In this episode, we're gonna take a look at how many data scientists does it take to predict your SMB success.

Hey, welcome, everybody to another episode. All right. So we've been looking at this problem around how do I help my own SMB to grow? Right? What does it take for me to have all of the sufficient technology that I need to compete today? We've heard the adage, of course, Hey, you know what, if you're not participating in AI, you're going to get left behind, right that there's a lot of large organizations that, of course, pursuing this, and we'll talk about that here for a moment. What I found is that most AI platforms today, they really require a deep technical bench, right? And a lot of that gets outside of the reach of the small to medium business, right. So one of the things that led me into this world was to say, How can I help and find a way to close that gap for the small to medium business? What does that look like? The drive for Business Insights, leveraging AI, through simple platforms is growing at a cumulative annual growth rate CAGR of 47%. From now up through 2030, the safe some of the researchers out there.

Alright, so the need for this and the desire and the appetite for it is clearly growing. But how can you afford it? What does that look like? So I was looking recently, at this came out in February of 2021. There's this report that was looking at some of the top 10 companies, right, that are hiring data scientists. These are large companies, right? And what were they paying for them? Right? Let's say I had the checkbook out. And I want a data science team. What does that look like? Pinterest? At the time anyway, they were paying the most 212k on average annual salary for one, one data scientists. Lift was 154k. Snap was 152k. Man take a breath, right? slack 148k, Uber, 139k, Microsoft 136k. Right. These are average salaries for data scientists, Oracle 132k, Intel 120 3k and Accenture 107k. So the average of that using fancy math 144k. All right, how many data scientists does it take? I don't know, how many does it take to, to, you know, screw in a light bulb. But let's take a look at this. If you're gonna build a data science team, there's 12345678, there's about eight different key roles that are typically done on the stains. Now, a role doesn't always translate into one person. Sometimes it's multiple people. Sometimes one role can go across, you know, a person. Now let me run this by here, there's at least four, four critical needs, right? Let's say as a small to medium business owner, you're going to go build your team, you're going to need someone that's going to take care of analytics, right. And so that's, that's a critical role. So that's a necessary one that's required. Someone's got to know some coding work. So in languages like R and Python, stay with me. A third area is of course, all of the data and database work. So you know, working with SQL and no SQL, those sorts of things. And then and then there's all the algorithms and the models, right.

This is regression models, right? And dimensionality and yeah, the list goes on. All right, that's four roles right there. So it let's just take that, let's say that, and that those are unique enough that I'm going to argue that you will need one person for each of those roles, and using the average of 144k. We're up to 576 $576,000 a year. That's it. Even fully loaded, you know, that's, that's, that that starts to become a big number instantly it goes outside of the reach of the SMBs. If you really had and I was looking across some of the large companies doing this, they've typically got around 10. Right? And so you're in the 1.5 to 2.5 million range. And that's still not fully loaded. Nor does it include all of the technology costs, and you know, et cetera, right, all of the hardware and everything you need for that as well. Is it a surprise? No, it's not a surprise, when you say, Hey, you know, the big companies, they are really getting an advantage here by using the resources to bring in this talent, and then to actually widen the gap between what they can do, and what's available to the SMB world. And that's really started just sink deepest in my soul, right? I was like, wait a minute, wait, hey, it's the small companies that have brought some of the coolest innovations, and, and great opportunities to the planet. Oh, and I didn't even mention, there's another role. It's called the chief analytics officer. Not every company has those. But that's yet one more. And of course, I'm sure you could tell that roll is going to be above the 212 k range. I didn't even include that in there. Right. So let's say that, at the very least, as an SMB, if you're really going to do this, if you're going to put together your own data science team, you're at least in the 500k range somewhere right in there. And that's actually on the low end.

So like, Alright, so how do you let's, let's just assume that we did that. All right, for whatever reason, I've got the money in the bank, let's say we're just pretending let's say I'm going to go do that. How do you integrate this data science team in your company, there are several operating models to do this. There's a decentralized model, that's where you take an analytics group, right, your data science group, and they're focused on a particular function or business unit, and each business unit, or each function in the company, they have their own data science teams, some large companies have that. Here's another operating model, it's more of the functional model, it's where you have sort of this key function, there's one analytics group, but they will reach out and provide occasional support to other teams. Then there's the centralized model. And this model is where you have one analytics data science team that spans across the entire Corporation. And they of course, then go and reach into the different analytics projects, which are owned by the different business units, and, and different business functions. It turns out, that that model, that centralized one tends to be the one that works best for SMBs, right, where, in reality, it doesn't make sense financially, that, of course, you're going to have multiple data science analytics teams, you'll have one for the entire organization. But you know, like I said, in the larger, larger companies, they'll certainly fun multiples of these. So for our purposes, here, we're going to talk about the centralized model. And there's a flavor of that called the consulting model, which means sometimes that analytics group, or that data science team is actually not in the company. It's external, in its reaching in and providing insight, and, and, and guidance and predictive work into different parts of the organization. So it's critical to know that when you're doing this kind of approach with the consulting model, that that the understanding of the business and the key problems you're trying to solve, that goes back and forth, right between the analytics group and of course, then the the SMB itself. But what does this really mean for an SMB? One of the most important things that it really means is that in order for you to do AI, it means that you're going to have to have one of these big teams. And I did not like that conclusion, I was like, this is this is actually really hurting the SMB teams.

So I went hunting for a solution, right? And the solution was, how can we get data science and AI and predictive capabilities into the hands of an SMB? Right? How can we do that in a way that it does not require a data science team within your organization? So there's, there's not only this monetary challenge, or hurdle to it, which is okay, at the very least, let's say it's 500k, for argument's sake. There's another problem though. And that problem is, how do you declare return on investment? Right? Have that all you know of all of that machine learning and data science team investment, and that's of course, where a lot of business executives still need to be convinced. Then you know, you can't can't complain them or Can't can't blame them, right? I mean, you're looking at, you know, at the very low end 500k definitely up into the millions of dollars, what is it that's going to turn around a return on the investment that will make that justifiable. And that right there makes it even more challenging for an SMB to say, I'm gonna gonna jump into this. So, as an SMB, one of the things that I found is that you can skip hiring that team. You do not need the team that the platform's AI platforms have matured, to enable SNB teams to get the benefits of AI without actually requiring bringing in all of that sort of expertise into your own organization. Now, that's that's a huge promise, right? That's a huge change. That capability wasn't really even there a few years ago, right now today, what we found what we've developed, what we've provided is a way to do that. That is literally pennies on the dollar. So what I'm offering up to you is to join me Thursday for a web class on how to take advantage of this 1pm Eastern 10am. Pacific, this coming Thursday. I'd like to share that with you. I will be talking with you soon. Listen to me on my next episode, and I will start letting you know how to participate in that. Thanks for joining and until next time, don't go buy a data science team.

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