In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the age-old question of whether to buy or build. They explore various aspects of this decision, including business strategy, in-house skills, regulatory requirements, long-term considerations, and the value of owning versus renting. They also touch on the growing importance of large language models and AI in the marketing technology landscape. Tune in to gain insights into the factors to consider when deciding whether to buy or build a solution and how it aligns with your business goals and needs.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher Penn 0:00
In this week’s In-Ear Insights, let’s talk about buy or build.
Now, this is a topic that has come up many, many times over the years from the earliest days of whether you should even run your own email server, versus using a commercial service through marketing technologies, open source products, for example, like matomo, and Mautic, versus commercial properties like Hubspot, and even to the in artificial intelligence, which is where there’s a lot of discussion happening today with open source models like the llama family and the Mosaic Family versus commercial services like ChatGPT.
So the question is, should you buy? Or should you build? So Katie, in you’re looking at this from a governance perspective, a corporate responsibility perspective and a hey, that thing vanished, that I was paying for a perspective? What’s your take on buy versus build? Well,
Katie Robbert 0:51
unsurprisingly, there’s a lot of dependencies.
And one of those dependencies is your overall business strategy.
Where do you want to be with your business? You know, in a month, six months, two years, five years, and so that will start to educate everyone on whether or not you should build your own thing and own it? Or if it’s just a small part of moving the business forward, and it’s okay to buy it.
The second piece is, you know, do you have the skills in house? Or do you have to build out that skill set first to maintain this thing? And so on and so forth? It almost kind of sounds like we’re talking through the five P’s? What is the purpose? Who are the people? What is the process? What platform? And then how do you measure performance? It’s almost crazy how, you know, you can always tie it back to the five Ps.
So the first question is, what is the purpose of this thing? And not? What is the purpose? Like, what does it do? But what does it do for the business? How does it move the business forward? How does it serve the customers? How does it fit into the workflow? How does it help our team members do their jobs? And so understanding all of those questions, so that’s where I was start with trying to understand builder by, you know, I think of it the same way that I think about projects around my house, there’s certain projects that I am completely capable of doing myself.
And then the other 99% of the projects, I need to outsource to a contractor.
Because I either don’t have the tools, like for example, I was putting a cart together yesterday for my garden.
And I didn’t have the correct tool.
So I ended up using an adjustable plumber’s wrench, because it was the best proxy of a tool that I needed to tighten the bolts on the cart, it was the wrong tool, it got the thing done, but it wasn’t done properly.
And so I think when you’re thinking about builder buy, do you have the tools? Do you have the skills? And so there’s a lot of questions to ask, before opening the wallet.
What do you think, Chris?
Christopher Penn 2:59
Well, I was gonna ask you, where do you put regulatory requirements in this?
Katie Robbert 3:09
I put that honestly, in all five P’s, you know, so let’s say you work in a regulated space, like pharmaceuticals, for example.
And you have to, you know, store your data a certain way.
And you have to report on your data a certain way to federal governments.
That needs to be part of your purpose that needs to be part of your user story.
So, you know, as the person who was up all night, every night thinking about the, you know, protection of our data, I want to bring on a piece of software so that I can maybe sleep at night and not worry about data breaches.
That’s a pretty strong user story.
And there may not be software out there to do exactly the thing that you need.
So you would have to build it.
The challenge, especially in regulated industries, is the there’s a general set of rules that applies to everybody.
And then when you get into each organization, the rules are unique, depending on the workflows, and the customers.
And so in those instances, a lot of times it’s, quote, unquote, easier to build your own thing to conform to the rules and regulations that you have, versus buying a piece of third party software that you then have to adapt to your rules.
Christopher Penn 4:30
Right, I was specifically thinking in this case of Google Analytics, right? We know that, for compliance with the EU’s GDPR rules, Google Analytics, even the new version, Google Analytics 4 is not fully compliant.
There’s some things that they’re still not permitted.
And that it still does.
And apparently, Google is kind of go ahead and try to sue us.
But if you’re a company, within the EU, for example, you probably want to use a piece of software like matomo which if you host it on your own servers, which is now what you’re in the, into the build territory, you can be fully compliant with EU law, which you know, as an EU Business you would want to have to do.
When we look at what’s happening with things like large language models, we’re starting to see a patchwork of regulatory differences.
So the EU is saying you may not use scraped data in your models to build these models.
It’s a violation of copyright.
Japan went the other way and said, scraping data for AI construction is not a violation of copyright.
And it’s fully permissible there.
So there’s, there’s no, there’s a question of do we buy? Because if we if we’re buying from a company that that host does mile construction hosting in Japan, we’re compliant with that law? Or do we build knowing that you have to comply with with certain laws.
So there’s, there’s a bunch of regulatory sort of constraints and a lot of these technologies.
In terms of good,
Katie Robbert 5:55
I was gonna say, but that’s what I was saying is, you know, it, you have to understand what your needs are first to see if there’s software that already does a thing, or needs to be only slightly adapted.
Or if there’s generic software that you then have to highly customize.
And so it really, it depends on what you need.
And so, you know, both cases are fine.
But you have to also think about the long term.
Christopher Penn 6:20
And the long term is something that really got me thinking about this past weekend about, I was actually thinking about stuff on Netflix, believe it or not, and all the different streaming services and Spotify and stuff like that, when you pay for the services, you don’t own anything, right, if you stop paying for Spotify, or whatever, you all that stuff just vanishes into the ether.
Whereas in the old days, like when we were young and rebellious, you will go out and buy a CD and you own this CDs, you have this piece of plastic for as long as it lasted.
And you and you chose never to do business with that company, again, you still have a CD that you can put in your music player and listen to that music today with the streaming services, you can’t.
And so much of our marketing technology is that same thing, right? Where if you say you don’t want to do business with Salesforce anymore, the moment you terminate your account, everything is gone.
You don’t you have nothing left.
Whereas if you build like if you’re using say, oh gosh, Sugar CRM, the open source product, if you build that thing, and that is running on your servers, that that is yours, even if you never do business with with sugar, again, you have the system, the server, the data and stuff and all that is something that never goes away, as long as you maintain that that piece of hardware.
Katie Robbert 7:43
But I guess that goes back to the question of your need.
Do you need all of that data? If you decide to part ways with your CRM provider? Or, you know, do you want to hire a team to maintain that information that maybe you only look out once a year? And so there’s no right or wrong answer.
In this conversation, it really comes down to the specific needs of each individual company and team.
So you know, in that example of Salesforce, you know, maybe what you do is you export all of your data or make copies of the information that’s in there and then put it into a different one.
And then it doesn’t matter.
You know, what the outside shell of the CRM looks like you have the data regardless, I mean, there’s different ways to look at the same problem.
And so you really need to go through that exercise of understanding what you need short and long term before deciding to build or buy.
So, you know, we’ve talked about building our own CRM and our own processes.
And for us at this time, it’s a nice to have, you know, we’re still a relatively small company in terms of the number of contracts that we have.
And so using a system that we buy, or rent rather, is good enough, at some point, we may get large enough that we want to build our own, but at this time, you know, we’re not there yet.
And so maybe that’s the solution for a lot of companies is that they sort of rent to own, like you do a piece of property or you rent until you can afford to build your own.
Christopher Penn 9:24
I think the other consideration and one that we talk about a lot with things like artificial intelligence is if it’s an ancillary system or service, like it helps you do your job better.
That’s probably fine to rent.
If it’s a part of your secret sauce as a company, you probably should own it, because otherwise you are.
You’re fully dependent on another company for a core functionality.
You know, one of the ways that you make money and if you if you lose access to that vendor if they go out of business, or they quadrupled their prices You’re kind of kind of stuck with them.
Katie Robbert 10:03
So let me push back on what you just said.
So one of our core sets of services has to do with Google Analytics data.
We’re not about to go out and build our own comparable Google Analytics system, even though we are reliant on the data that comes out of that system.
And when Google made their announcement to sunset Universal Analytics, and bring on Google Analytics 4, we had to rewrite and rethink all of our services, because we’re dependent on Google Analytics data for our core services.
But at no point in the history of our business.
So we had a conversation or even inkling of, should we build our own Google Analytics like system, because we are dependent on the data for our services to be successful.
Christopher Penn 10:58
That’s not entirely true.
Katie Robbert 11:01
We’ve never had that conversation we have?
Christopher Penn 11:03
Well, we’ve not talked about building our own we have talked about and we are in the process of doing for one of our clients that actually is building some of our core services for Adobe analytics.
And as we covered on the live stream a few weeks ago, which if you have not, if you missed the the episode, you can find over TrustInsights.ai AI slash YouTube is buildings, a system that allows you to extract data out of the matomo system, because if you’re going to backup your Universal Analytics data, you would have that data stored in that system as well.
So we’ve no talking about building our own analytic software, because that’s just crazy.
But we certainly talked about having alternatives to the the 800 pound gorilla in the room.
Katie Robbert 11:45
But without the analytics systems, our services don’t work true.
And so that goes back to the builder by, you know, we, regardless of the analytics system, we’re always going to have to quote unquote, buy, because we are not in a position to build our own.
Because, you know, for a variety of reasons, number one, being market penetration, we will never get the same market penetration of a Google or an Adobe or even a matomo.
With those Analytic Systems, therefore, we will forever have to buy,
Christopher Penn 12:21
right, and a bigger consideration on that font is, I don’t know what we could offer that would be better than the existing market alternatives.
Katie Robbert 12:30
You think that’s a bigger consideration than all the work that it would take to actually build the thing? I do.
Christopher Penn 12:35
Because if I think if there was a, if there was such a compelling value proposition, such a blind spot in the market that we thought was really important, we would build it in the same way.
Like, for example, we built our multi touch attribution system, because we saw a gap in the way Google Analytics does it.
And we couldn’t find anything else in the market that even came close to what we thought made sense.
We’re like, Okay, we’re gonna build this sucker.
Katie Robbert 13:00
Right, but we’re still relying on the data that comes out of Google in order for that to work.
Christopher Penn 13:04
But I’m saying even in the case of like, digital analytics, if there was a glaring gap that we’re like, hey, we could make a billion dollars on this.
Yeah, we’d give it a shot.
Katie Robbert 13:19
Is there? Can you think of any examples of any companies that have 100%, built all of their own stuff, and are not buying or renting things from any other third parties?
Christopher Penn 13:35
Not currently, the student loan network that I worked at a number of years ago actually did that.
And it was a very painful experience, like, we hand wrote our own CRM, because we couldn’t find anything on the market that we wanted.
We operated our own mail servers, we operate our own web servers.
We rent it, obviously, the hardware to do that on but we handled all that stuff.
It was miserable.
Katie Robbert 14:03
But did you also then use systems like Microsoft Office or payroll systems that were third party systems?
Christopher Penn 14:11
We used an open source alternative for like the first six years of the company, because the company couldn’t afford the licensing.
And we were we were not willing to steal it.
So we had so we used open source alternatives.
Yeah, it was like I could say it was not fun.
Katie Robbert 14:25
So it sounds like, but there was still some level of buying or renting other services.
Christopher Penn 14:34
Yes, there’s certainly as some other entities, we were more self contained.
than, say, the average company.
But yeah, I mean, no company can can do everything from scratch.
There’s no way to do that in the modern world.
Katie Robbert 14:48
And I think that that is where you as a company do to start making those decisions.
You know, so Microsoft Office, for example, is one of those things like sure you can build your own using something like Open Office, or you can use, you know, Google Docs or Google Sheets, but either way, you’re still reliant on a third party to create a Word document.
You know, whatever the system may be, and so we as consumers, we as business people, we as professionals, we’re okay with that.
Because that’s not a system we’re ever going to build ourselves.
But yet, when we start to think about large learning models, we’re like, oh, no, we have to build our own.
And so obviously, we’re talking about two different kinds of things.
But I guess my question, you know, from a psychology standpoint is Why is one Okay, and we’re not okay with the other?
Christopher Penn 15:45
I don’t know it’s not okay, I think it’s, like you said, comes back to the requirements gathering like what is going to be essential that you cannot lose access to.
Otherwise, it’s sort of game over.
And if you are building a large language model of some kind, and it is core to your, your product like that is what you do.
I feel like the risk of binding yourself to a vendor that may or may not be here in six months, a year, two years.
If you’re staking your whole business on that thing, that to me seems like risky.
And it’s one of the things when I see on LinkedIn every week, here’s the 200, new AI companies that have come out like well, you know, 198 of these will not be here in six months, because they’re one trick pony is built on someone else’s architecture.
And once they start to scale, they’re going to realize, hey, this is going to get real expensive, real fast, and we’re not making enough money fast enough to cover the costs.
Katie Robbert 16:42
How long? I mean, this is going to be a wild guess.
But how long before you think these AI companies and large learning models become essential tools in the mahr tech stack? So for example? Well, so for example, like if when people say, what are the essential tools that I need to start a business, we always talk about things like well, you probably need, you know, accounting software, and you need, you know, some sort of document repository and you need, you know, a basic CRM and an email server.
You know, how long before large learning models and generative AI tools are essential, not, you know, nice to have but like essential,
Christopher Penn 17:27
large language models skills will be a must have by the end of this year.
And here’s why not because you’re going to be building or buying out your own model, but because it’s going to be in everything.
Microsoft Office with copilot will be out probably in the next three months where you will have a an LM prompt right inside of Office saying hey, convert this press release to a proud to a PowerPoint, right.
So being able to work with prompts that you already see it with Google’s search generative results, the STE stuff that is now Google said we’re going to sunset this at the end of the year, because this is going into mainstream Google search.
So generated search results, which like, Wow, you are going to you’re going to see in Windows 11, before the end of the year.
So the very operating system is going to have a language prompt to be able to ask things in we already have it in Hubspot chat spot, and I have no doubt that Dharmesh is going to take that out of alpha, and move it into the product sooner rather than later.
We have it in Adobe Photoshop, the new generative fill.
So if you are working in Photoshop, there it is, you have an Adobe firefight, the new version of Adobe express just rolled out with generative AI built into it.
So from the buyer build section, in this in this particular skill set case, that it’s a moot point because the technology, the the core technology is going to be in every piece of software that you can imagine now, should you build your own would depend on the use case.
But what we’re seeing with a lot of the new open source models is because they’re open source, they’re commercially licensable.
And they’re much lower power and cost.
Those are going to be in everything you already see it this audience Snapchat, for example, you can talk to your AI friend, which is really weird experience, by the way.
Katie Robbert 19:14
I found surprisingly, I don’t use Snapchat.
So that just sounds odd.
Christopher Penn 19:18
Yeah, it’s going to be a Tiktok.
The thing that’s going to be very interesting is in customer service applications.
So this is one where I think there’s gonna be a lot of application where you’ll have a language model that powers a chatbot on your website that is more intelligently trained to answer to realistically answer customers questions better than the existing chatbot technology, which really still kind of sucks.
Katie Robbert 19:43
Marketing profs release that.
So you know, they tend to be more educational, they have courses, they have a lot of content.
So I would imagine that it’s if I was sort of imagining how this all played out, they’re like, Hey, we have all this information.
Why don’t we basically turned it into an intro We’re active FAQs, which is essentially what they did with their content, which was, you know, on their part a really smart move.
Christopher Penn 20:06
And so that goes back to, you know, buy or build in their case it well.
We won’t disclose anything confidential, but, but they saw an opportunity with their data like, yeah, and that is a big consideration and vulnerabilities, if you’re going to build something, even if you’re gonna be using other foundational architecture, you’ve got to have the data to be able to do that.
And you’ve got to be able to have the data be clean and ready to go.
So you’ve got a huge amount of requirements gathering to see, could we do this, I’ve been testing something and it’s not ready for primetime in the slightest, because it’s still really wonky.
But we have our own data stores of information that we could use to to tune a model, I’ve actually been training one, trying to train it on the data from our Slack group, if you go to trust insights.ai/analytics.
For markers, I can export the conversations as a server administrator, from there, D, identify them, remove all the personal data, stuff like that, but then take the conversations themselves, because we talk a lot about stuff like analytics all the time, unsurprisingly.
And if I can get one of the open source models and tune it, based on the conversations we have in Slack, we might get a model that is better at answering analytics questions than say, the off the shelf model.
Katie Robbert 21:26
provided the information is correct,
Christopher Penn 21:28
provided the information is correct.
Likewise, I could export our own internal slack data and to that and you know, tuna model on that as well.
So that we could, you know, continue our quest to make a, an AI agent version of you.
So you can just sit on your desk all day.
Katie Robbert 21:44
Christopher Penn 21:46
Exactly, but so that’s that’s an example where, because that would be something that eventually would be core to us, it makes more sense to build.
Now we’re not building the foundational model, but we are building the fine tuning to make something that we would eventually own and operate.
Also, we’re doing that with open source models, because it’s a lot lower cost.
And if if it blows up and doesn’t work at all, it was just, you know, my time on the evenings and weekends to mess around with this thing and not, you know, $100,000 worth of compute time over over it OpenAI.
Katie Robbert 22:18
So it sounds like, you know, obviously, there’s a lot of considerations if you want to build or buy, you know, but one of the things you really need to be clear about is, do you have enough of the raw material to build the thing first? You know, what is your long term plan for the business? How does it fit into the overall strategy? Do you have the skill sets in house? Or do you also have to buy those? Because that’s not inexpensive.
You know, do you have a process established to build this thing, maintain this thing, understand this, then what tools? What platforms do you need? Because, you know, if if you’re buying it, you tell it tends to be not always it tends to be self contained, and you have all the things you need.
If you’re building it, you tend to need multiple platforms to build one cohesive thing.
And then how do you measure you know, whether or not you were successful? Did you build the thing? Did it save time? Does it you know, move the company strategy forward? Does it do anything useful for your customers? And that all goes back to understanding your purpose?
Christopher Penn 23:28
And it doesn’t even work? Well, yeah.
That’s a really Yes.
But I mean, there’s a lot of things where you know, maybe it doesn’t work in some way there’s there’s a lot of products that never get to MVP.
Katie Robbert 23:49
So I know Chris, I know you are, you know, strongly in the build my own camp, but what products what services, what software? Are you comfortable buying and not building
Christopher Penn 24:02
anything is not essential, right.
Anything that that is not going to be part of the secret sauce, to me is something that I feel perfectly comfortable buying right? email, email systems.
That is not a core competency of our company.
I am more than happy to let Google do all the infrastructure maintenance for that because that is just a pain.
Running a web server.
WP Engine here’s my money, take my money because I don’t want to do that.
I’ve done it.
And there’s very little there’s there’s no extra added value.
For me running an Apache two server versus somebody else running and doing all this.
This there’s just no value add them.
Like you mentioned earlier office software.
There’s no There’s no extra value to add there.
The stuff there is value add or things like our multi touch attribution, our marketing mix modeling, those are things that yes, there are vendors out there that do that.
They are way more expensive.
And then we can afford that and their quality.
From what I’ve seen, what I’ve talked to the quality isn’t any better than what we can do ourselves.
And I’ll obviously put a big asterisk on that, based on what you were saying earlier, Katie, which is you’ve got to have the people with the technical skills to do this stuff.
It’s not, it’s not something everyone can do.
Katie Robbert 25:21
And I think that that is, you know, we could sort of go down the rabbit hole of talking about it, but that is one of the more overlooked pieces of this whole conversation, because we’ve been talking a lot about the software’s and technologies.
But you know, as, as a company is saying, Whoa, AI is the thing, and I want to introduce AI.
Well, guess what, you need to have people who actually know how to build and operate and maintain the thing.
And that may not currently exist as a skill set on your team.
And so you then have to go out and hire contractors or full time employees or vendors and consultants to do the thing and figure out even where it fits into your company.
And that is not an inexpensive endeavor.
Christopher Penn 26:06
And it’s also it can be very risky.
You know, one of the very fortunate things about about Trust Insights is that the technical skills that you know, and the stuff that we do also live in one of the co founders.
So it’s not like I’m just going to quit and leave tomorrow.
But that dag exact scenario happened at our previous company where I had all the technical knowledge to maintain the systems.
And then when we left a found Trust Insights, within a month, all their stuff stopped working, because it wasn’t constantly being maintained.
Katie Robbert 26:38
Yeah, the institutional knowledge.
I mean, that’s true of anything for any company, that’s a risk, regardless of I suddenly decided to walk away tomorrow.
Despite you having all of the technical skill sets, the company itself would cease to stop moving forward, because there’s a lot of things that I do that I know about the company, Chris, that it’s just not in your best interest at this time to focus on.
And so, you know, whether you leave or whether I leave the company would come to a screeching halt.
Christopher Penn 27:11
So there’s, that’s a strong argument for a by strategy, or I guess, I you know, I like what you said earlier, I really am thinking of rephrasing as rent versus own, because I think that’s a much more clear thing.
But the rent strategy makes a lot more sense in cases where, you know, you have retention problems, right, you know, where your, maybe your company isn’t the best place to work.
And so if you can’t hold on to people for more than 18 months, yeah, rent is a lot safer of a bet, because the vendor will probably outlast you.
Katie Robbert 27:45
And, you know, if you think about it, like, you know, sometimes renting is just a temporary solution.
versus you know, if you think about it in terms of buying, like that feels like a long term investment.
But if you think about it, you know, it’s just a slight mental shift in terms of renting, that can get you over the hurdles, to sort of like, okay, it might be a little expensive now to rent.
But while we’re renting, we’re training our team, we’re building out the skill set so that we can then you know, decide, Okay, we’re done renting, we’re ready to buy the thing ready to own it ourselves,
Christopher Penn 28:20
or you find out isn’t gonna work.
Katie Robbert 28:23
And that’s another, you know, great way to approach it is if you rent first, it’s a lower, I wouldn’t say lower risk.
It’s a different set of risks in terms of trying to figure out if you even want to do this thing.
It just didn’t make sense to your business.
And then you’re not fully invested in building and owning the thing yourself.
You’re like, you know what, this didn’t work out.
Let me go find a different property to go rent and see if that one suits me better.
Christopher Penn 28:51
If you’ve got some stories that you want to share about rent versus own buy versus build, and you want to share them pop on over to our free slack group, which we rent from slack, go to trust insights.ai/analytics for marketers, where you have over 3200 other marketers are asking and answering each other’s questions every single day.
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