#56 Why GA4 means you need to get on board with BigQuery – MeasureFest (with Katie Kaczmarek @ Measurelab)
This week Dara is joined by fellow Measurelabber Katie to discuss her recent talk at MeasureFest (part of brightonSEO). Katie talks though the process of putting together her presentation and getting up and speaking in front of a croud for the very first time. They also run through the talk content around why everyone using GA4 should get onboard with BigQuery asap!
Check out Katie’s talk slides and video recording.
Here is Katie’s step by step guide on how to set up the BigQuery exports from GA4.
In other news, Dara walks out and Katie tough mudders!
Follow Measurelab on LinkedIn – https://bit.ly/3Ka513y.
Intro music composed by the amazing Confidential – https://spoti.fi/3JnEdg6.
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Quote of the episode from Katie: “the cool thing about BigQuery is I think the only limitations to it is your own imagination and your courage as well“
Quote of the episode from Dara: “there’s an entry point for people, which is probably been made more accessible now with GA4, connecting to BigQuery for free”
Transcript
Intro
[00:00:00] Dara: On today’s episode, we’re joined by Katie Kaczmarek who has just done a talk at MeasureFest, which Dan and I promoted last week on the show. So Katie did a really good talk about exporting GA4 data into BigQuery. So we talk about the talk itself and also her experience of doing a talk and advice for anybody else who might be out there who’s thinking about doing a talk at a conference, enjoy.
[00:00:37] Dara: Hello and welcome back to The Measure Pod, a podcast for analytics enthusiasts to talk about all things surprise, surprise, analytics related. I’m Dara, I’m MD at Measurelab, and this is a first for me. I’m without my usual co-host. Dan is not, he’s not going to be joining me today, but I am really happy to be joined by Katie Kaczmarek, a fellow Measurelabber. So Katie, firstly, welcome to The Measure Pod, good to have you on board.
[00:01:02] Katie: Thanks very much, I’ll be the sidekick for the week.
[00:01:05] Dara: You will, and if you do a good enough job, which I’m sure you will, then Dan’s out of a job and you’ll be the new co-host.
[00:01:10] Katie: I’m in it for the takeover.
Topic
[00:01:11] Dara: Good, Good, let’s do it. So last week I saw you speak and we talked about it on The Measure Pod. So I think we gave it a little plug that we were going to be at MeasureFest, which is a fringe event of the three day brightonSEO conference that’s on, obviously enough in Brighton. But MeasureFest is one of the fringe events, and it’s our bag, so it’s all analytics related. And you actually opened the show, so you did a talk and you were the first one on the day which I sat in the crowd for and really enjoyed. So, really that can be kind of at least the jumping off point for our discussion today, but we can ramble, there’s no rules for this. But before we get into that, I know you’re an avid listener of the show, so you know how we always like to get people to introduce themselves. It’s basically over to you to give as much or as little detail about what got you into analytics.
[00:01:56] Katie: I actually, I really like this question actually because mine is not a university degree. It’s not anything I studied in any way, shape or form, but I’m where I am today, kind of thanks to a friend because of a very clever friend. So I was at a point in my life where I was doing loads and loads of formulas in Excel, and that’s what I loved. I loved doing a spreadsheet, I had spreadsheets for everything, with formulas all over the place. And I was like what am I doing? I don’t know what I’m doing with my life. And I went up to my friend and said this and he said, what do you like doing? I said, formulas and Excel. He said, well you need to be in SQL then. So from there on, I applied for a job at a very low level, got in, got taught the ropes in the job and then just went from strength to strength from there. So it can be done without a degree, I’m living proof.
[00:02:38] Dara: Absolutely, and I’m a big believer in that. I think you know what we do it, it can be, and often is the case that a lot of it is self-learned. I mean, some people do obviously have an academic background, but I don’t believe that anything that any of us do and can’t just be learned by trial and error and, you know, that is as we all know, probably the best way to learn in analytics because you get a lot wrong and you learn from that, and you figure things out, don’t you?
[00:03:01] Katie: Yeah it’s all about being asked a question to start with. I don’t learn in a way of sitting and reading a book and figuring out or learning a challenge, being faced with a bit of data and going, right, you need to get this bit of information out of it, and then you figure it out along the way with a lot of Googling, Google is your friend.
[00:03:15] Dara: It is, and at any level that doesn’t stop to be the case, does it? I think you know, there’s always a nuance or something that’s slightly different to how you’ve seen it before. So, I mean obviously there are those kind of bread and butter things that you just repeat and you know how to do them, and they become like muscle memory. But there’s also always those kind of slightly gnarlier challenges where you think, It’s like something I’ve seen before, but it’s slightly different, and I need to actually think about this and I need to Google it and I need to get a few things wrong before I finally get the solution that works. So you became a sequel SQL whiz and I think I’m right in thinking you’ve worked with, so Google and kind of BigQuery in the GCP is relatively new to you. Your background was more in, I think Oracle and Microsoft, is that right? And maybe some other systems too?
[00:03:58] Katie: Yeah, I started off with UK Power Networks and there was Oracle SQL, and then I moved into mainly Cabot, which was Microsoft SQL. So, the syntax sort of carry over and across both those formats and now into BigQuery, just slight variations. But again, you know, you can just Google this in Oracle versus this in BigQuery and generally you are getting response to it and figuring out what the syntax is for that program.
[00:04:23] Dara: So if you know one you can pick up on any of the others basical.
[00:04:26] Katie: Yeah, I think there’s a lot of crossover between all of the SQL languages. I think standard SQL is the, is the BigQuery one.
[00:04:32] Dara: Standard SQL is the BigQuery one.
[00:04:34] Katie: Yeah, so although it’s owned by BigQuery, it’s got a name for that type of language. I’m sure Oracle, Microsoft Oracle might be their own one, but yeah, BigQuery is standard SQL.
[00:04:44] Dara: Okay well, I might probe you a bit more. I might park that and come back to kind of probing you a bit about what your thoughts are on maybe some of the pros and cons of the different, the different flavours that you’ve used. But first, just for anyone who wasn’t at MeasureFest or hasn’t read your blog on our website about your talk at MeasureFest, can you give us just a kind of like overview of what the talk was actually about?
[00:05:05] Katie: Why having GA4 means you need to get on board with BigQuery. So the basis is we’ve all got to go over to GA4, the data isn’t going to stay the same in GA4 as it was in Universal Analytics (UA). So to truly utilise it in the way that you used to in Universal Analytics, you need to get BigQuery on board and start evaluating data within the BigQuery UI rather than in GA4. It’s just not, it’s not going to be in there in the same way that it was in Universal Analytics. So it was just taking the audience through that.
[00:05:34] Dara: Yeah there’s a couple of, I guess, a couple of points that we’ve talked about on The Measure Pod quite a few times before. So one, and these are points that you stressed as well. So one is that you don’t get the data backdated, it doesn’t backfill it. So there really is an urgency to kind of connect your GA4 account up to BigQuery as soon as possible. Because even if you’re not planning to use it right now, when you do want it, you’re going to want that historical data. And they don’t, sadly, they don’t backfill it, do they? So there’s a real need to kind of get this done now.
[00:06:02] Katie: Absolutely, any data worth its sort, is going to be a comparison. So you’re going to be looking at year on year data, and if it’s not in BigQuery, you are not going to have that backdated information. So really, as soon as you set up GA4, set up that link, because even though if it’s tracking in GA4. Unless that BigQuery links there, like everything in Google, it’s going to be from that date forward, then it starts coming over to BigQuery. We’ve actually probably passed the date now to get proper year on year analysis, because it’s the 1st of July next year. But I always like the quote, when’s the best time to plant a tree? The answer’s 20 years ago, the second best time is now. So it’s the same comparison with BigQuery. It would’ve been great 12 months ago, but the second best is as soon as possible.
[00:06:41] Dara: Yeah, so true. And this is something Dan and I talked about a couple of weeks ago where we said that there’s probably going to be this second, second rush, which is, you know, the people who weren’t organised enough when the news first came out, but are suddenly it’s going to click with them and they’re going to realise they’re going to need this. And you’re right, they should have done it arguably several months ago at least, but now is still better than waiting until July next year. The other thing that we’ve mentioned on here before and why it’s a bit of a game changer is in the past or well even now, currently with Universal Analytics, you have to be a 360 customer to get the data export to BigQuery, which ruled out a lot of companies. You have to be pretty big and be willing to, to kind of swallow the cost of GA360 just to get your data easily into BigQuery. Obviously there were other ways you could get it out through the API and then pump it back into BigQuery, but that’s nowhere near as easy.
[00:07:28] Dara: But with GA4, everybody gets this data export for free. So it’s going to open up BigQuery to a lot more businesses who maybe wouldn’t have had the need or wouldn’t have been able to justify the cost involved before. So there’s going to be more and more people relying on using BigQuery and learning how to use the data in BigQuery.
[00:07:46] Katie: Yeah, and I think that’s probably where a lot of people are nervous about things, because with the smaller companies, it’s not necessarily the abilities or the people within the organisation that are going to be there to have the SQL skills. So there’s a lot of nervousness about, oh, okay, so the data’s got to be in BigQuery, but I don’t know how to write SQL, so where do I possibly start with that? So I think, yeah, it’s brilliant that it’s coming over to everybody for free. And there is limits of what you can put in for free, which basically means you get 10 gigabytes of storage per month and one terabyte of queries per month. So as long as you are below that, which lots of smaller companies will be, then it’s going to be absolutely free. If it’s above that, it’s a kind of pay as you go model. So, depends how much you use it or how much you store as to how much it’s going to cost, but really really competitive, especially in comparison to other cloud warehouses.
[00:08:34] Dara: And as something else you mentioned in your talk there is a pricing calculator that you can use to estimate how much your storage and your queries will cost. But you really do need to be pretty big to be racking up a cost that you’d worry about. Obviously it’s possible, but you know, for a lot of businesses the kind of data they’d be storing and processing, we wouldn’t be talking about a huge bill really.
[00:08:53] Katie: No, not at all it’d be fairly minimal really.
[00:08:56] Dara: So what were some of the other benefits that you mentioned in your talk then around like, why should people not use the GA4 interface alone? Why else would you want to have that data in BigQuery, and what else could you do with it once you do have it there?
[00:09:09] Katie: Well, historic data is obviously your key one. The only data that’s going to be stored in GA4 is pre-aggregated data. So going to be down to whatever exists after that 14 months that you’ve got the retention period on there. You can pull all sorts of data into BigQuery, like you can get your CRM data in there, your Google Ads data in there, and get it all talking to each other, especially if you’ve got a link between that customer information and GA4 data and just really, really develop a sort of customer journey. Understand where they’ve been, where they’re going, what ads they’ve had, what your return on investment is for the ads that you’re putting out there. Also you can use BigQuery without, you know, not even looking at any GA4 data and use it as a SQL tool to build reports. And it talks really well with other software like Data Studio or Google Sheets. So you can really extract data and manipulate data in BigQuery and extract it to all of these places to build all your reporting for your business.
[00:10:07] Katie: So another major thing you can do within BigQuery is clean up the data. So in GA4, it really exists however it comes in, however it exists, there’s no sort of manipulating it in any way to make sure that it, it’s reporting in the way that you would want it to. So, you can properly clean up data within BigQuery. So for instance, two UTMs that are tracking source/medium for slight variations of exactly the same website. You can bring those into one row as opposed to reporting them separately. Another major thing is automating processes, there’s loads of schedules you can set up. You can schedule imports, schedule exports of the data, so properly start to finish of a report on your desk on Monday basically for any kind of manual process. I love getting rid of manual processes, it’s kind of my thing.
[00:10:51] Katie: What I also talked about in comparison to other SQL things like Oracle or Microsoft being really, really slow. So we used to sort of set off a code to run or set a report off to retrieve a report information. And you, you know, you go make a cup of tea not knowing quite when it’s going to finish, or get used to the fact of, that’s a half an hour report, let’s go and do something. Whereas BigQuery is so, so quick. Like if it’s longer than a minute, I really question whether my code’s correct, like that’s how quick it is. So yeah, really, really quick and efficient to go and retrieve data, which makes not just your code running quicker, but also if you’ve got those reports that are feeding from BigQuery, that is almost instant as well.
[00:11:34] Dara: Yeah, I don’t know what it’s like with GA4, and I don’t know if you’ve experienced this, but I know in the past, if you’re connecting Universal Analytics to Data Studio it was really slow because you had the slowness of Universal Analytics and then the slowness of Data Studio. But if you connect it to BigQuery, you obviously get that data much quicker. So I don’t know if that’s the same or if GA4 is sped up anyway.
[00:11:53] Katie: I think it probably would be the same because you’ve got that raw code, you’ve got that raw data in BigQuery and the code, or going to find that specific information rather than, I don’t know, maybe GA4 has some additional steps before it gets into Data Studio.
[00:12:07] Dara: And then the other benefit that you mentioned obviously, is then being able to have other data sources in there as well. Something else that used to always annoy me with Universal Analytics, this has gone quite a bit back, but people would always try and shoehorn things back into GA (Google Analytics) because that was what they were used to using. So they’d be like, Oh, can we get all our offline sales data in there? Oh, can we get our call tracking in there? Can we get this in there? And you were always kind of, it was never really what GA (Google Analytics) was designed to do. It makes so much more sense to have it the other way around where the Google Analytics 4 data is just another data source, and you have that sitting maybe next to CRM data, advertising data, sales data, you know, whatever else you have, and you have them all in that one kind of data warehouse. And then you can do what you want with them and clean up the data and feed it into various different reports rather than always thinking, Oh, well how can we get this back into an interface that doesn’t really let us do what we want to do.
[00:12:57] Katie: Yeah, and I mean, the other thing with BigQuery as well is there’s Python and R and SAS and all things that you can link in with the APIs and start utilising those. So we’ve recently done a project for somebody where they wanted Client IDs back into GA4 to use with their Google Ads to see who’s been marketed already, or has only been marketed once. All of that sort of stuff is possible with some additional, when you’re ready for the next steps of you know, Python and things to get that data back in.
[00:13:23] Dara: I like that so you can kind of, once you’re comfortable just having that data and maybe feeding into some reports, you can then continue to, because the reality is BigQuery is just one tool within the GCP. So you can then layer on all of these extra advanced features once you start to upskill or once you have more resource within the business, if you have people who know how to work with those different technologies. But you know, there’s an entry point for people, which is probably been made more accessible now with GA4, connecting to BigQuery for free.
[00:13:53] Dara: And then as people start to get more and more familiar with that, they can then start to do this more advanced stuff that you’re talking about, whether it’s using Python or using some of the other GCP tools alongside the data that sits in BigQuery.
[00:14:04] Katie: Yeah, I mean the cool thing about BigQuery is I think the only limitations to it is your own imagination and your courage as well. You’ve got to have a bit of courage if you’re not okay with some of these technologies. But yeah, coming up with an idea is key. What do I want to do with some data? And then you figure out the process after you’ve come up with the idea. So yeah, imagination is really key with BigQuery because you can do all sorts of stuff, but you will start off with the basics, but then you’re building it and building it and go, oh, I didn’t know it could do that, oh great, let’s do this then. And the other cool thing is that Google are always adding more things. If an API doesn’t exist today, it doesn’t mean a week or two down the line it’s not going to, so it’s always developing, always adding, I mean, it’s adding more functions all the time, it’s adding new APIs to connect with different software.
[00:14:51] Katie: I love the fact that it’s not stagnant in any way. Whereas I feel like some of the other, when I was using Oracle and Microsoft, it was very much kind like, this is what it does, it’s our finished package. Google isn’t like that, it’s always looking and it’s asking its users, what don’t you like? What additional things do you need? And it’s always developing and making it bigger and better.
[00:15:08] Dara: So do you have a wishlist item then? What would you like to see next on the roadmap, or is there anything lacking from kind of different data warehouses you’ve used before?
[00:15:18] Katie: Usually it’s like little tiny sort of things that you kind of put up with. I don’t notice them because I’m just kinda like, oh, it does it that way, that’s fine. But then a week down the line, they’ll fix that because somebody has commented about it when I haven’t. I’m one of those people that just kind of lives with however it’s performing or whatever it can currently do and then when something new comes you’ll kind of go, oh, that was handy.
[00:15:39] Dara: Pleasant surprise.
[00:15:40] Katie: Yeah, exactly. So they do have BigQuery release notes, which we’ve got a direct link into our Slack to fire off as and when a new update is out, because it’s always good to keep track of those new things that are happening. Google do tell you about them, but if you’re not keeping an eye on the release notes, you’ll either come across it and go, that’s different to last week, or you can find out about it on the release notes, so it’s a good thing to keep checking on those.
[00:16:01] Dara: And we can add a link in our show notes to those release notes and also to your MeasureFest slides and any other resources that we talk about in this episode. Just going back a tiny bit, one of the other things that you mentioned, which I think is a really great benefit of having the, again, I know I’m talking a lot about GA4 because mostly that’s what we’re working with our clients on, but whatever the data is that you have in BigQuery, the complete flexibility you have to clean that data up compared to whatever the data source is that it came from and that wouldn’t just be for GA4, but if you are ingesting data from any platform, really, if you use the UI of whatever that platform is, you’re so limited in terms of what you can correct. And what we find so often is like if it is GA (Google Analytics), you’ve got so many different people running campaigns for a website and they all use different naming conventions. The tracking could be a little bit broken, things could be named in unclear ways that might mean something to the development team, but it doesn’t mean anything to the marketing team or vice versa.
[00:16:57] Dara: So to be able to within BigQuery pretty much go in and change whatever you want, and not only going forward, but like to apply that retroactively, which is another thing you can almost never do with GA (Google Analytics). I mean, there are a few things that work backwards, but most things you make the change and it only applies from that point forward. So being able to have that complete flexibility on what that data is actually, you know, how it’s labelled, how it’s structured is quite, is quite powerful.
[00:17:22] Katie: Really powerful yeah. In my talk for MeasureFest that we did I discuss conversions and if you have an idea for a conversion, you can almost test it out with your data. Look at historically how that looks, how that performs, whether that’s what you expected and how you’d like it to look. Then you can choose to load that into GA4 and then in GA4, as you say, it’ll be from that point going forward. However Within BigQuery, we can go this is now a conversion, let’s apply it to all our historic data and really see those conversions coming through and what they look like. We have just done a really big project for a client, which was, they had an app Firebase, so app GA4 data, and they had GA4 data. So they’re a bit ahead of the game, but now they want to merge those two. But unfortunately, we’ve got all the app data, have event parameter names of a certain type that don’t correlate with what the web tracking is doing. So that was a massive, massive cleanup operation. So we had to extract all the app data, convert all those event names and create some new keys to manage it. So yeah, really extract all the app data, apply these naming conventions that we wanted so that they would correlate with what the event parameters were doing in web. That’s a massive example of a big cleanup operation.
[00:18:31] Dara: But you know, the fact that you can do it even, it’s not something you’d want to be doing, I guess on an ongoing basis, is it? But the fact that you can do that, like you’re never going to be able to do that within the platform itself. But to have that complete control of the data in BigQuery just means that, you know, if you do need to merge or separate or clean up, it’s just, you know, it’s just a matter of hours really, isn’t it? It’s just the time and the skill to actually do it. There’s no technical limitation, you’re not restricted in the same way that you would be within the UI.
[00:18:57] Katie: No, I mean in an ideal world, all data would be clean, but you don’t live in an ideal world, and it’s very, very unlikely. You know, many cooks spoil the broth, you know, so you’ve got all these people doing all this kind of tracking, there’s going to be data that just doesn’t correlate and is done one way by one person and one way by another person. So, It’s just inevitable it’s going to happen. I mean that’s basically what BigQuery is, is full ownership of your data, full control of the output. So take it away from GA4 and really do it yourself and develop it to benefit your organisation.
[00:19:26] Dara: And speaking of doing it yourself, and this obviously won’t be for everybody, but you also mentioned in your talk about the possibility that you can do custom attribution modelling with the data in BigQuery. So if you’re not happy with the kind of black box solution that Google offers, you can technically do that, right?
[00:19:44] Katie: Absolutely yeah. And then you can Google, there’s some great channel grouping bits of code that you can put in but tweak it to what you want your organisation’s attribution to be. But yeah, we did speak about how Google have got these attribution models, but they won’t release the code as to how they build them or how they define what attribution is assigned to the conversion. So you just very much have to trust it, so if you have BigQuery and have the data in there, you can develop it for your own organisation and decide what you want attribution to be and just go from there because yeah, it’s your data, you can do with it what you like and you won’t have the bias of Google Ads and their billions of pounds worth of income.
[00:20:23] Dara: And then I guess you could use other GCP tools like the BigQuery ML for example to try and create your own attribution or maybe even predictive modelling as well. Again, if you have the resources.
[00:20:34] Katie: Yeah, it’s totally there to utilise, I haven’t played with it yet, to be honest, but I know somebody else at Measurelab has. But I think it’s actually fairly simplistic as to how you can go about it, and Google are great for releasing notes on what all of these different features can do. There’s usually videos and notes on the website as to what they can do and how to do it. But it is, again, it’s just having a bit of imagination, a bit of confidence, and a bit of gusto to just go and do it and play with it. You know, if you take a small subset of data and have a play, it’s not going to cost you loads of money. If you hit all the data, it potentially will, so just be careful. I would always take just a sample of your data set and have a play with it and make sure those features are doing what you want them to do and yeah, just play, play, play. It’s a playground in SQL I think.
[00:21:19] Dara: Do you have anything in your mind that you want to kind of explore next? Like is there anything you’ve been kind of keen to try out, whether it is the BigQuery ML or any other features in GCP or anything else that you could do with the data that you have, GA4 data or otherwise really?
[00:21:35] Katie: Yeah, I mean the machine learning is really, really interesting and looking for those similarities in data and I would be very keen to learn a bit more about machine learning because I kind of in the back of my head think that it’s a really advanced bit of measurement, but then when I see it, I’m like, okay that makes sense to me. And I understand kind of what it’s doing, but again, I need to play to gain the confidence to do it myself, so at some point I absolutely will be playing with the machine learning elements of BigQuery.
[00:22:05] Dara: And then for anybody who’s completely new to it, anyone who’s kind of migrated over to GA4 and is thinking, right okay, I need to get the export set up to BigQuery. But if they don’t have any SQL skills, they’ve never used BigQuery before, do you have any suggestions on how somebody would start to upskill themselves? Let’s say it’s someone who’s really familiar with GA (Google Analytics) but just hasn’t used BigQuery or done any SQL in the past.
[00:22:27] Katie: So your first port of call is to set up your ingestion of the GA4 data into BigQuery. So we’ve got a, there’s a link hopefully we set up at the end, which we’ll send you off to my webpage, which tells you there’ll be a step by step process of how to do that. Then it’s just a case of playing, so you can either play with your GA4 data or you can go off and get some sample GA4 data from Google and have a play with that, which will just be a sample data set from the GA4. So it just gives you an idea of how the GA4 data look. As for learning SQL, you’ve got things like Code Academy, Coursera, so there’s once you can pay for, and there’s free tutorials as well. When you’re on the tutorials and you’re thinking, well that’s not the same syntax as Google BigQuery. As I said before, there’s a lot of crossover, if it’s a free one, go full heartedly into it and have a play and have a learn, and just find what the correct syntax is, but in general, there’s a lot of crossover, so you’ll be okay with any kind of SQL to get started with.
[00:23:26] Katie: I mean, lastly, just play with SQL. You know, it’s going to be a learning curve for you, but start from the basics of select some fields from a table where the date equals this. And then from that bit of code, you add some more columns and maybe do some functions on those columns, or add some more parameters, or create a subset of data and you just build on it and build on it and build on it. And eventually you’re writing code without any problem. Every bit of code I’ve ever written starts with a ‘select from where’ and you dump in there what you want and then you start thinking and the imagination starts going of, actually that would be a really handy bit of information, let’s aggregate that bit of information because that would be cool to have in that line of data. And it’s just built on like that and eventually you’ll have a really long bit of code that gives you exactly the information you want, but come with an idea or a challenge or a something you want to ask that data set and then just go from there.
[00:24:21] Dara: What’s funny, it’s like what you said at the very beginning about how you got into analytics. And same for a lot of us it’s like you, you don’t necessarily have a formal background in it, but you realise you’re quite good with spreadsheets or you’re quite good with using an analytics tool. And then the more you use it, the more questions you get, the more you have to kind of push your own knowledge, because you could say it’s the same with Excel, couldn’t you? People say, oh, well I know how to use Excel and there’s people who really know how to use Excel because they’ve just tried all the advanced formula and everything that you can do and really pushed it. And SQL, is it fair to say it’s not that different, that you could use it on a very basic level just to pull some fairly simple queries just to get some numbers that you need and then obviously you can develop that and get to, you know, a really good standard where you’re writing very kind of complicated code, but you don’t ever have to get to that point necessarily if you just need to be able to run some queries to get some data for some reports that you’re running, that’s a relatively low barrier to entry.
[00:25:12] Katie: Yeah, an awful lot can still be done in Data Studio or whatever reporting software you’ve got. So if it’s just a case of selecting these columns from this table, you know, that’s your basic entry level. Get it out into Data Studio and do all your formulas and things in there, creating fields. But trust me, if you’re doing the formulas in Data Studio, there’s so much crossover, you’re pretty much writing the same thing you’d have to write in SQL, you’re just not confident enough to do it in the SQL code, so get confident and put it in there. It’s funny you should say about people think they’re good at something because I always think any knowledge of any software or any sort of coding, it’s not about knowing exactly how to do everything, it’s about knowing what it can do. Because the minute you know what it can do, you can go and Google that very, very quickly and grab a bit of code that does exactly what you’re looking for. But if you don’t have the knowledge of what’s possible, then you’re not going to have forward thinking to do that and go and fetch that piece of information.
[00:26:04] Dara: That’s a really good point actually. It reminds me of something you said before where because you come from a more traditional SQL background and when you joined Measurelab, the GA (Google Analytics) world was newer to you. So you knew how to apply SQL to solve problems, but you didn’t initially know what GA (Google Analytics) couldn’t do. So until you kind of reach a point where you know what the different technologies can and can’t do, it’s harder to know what it is that you should be trying to learn. So you kind of need to know where all the edges are don’t you?
[00:26:31] Katie: Oh yeah, and I’m still learning all the time because I don’t have an awful lot of interaction with GA4 or Universal Analytics. I have a lot of interaction with data, but then the joy of SQL and SQL coding is that it’s ones and zeros, you know, it’s data at the end of the day, it doesn’t really overly matter where it’s come from. You’re just manipulating data so you can cross over from lots of different things. I think it’s all about having a logical approach and sometimes it’s about having a bit of forward thinking of, hold on, if I do that to the data, it’s probably going to do this, let me test it. So it’s a lot of forward thinking of what could potentially happen and checking and rechecking and testing the code to make sure it’s doing what you think it’s doing, because it can, you know, go on its own journey and kind of do duplications all over the place. And if you haven’t got a key eye for it, you’re not going to spot it. So I think it is for particular people, but I think if you are a logical person who loves a spreadsheet and loves a formula, then you are going to be okay.
[00:27:25] Dara: So I’m going to ask you something slightly different now. I’ve got two questions and this is about your, not about the content of your talk as such, but about doing the talk at MeasureFest. So the first one is, if you were going to do it again next year, or speak at another conference. What would be your next topic? And maybe that’s a tricky one because you’re probably just thinking, I’m glad that one’s done. Having done it and gone through the process of pulling all the content together is there anything that you thought, oh, do you know what, I could actually expand this bit into another topic? Or actually next time I would talk about this particular thing?
[00:27:57] Katie: Yeah first off, I’m pleased it’s done. I didn’t get the adrenaline kick that lots of people get, so I’m not sure that I’m overly keen to do it again. But if I did, there’s lots of topics and you know, and from watching other peoples, taking a particular job we did for a particular client and going step by step how we approached it and how we met that challenge and came out the other side of it, I think is a really good way of doing a talk. The key to a talk is fully understanding what you’re talking about. So if you’ve done a project from start to finish, you’ve understood all of the intricacies of that and you can really explain it.
[00:28:30] Katie: Another thing I keep thinking about, this is off topic a little bit, is why hiring a mum is a really good thing for any business, and seeing if I could do a 20 minute talk on why mums are brilliant, because they’re really good multitaskers.
[00:28:44] Dara: Definitely, I think that would be a great talk. I always like when, if you go to a conference, you expect to see the topics that are all very focused and very technical, if it’s a technical conference. I like those talks that get you thinking in a slightly different way, so you get my vote, I think you should do that. And so you’ve kind of answered what, at least partly answered the second question in a way. I was going to ask you what advice, because it was your first time speaking at an analytics event. I was going to ask you if you have any advice for anybody else, maybe who’s listening or who saw you at MeasureFest and thought, wow, that was a really good presentation, I’d love to be able to do something like that. Have you got any kind of tips having just gone through it for somebody who maybe is thinking about doing it but is, I don’t know either a little bit nervous or just maybe doesn’t feel like that they’ve, you know, got something to speak about.
[00:29:30] Katie: Well, I mean, Dan helped me, the old co-host. Ex co-host Dan helped me an awful lot and I remember when I was sending him proofs of my slides, I was trying to go and get all the information that I could possibly tell people about BigQuery and sort of dumping it in and going, right, they should know this and this because this website told me this. But he would say, you know, do you completely understand what you’re talking about there? Or is that something you do it day in and day out and a few bits weren’t. And I think just whatever talk you’re doing, make sure you wholeheartedly understand every element of it so that you can speak about it with passion and understanding, rather than trying to reel off lots of bits of information that the audience should know. Which is great because the audience should know this information or have that additional knowledge, but if you’re are not going to be confident talking about it then it should be dumped out of there, which is the key bit of advice Dan told me.
[00:30:25] Katie: And it helps to make it a bit fun and a bit fast paced with the slides. I think a lot of slide presentations, especially giving you lots of information, tend to be very word heavy. You know, all the audience are trying to read through absolutely everything. Or I know what would happen to me is I would read exactly what’s on that slide and then it’s just not a very good presentation, because you’re not really talking to the audience, you’re just reading what they can do themselves. So I’m pleased he pointed me in the direction of more images and less words because I absolutely would’ve said exactly what was on that slide and that wouldn’t have been very interactive at all.
[00:31:00] Dara: So stick to what you know, and less is more.
[00:31:03] Katie: Yes sorry, yeah if you’re going to say shorter, yes that.
[00:31:07] Dara: I’m a simple man, I always think in simple terms. That was good advice and it paid off I mean, I think it was a good topic, it was good content, and you delivered it really well. But I know we talked about it afterwards, you did say that, that you didn’t unfortunately get that adrenaline kick that most people do when they do a talk.
[00:31:22] Katie: Yeah, and there was a few bits I forgot. So, I think it was supposed to be a 20 minute presentation, when I was doing it at home it was running 18 to 19 minutes, and I was like that’s okay. When you’re on the day, it ran at 16 minutes because I was just clearly, even though I was trying to slow down, I was probably a bit faster than I usually was, or forgot little bits that I said previously. But again, if you forget things, it doesn’t matter this is what I told myself in the morning. I was just worried that I would forget to say a piece of information that even though I’m confident on and know what I’m talking about, because I didn’t have any cards in front of me or anything, I’d just forget it. But at the end of the day, the audience don’t know what was on there, especially if you’re not doing bullet points with all of those words on it so don’t worry about it. If you forget a bit, it doesn’t matter, they weren’t aware it was there anyway.
[00:32:04] Dara: Although you’ve just confessed now.
[00:32:06] Katie: I have yeah, there were loads I missed.
[00:32:08] Dara: Yeah. No, but you’re right nobody would know because nobody knows what the full list of things you were going to say was, so as long as it all flows and it sticks together well, which it absolutely did, then you’re the only one who knows, and now all of our listeners, because you’ve told them.
[00:32:20] Katie: I missed nothing, it was absolutely what I expected it to be.
Wind down
[00:32:22] Dara: It was bang on, exactly. Okay so you again, as an avid listener, you know what’s coming next, because you’re a guest, you don’t have to pick something from the last week that you’ve done, like Dan and I always do. So we’re always a bit on the spot trying to scrape the barrel and think of fun things we’ve done. So I’ll be fair to you, and you can pick anything that you’d like to do, you don’t have to have done it necessarily in the last week. But what do you like to do to wind down when you’re not working?
[00:32:48] Katie: I’m just an activity junkie. So I like to do everything and anything I can find. So, luckily my organisation has put me on the social committee so I can find all the things I want to do and say it’s a work event. We did escape rooms last week for the social, which were absolutely amazing. I love an escape room. I did Tough Mudder a couple of weeks ago, which I didn’t want to do, but I did do it. I don’t know, anything, any kind of white water rafting, I’ve only got to have somebody ask me, do you fancy this? I’ll be like, yes, absolutely. But escape rooms are my thing, I love an escape room.
[00:33:20] Dara: You’re an adrenaline junkie. But yeah, the Tough Mudder I think it’s really impressive. I’ve never done it, but I know people who have. I know someone who did it and she’s still injured, not badly, but she’s still got like a pain after about three or four years I think. So yeah, people tend to have their war stories about doing Tough Mudder, so hats off. So my, no adrenaline junkie behaviour for me, I went to the cinema twice over the weekend. Not once, but twice although it wasn’t a complete two times because we left in the middle of a film. I was trying to think if I’ve ever done it before. So we went to see on Saturday night, we went to see Mrs Harris Goes to Paris, which was a lovely film, really enjoyed it. Very nice, easy to watch, good fun. And then last night we went to see Blonde and we walked out about halfway through. Not to say it’s not a good film, I think it would suit a lot of people, but just the style and just the way, just the storyline and just everything about it really, it was just too much for us. So we actually got up and walked out.
[00:34:17] Katie: I’ve only done that one movie because I think it’s quite sacrilege, I like to respect people that have gone to the effort to make something. But I walked out Wild Wild West because I was just kind of like, what is going on? There’s a massive like mechanical spider, it’s ridiculous. I had to leave.
[00:34:31] Dara: Yeah that is, but going in the first place to that was the mistake.
[00:34:34] Katie: It was back in the day when you got the card, that was like £9.99 a month. So you’d like just plough through and see everything that came out that month and make your money back. I’m a frugal person.
Outro
Dara: Yes, we all have our own interests. That’s it for this week, to hear more from me and Dan on GA4 and other analytics related topics, all our previous episodes are available in our archive at measurelab.co.uk/podcast. Or you can simply use whatever app you’re using right now to listen to this, to go back and listen to previous episode.
Daniel: And if you want to suggest a topic for something me and Dara should be talking about, or if you want to suggest a guest who we should be talking to, there’s a Google Form in the show notes that you can fill out and leave us a note. Or alternatively, you can just email us at podcast@measurelab.co.uk to get in touch with us both directly.
Dara: Our theme is from Confidential, you can find a link to their music in the show notes. So on behalf of Dan and I, thanks for listening. See you next time.
Daniel Perry-Reed
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Further reading
Customer Data Platforms (CDPs)
Grand Designs: “The Warehouse to CDP conversion”
