How Railsware Reached $16M in Revenue and 200 Employees by Going Data-First (Interview with CEO Yaroslav Lazor)

Event context and introduction

Nathan Latka

00:00Quick context. This was recorded March twenty eighth and twenty ninth. So a couple weeks ago at my live event, saasopen.com. We had a thousand software CEOs there. If you missed it, we hope to see at the next one, September fifth and sixth in New York City, sasopen.com. But for now, let's jump into the recording.

Yaroslav Lazor introduces Railsware

Yaroslav Lazor

00:21>> So my name is Yaroslav. I run a company called Railsware. We're we're doing a lot of stuff with data, and then I'll show you the supple art of it. So I've been twenty three years in a pro tech, so basically working for technology companies and money. First, wrote my first code when I was 10 years old. Engineered by heart, they they make us drop some numbers in here so the company is like related with a 100

00:50>> plus million and it's 200 people altogether a bit more than 200 people.

Five times growth in five years through data

Yaroslav Lazor

00:57>> We we grew five times in the last five years specifically because of the data.

01:03>> The relevant experiences we one of the products that we run, a SaaS product, is called Coupler. It helps you move your data around and figure out your data. So that's where basically the information about this presentation goes from. There's some consultancy that we're doing including data consultancy as the guy said before like it's really great to onboard people into this thing because when you see a blank sheet and you're like go go do your data it's

01:28>> actually pretty hard.

Coupler product and data consultancy overview

Yaroslav Lazor

01:32>> There's a practical approach how to kind of make you see what is it that you're working with and then I'm gonna share it with you and then take your ideas that you think are happening within your business in a lot of different areas and map them with the reality and actually look at them all the time. Data is not about looking at it one time, data is about doing it all the time. And

01:56>> shared the lessons that we had across the time. I'm trying to figure out who I'm speaking with so who's doing analytics, who's working with data kind of on a daily basis? Can you raise your hand? Okay. And then who who are the CFOs in here maybe? Or CEOs and founders? Who's everyone else? Is it? CROs. CROs. Okay. Good. Okay. So this is the team growth specifically when we were start starting to tap into data and pushing

Audience check and team growth slide

Yaroslav Lazor

02:30>> the gas in the last few years and this is the evaluation growth. And I'll take it to my first part. So my my data path, I'm an engineer I was writing software I was super happy until the time when it was the time to to raise people salaries and I needed to know our revenue for sure. At that time we were consultancy company so our revenue was supposed to be time multiplied by rate equals revenue and

The $120,000 accounting error that sparked a data culture

Yaroslav Lazor

02:57>> what happened is that I multiplied time by rate it didn't equal revenue we were like a $120,000 short and there was there was a lot back then in the in the time when when we started we were a much smaller company. So I looked into the numbers and I was trying to figure out what happened and our the finance accounting lady did a sum of b two to b four rather than b two to b five.

03:22>> So she skipped in one line and overall across all the invoices we lost $120,000. At that day, I lost trust in humanity, specifically in accounting people and finance people and people that do sums in Excels like this is definitely not the way the software is being built and having it done in finances one of the most important functions that everyone is trying to stash away. It's it's a really bad idea right and then at that day

03:45>> an analyst was born within me. I was reborn and we became a data company and probably a bit overly data company because now we have about 600 dashboards that we look into. Then the second thing was when our CFO was paying, so we are remote company and we have people in 26 countries right now. Back then it was 20 countries. It was super hard to pay the payroll to everyone and she was paying it basically for

Payroll nightmare and automation to 4 hours

Yaroslav Lazor

04:13>> ten to fifteen days every month and then she was getting sick because it was so frustrating and stressing. So we we turned it into a data process. We build up the data.

04:24>> Data flows around it, dashboards something I'll tell you a bit more about and then now we're doing it in four hours instead of fifteen days. That was kind of a second big thing that pushed us into data and the third one was it was very hard to figure out exactly where we stand when when you have a generic P and L. Where does every product stand? How much profit does it have? There's a shared services that

04:48>> you have to split the processes. It was it was really hard to figure that out and then we built a hyper detailed P and L that actually showed all of that and I did it myself at the time using kind of our product and some other products and that helped to understand that you really need to understand your data if you are growing a lot so that you can push the gas into the panel. You need

05:13>> to know where the need to know where the floor is so we can floor it rather than under do it or over do it and kill your company right?

05:22>> And so those three things kind of pushed me into direction that you need to become a spider of your of of what is it that you're doing and and you need to understand how your data work works like and we'll go into it in a sec. And then the art of how to manage the data because it's it's great when you see all those dashboards but where do they come from? How do you build it? Right?

05:44>> And and we're gonna go into it. So this is the payroll nightmare that happened it's she was spending like twelve hours per person and then after the automation it dropped into like five and three hours per person now we're a much bigger company we're just leveraging our automation much better.

Building a hyper-detailed profit and loss model

Yaroslav Lazor

06:01>> So basically I did tell you about the one the 20 ks deal that pushed into data. So everyone has those horror cases where you actually understand that you're going into a very different direction than you were supposed to be going. And then basically you need to need to from that moment on it's better to create your own data or we'll jump into that. So we went through the so basically P and L is a very interesting

06:27>> case. So we had our P and L was spread between the the payroll multiple we had multiple entities multiple countries a multiple accounting software we were transitioning from one software to the other and it pays off to combine all of it together unify it and to create yourself a data that you can that you can trust in. Most of that is being done with the data analyst of course and build a foundation model with which you

06:58>> can reflect of what's happening like within your within your organization

07:04>> and and have an ability to drill down and actually see kind of the insights of of of where you're at.

The CEO dashboard and spider web of visibility

Yaroslav Lazor

07:12>> So this is this is something I looked at every day. This dashboard is basically it contains everything that I as a CEO look at and this is the dashboard that I built for myself. This is my spider web that I kind of gain control through. It's a spreadsheet and it gives you flexibility to you understand your own data. Everyone can do spreadsheets on some level and you can basically create a base model for what can be

07:43>> useful for you. Instead of just working with analysts and we'll go into that in a sec. This is like a typical setup that I have where there's a lot of monitors you can step back, can and then you can see the correlations of what's happening within your business around multiple dashboards of them. But the main one, you should have your main one that you refresh every day and then you'll get a sense of it. And and

08:09>> there's some more stuff as I said, have 600 dashboards. I don't have 600 slides but one of them is the employee employee satisfaction and the other one is time tracking and basically where do people, what what is the quality of time tracking in the company. So how to become a spider of your own data? So whatever you think is important to you, you can you can break it down in using extraction tools and and you have

How to become a spider of your own data

Yaroslav Lazor

08:42>> to place it in front of you. You need you need a place where your reality that the reality of your world is going to reflect it using some hopefully color coded data and it's actually very easy to start. It's much easier to start than than everyone realizes and you should always redesign your data for your visibility. If you're going to use data tools that you need to filter in order to answer some of your questions that

09:08>> you have, you're going to lose yourself. You will basically

09:14>> go what your intuition tells you rather than follow the the answers that that you might have if you pack your data correctly. And all of your executives need their own data so if someone if you have a head of product he needs to look at the product data. He needs to look at the churn data, activation data, everything important for the product. If you have the HR people, they need to look at how their funnel goes

09:42>> through and what is happening within the funnel. If they look at it every day, they will get an intuitive sense of what's happening within under their part of the business. And basically then you can take the data from your execs and combine it into something that you look at every day. And it'll it'll it'll create an intuitive sense for you on how to run the business.

Starting with spreadsheets and extraction tools

Yaroslav Lazor

10:10>> It is actually easy to start everyone knows Excel and and spreadsheet as I said. It's easy to start you just use data extraction tools like one of them is our product coupler. There's a lot of them in the market depending on what you need and you're pulling somewhat aggregated data into your spreadsheet and then you just combine it in in a way you want to see it and you place it especially is a great canvas it's

10:35>> like a drawing a picture So basically you take the data and you place it into a proper part of where is it that you want to see it and then you you add up to it and create a base model of what is it that you would like to see. The next step is working with a data analyst. So the very often what we see and mistake is that people start working with data analyst and none

10:56>> of them know what needs to be done. The data analyst doesn't understand fully, you don't understand fully what do you want and then but then you know and this combination of a pair is somehow explosive. So

11:12>> you're you're getting frustrated because you don't see what you want but that's because you yourself don't know what is it that you're trying to extract yourself. Okay? So and then the first it's better to go back to the first step and try to extract some of the data even dummy data and then drop it onto a spreadsheet for yourself to look into it and to actually understand

11:35>> the model that you would like to look at and then you can proceed with the analyst and basically work with them to do a long term kind of stable foundation that is built on something that you you wanted to build from. Okay? And then

Working with data analysts effectively

Yaroslav Lazor

11:52>> and then there's a trick that helped us a lot specifically in the spreadsheet it's called the three d data. It's when you you have your numbers you don't you might not fully know where they come from but then you combine them into this kind of like expect just text within the spreadsheet and and the we have a blog post about it how to do it this saved us a lot because when you combine your own data

12:16>> and then you show it you're able to see the underlying information that came from this and you are able to parse it with your eyes quickly and again build the intuitive understanding of where where does it come from and what does it mean.

12:35>> And basically that's it. So I'm seducing everyone not to I'm seducing everyone to go into building your own spider web into creating a visibility for yourself of what is it that you need. It'll take time, you'll build it over time but it it will become something that basically you want to check either multiple times a day or just every day.

13:03>> The then always redesign your data for visibility. So if the business changes, your reality changes and then your priorities and interests also changes. So don't be lazy and kind of redo it in a way that you want that you get exactly what you want to see. And then make your execs do the same thing and and you know then all the every time you synchronize about something with an exec, you're actually looking at reality and you

The three-D data technique

Yaroslav Lazor

13:32>> don't spend time of going over like slide decks, presentations or or just agenda items that are there are not that important. You look at the reality of your business that is being refreshed all the time. And then start with a spreadsheet and data extraction tools. Extract it and play with it until you create a basic model and then this basic model can then be extended with data analysts or if you didn't have them in the company

Closing advice on redesigning data for visibility

Yaroslav Lazor

13:55>> just some freelancers. And use the three d data shape. It's a bit tricky but if you scan the QR code and send me a note if you're interested we're gonna send you the blog post when it's out just just so that those spreadsheets are just easier to to start with And that's it.

Nathan Latka

14:22Hey folks, if we haven't met yet, my name is Nathan Latka. I launched and sold my first software company back in 2015 and went on to write a book about it, which you guys made a Wall Street Journal bestseller purchasing over 30,000 copies. Thank you so much for that. After the book, I launched this show and went on to create founderpath.com. I raised a large fund to do non dilutive deals with B2B software founders. So far,

14:50we've invested in over 400 software founders totaling $150,000,000. Here in 2024, we're doing three to four new deals per week. So if you're looking for capital and don't wanna give up equity, go sign up at founderpath.com for free to get your offer.