The 2 types of data & the 2 ways of understanding them With Jay Acunzo, founder and former Googler

Types of data matrix pills

Photo credit: Thomas Thomas via Visual Hunt

Think of the word “data”… what pops in your head? I’m guessing one (or all) of these things:

  1. Numbers
  2. Charts
  3. Percentages

It seems we have certain ideas about what data is (numbers-based, objective, tangible) and what data isn’t (language-based, subjective, abstract).

But these ideas are incorrect.

This tendency to wrongly dismiss valuable information, holds us back from using *all* of the data available to us.  

When Jay Acunzo looked up a definition for data, here’s what he found:

Things known or assumed as facts, making the basis of reasoning or calculation.”

This is the philosophical definition of data, according to the Oxford English Dictionary.

Subjective feelings, abstract ideas and prosaic descriptions, can all be facts.

For example, let’s say I publish an article. Then readers start sending messages, saying they feel insulted because the article misrepresents their professional ethos. Those messages would be data.

Even though the feelings expressed would be subjective and abstract, the reality that they are genuinely felt by several people would be a fact.

And I can use that fact as the basis of a reasoning or calculation.

This should matter to you as a problem solver and digital-era professional because different kinds of data are better suited to solving different problems.

In this final part of my interview with Jay Acunzo, we discuss the importance of understanding that:

“Data is a big, big part of the car, but there are more parts of the car —and you, the person, are the driver.”

Listen to part 4 of my chat with Jay – or continue reading the article below


Timi: What role, if any, do you think data plays in the creation of content?

Jay: I think it’s a wonderful place to draw insight, right? That’s what data is for. Doug Kessler talks a lot about this. So I should quote him and cite him, because that’s where I got that kind of direct connection where data is for not talking about things through numbers, because that’s not the most effective way to convince somebody or convey information. It’s also not like a bunch of charts on a slide, because that’s just a data dump. It’s about deriving insights so that you know what to do or informs what you do. I think the problem is we think it’s the only way to draw insight or the best way, which it very well might be in certain scenarios.

So what I wanna do with Unthinkable is encourage people to draw from every place that can get you insight, data, what other people are telling you to do, having gone through the data themselves, your own experiences that are directly related to your work. I used to write a fun blog, and now I’m a writer. The things that are indirectly related to your work. I whittle wood at home, and somehow that rewires my brain or gives me the mental space to solve hard problems back in my work. Like, there’s just lots of ways to get the insight that you need to go and execute something better than A-to-B advice, right? So data is a big, big part of the car, but there are more parts of the car, and you the person are the driver.

Timi: Absolutely. I don’t know if this was a thing at HubSpot in particular, but do you think that there’s a way to get a good mix of quantitative and qualitative data, like Google Analytics versus, I guess, for example, talking to people on the phone? How do you balance those two?

Jay: Yeah, I mean, I can speak to Unthinkable. So I look at email list growth and podcast downloads per month, per episode. The latter being kind of a weaker metric, because it just means the file has been localized to your machine, doesn’t mean listens. So email is really strong. That’s the stronger metric. And I also look at number of unsolicited emotional comments. So I know I’m on to something there, right? Like, so if my goal some day is to turn what I’m doing on the show into a workshop, or a talk, or a book, or a product, like, you know, I’m trying to play the long game. I’m not like this episode has to kill it and buoy the whole show. I’m like, “This episode has to teach me something so that I can keep playing this game over time.”

So what did I learn here? A concept that I love, nobody reacted strongly to. So maybe that doesn’t make the book. Maybe I don’t build out a workshop around it or try to sell a product or whatever. And so that’s what I’m looking at. And I do drop my email, which maybe is a little odd. My call to action is, “Subscribe, and if you have thoughts on this, email me here.” So I prompted a little bit. I’ve just started doing that actually in the last week.

Timi: I was surprised. I was like, “Wow, that’s bold.” Most people would try everything they can to hide their emails.

Jay: Well, yeah, it’s gonna cause me a little bit of inbox stress, and there’s some weeding out that I have to do with things. But I have gotten good at saying no in a way that’s tactful and helps me sleep at night being the sensitive guy that I am. And I’m gathering tons of data. Like that is data. I’m gathering tons of information about you, what you think of my thinking, or my show, or my product. It’s huge. So if I said something on this podcast or in this interview that you’re all excited about, like, I wanna hear that. Or you’re like, “I strongly disagree.” I wanna hear that, too. I’m looking for the emotion on either end of the spectrum. And so I consider that data. It’s just that I think a lot of people don’t consider that the same. They look at our report as data only.

Timi: Yes. That is such a good point, and actually some of the guys… An attitude that I’m trying to change in the world, which is people think of data as numbers. And you’re absolutely right. All those responses, all the behavioral insights, all the emails, that is all still data. That is all still information that is informing you, giving you insight, and that can help you proceed in an optimal way, shall we say. But I think when it comes to the world of content, and marketing, and images, and words, people are too focused on numbers.

Jay: Magic of the second screen here, I just pulled out my phone, and I searched Google for “define data,” and here’s what it’s saying, “Things known or assumed as facts making the basis of reasoning or calculations.” It doesn’t say numbers. It says things known or assumed as facts. So you assume that somebody really, really likes your work if they spend a lot of time with it. And your report is saying there’s a lot of time spent. That’s an assumption. The time spent metric doesn’t tell you they definitely liked your work. You assume they like your work if they say, “That was amazing. It changed how I think.” Right? Or maybe that’s a little bit more factual, I don’t know. But like in other words, you can derive meaning and insight from a whole lot of sources, and it doesn’t have to just be in analytics report.

Timi: And, again, what is your approach or what is your one piece of advice you have to make sure that you do use data in the way that it’s meant to be used, in a way that, you know, you’re in control, you’re driving the car, but you’re using the data to get the most out of the car?

Jay: Ask two questions always. So what? And what if? People are spending more time with your content. So what? “We’re gonna publish more articles.” So what? What does this mean for the customer? What does this mean for you? So what? And then to move forward to new ideas, ask what if. Okay, so what, so what, so what or why, why, why, same deal. “Okay, well, what if we did X?” All the experts say do, back to the easy example, list articles, “Okay, what if we wrote stories? What if?”

And there’s two ways to gather data about the world. There’s something called the Aristotelian model and then the Galilean model. The Aristotelian model is the data says, “Lists work. Do more lists.” It is observations about the past bucketed and categorized to make decisions about today or the future. But today and the future are changing more rapidly than yesterday ever did. So the Galilean model says, “Isolate the variable and test it in the moment.” It’s based on the question, what if, right? So what if, if we have an ad for a TV show, we put the time above the name of the show versus below? Will more people see the time? Will more people tune in? What if? I don’t know. I mean, the data in the past says that these ads worked. All right, now the creative process says what if.

Timi: Brilliant. Jay, that was a perfect answer. And that was actually the last line of questioning I have for you. So thank you very much for coming on our interview. I think our listeners will love this. I hope you’ve enjoyed it as well.

Jay: Oh, it’s been awesome. Yeah, thanks, Timi.

Qualitative and quantitative types of data

For the purposes of this discussion, we can split data into two kinds:

Qualitative data comprises information that isn’t collected in the form of numbers, and is suited to influencing subjective perceptions of a thing.

Quantitative data comprises information that is collected in the form of numbers, and is suited to influencing occurrences of an objective outcome.

This is the difference between saying, “I want more people to visit my website,” (quantitative), and “I want more people to enjoy visiting my website,” (qualitative).

One is about behaviour. The other is about the feelings which influence behaviour.

I won’t go as far as to say that one is better than the other—it simply depends on your goals and outlook.

But there is one aspect in which people often get things wrong—trying to influence matters of quality by collecting only data relating to measures of quantity.

Aristotelian and Galilean forms of interpreting data

Aristotelian and Galilean formsWe often collect and analyse data in the form of numbers, when we’re trying to influence an outcome that takes the form of feelings.

It doesn’t work. It’s hit-and-miss at best.

Why? Because we’re using the wrong tool for the job. People aren’t robots. We can’t simply look at numbers and programme others using lines of code and binary sequences. To influence people’s emotions, you have to work with data at an emotional level.

Jay explains that there are two ways of interpreting data (as well as there being two kinds of data).

The Aristotelian model is based on finding the absolute essence of a thing, devoid of context. A rock is a rock because it fits a set of criteria that applies only to rocks.

The problem with the lack of context in the Aristotelian model is it can only explain what things are, not why they are the way they are. Objects do not exist in a vacuum—they are acted upon and influenced by other things around them.

The Galilean model is less concerned about the absolute “essence” of a thing—and more concerned with understanding the variables which combine to make a thing the way it is.

The advantage of this model is that it can explain things. It helps us understand, rather than simply know.

As Jay explains, the Galilean model allows you to improve things because it gives you enough context to ask, “What if?”

What if I change this variable or that one… will that change the essence of this thing?

As a professional, the feelings of the people in your audience is a decisive part of the context that determines the essence of your work.

Irritating digital clutter? Or pixelated rays of sunshine? It comes down to how people feel.

If you’re curious about the idea of measuring and improving feelings, this intro to user experience (UX) and qualitative data should help.

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Timi is a London-based copywriter and full-time marketing sceptic – there are now more unvalidated opinions out there than ever.

He became a UX testing enthusiast after seeing its power while working at TUI – the world’s largest travel, leisure and tourism company. He then joined WhatUsersDo to sharpen his UX knowledge and work side-by-side with the field’s best and brightest.

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