“The scientific methodology would tell you that, if you’re going to really prove that something is correct, you first have to try to prove it wrong. So, if you have a hypothesis in science and you want to move that hypothesis forward, then the first step is to prove it wrong. And if you can’t prove it wrong, you’re probably on the right track.”

Intro:
Welcome to the Agile Digital Transformation Podcast, where we explore different aspects of digital transformation and digital experience with your host, Tim Butara, content and community manager at Agiledrop.

Tim Butara: Hello everyone, thanks for tuning in. Our guest today is Aron Clymer, founder and CEO of the full-stack data consulting firm Data Clymer. Today we’ll be talking about how to do data right, and Aron will also share his approach to being data-driven, and how you can instill such an approach into your own company’s culture. Aron, welcome to the show, it’s great having you here, excited to talk with you about this. Anything you want to add here?

Aron Clymer: Thank you, Tim. No, thanks for having me, it’s great to be here. I’m also excited to talk about this, as always.

Tim Butara: Awesome. And, from what I understood during our initial conversations, we were kind of deciding on the topic, you have a pretty unique background and hence a pretty unique approach to being data-driven. And I’d like to start off with you telling us a little bit more about this, about what kind of advantages, what kind of peculiarities you’ve seen following your particular approach.

Aron Clymer: Well, I think maybe half the story is some of the technology that’s come along in the last 5+ years here, and half of it is just methodology around how people can really access data in a self-service way. I always think that’s the key to really making a difference with data, is having a lot of people have access to the data they should have access to for their job. 

And being able to access it self-service, any time and answer most of their questions by themselves so they can quickly move on to the next thing and have ideas, generate quickly and have answers quickly. That’s how a lot of people can impact the business all at once with data.

Tim Butara: What about the part about you frequently wanting to prove yourself wrong and using data for that? That sounded really interesting to me, because typically we use data because we want one of your hypotheses or one of our theories to be proven right. So, what’s the deal with trying to prove yourself wrong?

Aron Clymer: Yeah, that’s true, very good point. And most people, of course, are going to try to prove themselves right. That idea comes, really, from the scientific method. I started out my career in science, really. So I have two degrees in science before I really got more into high tech and data.

And, you know, the scientific methodology would tell you that, if you’re going to really prove that something is correct, you first have to try to prove it wrong. So, if you have a hypothesis in science and you want to move that hypothesis forward, then the first step is to prove it wrong. And if you can’t prove it wrong, you’re probably on the right track. And then the question is, can other people prove it wrong? And if they can’t prove it wrong, then maybe it becomes more of a theory and more of a working truth.

So, I think that if you can apply that to business – and it’s very difficult, it takes a lot of self control and rigour and really trying not to be biased if you can – and if you have an idea about why something’s happening, you have some data you think might explain it, yeah, try to prove it wrong. Try to show how the data would show the opposite, or show nothing at all.

And if you can do that, then maybe you need to keep investigating. But if all the data does point to some outcome you’re looking at, then maybe that’s true. I think data’s still very hard to interpret sometimes; and you can prove anything with statistics, as they say. So, it’s worth trying to think differently about that.

Tim Butara: And it’s a win win, right? Because if you prove yourself wrong, then you save precious time and resources and everything else you would’ve invested in a wrong solution. But if all of your efforts to try to prove yourself wrong turn out to actually solidify your initial hypothesis, then that makes it even more fool proof in a way.

Aron Clymer: Yeah, that’s a great point. People waste a lot of time going down the wrong path because they think they have it right. And at the end of the day, if you don’t see a result from whatever action you want to come up with based on your analysis, it’s a big waste of time, energy and a lot of money, usually.

Tim Butara: So, this is one of the main negative consequences of not doing data right. What are some other ones here?

Aron Clymer: Well, I think that, not only can it be a waste of time, but, again, it can be very costly. If you go down the wrong path, you can lose a competitive advantage in your industry. You can waste a lot of time not gaining more market share, you can lose a bunch of customers… On and on. It can really destroy a lot of value at a company if you’re really chasing the wrong. So it’s actually really important to try to get it right as fast as you can.

Tim Butara: And how can you? Specifically, how can you instill this data-driven mindset that we spoke about initially into the culture of your company, and also help your team become – and I really like the phrase that we used here – how can you help your team become data heroes?

Aron Clymer: It’s very difficult. The status quo is hard to change a lot of times and people have momentum going a certain direction. So behavior change is the hardest thing you can do. I mean, imagine changing any habit that you don’t like, trying to form a new habit; it’s really hard, it takes a lot of effort, a lot of repetition, a lot of just daily practice, if you will.

So, it’s the same with data. There’s two sides of this coin. The first is, how you prepare data, how you get it ready for the business; how you make it easy to use, easy to understand; can you make it trustworthy? All of that. But it takes a lot of work. And that’s a lot of what we do at Data Clymer, is getting all that data ready for business to be able to analyze it from lots of different data sources, so you have a 360-degree view of your business or your client or your product.

And then the other side of the coin is getting the business data ready. So, how do you get the business ready to use the data? And that’s often the harder part because it involves more human beings. So, that’s more the behavior change I’m talking about. The beautiful thing is… There’s a couple of things I’d say about that. One is, regardless of the tools, technology, the systems and the data, there’s lots of ways to interject data into your daily business processes. And again, it’s more about habit breaking, right, or habit making in this case.

So, just to give you a couple examples. You can require the data is brought to every important business meeting. I used to work for a bunch of product companies, and we would require that, every week, every product manager brought data to the table to back up whatever their roadmap was, whatever their opinion was about the way a product should go; whatever decisions they’re making. But they had to show data to be able to move the product forward. So, it just became part of the business process. And you can do that in a lot of ways. You can just require data as part of logs and meetings in the company.

And so, putting data into everybody’s business process– and it doesn’t matter whether, you could be in sales, marketing, product. I have a great story about PR. I once gave a data set to a couple PR folks, and they were able to come up with stories every couple days they could publish as a press release. Self service, without having to ask anybody else in the company for the content for their story, because the data told the stories they were trying to tell.

So, putting that in your process. And then the other thing is, back to the technology for a minute, this is becoming night and day more easy to do more recently, in the last five years, I’d say. There’s this proliferation of what we call modern, cloud, stack data tools, or modern business intelligence tools. Most end users really just see the user interface, you know, the piece that integrates or interfaces with the data.

And there’s a lot of popular tools out there that people might be familiar with, like Tableau or Looker or Sigma Computing. Even Power BI is becoming very popular with the Microsoft crowd. But all those tools, they’ve come so far in the sense that it’s much much easier to use. They often will query live the data in your data warehouse, so you’re getting up-to-date data. And you can query it at detail, you’re not stuck with just these high-level aggregations. If you’re looking at, let’s say, a monthly sales report, you can drill and drill and drill and get down to every single transaction underneath the report really easily.

So, the technology has become so much easier to use, that people can become data heroes in their company. And they don’t have to be technical, is kind of the point. Because they can take these tools that are so easy to use and push them really far across the company without having to know data engineering or SQL or really anything else technical – which is the beauty of it all. 

Tim Butara: So, the cloud is really a major player here, because of all the abundance of data and everything. So, what are the specifics, the benefits of storing your data in the cloud? I mean, you already started talking about this. And I’m also interested in, if there are particular use cases where this is really a must versus just a nice to have; what’s the deal here?

Aron Clymer: I always think it’s a must, because I do think this is a competitive advantage that every organization will have with their data. There’s always ways to get a lot of value out of your data. Sometimes you monetize your data directly as well, so that’s really obvious. But other ways, it just influences all your strategic thinking when you do it right.

So, yeah, the cloud is just a de facto requirement now. The cloud has solved– not the cloud itself, but all of these tools that are cloud native have solved 90% of all of the headaches of this whole space. So, data warehousing, for 30 years, was really hard because the performance was just not there. 

Even ten years ago, you just couldn’t give people access to the real low grain data, the detailed data, it just wouldn’t perform, all the queries would bog down the system if you had everybody in the company querying this data all the time. Whereas today, you can. So you can give 500, 1000 people, 10,000 people access to the same data, and there will not be any performance issues, because in the cloud, the performance scales indefinitely. 

Not only does the actual data storage scale indefinitely now, but the actual performance, all the compute, can be scaled out– scaled, sorry, I shouldn’t say scaled out, cause it doesn’t scale out. But scaled indefinitely, you can support an unlimited number of users. And you just could never do that with on-premise solutions, eventually you always ran out of resources. So, this idea of flexible, on-demand, elastic and really practically infinite resources changes the game.

Tim Butara: And this really ties back to what you said and what you highlighted as the most important thing in the beginning, when you said that data needs to be accessible to everybody that’s working with it. And if I understand correctly, this wasn’t possible, or still wouldn’t be possible, without the cloud, without everybody having access at the same time to the same data without any drops in performance or something like that.

Aron Clymer: Yeah, exactly. The companies that did that best 10, 15 years ago also spent about 10 to 20 times the amount of cost to be able to do that. So, it’s just much more practical and cost effective these days as well.

Tim Butara: And, also, you mentioned before that you need to find good ways to include this data– it’s not just about making it accessible to everybody, but in what kind of way you make it accessible to everybody, right? And here we need to talk a little bit about data visualization. And I’m guessing that a lot of advancements have been going on in this space as well recently.

Aron Clymer: Yeah, those tools I mentioned before just make it so much easier now to create a story with the data through visualization. I always have to go back to also just the basics of understanding, when we talk about data, what I’m talking about is, having all of the data that you need to see your business in one place, in a data warehouse specifically. That’s just the best practice. 

Usually, companies these days are running themselves on 20, 30, 50 SaaS applications sometimes. There’s so many different solutions out there that companies are running on. You have your CRM, you have your marketing automation tools, you have social advertising, social apps. You’ve got– on and on and on, financial, lots of different sales tools as well.

So all those things have different data in them about your customers and your products. You can’t see it all if you go to those individual tools, you have to decentralize that data in a data warehouse. Then you can see it all in one place. And once it’s been modeled and transformed correctly, so it’s easy to use and easy to tie it all together, then you can make some really powerful visualizations and really tell a story quickly.

Again, it’s usually the power of having data side by side, really hard to measure, it’s so powerful. So, just an example, often if you’re trying to look at– so, you’re looking at a bunch of clients and you’re looking at three different parameters about those clients, and those three different parameters come from three different systems.

You often would never be able to put those side by side, cause it would just take too long. It would take you all day to put together one graph with these three numbers trended, let’s say. But if they’re together in a data warehouse, you can do it in 10 minutes in a nice visual tool. And it might reveal a whole story that you didn’t know was there.

Tim Butara: And, also, you mentioned earlier on that one of the main characteristics of data is that it’s really inherently hard to interpret, and visualization is the kind of main way of interpreting it.

Aron Clymer: Yeah, absolutely. And there’s a lot of little tricks that often people don’t fully follow through with. Like, there’s this idea of reducing cognitive load of the reader or the end user, the consumer of your graph. So, for instance, if you’re showing two trend lines, but what really matters is the difference between those trend lines, cause that’s really what’s important, because it’s a difference of positive or negative, you should calculate that difference and trend that difference.

Cause that’s what people are doing in their head, they’re calculating, oh, what’s the difference between those two things? And so, making things really easy for the end user to just see exactly what they’re trying to see can make all the difference in the world. And it’s one just one extra step or two often when you’re developing a nice visualization.

Tim Butara: That makes a lot of sense, yeah. Well, the last thing that I want to talk with you today, Aron, is privacy, of course, we haven’t yet touched upon this. And specifically, I’m wondering what kind of impact do you expect future privacy regulations to have on everything that we talked about, these attempts by companies at being data driven, at collecting as much data as possible?

Aron Clymer: Yeah, that’s going to be very interesting over the next few years. It’s always been tough to– there’s so many different contexts for this, but let’s take marketing for instance. It’s always been really a challenge to identify prospects in your marketing funnel before they’ve revealed themselves to you, right, before they’ve signed up and given you their contact information. And that’s tightening down quite a bit, right, so what do you do?

Well, I think, because the patterns are always what matters more usually than the individual, I think we’re still going to get plenty of information about patterns, buying patterns, engagement patterns with your company. And there’s a lot of tricks under the covers once a person has identified themselves to you to be able to stitch that back together with some other data to see how they’ve been engaging with your company.

So that’s one thing, just how do you continue to really identify people. On the other hand, there’s the privacy of their data themselves, and I think that that’s actually been a long time coming. I was really glad to see a lot of requirements coming out like GDPR and in California CCPA, and others. I think that’s really going to help keep people’s data private to a certain extent. 

And I think it’s being implemented pretty well, I mean, there’s still a long way to go. But I think that’s going to help people trust these systems more and maybe engage more with companies, which will be a good thing. And, overall, it seems like a good balance about give and get.

Tim Butara: Yeah, definitely a lot of changes still coming. I think that phasing out of third-party cookies was planned for this year, but because of everything that we just talked about, it’s been postponed, right, till 2024 if I’m not mistaken. It’s hard to have your finger on the pulse on every single little thing, especially with the advancements in GPT and whatnot. But definitely a lot more exciting stuff on the horizon, and a lot of changes coming, especially, as you pointed out, for everybody working in digital marketing.

Aron Clymer: Yeah, that’s right. It’s going to be very interesting to watch what happens there.

Tim Butara: Well, this has been a great, excellent, very fun and insightful conversation, Aron. Just before we jump off the call, if listeners listening right now would like to reach out, get in touch with you, learn more about Data Clymer, how can they do that?

Aron Clymer: Oh, very simple. Our website is the best, it’s dataclymer.com. And Clymer’s spelled “Clymer”, that’s my last name so it’s a play on that and it works well. So, dataclymer.com, and there’s ways to contact us there.

Tim Butara: I love the wordplay, by the way.

Aron Clymer: Thank you. A lot of great analogies. I know you– if you can’t see me, there’s a picture of people hiking up a mountain in the background. And we look on ourselves as mountain guides guiding our clients up the mountain of data that they are creating.

Tim Butara: Wow, that’s such a cool analogy. Awesome. Aron, thanks so much for joining us today. As I said, this has been fantastic.

Aron Clymer: Yeah, thank you.

Tim Butara: Well, to our listeners, that’s all for this episode. Have a great day, everyone, and stay safe.

Outro:
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