Dr. Jeremy Weisz: 16:14
I thought you were going to say CTO for some reason on this, but.
Craig Dunham: 16:18
And those technical leaders do tend to bubble up to the CTO. But at this point, most of the buying decisions have been made at sort of like an executive VP or VP level. But sometimes, depending on the company, they’ll bubble up to the CTO. But the CFO has been carrying a whole lot about this because think about the cost associated or the capital expenditure that’s being doled out by all these companies to try to support the needs of the business. And they’re all looking for ways to reduce the cost associated with running their analytics.
Dr. Jeremy Weisz: 16:50
I’m curious, Greg, what does onboarding look like? It seems so technical, like this large retailer has so many moving pieces they’re using, who knows. Lots of data points. Inventory software. What’s the onboarding look like? Do you have like a team of like developers that go in and meet with their team of developers and like, integrate Voltron into the system? I mean, it’s probably way over my pay grade to understand this, but I’m just curious at a high level, what is that integration look like to go in there? Because there’s a lot of moving pieces here.
Craig Dunham: 17:21
Yeah, you and me both. Jeremy. So I am, I should say I am while I run a deeply technical company. I am not a deeply technical leader. I am like my background, my history. I’m a go to market guy. So sales, marketing, customer success. Like how do we take a story, tell a story, find repeatability. Like that’s where I really specialize. But to get to your question, you know, I like we are and without nerding out too much but we run and we can integrate and implement really easily to anything that has like Kubernetes, which is like a simple and easy standard that your more technical listeners will certainly understand. But we’ve built our technology where the core IP, this core sort of SQL query engine that does the processing is closed source and so it’s protected. We don’t sort of give out the code or anything to that, but everything around that engine is open sourced. And the reason we architected it in that way is to make it really easy for people to onboard and integrate it into their existing systems. And so we don’t go to a customer and say, actually, you need to rip out your entire data stack and replace everything with all new infrastructure. It’s actually no. If you leverage one of those open source technologies called Apache Arrow. If you leverage arrow anywhere already in your infrastructure, it’s really easy for Voltron Data to just connect our query engine in through Arrow. And it’s just really graciously just integrates with existing systems. And we have a concept and we talk a lot about this, which is, you know, not every data processing workload makes sense for Voltron Data. And that example of that large retailer that did make sense. But there are some smaller workloads which may make sense to run on some other query engine. And we’re completely okay with that. We’re just saying, hey, it’s really easy to integrate our stuff. Leveraging these open source technologies, use us when you need us, and then point your data processing somewhere else when it makes sense to do that thing somewhere else. And over time, we’re going to start to help our customers make that decision for them. You know, we’ll be able to define the characteristics, the size, the types of mathematical computations that are being done and say, actually, you should use this other technology for this one. You should use us for that one and help our customers with that.
Dr. Jeremy Weisz: 19:27
Yeah. So you’ll have to go in and kind of implement the code inside and then connect it to whatever data points they have.
Craig Dunham: 19:36
They can do it on their own though. It’s actually pretty cool. It’s like, yeah, we give them a package. They install it, and sure, we’ll white glove it to the extent necessary if they have questions. But we’ve got documentation. We try to make it as easy as possible.
Dr. Jeremy Weisz: 19:47
Yeah, I guess you could tell me if this analogy is right or not, but I kind of visualize it similar to like Zapier helps connect apps. But you’re like helping connect things but then also streamlining the efficiency and, you know, decreasing the time as well. Like if you go on Zapier, it’s obviously like pretty self-serve. You go and you just connect a bunch of stuff. Yours is kind of I mean, obviously this is enterprise level stuff, but it’s helping connect it. But also just, you know, increase the efficiency and everything like that on top of connecting things. Is that accurate?
Craig Dunham: 20:22
That’s pretty accurate. And there’s a, you know, a tuning and optimization process. And so, you know, you plug it in and yes, you’ll see some uptick in efficiency. But you know, imagine a number, a series of knobs and dials that you could turn up or down to. Really find the right configuration that works based on the needs that you have. And we do. Work with our clients to make sure we optimize their specific workloads for, you know, all. Of the compute capacity that we have inside of their inside of their specific system.
Dr. Jeremy Weisz: 20:55
Talk about pricing. And how do you come to pricing? I know every client is going to be different. I’m not talking like the specific amount of pricing, but like, you know, obviously there’s data. Being used. How do you decide to price this type of model for people?
Craig Dunham: 21:12
Yeah. We use a pretty common model in this space. It’s, you know. It’s priced per terabyte scanned. And so we basically look at the amount of data that’s scanned. And we’re not we don’t sort of, you know, and we, we sort of t-shirt size it, you know, on an annual basis. And so, you know, call it small, medium or large and there’s a per terabyte, you know, estimates, estimates on the number of terabytes they’ll use in a given time frame. And then, you know, if they exceed that in a given year, we’re not going to come back and charge more. But we’ll look and say, okay, did we pick the right t-shirt size for 2025 and maybe for 2026? We’ll look at if that makes sense to continue with that size or to go up or down depending on their usage.
Dr. Jeremy Weisz: 21:57
Is that because, you know. From predictability from their end. Right. So they’re not, they’re not paying like a monthly because I could see it’d be like, okay, you processed this amount a month. This is the charge, you know, it sounds like you’re kind of guesstimating on the year.
Craig Dunham: 22:14
We’re guesstimating it. But they typically know, again, what we’re doing. The amount of data that’s being processed is not like a new thing. They have been processing data for many, many years. And so they’ll have a really good idea of the amount of data they need to process. And you can imagine this does grow annually. And so there’s some rough estimation of that.
Dr. Jeremy Weisz: 22:36
I mean, that’s what I was thinking because they may be doing like you said, well, this is taking too long. We’re only doing this subset of data, but they have like ten other sets that they’re not doing. So I would think and maybe it’s hard to predict that sometimes because they have limited what they’re actually processing because of that.
Craig Dunham: 22:55
You are right. You are. That’s a really good point. That’s part of our value prop actually, which is like the art of the possible. Imagine if there are all these analytics you’re not even doing today because your brain couldn’t fathom doing them because the technology you have can’t, can’t do it. And now all of a sudden we open up and unlock this art of the possible. But for a given first year, that’s great. Like we want, I don’t have a single problem with if we charge someone for a small t-shirt size, and in that first year they use a large if they’re getting a ton of value and they’re using it and they’re talking about it.
Dr. Jeremy Weisz: 23:27
Yeah, it’s a good problem to have.
Craig Dunham: 23:28
Great. That’s a great problem to have. We’ll figure it out next year or the year after. But I’m okay with that. We want to encourage our customers to use it. And that’s why we don’t sort of do this every month. We change the price. It just gets too complicated for them. We don’t want to give them any reason to not use it. It’s we agreed to a price. You’ve got it for the year. Go nuts.
Dr. Jeremy Weisz: 23:47
Yeah. No. It’s nice, I like that. I want to hear about the evolution of the client. Right. You’re a go to market person. I’m curious how the ideal client has changed through Voltron Data. I don’t know if you want to start with now what’s ideal? And then go back to, you know who are some of the client customers coming to you before that maybe aren’t a fit now?
Craig Dunham: 24:10
Yeah. Our ideal customer tends to be larger enterprises or government organizations that have just large quantities of data. And so you think about us being a large scale query engine. And, you know, if anyone in the audience, again, is more technical, we’re talking, you know, ten terabytes sort of queries or ten terabyte data processing workloads or greater. And we have scaled and run benchmarks up to 100 and are confident we can go even higher.
Dr. Jeremy Weisz: 24:43
And that’s like a per month thing or that.
Craig Dunham: 24:47
That’s a per scan. Not even a per per scan.
Dr. Jeremy Weisz: 24:50
Yeah. So just to give people an idea, I mean, if you aren’t technical, which I’m maybe like on the end of not as technical, but I think I have three terabytes of data on my Google account which I haven’t used in ten years. So like and you’re talking this is maybe scanning on a daily basis type of thing.
Craig Dunham: 25:11
That, that that’s right. And bigger than that. And so you know here the example I use, we have a large global bank that is currently right now running a proof of concept with us. And, you know, they’re looking at trade surveillance data. And so they want to know like the bid, the offer, the ask prices for every ticker on every stock exchange in the world. And they’re looking for anomalous behavior in this trading activity. And so imagine and today imagine they’re only doing it. And this now, this is not the exact example. But imagine they’re only doing it today for the New York Stock Exchange because they don’t have the infrastructure that can do it for the New York Stock Exchange, the Nasdaq. And think of all the European and Asian exchanges. ET cetera. ET cetera. And so imagine the billions and billions you think of, think of an Excel spreadsheet with columns and rows, but literally billions or tens of billions of columns and rows of data that you need to sort of think about and like, make sense of and aggregate and join and, you know, and run analytics on to derive some insights or to look for in this example, some anomalous behavior of somebody doing something they shouldn’t do. Quite bluntly. And so we help with problems like that. And so our hypothesis is, you know, those larger customers are the ones that typically have data volumes of that size. But the problem of the largest bank today is going to be the problem of the mid bank a year from now, 18 months from now, two years from now, as the volume of data and the complexity of data continues to evolve.
Dr. Jeremy Weisz: 26:47
Yeah. I mean, they’re doing this anyways. Yours just maybe makes it more efficient, faster, cheaper. Because if you think of, you know, I’ve gotten alerts if we’ve any of us have gotten alerts from our credit card being like, okay, Jeremy, you made this purchase in California at this store. Well, we already have all these data points. That’s an anomaly on your account. We’re going to immediately shut it down. Right. And they’re probably using all these data points to make those snap decisions and shutting my card off so they don’t charge more stuff on my account.
Craig Dunham: 27:20
Yeah, that is exactly right. And so we tend to see, you know, the use cases fall into sort of four buckets. And it’s hey, I need to pre-process and prepare data to go into some LLM or machine learning algorithm. It’s I want to take a bunch of transactional log data and use it to make recommendations. So I think recommendation engines like I’m customer facing and these are the kinds of things that they might like as a result. We see it a lot with market forecasting sort of predicting predictive analytics sort of data. You know, maybe if you’re an automotive company and you want to look at, you know, if you run autonomous vehicles, for example, and every single behavior that the consumer does, the driver does from the nav system to, you know, how they brake and when all these things are creating logs. And so you want to be able to sort of predict when maintenance may be coming up or how much of this like infotainment center they’re using. And how do I make that experience better? I mean, there are all these like an infinite number of use cases that we can solve for. But it all really boils down to is it a ton of data? And do you need to really quickly analyze a ton of data? And is there a risk to you not analyzing the full set of data possible to you?
Dr. Jeremy Weisz: 28:35
And so at a general level, I’m curious, maybe you can speak to some weird use cases and non weird use cases in general. So banks obviously right. Large government agencies. You mentioned large retailers. I could see large manufacturers. What are some other use cases that have been any strange use cases that have come to you that you didn’t expect?
Craig Dunham: 29:00
Interesting question. I don’t know. That’s strange. I mean, they tend to fall into those same buckets, you know, and some of the things you might imagine now, you know, maybe an obvious statement. We don’t always know the ways in which some of our federal customers like what they’re doing. That’s just highly confidential. And even with some of our more regulated customers, like the larger banks that we have, you know, they’re really confidential about how and what they’re doing with the data. I probably for competitive protection.
Dr. Jeremy Weisz: 29:26
I mean, not what they’re doing necessarily, but like, I don’t know, some weird industry that you didn’t expect would come to you. And maybe there’s not. Maybe it’s like it falls into those buckets and that’s what you get. But anything that’s come up. Yeah.
Craig Dunham: 29:43
The automotive example I just gave actually would probably be the one that I just didn’t even consider around sort of how they’re using like the infotainment center. Right. And so think of all the bells and whistles that exist in like, you know, selecting your music and plugging in your device to be able to control the map system and all those kinds of things. I think, you know, is a use case that I just didn’t anticipate and realize that there are all these logs that are just constantly kicked off as a result, and someone might want to take those logs and figure out how to do something smart and special with it.
Dr. Jeremy Weisz: 30:13
Are there any industries that you think it’s underserved and they should be using it right now? They’re like, we haven’t really gotten these types of customers, but you can see like a direct path for them to really benefit from what you do.
Craig Dunham: 30:29
I think there’s probably a really good use case with hedge funds. And, you know, we’ve had some conversations. But, you know, think of the predictive analytics that go into sort of running regression models and trying to understand how the market has moved historically based on whether it’s, you know, macroeconomic factors or signals that have occurred. And how do I take this data and process it really quickly to be able to make smarter decisions in predicting what might happen in the markets in the future. And so I think that’s a really interesting use case that I would love to sort of dig in and explore more with, you know, some of the sort of larger to mid-size, mid-size hedge funds. I think that’s an interesting one.
Dr. Jeremy Weisz: 31:04
Yeah. Anyone moving large sums of money, It would be helpful, I’m sure. Like trading companies, banks and those kind of things. There’s a lot of data that’s moving around.
Craig Dunham: 31:15
Yeah. No, absolutely there is. There is.
Dr. Jeremy Weisz: 31:17
What about from in the beginning of Voltron. Do you remember when they first started? What did that look like? I mean, how were you even? This is a very technical explanation. Well, who are some of those people early on that were using it that maybe it’s not a fit anymore for Voltron. Maybe it is still.
Craig Dunham: 31:40
Yeah. So we I mean, we don’t have a ton of customers that have sort of we started the journey with 3 or 4 years ago that aren’t sort of that have sort of we’ve outgrown them or they’re no longer, you know, there’s there’s a similar level of applicability. Now, I should be really clear. I wasn’t at Voltron at the beginnings. I’ve been at the company for about a year, and the company was founded in 2021. And so we did start our go to market early, really focused on, if you recall, I talked about the core IP, the query engine itself is closed. It’s proprietary. It’s ours that we license that technology. But everything sort of around the ecosystem of that engine to make it really easy to embed that engine into existing data systems. A lot of our early customers, we focus on sort of driving adoption of those other technologies such that when we’re ready to launch the core product, it’s easier for our customers to work with us. And so a lot of the early customers were, hey, you should be using these open source technologies Apache Arrow, Ibis substrate, etc., these technologies. And then the more adoption we get of those technologies when we release our product itself, then it becomes that much easier for the customer to work with us. And so early on, we focused on that a lot. And then about a six months ago, really, we started really turning our attention more towards the query engine first as like SKU number one and then the, you know, driving adoption of those technologies in more of a services offering becomes skew number two for us.
Dr. Jeremy Weisz: 33:11
Craig, talk about your path for a second. I mean, you’re in this, you know, with Voltron Data. You started off as an investment banking analyst. Did you ever think I’m going to be working with, like, SaaS companies someday?
Craig Dunham: 33:24
No, no, no, I did not. And yeah, I still to this day, I somehow don’t realize how I got here and it’s so here’s the path. You’re right. So I was a finance major. I went to Hampton University. Big shout out to my home by the sea down in Virginia. And I went to Hampton University, and I graduated in oh two. And I took this job in investment banking, because when you’re a finance major, someone tells you that the creme de la creme of corporate finance jobs is investment banking. And I was fortunate enough to get one of those. And so I took it up and about a year and a half into that job, I said, yeah, there’s got to be a better way because, you know, I love working and I’ve learned a ton. But, you know, I was working 100 hours a week. And, you know, I just knew that I wanted to do something different longer term. And I came across this company called Capital IQ, which was a financial services data company that was selling into investment banks. And so it was SaaS before SaaS really became a thing. And it was one of the dot coms that survived the sort of.com crash in 2000 or so. And, you know, Cap IQ was looking for investment bankers who can come in and say, hey, built by us, you know, for us or sort of, sort of, sort of mentality, which is hire as many former bankers as possible, sell this really cool tech into bankers. And that sort of made my transition into technology. And who knew that company would go from, you know, less than 50 people or so when I started to, you know, a few hundred people before I left and, you know, had grown to a couple hundred, you know, 100,000,000 in AR at the time that I left. And so suddenly I went from this financial services guy to this financial technology guy. And I decided we got acquired by S&P or Standard and Poor’s. And then I decided, okay, this is getting bigger, slower, because when you work for a bigger company, that’s just naturally what happens is just the evolution of businesses. But I knew I wanted to do it all over again. And so there was one of the founders of a company called Seismic, which you mentioned you made reference to in my bio earlier, who also used to work at Cap IQ, and he gave me a call and said, hey, we’re looking to do this thing and we’ve got this great company and we’re headquartered in San Diego. And I was like, oh, sunny San Diego. That’s amazing. I can’t wait to go live there. And then he said, no, no, no, we actually want somebody in New York. And I was like, oh, okay, fine. I guess New York will have to do.
Dr. Jeremy Weisz: 35:30
Where are you from originally?
Craig Dunham: 35:32
I’m from New Jersey. And so New York is kind of like going home in some ways. Not quite the same, but I.
Dr. Jeremy Weisz: 35:36
Yeah, I’d be jumping at San Diego as well for the weather purpose. Yeah, exactly.
Craig Dunham: 35:41
Yeah. I thought it would be interesting to live on the West Coast. I was living in London at the time, and then. So the idea of going to San Diego was more interesting than going back, quote unquote, home to New York. But I got to Seismic and it was again, hey, we’ve got this really cool problem. And we’re solving, you know, this sales enablement problem. And, you know, so I started working with sort of all customers. But as you might imagine, my background being in financial services from investment banking, from capital IQ as a data financial data company. Suddenly we started finding these niche solutions for seismic within financial services. And so we said, hey, we should really focus on this. And so we carved out about half the company and said, Craig, you’re going to be the general manager. You’re going to run all of the financial services verticals. So sales, marketing, customer success, you’re going to make sure that we’ve got a story that’s repeatable. And, you know, we know how to tell that story. And we’re just going to really focus a subset of the company on that. And so that really transitioned me into this like cross-functional leadership role where suddenly I’m not just, you know, focused on sort of pre-sales or post sales sort of consulting. I’m now focused on broad management and really focus on how do we sort of drive a go to market motion around a subset of customers, specifically within financial services. And then it just sort of evolved from there. And it was one GM job to another GM job to another GM job. And suddenly, you know, I’m I hate the –
Dr. Jeremy Weisz: 36:58
Seismic the really bad really quickly. I mean, we’re talking there are over a thousand staff here. I mean, this is a large company. I mean.
Craig Dunham: 37:06
Now it is. When I started there, there was, I was I think I was somewhere between employee number 20 and number 25. Now Seismic is probably, I don’t know, 1500 or so people. You know, when I left, it was probably just over a thousand or so people. So I was there for six and a half years of 100%, you know, doubling every single year, both organically and inorganically as a result. So it’s a really fun growth story to tell.
Dr. Jeremy Weisz: 37:33
Talk about the leadership, you growing as a leader there obviously starting at 20. And then you’re in charge of this you know large financial services department. And how you grew as a leader.
Craig Dunham: 37:48
So yeah, that’s a really good one. I mean, you know, there’s a couple things. There were a couple people at Seismic, I think that were really influential to me. You know, the CEO is still a good friend and mentor of mine today, Doug Winter. And so I adapted a lot of my leadership style from him, the CTO, Mark Romano. I often joke with him and I say like, the reason I joined seismic was because of Mark. And, you know, we did this interview and I was just so blown away by his intelligence and smart. And I was like, if he’s running the product direction, this thing is going to really go somewhere. And, you know, I think so the two of them really had a lot of influence over me as a leader and becoming who I am. And, you know, there’s just lessons that you pick up along the way as you think about, you know, what the company needs from 5 million to 20 million or 20 million to 50 million and 50 million to 100 million. And at each stage, the company needs something a little bit different. And you just have to adapt yourself. And, you know, I’ve become pretty, pretty good at doing that. I’ll give one other little example or tell a story, which I think has really served me well. But I had a leader just before I joined Seismic. Her name was Amy Kadomatsu, and one of the things she said to me right when she left Capital IQ to go to her next gig was, you know, she’s like your biggest strength and one of your biggest strengths is people. And she’s like, people like you, and they like to work with you and they like to work for you. And you’ve done a really good job at just sort of, you know, being able to get buy in and being able to influence without being sort of forceful or heavy handed. And she’s like, if you can figure out how to just take that people skill and leverage it, you’re going to have this exceptional career. And so I really started to think about that advice. And I don’t know if I, I don’t know if I intentionally do this or not, to be honest. But, you know, it just made me really appreciate the importance of hiring great talent, letting those really talented people, giving them the space to do the things that they do well, not having a big ego about it, looking for people that are smarter than you, and just like building relationships and bonds for for people to work really well together. And I think that has served me well throughout my career.
Dr. Jeremy Weisz: 39:50
I thought there was going to be a but after that, where she was going to say, okay, like you’re really good with people. But I was waiting for that.
Craig Dunham: 39:58
Maybe there was. I tend to remember the positives. I don’t know, maybe there was some negatives in that in that final interview or final exit interview before she left. But I remember the positives.
Dr. Jeremy Weisz: 40:07
Yeah. No, I love it. And then what about with Lumar?
Craig Dunham: 40:13
Lumar was a different situation. You know, it was you know, the company had historically been sort of a product led growth, and they were moving to an enterprise led growth motion. And so they wanted to bring in a leader who could really help the company go through that transition. You know, I’d say the big lessons I learned there were, you know, it was the first time I was at an entity that, you know, we were doing well, you know, growth had slowed a little bit. And so I got hired in to really sort of kickstart growth. But coming from Seismic, where we were growing 100% every single year. We were just rolling, rolling. And you get to Lumar and it’s a more crowded space with more competitors. We were, you know, a smaller player and there was a in some ways a misalignment of, you know, as we move from this product led motion to an enterprise led motion, we needed to sort of match the talent to be that of an enterprise led motion. And so it required us to make some additional hires and, you know, move on, unfortunately, from some people as well. But that was the lesson there. It was the importance of aligning the talent with the, you know, with the task that you have at hand, which maybe feels like a blatantly obvious thing. But it’s sometimes hard when you come into a company and there are people who have been there for a long time and, you know, they’re really nice and great people, but maybe they’re not the right fit for the role. And so my big lesson or takeaway was you can’t wait too long if your gut if your instinct says someone’s not right for the role, you unfortunately need to make the decision for the company, but also probably for them. Like when someone’s not right in a role, they know it, they feel it, they’re probably unhappy, and it’s best for everyone to just make a decision as quickly as possible.
Dr. Jeremy Weisz: 41:44
What’s that conversation look like? Because that’s a tough conversation, especially maybe if you’ve known them for 20 years and like, you know, but you’re coming in and you’re like, we need to shake things up and improve. And obviously you’re a people person, so I’m sure you gain rapport quickly. But what does that conversation look like? How do you initiate it and then have that conversation knowing in the back of your mind it’s going to help the company and ultimately help the person. But no one likes change. I mean, even if they want the change, no one likes it. So what does the conversation look like in that situation? Yeah.
Craig Dunham: 42:23
I mean, so having tough conversations. You’re right. It is, it is difficult. And I tend to usually open with an acknowledgment that it’s going to be a tough conversation just to sort of set that framing first. And, I usually will try to get to the point first, because I don’t think it’s good to just have this anxiety building and building for 30 minutes over time. It’s like, look, I’m just going to get to the conclusion and then we can go back and talk about how we got to that conclusion. And so if it’s delivering news like not the right fit for the right role, I usually open with that. And then I talk about the logic of what I see in terms of their skill set and their qualities, and what we need specifically for the role, and try to be as logical and just point out the mismatch of the skills with what we need at this moment. And it’s typically and oftentimes it’s not a reflection on them as being an underperformer in any given way. But it’s more so that, hey, I think this kind of role is probably a better fit for what you want to do long term and better fit for your skill set. And if there’s another space in place within this company where we can put you in that. I’m open to it. And if not, then I’m also open to supporting and endorsing you to go find something else that’s probably and perhaps a better fit for you. And I’m happy to be a recommendation to talk about all the skills you do have that align with this other role that makes, I think, more sense for you.
Dr. Jeremy Weisz: 43:41
Do you usually come in with a conversation thinking of, okay, there’s another role or this is not going to work out or you kind of explore it with them.
Craig Dunham: 43:54
The answer is always it depends. There have been situations where it’s been both. There are times where I’ve known conclusively that you know, someone is not right for a given, a given role. And so that decision has been made coming into the conversation. But in all those cases, you know, I have another really good friend of mine. She’s super experienced and her name is Nicole Maguire. We work together really closely at Seismic. And one of the things that she always taught me was, you know, if someone is, you know, if you need to move on from an employee and they are surprised about it, then that’s your fault as a manager. Like no one should be surprised that they’re not a good fit or that things aren’t working well because you should have been giving them feedback along the way. You should have been sort of like, you know, helping them and coaching them and guiding them. And, you know, and all those things should have been happening. And so they should have known that something wasn’t 100% right. And so it shouldn’t be shouldn’t be a surprise. But then there have been other situations where it’s like, you know, and I would view those more as like feedback, conversations or even quarterly or annual reviews where it’s like, hey, we’re going to go through more formally, like what you’re doing, what you’re not doing, where I think you can do better. And in some of those situations, we can kind of really work through how do we get them to a good place where they can be more productive?
Dr. Jeremy Weisz: 45:03
Yeah. So I guess I visualize the approach as kind of like a bit like ripping a Band-Aid, like letting them know this is going to hurt and just go like, just rip it off and then maybe go back and do the explaining. So there’s no anxiety around.
Craig Dunham: 45:17
That’s right.
Dr. Jeremy Weisz: 45:17
Doing it slow. Actually there’s a Dan Ariely and Predictably Irrational talks about I think it was his book that talks about, you know, just ripping a band, like he was a burn victim and had when they took off the bandages really slowly. It would, they thought the nurse thought they were doing something good by him to like, let him adjust to the pain. But he’s like it was way better when they would just rip it off and not like make them toil slowly. Anguish over taking these off. I’d love to hear you know some of your favorite resources. They could be books. They could be podcasts. They could be, you know, software that you like. For some reason, you’re. This journey reminds me. I had Richard Wilson on the podcast who runs Billionaires Comm and Family Offices Comm, and he recommended a bunch of these books written by billionaires. And his argument was like, hey, Jeremy, do you want to read a book by someone who’s never made money before or someone who’s accumulated billions of dollars? I’m like, okay, it’s a good point. So he’s got a list on his site, and one of them was What It Takes by Stephen Schwarzman, who co-founded Blackstone. And for some reason, you’re like this winding journey, like when I listen to it, somewhat reminds me of that book, What It Takes, because he was kind of like all over the place with different things, all finance and things like that. But I’d love to hear some of your favorite resources that you’ve, you know.
Craig Dunham: 46:53
Yeah.
Dr. Jeremy Weisz: 46:53
Appreciated about your journey.
Craig Dunham: 46:55
Yeah. So I’d say a couple podcasts I listen to quite a lot. How I Built This with Guy Raz is one of my favorites. Masters of Scale with Reid Hoffman is another one that I listen to a lot, and they’re all stories about like, how do you grow businesses and really practical success stories. And then I am like an avid reader. And, you know, oftentimes I read a lot of fiction, but when I do read nonfiction stuff, it’s, you know, it’s business books. And, you know, some of my favorites are The Hard Thing About Hard Things is one of my favorites starts with the why. All of the like, big fan of Patrick Lencioni. And so he’s got like Five Dysfunctions of a Team. He’s one of the other ones. Oh my God, the book about meetings. It’s driving me nuts. I can’t remember the name of it right now, but there’s a number like that. Brené Brown, Dare to Lead, I think is an interesting one. It also talks about leading with vulnerability and sort of the human element of being a leader are some of the ones that I would that I would that I would recommend or the authors that I would recommend.
Dr. Jeremy Weisz: 47:53
Yeah, I think the Patrick Lencioni one, I remember, if that’s what you’re referring to, I think it’s Death by Meeting or something.
Craig Dunham: 47:59
That’s it. Thank you. Yes, that’s the one. That’s the one.
Dr. Jeremy Weisz: 48:02
He’s got a lot of good ones, though. No, I appreciate that. Yeah. No. First of all, Greg, thank you. Thanks for sharing your journey, your knowledge and your lessons. It’s been super valuable. I want to encourage everyone to check out VoltronData.com to learn more and more episodes of the podcast, and we’ll see everyone next time. And Craig, thanks so much.
Craig Dunham: 48:21
No thanks for having me. I appreciate your time.
