me and why didn't you ask me about this I'm the expert on this data or I know this process so it was part of our deep discovery sessions we start to really drill into those details to find out what's truly going on and where those pains are and they tend to be well beyond either the data IT or finance functions
it's in support of the subject matter domain experts in general and so once you start to find that you then start to you want to identify well let's see how we build relationships with these people and get them bought in and that's not a consultant thing we can guide that process and we do that all the time but ultimately it has to be the internal stakeholders because we
want to see them succeed because once we're gone it's on them so they need to go start to facilitate engagement and excitement for these types of initiatives and a lot of that comes down to let's say you've never met with somebody and you know somebody in HR is struggling with data or somebody in sales or marketing and maybe they're a little introverted they kind of like
stay in their lane I would just set up a meeting with them we've seen this time and time again just set up a meeting hey John want to talk to you just to understand some of the data pains working on this initiative and we think it might help it's helping us in finance so we want to see if it can help in HR which am I meeting just so we can chat and then you spend that time having
mostly just listening asking the questions that are relevant to this and understanding what their data challenges are their analytics challenges where there are a lot of their time is taking what's frustrating what's keeping them up at night what at the end of the day are they dreading the next day because they have to do it and then you document and you capture
all of those pieces and then not only do you say okay well John thanks for meeting appreciate it and then never talk to them again and that's what happens a lot of times there's no execution there's no next step so there's no project management in that meeting I would say hey John can we get a meeting on the books for a week out two weeks out let's figure out I want to work
around your schedule but I want to make sure we keep the momentum and I come back to you with some recommendations on how this new initiative can actually help you then I'm going to have conversations with our internal teams and then when you have that second meeting with John then you start to set up timelines and say hey this is something that can be a quick win for you
our team's done this before we have an idea for it if you're open to us trying it but let's do that we'd love to spend some time with you to get this set up look for those quick wins you start to build that relationship and by the time you say our guys we're going in to launch this bigger initiative across the enterprise maybe it's a few functions however you approach it
just like cool yeah like let's do it in fact these guys helped me out already I'm excited for it and let's get some momentum going here now you have an even bigger sponsor for your project then just the original stakeholder who is focusing on this and I say all these things it's not easy it's not easy at all but it takes you to go out of your comfort zone to build relationships
and we've seen it time and time again not only our project successful but these people get promoted they move on they do well in their careers so yeah it takes being uncomfortable to make a lot of progress in these areas and so that's the people aspect of it and to answer your second question there around how do you actually approach from say you want to deploy this modern
data stack what's a good approach to do it well historically we look at oh sales vendors come in and say hey you're going to get all this ROI out of this analytics tool out of this database wow look at these cost savings over here based on these few variables you'll tell me very cool sign now for this three-year agreement multi-million dollar agreement those times
have changed and I caution you for any vendor via technology or services or pushing something like that on you definitely question it definitely get a second or third opinion because this is the era of easy to stand up data infrastructure no it was never like this before we're very fortunate to have that technology so what we recommend is there's two things one to
do an assessment so an assessment is could be anywhere from a couple weeks to a couple months to say what do we actually want to focus on what are our needs well how do we want to plan these in the near-term and long-term because of course everyone loved to have everything at once but that's not realistic so you need to prioritize things what is priority for the business
what's going to drive more savings in the near-term long-term you figure out all the challenges you document the gaps you look at your current infrastructure and see what the future state could look like and then once you've defined that future state on paper like that seems pretty good I think this is how the data should flow we're going to source out of these different
systems we're going to ingest it into this data platform what a reporting tool on top of it then you want to do a proof of concept or a prototype don't go by all of these technologies as much as they'd like you to do that what you should do is say either work with a partner or if you have the internal resources to do it say hey I'd like to do a proof of concept with snowflake
and I want ingest the data with five-trane I want to transform it with coalesque I want to report on it with sigma or tablo there's a lot of tools in these ecosystems you need to be cautious of not bringing in all of them but you want to focus on the ones that actually can add value to you and then you could do a proof of concept in its shortest three weeks maybe ideal is six
weeks and then you say oh wow this actually did everything that I wanted it to do now let's focus on how this impacts the longer term and we're going through that right now with one of the largest manufacturers in the United States he was ready to say hey like let's get this project going it's a multi-year project and I said well let's take a step back and say let's actually
prove this out and of course to me it would be oh yeah like let's sign let's get rolling no because that leads to failure so what we're doing is a proof of concept and I even said to him hey if you hate working with us at the end of this then you can look at other service providers and you get the proof of concept to see the results of that and of course that's never gone that way
they've always chose to work with us but that's the reason why is because you're hedging your bent you are showing that you understand the value of this technology you're showing the business the value of that and once you define that proof of concept you can say cool this will work for other situations because you focused on the most complex issues early on and then
you can focus on signing the contracts with the software vendors and the providers and whatever is going to get you there we're doing internally but don't take on all this unnecessary risk when we are in the era where technology is so powerful and easy to stand up just to approve a concept yeah and as an agile guy I love hearing your agile mentality come through and everything
you're saying here with these small wins and you know just addressing one component of time rather than doing this massive waterfall and I you know I've seen it so many times where you get a really slick software salesman comes in and just tells you this software is a magic wand you just buy the software and it's going to fix everything whereas whether it's an ERP or a data environment
or or the full finance tech stack you don't just buy the product and then try to fit your problems to the product you first so you know like what you talked about going through and looking at the steps so if you're making a roadmap here's the road here's a pothole here's a speed bump let me let me first fix the potholes figure out what's going on speed bumps you know maybe
there's paths where whatever you're doing takes a hard right turn you've got that noted in your in your process and then maybe there's parts of the road that are fog covered and you have no idea what's going on there well there's a gap that's something we need to fill and then but if you can I don't know if physical process is right because everything is so digital these
days but understanding what people are doing where they're having problems what exactly you're trying to solve for and then plugging in the appropriate software solution is much better than this sort of square peg ground hole just jam everything into into one solution so I love here in your your approach there yeah and I guess we're going along here and I've got
20 other questions listed here I want to I want to cover with you but I'd in the interest of time and because it is top of mind for everyone and it's one of my mantras that I'm I'm using all the time right now is because everybody wants to talk about AI a generative AI in particular and it's super cool you see the potential of it you know we're in the dial up aol prodigy era of a generative
AI so it's we're going to look back at this and say I can't believe how bad it was but we're all seeing the potential of it right now so people always want to talk to me about what do we need to do to be AI ready it's like well you have to have BI before you can have AI and if your data is still a mess AI is not going to help you so you know and we've talked about the implementation and how
to increase data maturity but from someone in your role knowing analytics as well as you do knowing data and how it can be used and what its power is as you see the potential of generative AI right now I mean how do you see the role of technology and AI evolving in FPNA with the rise of AI and machine learning and I think you know sophisticated FPNA departments have been
using machine learning for years now but it's it was only accessible to a handful of people so you know you're seeing the potential you kind of know state of the art where we are what do you put on your you know crystal ball here and and let us know what you're thinking about the future yeah I'm glad you said that too about machine learning because that's something I was going
to touch on as well is that a lot of these technologies whose financial planning budgeting software as well as just some third party software outside of say the major players they've been focusing on machine learning and statistical models within these tools for a long time now in all fairness there it's a bit clunky you still have to kind of know what you're doing
to inform that but a lot of people were always looking for ways of okay well how do I propagate data in some of the things that you always know are going to be consistent or you can use historical data to inform those models maybe there's some other variables that drive that based on success or failure fresh thresholds for particular product or successes anything
like that so I would say that's not necessarily new a lot of people are doing that but you're right it is again talking about barrier to our entry now we're looking at ways for how people can do more of that and I think Jenni on its own let's talk on that before machine learning because I think machine learning I was at the snowflake conference a few months ago and someone
was saying oh yeah you can do machine learning in this integration tool and they're talking about variables and all these things oh I was just great but I still don't really understand statistics that well so I'm not the best one for so people have tried to make it simpler to use but you still need someone with that knowledge to say well how is this model actually going
to work because when you get numbers out of that model when you're talking to your leadership I can assure you your leadership likely does not know how that's going to work so they're going to want answers how did you come up with this number what's it driving that and if you list off like 10 different variables and it's a statistical mumbo jumbo that they don't understand they're
going to be like why don't we try this again with something that we can actually explain and take to our board or our shareholders in that case too so I think there is a longer way from a machine learning perspective to get there and that's why some of these larger orgs can afford to have data scientists to actually do that and it does help expedite some of their FPNA processes
and better informed so they're tighter on their budget and forecast and I think that's great let's talk gen i gen i for a little bit so one of the biggest things in FPNA over the years that took so much time was variance report so you think about okay why is this variance 20% I now need to type okay well I need to drill into the details to look it up what's causing this and maybe
I need to go talk to somebody then type it up and then that's a lot of time for one person to even spend on say a single line item to accomplish that so for a long time when there was a lot of talk of nlg or natural language generation that could help automate some of these the generation around the commentary capture that needed to go into these reports that's where I see
gen i gen ai taking off for these teams because now somebody of those tools there were third party plugins they weren't that easy to use they're a bit costly now with this advent of these models being available and you can now actually use them within your own ecosystems you're not sharing your data with the outside world it's all secure now you can focus on training
these models so it can actually auto generate that commentary capture for you and go say oh why I've access to the granular details in this data model to actually go find this information what I said there is data model because finance data needs to be right as you know with gen ai there's a lot of things that appear right but are not right and so some of the bigger companies
out there are at least who we've talked to in other departments they're doing they're putting out these initiatives where they call them data quality initiatives where they're building these AI models and as soon as it spits out a result they have a team or a process that's a okay does this actually check out is this valid and until they meet a certain threshold of consistency
and that the model is actually trained to accomplish that then they actually allow for productionization all that being said I didn't hear that necessarily with any finance or fpna data given how accurate that needs to be so yes and actually somebody who I'm close with he's actually building an AI a gen ai model on top of some of the data models for for exactly what we're
talking about so your epm your databases to get everything there to assist with that so I guess the long term vision there is a mansion where you hit the month then you need to do your monthly reporting package and usually it was okay if you have nothing in place let me go download all this data let's put an excel let's pivot it let's do the graphs let's screenshot it let's
put an empower point let's type up the text here let's review it all I made a mistake so go do it all again for this one all right so but you've got the point there it's very manual now imagine a world where it could either be in the web or could be maybe it's automated in power point but you just run your report everything's auto generated the commentary is there the data points
are there and it could tell you something like okay here are the variances that really stand out that you that seem outliers compared to the past that you should really focus on because you're not going to go through and want to check everything you probably will initially until you build some level trust with it and come for it but that way you can spend your time actually
where the attention needs to be and oh by the way it didn't spend all your time putting together these reports and that somebody you could have paid like $10 an hour to do we're paying someone like $200,000 a year to do that when now it can be automated through technology so I think gen AI coupled with strong data models and a good process with help automate a lot of this repeatable
reporting and of course the next thing being ad hoc questions when you say hey what you know what's my revenue and this particular business unit and everything a lot of things that data analysts will usually do and it takes time to do I think we can get there and it will take time so there are a lot of exciting things but it always comes back to the data quality the consistency
have you built a data model that can support all of this especially when it comes to finance and FPNA data yeah absolutely and it's funny as you as you're talking through that I mean that that is exactly what I'm trying to do in my day job when I'm not flapping my gums on the on the podcast that's where my focus has been so yeah yeah so we're fully aligned there okay well
we could we could geek out all day I feel like you and I could have a 48 hour workshop and we just we just watch people in the room start falling off as we go and lay into the night talking about data geek stuff but we you know and we did we dove straight into all the data conversation and we do at the end of every show like to remind our listeners that we are all outside of our
our geekdom that we're people too so one of the questions we always ask our guests is what's something that not many people know about you that you you could share with our our listeners today yeah so I think one thing I'd like to share is that just around this rare eye condition that I have it's called the chromatopsia I want to bring it up because it doesn't get a lot
it's not a very known condition that's out there but essentially it limits my ability to do some day-to-day things like driving a car I can't drive so it's difficult to cross the street sometimes I'm very light sensitive I have color blindness so all of these things and I say that in a way to say yes there are a lot of things I struggled with growing up and have to deal
with of how to get around kind of like the norm or how people live normally just some basic things that we take for granted but I've been able to be successful because I figured that out along the way and found some amazing people to support my career and grow in that way I mean something is these even as simple as being in a conference room around a conference table and it's
the blinds are open in the bright room it's really difficult for me to see and then like I can't see people's faces or the responses that are on their face so it's one of those things you kind of learn and embrace to adapt to those situations and one other thing I'll say with COVID now that everything is over zoom and virtual for me personally that's made things a lot easier
because I can see things a lot better now that I'm closer to the screen it's helped me even though there's so much lack of engagement by not being in person I almost feel like I get a lot of value out of virtual because I can do things that I couldn't necessarily do before so that's something I just wanted to share in general yeah that's so interesting and you know having
to overcome those challenges to work in a world in especially I mean people think about eyesight and how important it is whether you're doing virtual or in person and then you know just the visibility and all the data and the stuff you work with that's a that's a lot to overcome so it's it's great that you have found the path through there with all this so all right our
other question that we ask everyone and we've been so like so geeked out on such a different level I don't even know how we're going to bring it back here but I am always fascinated to hear what is your favorite Excel function and why so favorite Excel function for me because I used it a lot in early in my career and I still use it sometime today is some if and so especially
when you're doing any type of data validation across different grains of data and you need to match it up and this comes into play especially in major migrations of data from different environments and yeah you can write a lot of code to do that but there are times you had to do it in Excel and so the sum if saved me a lot of times a lot of manual effort to actually accomplish
that and I still teach my team that sometimes today when there are when there is that need and then I think the other thing I'm just a big pivot table person I mean all the time because we're a smaller company right so we still it's funny word data company but at the end of the day we still have a lot of these challenges as well but we also don't spend that much time doing these
data challenges the company space so what I'll do is I'll take data out of certain systems do some pivots then enrich that data with my own thoughts and analyses and it's just I still like working Excel it's so I'm a believer of we're never going to eliminate Excel Excel is a very very powerful tool when complemented with data automation with analytics tools and that way
they go hand in hand instead of just like oh yeah let's get everyone out of the cell.
Yep yep that's excellent and I guess in a conversation for a whole other day I probably need to do a podcast on this is you know now that Microsoft's integrated Python and do Excel and I have personally not messed around with that at all but I know people are very excited about that so I'll probably start as people start figuring out the Python in Excel we'll probably
start getting different answers there as well so yeah this is this has been great I guess one you know one thing I want to be sure for listeners who want to connect with you and learn more about your work what's the best way for people to to get in touch with you.
Yeah I'm a big LinkedIn person so I'm regularly posting content mostly on a daily basis about all things data analytics infrastructure data modeling anything related to how you can get more out of your data so Nicholas man on LinkedIn will put it in the show notes and then you can check us out stratoes consulting.com and lastly I would say if you are in that journey
and you're wondering how mature your data strategy is or where you're at with your FPNA data check out that FPNA data maturity assessment I think it'll tell you a lot of things some you might already know but there might be things like that that's pretty interesting so and definitely happy to have conversations with other people who are out there who want to just talk about data.
Well Nico thanks so much this is a great conversation and really appreciate you coming on.
Thanks a lot Glenn really appreciate the opportunity always a pleasure to chat with you.
