So What? How to Migrate Your Universal Analytics

Well, hey, howdy everyone. Welcome to So What the Marketing Analytics and Insights Live Show. I'm Katie, joined by Chris and John. Hey, fellas. Hello.

Oh, John with the double hands today. Yeah, this is for the hearing impaired. Um, on today's episode, we are talking about how to migrate your universal analytics. Some people call it Google Analytics 3. Um, today we're gonna cover why you would want to migrate your universal analytics, the processes, the processes, processes to use, uh, oh my god.

The processes you use to migrate your universal analytics, we're off to a good start. And what you can do with your migrated data. So this is a timely topic because we are literally running down the clock on when you will have access to your universal analytics. You can see if you have a universal analytics um profile still, there is a literal clock running down second by second. John, I believe you called it the doomsday clock.

You can tell it was done by developers because that thing is hideous. No offense to developers, but you are not designers, and that's a whole different show. So we wanted to talk about this because there's been a lot of conversation, especially in our Slack group and on LinkedIn, of should I or shouldn't I? And if I should, how do I do the thing? And so I wanted to sort of start with well, how do we figure out if I should?

And I know this will come as a shock to no one. You're gonna need the five P's. You need to figure out what's the question I'm trying to answer. Who are the people involved? What is the process I need to go through?

And that's what we're gonna focus a lot on today. What's the platform? We know it's universal analytics, but where the heck does the data go and how do you retrieve it? And then performance, which is did we answer the question? Did I get my data out and can I use it?

And so, Chris, John, that's sort of the big question on the table is should people even go through the process of exporting their universal analytics data? Let's just start there. Depends on why. Why do you need it? Um for me, I as a data junkie, a hoarder and stuff like I want it for souvenir value, right?

Because I have on my website, I have 17 years of data. Well, 15 because you know it stopped recording a little while ago. And so having it for souvenir value, just to just have it is kind of like a is kind of like a a digital security blanket. Uh, am I going to use it infrequently at best? And by infrequently, I mean maybe once every five years.

I'm like, oh, look, I'm bored tonight. I'm gonna go look at my old data. This is what I do because I'm I'm lonely person. That is not true. He has a family, he has friends.

All right, John, go ahead. From a business perspective, probably not, because at this point, Google Analytics 4 has been out since October 2020. We all forgot that because of that whole you know, pandemic thing. Um, but it has been in production for four years and you've had it running now for at least a year because you you had to as of uh last July. So you have at least almost a year's worth of data if you migrated late.

If you were early on, like if you look at the Trust Insights account, we turned it on October of 2020. So we've have almost four years worth of data in our GA4 account. So that old Universal Analytics data, there's not a whole lot of value to it. The exception, I would say, is if there's specific types of data that you might want, maybe from a demographics perspective pre-pandemic, if the pandemic changed your business. What about you, John?

You marketing over coffee has Google Analytics running in the background, correct? Yes, but it is hilarious. I'm the exact opposite of Chris. I'm like, we've already taken credit for the wins. So the sooner that data vanishes, you know, the sooner no one can go back and blame us for losses on that.

We can just confidently say, yes, that data is no longer available. We've got to move on. So yeah, I I can totally see you could use it for future reporting and value, but um for me it's like, well, it's not on fire today, so maybe if I close my eyes, it will go away. I re you know what? I respect that.

Because I feel like there's a lot of things that are calling our attention at the moment. And so it does in some ways it feels like a big distraction from Google of like, hey, you have to do this thing. And a lot of companies aren't stopping long enough to question why, like why? So the only for me, because we've had uh Google Analytics 4 running for a few years, we do have year-over-year data. I feel like the best case scenario for us for Trust Insights to keep that universal analytics data is to do predictive forecasts with, is to do trend with.

Like so you sort of have pre and post uh pandemic because we have it up through. And so that is now here's the thing. To date, we haven't done that. So it's really just a distraction. It's just like a hey, we could do the thing.

And I don't think I have a strong enough business case to justify that we should do the thing. But a lot of companies, to your point, Chris, the pandemic did change their companies, it changed how customer behaviors, how they interacted with the website, how they purchased. So there is value. So I think what we're saying is first of all, before you go through the process of exporting and saving your universal analytics data somewhere, go through the 5P framework and make sure that you have your user stories, which is a simple three-part sentence. As a persona, I want to sew that so that you're clear about the expectations of why you're doing this in the first place, so that I can once a month run a predictive forecast, or I can understand my customers pre- and post-pandemic, whatever the thing is, but make sure that you have a good reason to do it, not just because Google's telling you to do it.

Uh, Google, please don't come for me. So let's say we went ahead and justified enough of here's why we need to do it. So, where do we start with this, Chris? Can we do it ourselves? Are there services?

Like, what is this ecosystem look like? So there's there's I would say four different options for doing this. Um, the first and simplest, if you only need very, very rough aggregated data. I'm gonna go into Google Analytics here. This is the old Universal Analytics.

If you only wanted like really, really early stuff, I'm gonna set this to February 2017. You could just say, I want this chart exported. I want this table exported. So if you just want to know, like, hey, how you know when when was our first users to the company website? Okay, I can export this and you'll have this as a CSV file.

This is the simplest. It is the easiest, it is top-level data. So this for some of that look back, you know, as you're saying, Katie, for forecasting, uh, maybe doing predictive forecasting, this would be the the bare minimum. So that this is option one. Go through and find the reports that uh and it may be one of those I've gotten used to the GA4 you know confusing interface now.

Have to go remember where everything used to be in in universal analytics, but you can go in and export that information uh manually. That's one way of doing it. And I would say, probably for a good number of use cases, this is okay. Like this is this is good enough for for just those bare basics. Okay.

Second uh option is to use a service of some kind. So there's a couple different services. Um, the we talked about in the past uh the Matomo uh analytics system, uh we did a whole show on it. There's a plugin for Motomo that will plug into Universal Analytics and essentially vacuum the data out of it. So if you've got Motomo installed and it's a fresh instance, meaning it's there's there's no current data in there, you can vacuum up that universal Analytics data, put it in Matomo, and it's available with a user interface, uh, with the interface that we talked about in the past, and make it available to you.

It takes a long time, it takes weeks sometimes because of Google's API limits for how fast you can get data out of the system, depending on how large your your old GA account is. So that's option two. Any questions so far? Um, no, I mean, uh, just as a reminder, for those who didn't catch our how to set up a Motomo uh instance in 2024, you can catch this on our YouTube channel, trust insights.ai slash YouTube. Go to the so what playlist, and you will find that episode.

Uh, it's only a couple of weeks old. It is a really good option. I've been playing around with Motomo since we uh did that episode, and I'm really liking what I can get out of it. But I also have very simplistic analytics needs. So for me, like this is probably the option I would go for personally.

But um I'm curious to see what else is out there. Option three, there are paid services that will do this data export. Over in our analytics for marketers community, um, our our friend and longtime contributor Todd Bullivan recommends the Analytics Canvas service, which will take your your universal analytics account, vacuum up all the data out of it, and then spit it out as both CSV files and into your own BigQuery database. So just to give you an idea of what this looks like, you go into their service, you spend the 99 bucks on it, um, you run the backup. So let's go ahead and just walk through what the steps for doing this.

If you want to do it where it is connected to a bigQuery database that you can then manipulate, if you first go in, you choose which view you want, you pay per view. Uh so if there's multiple views that you have in in universal analytics, you're gonna want to, you're gonna spend 99 bucks a shot. You choose the view, you choose which of the 45 tables um that they can export you want. And it this the cost is the same for whether it's one table or all so I just choose them all. Um then you choose your export option.

Your export options are just Google Sheets, Excel files, CSV files, and BigQuery. If you go with BigQuery, you then have to set up the connections and access to connect it to your company's BigQuery instance so that um the top software can talk to it, uh, which is a multi-step process that requires you to have administrative access to your Google Cloud account. So this $99 is really you're talking about at least you know a few thousand dollars, if not more, for the time commitment, depending on who it is you need to help you. Plus, you also need to understand how big query works, because if you have 45 different tables, you probably I'm assuming have to find some way to join those tables or query multiple tables to make one coherent data set, and then you still need to you still need to understand how when the data gets into big query, what the heck it means. So you probably need to have some kind of a data dictionary translation.

Because I'm assuming it's not gonna be as simple as this metric means unique users, and this means the number of unique users on this day. Like that would be way too easy. It's worse than that. Excellent. Woo.

So let's go over to BigQuery and see what's in the box. Um, what it does is it spits out because the Google old Google Analytics API gives you limits. The limits are 10 metrics and nine dimensions per per API query. You can't query more than that. So like you can't say just give me everything.

And so what this software has done is essentially do multiple combinations per report per view of what's in Google Analytics and puts that into BigQuery tables. So for example, let's look at acquisition overview daily detailed. That gives you the field names from the API. So yes, you need a data dictionary if you don't know what these mean. So what it does is it spits out by by goal number what uh was in those goals on a per day basis.

So you get all this. Here's the challenge. Wait, that wasn't the challenge. No, that that's not the challenge. All right.

Um, you get the same thing in the CSV files too, right? When you um when you do the export, you get a nice folder filled with files. Um my god. Oh my god. The challenge is this.

There's no primary key for any of these tables, so they're all standalone, they do not unite together. Oh my god. Well, and I would imagine too. I mean, so this is something we talk about, even with just sort of like general setup, is having consistent naming conventions and writing things down. So, like if you didn't really name your goals other than goal one, goal two, go three, goal four, you have no idea what you're looking at.

Well, so you in universal analytics you can't do that. They are you are given those hard-coded names in universal, you can't name them. I mean, you could name them in the interface, but it does not make it. But that's what I mean. It's like yeah, I know that it assigns like the numbers, but you can you can either leave it as goal one or you can change it to newsletter subscription.

So at least having some sort of like understanding of what the goal is. Um, yeah, exactly. Which the whole thing is. So you need to screenshot this because this does not come over. Yeah.

I mean, this is a mess. I'm okay. Let's keep going. The data is all in there, right? And it is all um it it is available.

Uh that's and you get a file spread feeds. So I feel like this goes back to where we started you really need to have a strong business case as to why you need this data. And to be to be fair, some companies, some teams 100% will need this data for whatever reason. You gotta be clear about why you need it because this is not easy. Like Google did not say, like, here's the magic wizard of just pick all the things you want and we're gonna export it into something that actually makes sense.

They said, we're gonna give you what we think you deserve, and then you're gonna have to spend another three weeks or four weeks or two years figuring out where we put it, what we did with it, and what the heck it means. So good luck. Looking at you, John Wall. Yeah, that's about how it goes. It just gets uglier.

Yep. So that's option three. To use a service, a paid service to do it for you. It's again, if you're the if you're the data hoarding pack rat, this is actually a this is actually nice. This is thorough.

You get all the data. Um, you can you can lock it away, put it on a backup hard drive, and just have that that the comfort of knowing you have the data, even if you will never ever look at it again. All right. So option one is to just do some simple exports directly from the Universal Analytics interface. Yeah, I can feel my blood pressure going up.

Well, uh remind I've already forgotten what was option two again. Option two is install a Matomo instance and use its plugin to vacuum up the data. Okay, and that's the option I like so far, but I'm sure that has its own set of challenges. And then option three is to use a paid service like this $99 one that will give you a whole bunch of big query tables or a whole bunch of CSV files that you have to join together but has no join key. Great.

What and I'm almost afraid to ask, what is option four? I'm nervous. Option four is to code it yourself. Um one of the things that you can do, because all of those fields in the Google Analytics for in the Universe Analytics API, you may not need them all. If you've done a good job with your five Ps and your user stories, you may be saying these are the uh of the 10 metrics and nine dimensions we're allowed in an API call.

These are the ones we actually care about, but we want to see it maybe more granularly than what's in the backup service. Because the backup service, for example, does not bring in anything other than day levels. If you want an hour level or minute level, which is in the UA uh interface, you won't get that. Um there is some there are some use cases where that you would want to do that. Also, there's some uh some secondary dimensions that are in not in the export either, like previous page, next page.

So you would go into a tool like ChatGPT or Gemini or one of the big models and say, hey, I want to code a Python script or an R script to talk to the Google Analytics API. You would have a long conversation with it. At the end, if you are technically inclined, you'll have a piece of code that you will run that will vacuum up that data that you care about, the the fields that you care about, and store it somehow. So for example, long before we heard about the $99 option, one of the things that we did was we we wrote this code for ourselves. Uh we picked sort of the 10 metrics in the nine dimensions we cared about, and we vacuumed up the data from our Google Analytics instance into um uh in this case a SQL database.

So we said this is what we know makes the the most sense for the you know looking backwards, and we have this stored in case we need it. And we did this um June of 2023 because we we got wind, like, yeah, this is just gonna go away if we don't get all this data. So that's option four. The reason option four would be good is if you if you don't need all the data and you've got your user stories really well designed, you can just get the data you need and then store that somewhere. And then the the advantage of it, this is that it A, it's not everything, and B, you could store it in the format that you want.

So maybe you want it in a CSV file, but maybe you wanted a SQL database. Maybe you want it in who knows, some arcane system. You get that choice because the code supports that. So those are your four options. Simple export of just the stuff you need from right from the interface, install Matomo on your own servers and then run the import plugin, which that comp the cost there is setting up a server.

Um export service and getting a you know the exports that way, and then writing your own code with the help of generative AI to extract the information that you want and put it in some storage format. So all I'm hearing with all of this is there is no getting around doing the requirements up front because it's gonna be a mess on the other side of it if you don't plan for what it is that you actually want to get out of this. So if you say I want to understand my attribution trends, for example, like throughout the lifecycle of my company, pre-COVID, post-COVID, you know, or just through all of it, you know, we've changed, we've restructured the company, we've changed how we sell, we've changed the products, whatever the thing is. Make that clear and get that data. But what I'm all I'm seeing is a gigantic nightmare if you don't do that planning.

And I've you're I've seen it happen, and I'm guessing it's gonna happen. You're a hundred percent correct. So one of two things will go wrong. Either you will vacuum up so much data that it's unusable, right? Or you will, if you don't do your requirements planning, you will just get the things you think you need, and then July 2nd, like, oh, we need to get oh crap, it's not available anymore.

Uh, because you didn't do the requirements planning first. And the other thing that I think is really important to point out, because you Katie, you and I talked about this. It's not apples to apples. A user in universal analytics is not the same as a user in GA4. A conversion, the goals are different, how conversions are measured, the events, for example, event tracking is is a new model.

Well, it's not new anymore, is the current model in GA4 that did not exist in the same format in universal analytics. So even if you're doing historical lookbacks, I would be very hesitant to use predictive forecasting on these two different data sets because they're not the same. And you do have to do a lot of manipulation of the data to get them to be on par with each other. You typically will have to use some kind of a third system, like a C like CRM data to level it out to basically say, okay, well, what multiplier do I need to you know add or subtract the universal analytics data to make it even with GA4 data? Because you have the baseline of like how many leads you had, say in your in your HubSpot instance.

So there's that aspect too. I think from a from a like the data pack rat perspective, yeah, this is great. Like you just get all the stuff and you can play around with it, but I don't know that it adds a whole lot of value anymore, particularly even if you want to do like complex attribution modeling. You could do Markov chain modeling just with the data inside Universal Analytics. You can't do that with just what's in univers GA4 anymore.

You have to use the big query exports to do that. And again, it's not apples to apples. Right. Yeah, it's it's tough because you know we are gonna run into a lot of uh people who are of that pack rep mentality of like, but what if? What if I need it?

And I think that the challenge there is really um digging deep into the user story exercise. Like, all right, let's walk through those what if scenarios. Let's see how realistic they are. So, what I would recommend there, you know, Chris, if if you were coming to me saying, like, I just I have to have the data for a rainy day, I don't know why yet. What I would be asking of you is all right, let's write down all of the potential user stories, real or not, and then try to prioritize them to say which of these is most likely to happen, which of these is least likely to happen.

Like, I'm sort of simplifying the exercise, but it's an important exercise because data storage costs money. Data storage takes resources. And then at some point, if you decide that yes, one of these scenarios, one of these user stories is likely to happen. How do you get that data out? You still need to go through the process of doing the requirements to know where does the data live?

What data do I have? What data do I need? So for those of you who are just wanting to buy the $99, stick it in a big query and forget about it, not a good idea. I'm telling you right now, don't do that. Do some at the very least, just answer these five questions.

What is the purpose? Who are the people? What is the process? What platform? And how am I going to measure it?

What's my performance? Like just very basic questions before you do it, just to at least sort of like start the conversation. That is my like begging you PSA for today. And it's important because when you look at what's in the data itself, um, we go back to sharing my screen here real quick. When you're looking at things like behaviors on your website and what content people are looking at, for example, this is let's see if the preview is not going to enjoy a 61 megabyte uh spreadsheet.

Um it's not, it may not be granular enough. Uh, it may not dig into the level details. So, for example, with with uh trusted insights, we look at things like you know, specific goals. When I look in what people are doing with the data, the the behavior page views that's in this report, goal completions is not in here. So you don't know if a piece of content could contribute towards one of the goals you care about.

So that's not available in in the stock report. It's not available. Uh it might or may not be available depending on how well things your instance maps to Matomo. So if that is of critical importance to you, you probably are gonna go with option four, which is pull the content that you want. So, like here's the page, here's the goal completions, and so on.

So you'll you'll have a custom binding together those those metrics, but you need to know that from requirements gathering before you embark on this journey. Because this part also takes time. This takes about three and a half days to pull all this for seven, you know, the granted, 17 years worth of data, but it takes three about three or four days to get all that information into a database. Well, that's not including the time that it took to put the code together. And you're pretty savvy with writing code at this point.

You know, if you said to me, um, I need you to put together some code to against the Universal Analytics API to pull this data that we've already done the requirements for, I would be like, all right, well, I guess I'll see you in six months. Oh, it won't take you that long if you use generative AI. Well, but see that but you're you're making assumptions about people's skill set and their ability to put these pieces together. So, yes, maybe six months was an exaggeration. Point being is that it's still gonna take time to write that code, but also then to execute that code.

So you have to have the environment set up to run the code, you have to know how to check the code, you have to know how to check the data coming in. So there's a lot of different steps. It's yeah, I don't know, John. Are you convinced? Or if we talked you out of even touching this data altogether?

Yeah, it's I mean it's just one of those things. It is it's like uh opening Pandora's box, you know. If you say, hey, we're gonna go grab this data, now you're gonna have to explain what to do with it, and if there's any problems in there, you have to make those go away. So um, yeah, I I'm still I just like sit here and hope that the problem will go away on its own. So, Chris, knowing that you're a data hoarder, and like, well, and I say that because I know that you have all kinds of different uh historical data, how often are you referencing the historical data that you've uh collected that you've stored away?

Never. Which I think is a really good point to bring up because you, Chris, are of the three of us the most data driven. Let me see what the data says. And if it's not something that you're using, that makes me question well, do we even need to go through this process? Again, there's going to be companies and teams that absolutely need to do this for a variety of reasons.

For Trust Insights, I don't know that it makes sense for us to do it. Um, I know you have some of it already, but I'm having a hard time in coming up with a valid user story that would justify us taking time away from other things to do this. The the only user story that I can possibly come up with for this data, and it is not revenue driving whatsoever, is that come, you know, 2028, whatever, when we say, like, hey, 10 years ago, Trust Insights opened its website, and you know, this was the these were the first pages people visited, kind of a nice walk down memory lane. That's it. There really is no other value.

Um, for myself, I had the data because yeah, I want to be able to look back at like what blog posts were popular in twenty two thousand nine or twenty twelve for fun. Have I done that? No, because there's been more fun things to do than that. Is the data there if I need to? Yes.

Um from a business perspective, here's the thing. If you were early ish on in adopting GA4, like say you installed it in 2020 or 2021, I would not bother going backwards. Because the world since 2020 is so radically different in so many ways. Even if your business fundamentally has not super changed out. Like Trust Insights is still pretty much the same company as we were when we were founded in 2018.

We help people do more with their data and their analytics and their AI systems. Um, you know, chat GPD certainly made everyone more aware of what AI is, but we're still pretty much doing the same work of helping people make more with the stuff that they have. The audience has changed, right? So many more people are working from home still, um, and will continue to be. And so many more people so many more businesses are now hybrid businesses that looking back at 2018 and 2019, other than for historical curiosity, doesn't offer us any valuable business insights.

Even things like in our in your CRM software, for example. When uh John and I were talking about this um this week on marketing over coffee, it used to be for account-based marketing, you could rely on okay, well, we're getting traffic from this set of IP addresses, so it's coming from Salesforce.com, it's coming from ATT. We know that's their building, and so we know how much, you know, we could we can target people. Now, half those employees work remotely, and you're like, uh, we're getting a lot of visits from like people's Comcasts accountants, you know, because they're working from home. And so even that data is not helpful anymore because the world has changed.

So looking at your universal analytics data, unless you literally just switched over in July of last year and you needed one year's one full calendar year for doing year over year reporting. I don't see any value in if you have data in GA4 from onwards from 2021, I don't see value in having the universal analytics data other than to cover your butt and you just say that you have the download in case somebody needs it. Question. Theoretically, if you've had the data, you've also theoretically been doing reporting on said data. So to you know, to your question, Chris, about what blog posts were the most important in 2019.

I would wager a bet that through our monthly reporting, we probably have that in a PowerPoint somewhere. So I would I think that's also part of the conversation for companies of do you already have the reporting done on the data that you'd probably care about so that you don't need to go through the process of pulling this data again just to look for answers that you've already got from the data by doing the reporting. And I think it's a really good opportunity to audit what you've been doing. Again, sort of going through ta-da, the five Ps. And what do we already have?

That's a big part of the conversation is are we creating something net new? Are we doing this just to do it? Or do we already have answers to these questions? So if your question, Chris, is I want to understand what blog posts were the most important in, you know, 2019, which ones were converting. I can almost guarantee you that we answered that question in 2019 because that's a question we've been asking for since we started the company.

Yep, exactly. However, for small organizations or in cases where let's say a new stakeholder has taken over, taken the reins of the company, and the uh there's uh been a loss of institutional knowledge. We had this happen, you know, at several clients relatively recently, where new stakeholders have come on, old stakeholders have departed, no one remembers where anything is, no one can find anything. Um having the raw data might be situationally useful. I don't, again, I would not put a whole lot of stock in data prior to the pandemic.

So if you've got 15 years of universal analytics data is a here, I think it's still a historical curiosity. I don't think that it's something that you should be making decisions with. Well that is there's one angle of you kind of a good insurance policy is just like grab the raw data and throw it in a folder somewhere and you know leave it at that. Like that does insurance is worth something. There's a whole industry about people just selling peace of mind.

Yeah, exactly. And I think having so this this lovely folder here occupies about uh 600 megabytes of disk space. Um that it compressed it goes down to 60 megabytes. So it goes into an archive folder and it it just stays there forever. You know I like this approach for when you don't have a solid use case because this gets put on a hard drive somewhere it doesn't cost you money to store it because it's already on someone's hard drive anyway or it's on a server backup server somewhere and that's that's good enough.

The Motomo approach I think is easier if you want to use the data because it has a working interface but it does cost money to keep that operating and stuff. So I would say this approach of just back up the data somewhere and just stuff it away is the least costly approach. I think that's fair. You know and just sort of to give a nod to the highly regulated industries that will be required to do this. So we were saying we know that some companies some teams are going to have to do this.

I'm thinking about pharmaceutical insurance financial where there's you know regular audits of everything. Um, you know, really exploring these options, what makes the most sense? Still again going through the five Ps to figure out, you know, what are we audited on? What do the auditors need to know? If you're in pharmaceuticals, it's probably what is the FDA care about?

What is the trail? Um, you know, so again, it's a really good time to do a refresher on your data governance, on sort of what you have, what your tech stack consists of, um, and what you have to have to answer those questions. But to your point, Katie, if you're in a highly regulated industry, you've already been doing this with this data. This is this should not be a surprise now. No, it shouldn't be.

But for some companies, first it they're to, you know, they they sort of have the John Wall mentality of let me ignore it until it's on fire. Right. Or if you're like, for example, a defense contractor and you're working with several three-letter agencies, yeah, you've got to have all this data stored somewhere. You've all had to all along. Um so you will want to have this available to be produced when when the their auditors come knocking and say, hey, we need the last 10 years worth of data.

Um, I will say to that, so you're saying if you work in a highly regulated industry, you've already been doing it. Having worked in a highly regulated industry, that is not the case. It is the it is the we're gonna ignore this until it's on fire and then everybody scramble uh and make it look like we've been doing it all along. So you have to backtrack and clean your clean the tracks. Yeah.

So in a perfect world, people plan ahead. In reality, it's oh crap, I needed this done yesterday. What are we missing? Let's hope nobody notices. So if you work in a highly regulated industry and you need to cover your tracks and get the process all your delayed data last 10 years, please contact us.

We'll be more than happy to extract the data for you and process it. Yeah. If you want to know what it's like to be audited by the FDA, uh reach out, I'll tell you. Awful. Um, but with that, you know, we really can, you know, so we're sort of talking through from a trust insights perspective, but you know, we're talking about it because we've gone through the steps.

We have seen um what needs to happen. So if you do, in all seriousness, need help with uh exporting your data, putting it somewhere where you can actually access it, please feel free to reach out to us, trustinsights.ai slash contact. And uh we will not tell you not to do it. We will help understand what's going on and actually help you do the thing. That said, I would also encourage you to do your data governance on your current analytics because if you haven't been doing it on the old stuff, you probably haven't been doing it on the new stuff either.

Yeah. Again, we can help with that. Uh, yeah. I don't know. I mean, this it's a whole different topic.

I don't know why data governance is so hard for people to do. Just do it. It's gonna make your life easier. Yeah, I mean it's the same reason people people people don't comment their code. Oh, I know.

Yeah. Eat right, go to the gym. It's all right there. Buy low, sell high. Well, it it sounds easy.

Well, on that note. On that note, um, thanks folks for tuning in. Please do do look at the options, but do your do your user stories and do your five piece first before you go grabbing a whole bunch of data that you can't use. And really critically, if there is data you do need that makes you make sure you actually get it. So uh that's gonna do it for this week's show.

We will talk to you all next time. Thanks for watching today. Be sure to subscribe to our show wherever you're watching it. For more resources and to learn more, check out the Trust Insights Podcast at TrustInsights.ai slash TI Podcast. At our weekly email newsletter at trustinsights.ai slash newsletter.

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