So What? How do I clean and prep my data for analysis, part 1

Well, hey, it's been a minute since we've been on the air, probably since sometime last year, but we're back. I'm Katie. I'm joined by Chris and John, and this is so what the Marketing Analytics and Insights live show from Trust Insights. This week, we're gonna start our series on uh data prep for analysis. So there's a lot to cover.

So on in this show in particular, we're gonna start with the basics, some terminology, how you get yourself set up for success, and what tools you should have if they're ready. So we're just we're really hoping, you know, if you have questions, feel free to drop them in at any time. Um if we don't cover something, then feel free to ping us um after the show and we'll try to cover it next week. So Chris, John, so what prepping your data for analysis. Why is this a topic that has that we are considering talking about over a series of a few weeks?

Why is it such a big deal? Well, you know, data is an ingredient. And if you're cooking and you don't have ingredients, or you don't know what they are, or you don't know what you're supposed to do with them, it makes cooking really difficult. Um it also makes for really disgusting food at the end. It's like, oh look, broken seashells, that's an ingredient, right?

No, no, it's not. So uh there's there's a couple of different things that are important to think about with data. You know, first is understanding like what good data is and and where you get it, and then what do you do with it is are kind of the the big three questions. So to start off, I think it's we should probably set some some basic foundational stuff. Like what is data?

Um there's really four-ish big kinds of data, right? So there's uh subjective data and objective data, which is opinion and fact, and then there's number data and non-number data. And you can kind of like draw a two by two matrix, if you will, of those different kinds. And a lot of what we deal with in marketing analytics is in the uh the objective data, it's like facts of some kind, whether it's not numeric or not numeric, but there is also still some subjective. So a couple of examples, I think, are good.

Uh numeric objective data, really easy, like website visitors, it's it's a number. Um, and there's no shortage of that. And that's what a lot of the software and tools that most marketers are used to dealing with uh are accustomed to. There is um objective uh non-numeric data. This is like emails, text dimensions in in data sets.

These are things that aren't numbers but are still important. Um you would need to be processing this if you want to understand, for example, sentiment analysis of a piece of text, or even just like you know, what topics are on a web page. Then you get into the subjective side. This is where things get really interesting. So you have things like subjective numeric data, which sounds like a contradiction in terms, but it's not when you think about like a survey on a scale of one to five.

You know, how much do you like this coffee? And everyone says it's a you know, it's a four, okay, that's a number, but it is also subjective, it's not a a fact, it's just a person's opinion. We have a lot of that data, and then you have subjective non-numeric data, and this is where you have opinions, review data, um, you know, people writing blog posts about your uh content, uh, social media data, there's tons of it, even things like you know, focus groups uh in and market research and and opinions polls are all subjective non-numeric data. And so one of the toughest things we have to do as marketers first is figure out what do we have in each of these four categories. Well, and I think it's interesting too, because and this is something that we've been talking about a lot internally is you know, what is data?

Like obviously it's you know what you describe, you can put it into one of those four buckets, but I think the most common understanding of data is that it's a set of numbers and that's it. And so I think giving it those qualifiers of subjective or objective, you know, whether it's fact or opinion, you know, and then whether it's structured or unstructured, numeric or non-numeric data, I think really helps move along the conversation as to why we want to dig into this over a course of a couple of weeks, because it's not just looking at a set of numbers and saying, okay, that's the biggest number, therefore analysis done. Like there's a lot that goes into it. Um, you talk with a lot of you know, people in the network. What kinds of you know, misconceptions do you think there is around, you know, what is data?

Yeah, well, I a huge part of it that goes straight to our challenge as marketers is the fact that it's the subjective data that tends to be most useful as far as what you're going to be doing for future products or also in the B2B space where you don't have tons of objective data, that's kind of where you can at least make some some movements. So uh the easy uh action is always in the objective data. You know, you you can look at website hits or likes or whatever, but really until you dig into the subjective side, that's where you tend to find uh tougher questions and the the value. So that's the the real data challenge for me is kind of where do you want to land? You know, where do you look first?

And the easy answers are always uh, you know, tend to be on the objective side. But again, the subjective side is where you get to purchasing, which is irrational. And so you know, you need that data to help guide you for where you want to go. So, Chris, where do we start? What what are some of the things that we need to understand about good data before you can even start to think?

Okay, I need to do an analysis. Yeah, so data, good data has six qualities. I'm gonna switch over our display here. You can actually go over to trustinsights.ai and get this from our instant insights uh section of the website. This is our uh uh qual data quality framework.

And good data is three six things, right? It's clean, which means that it's it's free of errors, which if you've ever looked in your CRM or your marketing automation software and you've seen test at test.com, your data is not clean uh because you've got got junk in there. Um it's complete. Uh this is one that's a big problem. So we were just looking at a client's uh Google Analytics uh yesterday, and there's a huge period, about three and a half weeks where it looks like they forgot to put their tracking code back on their website.

So there's like 21 days of zeros on their site. And I was like, uh that that's a problem because you got a major analysis gap now. Um data has to be comprehensive, which means it it covers the questions being asked. If I am asking you for return on investment and you give me all your revenues, but you don't give me any of your costs, I can't do return on investment because the formula is earned minus spent divided by spent. If there's no spent data, you can't do ROI.

So that's uh one that again, you have to be very clear at the beginning of a data project what your goals and strategies are so that you know whether your data answers the question. Um the corollary to that is your data has to be chosen well which means try to have as little irrelevant data as possible. This is marketing's bane uh we're in the middle of a a very large attribution project for one of our our large automotive clients right now and we started out that project with something like 215 different data points uh different data series which you know is if you imagine a spreadsheet that just goes on and on and on um a major part of the data preparation process to even do analysis for that says okay we got to figure out which of these 215 are relevant and then remove the stuff that's not data has to be credible which means it has to come from good sources and it has to be uh chosen from a relevant time period um so making sure that it's it's fresh and it's not biased in any way which is really hard to do and most people don't think about it. And the last part is has to be in a a calculable format that both humans and and machines in particular can use. And again this is something that you don't think a lot about until you start working with data and realize just how messy data is show you a very quick simple example when you go to for example Google Analytics and you just say you hit the export button and it spits out a spreadsheet and you look at this and go, okay that's cool.

But when you would zoom in, the first six lines of this thing are are junk. Right? So you can't just put this into a piece of software and expect it to go. Something as simple. Like if Google Analytics can't even spit out uh you know calculable usable data, what hope does you have for anything else?

At the bottom of every Google Analytics export, they always put a summary. Like and then again, if you don't know to look for it, it's like, oh, this completely screwed up your analysis. So it's just little things like that. That each of these buckets, you've got to have almost a checklist, if you will. And you can almost like score on a clipboard, like you know, a score to one to five, a any given data source, say, how does it rank for each of these six factors?

And I guarantee if you just spend some time with your data, you're gonna find that some of your data scores really low. So a little quick anecdote. So a lot of people know that I used to work uh in risk management in the pharmaceutical industry. And one of the challenges that we always had with reporting data to pharma quarter over quarter was that we were never getting consistent data from the uh agencies who were supposed to be reporting it. So basically we had this network of clinics that were supposed to be reporting data every single day, and every quarter the network would look different.

Some states would come on, some states would come off, the time frames would all be all over the place. And it was pretty much a nightmare to say that consistently this is the data that we can compare quarter over quarter and do any kind of analysis to say, you know, year over year, this is what trends look like. Like obviously, you know, we made it work. We had large quantities of data, so there was a lot of inference that could be made, and that's a whole other topic. But you know, when you're even, you know, Chris, to your point, when you're talking about things like Google Analytics and you're trying to do, you know, month over month, week over week comparisons.

If it's not consistent, if it's not complete, if it's not clean, calculable, credible, you can't do something basic like what happened last week and what happened the week prior. Exactly. So that's sort of the the things you have to be able to score your data with in order to be able to start using it. When it comes to getting organized and setting up for success, one of the things that you need to know is what does the what is the data analysis process look like even before we start talking about fancy stuff like data science and machine learning and advanced analytics, just even being able to look at our data and go, huh, what do we need to know? And uh I know for next week we're gonna start digging into examples of how this looks, but I want to give you a preview of what just the overall framework looks like.

So data analysis is really sort of a an eight-step process, right? First thing you figure out is what's your goal and strategy is like what are you trying to prove? If you don't know what you're looking for, it's kind of a lost cause. Um the second part, which is surprisingly difficult for marketing, is the data collection itself. How do you get at the stuff?

Um just this morning on one of our uh inside our Slack, if you go to uh uh trust insights.ai slash analytics for marketers, you can join our our free Slack group. But we're having discussions about getting data into different dashboards, right? Getting data from one place to another. It's really hard to do. A lot of different systems out there, you've got your marketing automation system, your CRM, your social media channels, your social management tools, your ad systems, you name it.

So, how do you collect all this data and get it into one place so you can even start working with it? Uh the third step is attribute classification, right? Which is a fancy, fancy term for saying, what's in here? Right? What what what's what is what kinds of data is it?

Like we're talking about is it subjective? Is it objective? Is it numbers? Is it not numbers? Um, what's in the box?

And again, this is something where you you will find surprises. Um when you look at like social media data, it on the surface looks like it's objective data, New America, not New America, but sometimes there's really challenging stuff in there. One of the easiest ones to really understand just how much of a problem this is is Instagram. When you look at Instagram data, you get a URL to the image or the video it was shared. You get uh the username, the engagement numbers, and then the description.

Unless you have really good machine learning software that can describe the image, you may have a total mismatch between what's in the description and what's in the actual image. Uh, we all have that one obnoxious friend, right? Who who posts a picture of like you know, them well, before the pandemic, them on a beach with a drink of some kind, and you know the caption says, Well, this sucks, right? We all know that they're being silly. Um, but if you didn't have the image, you just had well, this sucks.

Like you have a you could have a pretty severe disconnect if you're trying to do sentiment analysis. So, even something like that in attribute classification is important to know. After that, you do initial analysis. So looking at one variable, looking at multivariables. This is where you do your quality checks, like stuff missing, is stuff broken.

Uh are there unusual things in the data that you didn't expect to be there? Uh anomalies. Uh, I have had this experience with my own website. I was pulling uh submissions from a contact form, and uh a couple of weeks ago, uh a few spam bots stopped by and saw they were right in the middle of, you know, you know, download, download the first name, last name stuff came with a whole bunch of like porn links. So, like, oh, that's not what I expected to get out of my contact post fix my website.

But you have anomalies like that in your data. Anytime you're getting data from the general public, you're gonna get anomalies. Well, and so Chris, I think that you know, as we're walking through the basics, um, you know, so you just gave a good example of a type of anomaly. Can you in numeric data, for example, can you give uh an example of let's say you have you're looking at your website traffic data and you're looking at it for the past month? What is an example of an anomaly that somebody could or should be looking out for?

So there's a couple of different ways that can go. Like if you're it just goes to zero, like, oh, that's an anomaly. That clearly you broke your Google Analytics. Um, another uh opposite one uh is like, oh, look, there's like 10,000 people stop by your site in one day because somebody posted a link to a blog post of yours on a popular subreddit, right? And that would be a clear a clear case of an anomaly.

It's not something that you could probably sustain, but it was just one of those weird one time things. If you owned, say, GameStop stock, this whole week has been an anomaly for you compared to the last five years. Um, one of the important things to talk about in that in is understanding when, and this is a more advanced statistical discussion, is when is something is an anomaly versus when it's a breakout, which is where something changes uh and then stays for a little while, or then a trend when something changes and it stays changed, right? Being able to differentiate between the three. But all that has to happen in that initial analysis step.

If you don't do that, you don't know what's in the box. Well, and I think that that's an important point too that we could um you know explore for a second is that you know, just because you have an anomaly in your data, is that a bad thing? And should you immediately remove it? And then, you know, let's say, for example, you know, you have that one spike on one day because you had a bot attack, for example, or it was a legit spike on one day because it happened to be, you know, national data processing day, and everybody came to your website to learn about data processing, for example. You know, do you have to remove that from your data?

And how do you go about acknowledging that? Like, where do you even say, hey, when I did this analysis, there's some data missing because it didn't make sense? You're jumping ahead. Um, pull it back, pull it back. So after you've done the initial analysis, that's when you do requirements verification.

This is when you say, okay, we set out our goals, we set our strategy. Can we answer the questions that we've been asked to answer with this data? And at this point, you may have to stop and say, no, we can't. We need more data, we need better data. Um, you know, in in going to the example of our our attribution projects, we're doing one of our clients.

We've had to stop and start this project five or six times now. Um, because we've been like, okay, you know, this department didn't get us the data in time. All right, restart the project. Um that's one of the reasons why that that first step, that goal and that strategy, you know, all that planning is so important because otherwise you do this process a lot. Um the next step, and we is sort of where we're really going with lots of is in that preparation.

We're talking about centering and scaling and cleaning. This uh Katie is is where you were talking about like what do you do with some of those anomalies? What do you do with missing data? Um, so for example, if you're doing time series analysis, centering and scaling is really important because what you want to do is you want to try and get as close to apples to apples with your time as time date as possible. If you've got website traffic and you know retweets, right?

They're good, they're very different scales. Website traffic's probably in the tens of thousands of visitors, uh and your retweets are probably in the tens, right? So you you want to normalize that using any number of mathematical techniques to get it closer together so that you could do your time series forecasting. Uh, that's true for like really advanced machine learning models. You they will actually automatically do that for you because they know that you're probably not going to because you're lazy.

Um, and so I am anyway. Um, so it knows to to do those things. But the the other one is that cleaning stage. What do we do with an anomaly? It depends.

It depends on what you're doing with the data. If you're trying to get like you know, basic analysis, like what happened this month, you have to leave it in, right? If you're trying to do trend analysis, you might want to smooth it down with something like a seven-day moving average, or um, if you're trying to do um, you know, uh get a sense of what's really happening, you might use a median and then have like a five percent cutoff. So you top cut off the top five percent of values and the bottom five percent of values of of any data set, and that can take out those short, bursty little anomalies, but it all depends again, going back to that goal and strategy, what you're doing with the data dictates how you prepare it. So I cut my teeth on data analysis in the academic world, and there's very strict rules with um, you know, clinical trials around how you can analyze the data.

And I don't want to say that you know, when I stepped out of that world into the marketing world, like the rules went out the window because obviously they didn't, but the rules do to me feel a little bit more loose. So, for example, um, you know, in terms of like the consistency at the date range, if you don't have a hundred percent apples to apples data on the date range, so from the first from at midnight on the first to midnight on the 31st, in every single data set that you're putting together, it's invalid. You can't do it. The same is not true, at least in my experience, in a non-regulated, more like marketing data, digital analytics, those kinds of things. Why is that, Chris?

Like, why is it more acceptable to have less strict rules? And how do we, I guess, compensate for that to make it feel more valid? It it it that goes back to goal and strategy, right? It's level risk, right? If if I screw up a drug analysis, I could literally kill millions of people, right?

If I screw up a time series forecast for when to send an email, probably nobody's gonna die, right? Um, you might not get your bonus, but you're you know, nobody's gonna die from that. And so it all comes down to what level risk is acceptable, and honestly, and which is not part of the the exploratory process, but it comes it's it's the literally the namesake of the show is the so what? What are you gonna do with the data? If there is a you know 10% variance in your email marketing forecast, is that enough to care?

Or you know, or is it you know, knowing you gotta send on Tuesday, whether it's 10% you know, uh variance or 50% variance, it's still you're still gonna send your email on Tuesday, right? You got seven choices for what day of the week you're gonna send your email on. Um, there's much lower risk. And so you can be more lax in some of these statistical techniques, depending on the level of risk. If you were building a data analysis to tell a company where to spend 10 million dollars, you might want to employ a lot of rigor because that that could be a very expensive mistake.

Uh again, still no one's going to die. Um, but it it could it could endanger the company's you know profitability. So it sounds like one of the things that needs to go into the goal and strategy section is that risk analysis of you know, if we get it wrong, or if we you know spend a hundred thousand dollars and it's the wrong thing, is can we live with that? Um, you know, I know that we've talked with other marketers who purposely have a what it almost like a negative ROI on their campaigns where they know they're spending more and it's they're losing money, but they're okay with that. And you know, as someone who does the books for the company, like that freaks me out and it gives me hives, but you know, it's a risk that they're willing to to live with because that's just part of their business model.

And I think that all of those questions, what ifs and still what all has to get sorted out first. And you know, I think that is one of the things that we see. And again, John, I know you talk with a lot of the network and the prospects of you know, people are just sort of jumping ahead to the outcome and what they want to see versus what do we even have to work with. Yeah, there's uh you because they're often too our two different camps. You know, there's the one side where you're just gonna go and dig once and try and find an answer to one question, and that's totally different from the projects where you're like, okay, we're going to report on this every month for the you know the rest of our lives.

And I definitely want to give a plug for documentation, you know, talking about all the stuff where you're validating the data and you're maybe having to delete columns and change things. Every time you do that process, you really want to have a set of running notes where you're saying, okay, delete columns X, you know, double double J and all that kind of stuff, so that as fast as possible, you can get to the point where you can pay another human to do it or automate it or whatever, but um, you want to learn those lessons. And another thing with that is because you don't want to three cycles later forget one of the steps and pollute your data uh pile. So yeah, do your documentation. That's my PSA for the day.

I think it's a really good PSA. And again, like I mentioned, I cut my teeth in the more pharmaceutical academic world. Methodology is everything. You have to document every single thing you did with the data. So if you removed a number on line 157, that better go into your methodology.

And it's something that, you know, when we're doing this analysis sort of in the marketing space, less regulated spaces, I very rarely see methodology statements from a team doing an analysis, you know, for themselves, for their, you know, upper management on behalf of a client. And to me, that's such a miss because there's if you're doing the analysis right, there's no reason to not talk through what it is you did to get there. It doesn't mean you're giving away all your secrets, it just means that you're being transparent around how you arrived at the answers that you're sharing. So I think the methodology and job security diagram, there's not much overlap in that event. That's the problem.

That's true. Um, the other thing on the risk side too is again what happens with the data. Like uh, we used to work in in a in an agency where you know, uh there was a tremendous amount of reporting, but honestly, we could have colored with crayons on napkins and handed it to the clients and the you know, because the clients never read any of the reports ever. Um, and so there was no risk level. So you literally could have made anything up.

Now we are fortunate that since starting our own company, we've been freed of a lot of the restraints from you know the way that our our old shop used to do things, and we actually do insist that clients read what we produce now for them. But again, that comes down to risk. If you're just cranking out PowerPoints every month for your reporting and you know nobody looks at them, uh your risk level is low, and so you don't have to be particularly strict about the rules from a a risk level. That said, it's not a bad habit to get into it of being strict with the rules just for your own professional development, so that you if you do change jobs and you work in a company where it's more stringent about the rules, you'll be okay uh in that company. Well, and I would like to sort of backtrack a little bit of the disclaimer, just because people were not looking at the data does not mean that we weren't stringent about how the data was being collected.

We were still very, you know, we took a lot of pride in. And I think that that's something that um is also worth mentioning is you know, you talked a little bit about that QA process, Chris, but I think that it's you know something that you know, we've asked this question a lot to like our partners and our friends, you know, in the marketing space of what is your you know, QA process. And a lot of times we'll just sort of get a blank stare of what do you mean? And so that seems like another, as you're thinking through, you know, I need to do this analysis, even if it's a small analysis, there should be some level of QA built in to make sure there's no copy and pasting errors, or if you've set up an automated process that it's working correctly. Um, Chris, do you want to talk through a little bit, you know, what you've experienced with and without QA and what people should be looking for?

Um the number one thing is get humans out of the process uh out of your data as quickly as possible because humans are the source of errors. Um as much as possible, automate this stuff. And you know, this is something we'll we'll tackle in future episodes, is how do you automate as much of this as possible and have the machines enforce rules that the humans may have forgotten, whether it is you know normalization or deletion or anomaly detection or imputation, you do not want people doing that stuff because people just screw that up all the time. Um I screw it up, even when you know, looking at my own work and just sanity checking things, going, is this within the expected range for you know for this variable? I screw things up all the time.

So one of my personal imperatives is how can I get myself out of the process as much as possible and double and triple check the code itself, walk through it and and and talk it through with with other members of the team or with the customer to make sure the process is sound and then get humans away from it and and let machines enforce the process for you. Katie, I wanted to ask you about that because you know, for so many of the business things that I've done, methodology is and documentation, even documentation is an afternoon, you know, if it's even done, because uh, you know, again, management just wants the answer, they don't care. But in the realm that you came from, where methodology is legal requirement and has to be done, how did that normally work? Was there because you know, for us, maybe it's a Google Doc, you know, of some kind, just a running text pad. But did you actually have materials that had to always ride along with the data?

Was it you know how what how did you manage that? Um, well, not to date myself, but a lot of stuff was actually collected on physical pieces of paper that were then kept under lock and key, literally the like the box with all the data in it would be locked, and then the keys would have to be kept somewhere else because it was um protected health information. Um, but then somebody would have to input it into some sort of a system. And so all of these steps had to be documented a lot of times before the project even started. So you had to outline the process before you could even get started and have a committee approve the process to say, yes, this is a valid process in order to ensure that your data is correct, and then you had to have one or two research assistants double checking every single number.

So it's a lengthy process, it's why clinical trials tend to get more expensive because there's so many quality assurance checks involved, but it's for the safety of you know, human lives. Now, in this respect, like in the marketing space, Chris, to your point, like if you're sending out an email, it's probably not life and death. However, if someone's paying you, you know, six figures for some kind of analysis or even you know, five hundred dollars for an analysis, you want to make sure that it's correct. And so one of the things that I've seen a lot of people skip over is that QA process. And so even if you're pulling, you know, really straightforward data directly from Google Analytics, it never hurts to have somebody else uh take a look at it be like, hey, can you check my work?

Just make sure I didn't copy and paste something incorrectly. You don't even have to go down the road of having lots of code and processes and automation. I, you know, mistype numbers all the time. Like when I meant to write 75 or 57, that could be a big error down the line if I don't catch it up front. And so as you're going through this exploratory data analysis process, make sure that you're building in those QA checkpoints along the way.

Because if you get too far down the road, backtracking to find out where it went wrong can become a very expensive endeavor for something that was meant to be simple in the first place. Yep. So the last two steps in the process, feature engineering is when you're taking the data that you have and making new data out of it. And the easiest example of this is when you have a date, you can take a date and break it into the year, the month, the day, the day of the month, the day of the year, the weekday, uh numerically, the weekday by name, and you can create all these additional features from just that one data point that you can then use for analysis. So a lot of you see this a lot in stuff like email and social media marketing.

What day of the week is the best data tweet? You know, is a silly example, but all that all has to be engineered from the data you already have. And so that's something that again is done in the requirements process, but it it is a discrete step because you have you do have to typically either write code or have tools do that for you. And then the last part is once you've done all this stuff, you got to do something with it. What happens with the data?

Does it become insights and analysis? Does it go into uh a formal data science process to become a machine learning model or a piece of technology, a piece of software? Not everything does, but you at least have to be able to say, okay, after this whole process, here's what we've shipped. Here's the thing that that you can use to make a decision with. One of the big pitfalls that we've seen way more than is comfortable is you do all this and then you create a you know shelfware.

Here's your PowerPoint slide deck that gets put on a shelf and nobody ever looks at it. It's like, okay, well, what was the point of spending all those hours and all those resources if you never actually do anything with the data? And it goes back to that goal and strategy of what decisions do you want to make with this data? And in terms of, you know, when we talk about the data preparation process and the data analysis process, you've got to have that goal in mind up front, because otherwise it's just an academic exercise. I'll be honest, a lot of times people will say, I'm just curious.

I want to see what the data says. If that's the response that you get from the person asking you to do the analysis, push back and ask for something more specific because that really is gonna be a waste of your time. Yep. And then in terms of the tools to do this stuff, you can do a surprisingly large amount of it uh in a simple in simple spreadsheet software. Like Microsoft Excel is a pretty darn good tool, right?

It's the number one data analysis tool on the planet for a reason because it's good enough for a lot of different tasks. Um, some of the other tools that we use typically, um, we do use a lot of databases, uh, SQL databases and stuff to store larger data sets, big query. Uh, we do a ton of coding in the statistical programming language R, but you can do that in in Python and Julia and Scala, you know, depending on the language you're comfortable with, if you're not, if you if you are going that route. There are also tons of off-the-shelf tools that you can use um for visualization for you know steps like the your initial analysis, software like Tableau, which is a Salesforce product, is is a really great tool for some of the preparation and feature engineering. You can use tools like IBM Watson Studio.

Uh, and it's very, very varied modules, uh, some of which are no code, you just click and drag stuff that you want to cleaned up. So there's plenty of options for tools. The trick is as you're going through this, is to say, okay, what tools do I need at each stage in the process? And do I have them? Or if there's a stage in the process that doesn't seem familiar, say, okay, well, what do I need to learn?

What what skills do I need to learn? What processes are recipes, and then what technology? So, really the whole people process platform. There's kind of a mini checklist at each stage of the exploratory data analysis process to say, what do we have, what don't we have? Like, what do we know?

Well, what what recipes have we already cooked? And it might sound daunting to sort of go through this whole process, do the checklist. But one of the things we talk about a lot is getting yourself organized up front with your requirements, knowing what tools you're gonna need. It's a lot like you know, Chris, to borrow your analogy, it's a lot like cooking when you have your museum class and you have everything organized. Once you do that step, everything else goes so much faster because you know exactly what you're working with.

It actually helps keep you focused as well, it keeps you on task. If you're working at a company where you know everything is like at an hourly rate and you have to stay on budget, it really helps you understand how much time you're gonna need to do these things. So doing this work up front to set your goal, set your strategy, organize your tools, ask your questions, set up your process of QA, how are you gonna check, sort of look for anomalies? You really can't skip this step. Now, if you have bazillions of dollars, you know, basically the FU money, and you don't really care, then yeah, go ahead and skip right to analysis.

It doesn't matter. 99.9% of the people who are going to be doing analysis do not have that FU money. So it's probably a good idea to get organized up front and really, you know, explore what it is you're going to need to do, even if it's a simple analysis, because, you know, to your point, Chris, it can become a repeatable process and something that you can eventually just automate and you can move on to more interesting and exciting stuff. Exactly. I mean, simple music example.

I was getting ready to make some chicken soup uh earlier today for dinner tonight and get everything ready, turn the Instapot on, then I realized I forgot to put the chicken in the chickens. Exactly. You think about something as exactly. Well, yeah, as central as that. Now think about that from a marketing data perspective, though.

Like if you were trying to do social media engagement analysis and you forgot like Facebook, even though it's like one of your big channels, that'll be a pretty big oops, but it happens. So the when we talk about getting ready to do data analysis, you know, the data preparation process and the the exploratory data analysis process. Having that cookbook and following the recipe is so important. If there's one place where I know personally I go wrong the most, it is not following my own processes. Um there's one thing I I've done that that makes things go way faster, it's having the existing code, and then me, I'll just take that code and port it to new things and be up and running much, much faster.

The worst thing I've seen is watching somebody, you know, when they start a project and just have to, you know, they open up a brand new template and tableau or whatever, just start from scratch again. Like, why are you doing that? You should be taking your existing code and tools and you know, update them, sure, tune them up all the time. But you should never be starting from scratch if you can possibly avoid it. I think another really good example of this, and this is something we were talking about with one of our partners this morning, is not having even the right systems in place in order to collect the data.

So a very common issue that we see that we are happy to help with at any time is companies will install Google Analytics, but they won't also then install tag manager. And so their ability to collect data, collect data accurately, collect goal data conversion data, which is most likely what they care about. What are people doing? Are they buying stuff? Is compromised because you're not collecting it efficiently, you're not collecting it correctly, you're not collecting it completely.

And then they want to go ahead and start running campaigns and making big business decisions, but they didn't step back to first do that foundational work to say, do we even have the right data? They just jump ahead to step six, and all of a sudden, you know, they're making decisions based on things that aren't real. Yep. So that's the process and and sort of the lay of the land, if you will. It's it's really not about the tools.

Um, you can get by with a lot of the tools you already have. It's it's very much about the process and the skills that the people put and the process in order to make this stuff work well consistently uh uh over a long period of time. Um any final parting thoughts? Plan on having to do it again and plan on having to explain every part of it. Oh, so true.

But John, to your point, your PSA, if you document from the get go, when people ask questions, you have it ready. Like you can answer that question. What happened to line 32? Hold on. Let me tell you exactly what happened to line 32.

And it just you also build that trust with the people you're doing the analysis for. So next week, we actually get to talk about the data itself. All right, so stay tuned. We'll see you next week. Thanks for watching today.

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