{PODCAST} In-Ear Insights: What is Data Analytics?

{PODCAST} In-Ear Insights: What is Data Analytics?

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris dig into data analytics. What is data analytics? How is it different than, say, marketing analytics? What are the prerequisites for data analytics? Learn all this and much more in this episode.

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{PODCAST} In-Ear Insights: What is Data Analytics?

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:16

In this week’s In-Ear Insights, we’re talking about data analytics.

And data analytics is kind of a, a really big umbrella term that doesn’t have a super clear definition, particularly when you compare it to things like business analytics, marketing, analytics, and so on and so forth.

Fundamentally, data analytics is the discipline, or profession of data analysis, right? Just like marketing analytics is the profession of marketing analysis.

So Katie, when you think about data analytics, and analyzing data as a huge, broad, vague term, what springs to mind?

Katie Robbert 0:56

Well, you know, it’s interesting that you’re talking about the lack of a definition, because for the longest time, I thought, data analytics was a redundant thing.

It sounded to me like data and data or analytics and analytics.

And I think that that’s part of the confusion around what it is, but it’s the act of analyzing the data that you have, which, to be quite honest, is, to me, when I think of it, it does, the word data itself almost becomes irrelevant, because if you say business analytics, or marketing analytics, you need data in order to do the analysis, or else you’re analyzing literally nothing.

So you know, zero times zero is still zero.

And so that’s sort of where my head goes is the word data.

In some ways, it’s not that it’s misleading.

It’s just not descriptive enough, because it really does apply to anything.

Because at the core of it, like you need that data in order to do the analysis.

And so I feel like, call it whatever you want, you can’t get around the fact that if you have terrible, you know, processes for collecting data, or really bad governance, bad data quality, it doesn’t matter what you call it, it’s still going to be terrible.

In the end.

Christopher Penn 2:19

It’s sort of second on the rung of a very short ladder, but you think that you have data engineering at the bottom, which is the storage and manipulation of data, how to run a database, how to store data properly, how to retrieve it, how to back it up the things you need to make the stuff work, then you have data analytics, which is the analysis of all that stuff that you store to get very generic.

And then on top of that, you have data science, which is the experimentation application of the scientific method on the data, particularly on the analysis that you’ve done to understand the data.

So there is kind of a logical progression to this very, very vague, broad term.

And when we unpack Data analytics, you can split it into kind of five areas, right? There’s descriptive analytics, which is what happened, diagnostic analytics, which is trying to understand why those things happen.

Predictive analytics, which is trying to forecast from your data, pro, prescriptive analytics, which is trying to make recommendations from your predictions, and the proactive analytics, where you’re building systems to essentially manage the data analysis on automatically where you don’t have to touch it.

It just does its thing.

And so this hierarchy within data analytics, is a nice set of clues as to what data analytics should be.

And again, like you said, it can apply to anything that has data, right.

So it’s a sort of an umbrella set of disciplines.

And this is where you have now transferable skills, right, if you will have, if you’re good at descriptive analytics and marketing, you’re probably can take those same skills and port them to sales, report them to finance, because at the end of the day, you still got data that you’re trying to analyze.

Katie Robbert 4:04

So, you know, I appreciate the explanation.

But I’m still you know, it’s funny, we’ve talked about it a bunch, but I get stuck on the order of data engineering, data analysis, data science, because you’re talking about applying the scientific method to the data you’re analyzing, and to me, and maybe this is just how my brain operates, it almost feels like those are backward.

And so because I feel like you need because to me, the scientific method, in some ways is a plan.

It’s a plan of how you’re going to approach the data analysis.

And I feel like if you’re doing the data analysis, and then applying data science on top of it, you’ve done it in the wrong order.

Christopher Penn 4:47

I would disagree with you there because data analytics and data analysis tells you what’s in the box to begin with.

If you don’t know what’s in the box, you can’t design experiments around it.

It’s it to me that that is putting the Before the lesson, I don’t even know what’s in my Google Analytics account.

I can’t set up Google Optimize, for example, like I could set up Google Optimize, but I would have no idea what I’m measuring.

Whereas I set up Google Analytics first, like, okay, now I know what’s in there.

Now I can start to design experiments around it.

Katie Robbert 5:15

I think you just described the number one problem of the majority of companies is that cart before the horse.

So, you know, I’ve seen a lot of companies and our clients talk about Google Optimize, and we’re like, Yeah, but where’s Google Analytics? And I think that that kind of mindset is just it’s not.

It’s not shared, it’s not the understanding of, if you set this up first, then you can do all of these things, people like No, I, I need to test in order to know what to set up.

And so I think that is a lot of where data analytics gets confused.

You know, even I was just saying, like, to me, it feels like it’s backward.

But you’re absolutely right, you need to have the thing set up first, before, you know, you can do the experiment.

It’s, you know, if I think back to my, you know, fourth grade science class where I was learning about the scientific method, you know, you have to have all of your materials set up in front of you before you can start the experiment.

So I guess it is, you know, that does, as you’re describing, it makes a heck of a lot more sense to me now.

Christopher Penn 6:21

Exactly.

It’s like cooking, right? Like you, you probably should know what ingredients you have before you start trying to make different variations.

You can do consequences.

But that’s, I think that’s the confusion about like you said, data analytics.

A it sounds redundant, even though it’s not and be it’s so broad, because it’s describing a set of skills, rather than, well, he let’s put it this way.

This is something I was talking about this past weekend.

In everything we have, there’s verticals and horizontals, verticals like marketing, finance, HR, manufacturing, farming, etc.

And then horizontals are things like analytics skills, data, science skills, right? engineering skills, management skills.

And it’s those intersections that you have to look at and say, Okay, well, when I take analytics as a skill as a broad set of skills, and I intersect it with marketing, now I have marketing analytics.

Now, when we say data analytics as a whole, it’s that entire horizontal band.

So the skills that you have in analytics can go from marketing, to finance to agriculture, relatively unchanged.

But because we don’t think of horizontals very much in our skill sets, when we’re talking about people, it sounds redundant and confusing the same way that you would say, you have a manager, right.

And in a PR firm, you have an account manager, whereas at a retail store, you might have a store manager, it’s still a manager, but and you have management skills, but you’d be talking about management overall as a as a horizontal skill set, how do you move that from vertical to vertical, so data analytics is the whole horizontal, and we have to break it down into the different categories.

Katie Robbert 8:08

You know, it’s, it’s interesting that you bring that up, because we’ll be talking about the people doing your marketing strategy on our live stream this week, which is every Thursday at 1pm, you can catch it on our YouTube Live Channel.

Um, and I think that you just hit on, one of the big challenges with data analytics is the people.

And so not thinking about what kinds of skill sets you need, yes, you know, if you can, you know, run a mean Excel spreadsheet, your skills are likely transferable because data is data is data.

But then when you get into the nuance of each vertical of each industry, understanding the definitions of these things, having a data dictionary, you know, I mean, I could probably still recite for you the full data dictionary of the 3000 variables in the assessment tool that I worked on 10 years ago.

But it means nothing to the job that I do today.

So it doesn’t really help anything.

And so that’s not a transferable set of skills other than just like a cool party trick.

So I need to now understand the different verticals of data as they apply today.

However, my data analytic skills are transferable across all of those verticals.

So it is interesting to think about it in that respect, because you know, what we’ll be as I mentioned, what we’ll be talking about is the lack of thought around the people who have to do the thing.

Christopher Penn 9:39

Exactly.

To your example.

Yes, the coop is cool party trick to be able to recite all 3000 variables also makes it really easy to design long passwords.

But the principle of designing a data dictionary, it which is more of a data engineering skill than just a data analytics skill.

That is Is 100% transferable? And if you look in the vertical of marketing, it’s entirely absent.

We don’t do that at all, like, I was just working on paper on Tiktok.

And looking at the 41 different variables that are available.

There’s no dictionary for it.

Literally, this paper is the first time I’ve ever seen it written down, like, here’s what these things mean.

Until then, it was just kind of this black box.

And so you can’t do data analytics, the profession on this Tiktok data, because without having done the data engineering first to say, what is even is in this box, I have no idea like this is 40.

When didn’t variables, what do they all mean? How, what are the format are they and in marketing in general, that’s just not there.

When you look at what Facebook gives you from its reporting, right? It gives you that lovely spreadsheet with 44 tabs, and you’re like, up your Zuckerberg.

But that’s an that’s an engineering problem that then becomes an analytics problem.

Katie Robbert 11:04

Well, and a really another really good example of that, we’ve been working with one of our clients who are using both Google Analytics and Adobe analytics.

And so the definitions between each system vary.

So you know, let’s say, for example, each system captures session.

Okay, you could say, All right, well, the session here is the same as a session here.

Not necessarily because you then have to dig deeper into the engineering of how that variable was constructed to say, is it a session that last 30 seconds? Is it a session that last 30 days, those are two very different variables, even though they’re named the same thing and theoretically, capture the same amount of information?

Christopher Penn 11:47

So here’s a question for you, since you’ve done the engineering side, in designing assessments and things as well as the analysis side, when you see a governance problem in analytics, how often is it an actual data analytics problem? And how often is it actually a data engineering problem where the engineering was done poorly enough, that that problem now bleeds through into analytics?

Katie Robbert 12:09

I would say it’s almost 100% of the time a data engineering problem, because there is, you know, we we all do it, we tend just to rush to get the things set up.

Because doing requirements, doing all of that, you know, documentation upfront, you know, aside from me, everybody hates doing it.

But there is a reason that you need to do it.

There’s a reason why, you know, my old epidemiology team, they had this, like, I don’t even know if like, it was like four encyclopedias thick of SPSS syntax, that every time we introduce a new variable had to be updated.

And there was constantly complaints with like, why can’t we just, you know, do the thing? Why do we have to document it? Well, you have knowledge transfers, you have that QA process of okay, the analysis didn’t come out correctly.

Where did it break? Did we capture everything? Did we forget anything? This introducing a new variable, break the whole syntax, which is essentially the script to run the analysis? And I would say a lot of the time, it’s just that data engineering of how’s the variable constructed? What elements? Does it contain? How is it captured? Because that’s a big part of what’s forgotten.

Now, I’m gonna go on a little bit of a tangent, you know, in terms of, so let’s say I collect user information.

And I’m unlike Okay, great.

So we’re going to collect the person’s demographic information.

Well, what does that mean? Are we collecting first name? Are we collecting last name? Yeah, absolutely.

Okay.

Is it one single field? Is it two fields? Is it first name as its own field? Do we care about case sensitivity? What about stray characters, you know, all of those things that go into constructing a variable.

And then if we don’t consider those things, when we’re doing the data engineering side, it’s all going to be FUBAR on the data analysis side, because you didn’t set it up correctly in the first place.

So that’s my little tangent.

You know, definitely having been there done that multiple times.

Christopher Penn 14:18

So then, when you are managing a data division of some kind of a company, how do you remediate those engineering problems, especially if you put yourself in the shoes of like a marketing manager, you’re like, Okay, I need to do my reporting.

And you know, that the fundamental underpinnings the foundation on which that reporting is built, is shaky at best.

I think

Katie Robbert 14:42

that’s when you start going back through and you can’t retro actively necessarily change the information.

But you could start to put more processes in place as you’re auditing the system that you’re working within.

And so you know, You can prioritize it by, you know, your KPIs, what are the most important metrics? Versus what are the least important metrics? And maybe you actually do the reverse where the metrics that are the least important are the ones that I start to correct, because they have the least amount of impact on the overall report.

You know, it, that choice really depends on you know, what the situation is, a big part of that comes back to the people and that communication.

And so a few weeks ago, we recorded an episode of this podcast about analytics, amnesty, and talking through if you know that everything is incorrect, what does that look like to get your organization to have a correct set of data, that analytics, Amnesty of making sure that everybody knows on the state, this will change, this is the new process, everybody has to buy into the process, everybody has to sign off on this information has to be part of changing that.

And that’s not a small ask.

And so if you’re a marketing manager in a large organization, it may feel like an impossible task.

And so I would say the way to start there is to start to prioritize the data and start to do almost like a risk assessment.

You know, what is the? Like, how big is the risk of us not fixing this thing? What are the impacts? Is it a financial impact? Is it a customer impact? Is it a, you know, overall ratings impact and start to break it down that way, so that when you’re having the conversation with, you know, the team, so the stakeholders, whoever it is, who has to be involved in this, they then have a frame of reference of if we don’t fix this thing? This is what’s going to happen, the sky will literally fall or if nobody’s going to notice, so we can let it go for a while.

Christopher Penn 16:47

Okay, so what about situations where you don’t have control over the engineering? So for example, if you’re using Google Analytics, you have literally zero control over the overall data engineering, you have no control over the variables that come out, you have no say in how the data is collected.

To your point earlier, we were talking about different analytics systems, they have different standards of collection, we have absolutely no say in the matter whatsoever, with systems like Google Analytics, or Google Search Console and things like that.

And yet, we are still tasked with providing analysis from them.

In those situations, what do you do,

Katie Robbert 17:25

and that goes back to your point about those data dictionaries.

And something that isn’t done a whole lot in marketing, but making sure that everyone who’s involved is 100%, clear on the definition of this variable.

And so this is going to be a poor example.

But you know, let’s say I need to know, down to the second, the information about people who were hitting my website, but for some reason, the way that the website visits are collected, it’s, you know, a 60 minute window, but I needed a shorter window, you know, I need whoever’s doing the analysis to help me understand what that means.

And so maybe it’s putting a little bit of a data dictionary at the head of the report or at the end of the report so that as I’m looking at it and saying, these aren’t the numbers that I need, or these aren’t the numbers that I thought I wanted, I can then have a frame of reference, like, oh, okay, this number is never going to get me the thing that I need, maybe there’s a different alternative for how I can look at that information.

Christopher Penn 18:28

Right, that was where I was gonna go with that, which is fundamentally, you either can’t make the decision, because the data is not there.

If you don’t have the second level data, or you have to build your own, you have to build your own system, if it is that mission critical.

And that goes back to KPI mapping, if if by the second data is mission critical, you’re probably gonna have to roll your own because what you’ve got from off the shelf tools is not going to be okay.

At least on that side, if you do that, then you at least have control over the data engineering, you will just have a lot more

Katie Robbert 19:00

work.

Well, in that that’s exactly it that is not a small ask either to sort of build your own system that takes them manipulate the existing data in such a way that you want it to operate.

Now, it may be straightforward, you may have a full team of people, but I know a lot of marketing.

agencies don’t necessarily have that kind of discipline, which goes back to the conversation about the people who are doing the work.

And so you know, if I, as you know, a out of touch bossy CEO, come to you, Chris, you know, my data scientist and I start making demands.

Your job is to help me understand what’s realistic, and if the things that I think that I need are so mission critical, what kinds of skill sets do you have versus what’s missing? And that’s the kind of conversation that the people should be having about the data.

Christopher Penn 19:58

Exactly.

And there’s something we like to say Folks, the answers never know, the answer is always how much are you willing to invest in it to get that answer? Because everything is measurable everything.

You can get data on anything, the question is whether it’s worthwhile doing or not.

Second level data may be great.

But if you’re gonna spend a million bucks on building a new tracking system, maybe the decision is not worth that much.

And that brings us to the like the last level in this hierarchy of data engineering, which is the stuff Yeah, data analytics, which is analyzing stuff, you have data science, which is proving the stuff.

And then you have data decisioning at the top of the ladder, which is the decisions you make from the first three layers.

And it is very much like a building a structure, each layers ability to be useful is contingent on the previous layers, stability, and its structural soundness.

So if you have fundamental engineering problems, none of the rest is possible, you will literally have something that’s like a Monty Python way you build a castle, the swamp and it falls over you.

But another one, it falls over.

The third one, it falls over, the fourth one burned down, fell over and sank into the swamp, but a fifth one stayed up, because it’s not built on the ruins of the first floor.

And the same thing is true in in, in data, except that the swamp is never ending.

So you can build as many failed structures on top of it as possible, you will never get caught up unless you fix the previous layer.

When you have to explain to stakeholders, Hey, you can’t make data driven decisions, like you cannot legitimately claim to be data driven.

Because the data, the analysis and the the experiments are all fundamentally flawed.

How do you communicate that to somebody? When they’re so hell bent on saying, Yeah, we want we’re, you know, we’re a data driven, whatever, we’re a data driven PR agency, and you’re like, that’s not true.

Katie Robbert 21:51

I feel like it starts with, you know, telling people that they’re wrong, telling people that they’re doing things wrong, at least in my experience never goes over very well, because people will get defensive.

And so the way that I start to handle that is I start to, you know, look in my bag of tricks, and I start to pull out things like user stories.

And so I would say to you, Chris, you know, okay, Chris, as the chief data scientist, I want to be data driven, so that my friends are impressed by me.

Great.

That’s your user story.

So then we can start to walk backward and be like, okay, in order to be data driven, you need to be using data, and I can start asking this question, what data are you using? Um, you know, it’s all anecdotal, it doesn’t matter.

It’s fine.

Okay, so we know that anecdotal data does not necessarily mean that you’re data driven.

So let’s try again, what data do you have access to.

And so that’s how I approach that as I’ve tried to walk it backward of let me understand what it is you’re really trying to do by using a user story.

And then we can start to more gently problem solve.

And the reason I like to do that is because the person that I’m working with the stakeholder I’m working with, they need to buy into this whole narrative.

And when I say buy in, I don’t mean like, it’s a fake narrative, they need to buy into it, but they need to buy into, this is the process that will get them to where they want to be.

So they need to be, you know, hand in hand with me the whole way, as I’m trying to problem solve, they need to be the ones coming up with the solutions, not me.

And so I may already know what the solution is.

But if they don’t also see, see the solution, which is, you know, oh, we need to be using data to be data driven, it’s never going to work.

And so you need to engage the people in the solution, part of the process so that they feel like they have ownership over it.

Christopher Penn 23:50

So yeah, I was gonna say if your friends are impressed by your use of data, you might want to get some better friends.

Katie Robbert 23:58

Yeah, I mean, that’s a whole different topic for a different day.

Christopher Penn 24:07

So I would say in to sort of wrap up when we talk about data analytics, we are talking about one piece of a multi level structure engineering, which is where the data is analysis, data analytics, the profession or the discipline of analyzing data, regardless of the vertical data science, which is the the application scientific method to your data to prove things that you think you found in data analytics, or to prove things that you want to experiment.

And then decisioning, the ability for you to make decisions from your analysis, your insights and your experiments, so that you can hopefully get better results than you would get by simply guessing or doing what you’ve always done.

Now, if you are in a situation where one or more of those layers is broken, you have to fix the previous layer before you can move on.

So if you’ve got got comments or questions or thoughts about the data hierarchy and data analytics in specific, you want to share those questions, pop on over to our free slack or go to trust insights.ai/analytics For markers where you know over 2200 other marketers are asking and answering each other’s questions every single day.

And wherever it is you watch or listen to this show.

If there’s a challenge you prefer to have it on.

We probably have it go to trust insights.ai/ci AI podcast where you can listen you can even catch those snippets you know, on like Tiktok and stuff, but thanks for tuning in and we’ll talk to you soon.

Take care


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