It's incredible to see how Tab

BFN has gone from strength to strength in a relatively short space of time. So how does anyone go about setting up a successful tech company like that? In episode 865, I talked to Cal Aldubai about how to start and scale a data science consultancy using his wildly successful company Pandata as a case in point. Let's talk about the kinds of things that made Pandata so successful. We have already, you know, this make it boring idea of making it boring for data scientists easy for your clients to be able to understand the data science that you're delivering. What are the other keys to scaling a successful data science consultancy?

So something that I didn't quite nail in my first startup that really stuck with me is this notion of product market fit. And anyone who's in the space of entrepreneurship will hear this term bandied about. And for those of you who haven't been in the field of entrepreneurship, what that means is you found a pain point that someone is willing to spend something on solving. And there's enough of those people at enough scale, you know how to reach them, and you can consistently deliver that thing that they're willing to pay for. And clients vote with their money. And I found early on, because I bootstrapped, that meant I didn't raise any capital. The only source of growth I had was when a customer is willing to pay for it. And so it's one thing when somebody says, hey, that's a great idea. It's another thing when they're willing to sign a big check for you to solve that problem. And then they come back to you to solve that same problem or similar problem again and again and again. So product market fit and listening to what people were willing to spend on was a really big part of Pandata. My first year, all I had to do was say, hey, we can do data science things. And I was able to land a few contracts here or there, but it was a rotating window. I'd work with one enterprise and then they'd go away. Another enterprise would come. And that's a very common story for consulting companies. There were maybe one or two clients that stuck around or kept coming back to us. And I remember having a conversation with my stakeholder there. I finally worked up the guts and I said, not that I want you to question this situation at all, but why are you coming back? And I was like really trying to do some market research and understand. And it turns out that they really liked that we were approachable, right? That was one of our core values is hold back the jargon, always speak plainly. And then there were a couple of formulaic things that we accidentally ended up doing. We have this process called discovery and design that now is a mandatory requirement. Anybody that hires us to do any work, I say, you have to do this up front or I won't work with you. With those clients, we accidentally did it. And that's where we spent just 30 days, six weeks, diving into a problem, trying to figure out where are the skeletons? Is this solvable? How can we approach this? What are the unknown unknowns? Which is a really big part of solving problems that have not been solvent before with pattern matching algorithms, just to simplify it. And so I tried to recreate that magic. So there were these attributes that we had that became our core values. We had five core values that I can talk about later. And then there are these processes. And one of these processes was discovery and design. Now, the funny thing is, I decide, all right, I'm now no longer going to work with any client that doesn't want to do this. And we're going to charge an arbitrary amount of money. That engagement size is now $50,000 at that time. That was a measly $12,000. And I was really a first-time entrepreneur nervous about throwing that about. But I'd say, hey, you know what, unless you're willing to spend this, I don't even want to work with you. And it helped me weed out two things. One, clients that weren't serious. If they weren't willing to pay that, they definitely weren't willing to pay for the rest of the engagement. And two, if they didn't philosophically agree with the importance of that step, then I knew that they were likely to be a client that was consistently disappointed by the results because they didn't quite get the data science process. So I went from spending a lot of time talking to a lot of people that seemed interested at first in data science. And then I got no, no, no, no. My pipeline started to dry out. And this is one of three times that Pandeta's bank account reached like less than, you know, a month's worth of expenses. And I was like, this was the end. This was maybe the dumbest idea. And within that same period of time, I landed three of the biggest clients I had ever engaged, two of which remained clients until Pandita's exit. So over a period of about six years. And that process became a part of how we were able to scale so much larger than most small solopreneur consulting shops.

Right. So the key was having this 30-day discovering design initial engagement at the beginning of trying to consult with somebody. And you'd say, you know, there's going to be this $50,000 price point to do that initial 30-day engagement. And so that initially seemed to put you in peril where your pipeline dried up, everyone was saying no, but then it did ultimately lead to discovering

solid long-term clients that were with you for six plus years. Cool. Well, and so I would use this tactic and now I use this tactic to scare off non-serious people. And it actually allows me to save them time. It allows me to save time. And then I find the companies and the groups that say, heck yeah, that sounds amazing. I love how you think about this. And there's a lot of fish in the sea and it's all about this matchmaking process. And one of the counterintuitive lessons I learned was the art of saying no, or ruling others out by saying no to them. And it really allows you to spend more time on the bigger things, the higher value things. And this is a common tactic I see a lot of most of my friends who are wildly successful.

Right, right, right, right. That is tricky. It's very hard to say no to smaller or more challenging projects because you remember those times where you got to only a month of expenses. Oh, my God. You left in your bank account. You're like, well, I guess I better say yes to everything. But then that ultimately it slows you down. You have the death by 1000 cuts of just all of these low value touch points. Well, it's funny, when we were going through due

diligence on this acquisition, there were about three points on the balance sheet in the financials that they had virtually circled. And they're like, we want to talk about this, this and this, we don't like that. I said, I didn't like those either. Really really bad moments for me too. All

right. That's it for today's in case you missed it episode to be sure not to miss any of our exciting upcoming episodes. Be sure to subscribe to this podcast if you haven't already. But most importantly, I just hope you'll keep on listening until next time. Keep on rocking it out there. And I'm looking forward to enjoying another round of the super data science podcast with you very soon.