Principles for Better Data Design - Create Context - Landscape [Replaced on February 25, 2026]

So to those in the know, we clean data, we analyze data, we visualize data. There is a missing layer here for those that maybe aren't so privy to this process. So like our desktops and our glove compartments, your basements are messy. They can be at least. And usually, there's only one person who actually understands the system, that being you. If it's a dashboard, if it's a metric tree, if it's a broader analysis or report of some kind, it rarely lives in isolation, is accessible to one person it's supposed to be shared. So you start to get these other questions, this hidden layer of, like, needing context. Why does this look different? Make this a three d pie chart. So anyway, a layer of context could be very useful. To illustrate that, let's look at a couple data sets to answer this question. Pause to drink some coffee. If the way in which current events are covered by the news media is labeled as having a negative tone, do we listen to music that reflects that tone? In other words, if the news is bad, are we mad? For the year twenty twenty, the dreaded COVID year, we can see from a dataset of the Billboard Top one hundred songs how the most popular songs in the United States trended in terms of emotional labeling. So in other words, green peaks mean that there was more happy lyrics among the most popular songs, and the red peaks mean that there were more angry lyrics among popular songs. So let's add one more dimension to this, which is called the Goldstein metric. Is an expert based measure that scores the potential impact from an event on a country's stability on a scale from negative ten to positive ten, which represents highly conflictual to highly cooperative. And if that seems really confusing, that's the point. So let's make it less confusing. First, using analogy and metaphor. So I mentioned this. It's kind of a convoluted description. Basically, negative ten means the news, and this is analyzed from a from a GDELT dataset for those familiar. Very big, very cool. But they label news events as things that are highly conflictual and therefore, like, might make a country less stable or very cooperative and therefore more peaceful. But it's not immediately intuitive, so there's visual analogies we can draw, for example. In this case, using, like, a very familiar weather app. When the sun is out, sales executives, whoever you're talking to, when the sun is out, we all get along a little bit better. When the chart dips, it gets dark and chilly, and we're more likely to retreat. Right? We're more likely to maybe be just a little more pissed at one another. And so visual analogy, right, g dealt high, we're getting along, g dealt low, or excuse me, Goldstein high, we're getting along, Goldstein low, we're not. For those that are a bit more creative, you could rely on a metaphor. In this case, it's a visual metaphor. You could write one. Imagine if world events were an Olympic sport where cooperation is rewarded with higher scores. So that score is the Goldstein metric. In this case, with this crappy AI generated art, we see some judges judging a military parade, giving it an average score of negative four, which is somewhat of an act of aggression, but not as bad as, say, a drone strike, which would have to be, like, a negative nine or something. Second principle within creating context is all about baby steps. So sometimes to communicate your findings, you need to show the entirety of your analysis. It's important, but it can again, like, this is a through line for this whole presentation. It can get really dense. So I'm gonna share an anecdote in order to fuel what comes next. I have this memory, again, staying within the year twenty twenty, the COVID year of Augurl, dancing, which is strange. To Rain On Me is a Lady Gaga Ariana Grande song. If you haven't heard it, do yourself a favor. It was a very big deal. It came out in May twenty twenty. I was with friends outside, obviously. You had to be. And it was like this brief respite from everything happening in the world, and I kinda had this question. Like, was I enjoying a rare good news week at the time? Like, were things just, like, slightly more positive? Or was I, like, trying to dance the bad feelings, the bad news away? Was I dancing along with the news or in defiance of it? Is the question. If we look at the Goldstein metric, again, US news, from the beginning of twenty twenty to the end, yeah, news got worse, right? Worse and worse and worse. There are some peaks in there, but it was largely pretty bad. And you can see there are some reasons why coverage became kind of negative. So it turns out that during very negative news weeks, the Goldstein metric was low, and angry lyrics increased by thirty seven percent. This makes some sense, whereas happy music on average decreased a little bit. This also makes sense. What was less intuitive that actually Count AI helped me understand a bit was this correlation. It's not the strongest correlation in the world, but a correlation nonetheless, where happy lyrics experience an uptick one week after the Goldstein metric goes down. This suggests that there might be some kind of delayed response where new sentiment takes time to influence music preference. Like maybe I needed seven days process. Rain on Me was actually released May twenty second of twenty twenty, and it hit number one on the charts. And one week earlier, US COVID deaths surpassed a hundred thousand. Just a correlation, but kind of interesting. Last one is pretty simple, point to parent. To create context, we need to go back to the beginning. Like, is so easy to get lost in this presentation's been going on for not even twenty minutes. I've been on this section for probably five. You maybe have already forgotten initial question, which is if the news is bad, are we mad? Yes, but we can escape it rather than amplify it. So it's always good to point to that original question. We have a lot of templates available on our website, again, shameless plug, I'm sorry. But one that illustrates this idea really well is this revenue canvas from one of our customers. It's a giant metric tree. It's very cool. But notably, the one metric that people care most about is the revenue target at the very, very top. And next to it, where anyone visiting this canvas would go, are the team objectives. Like, let's remember why we are here and what we are trying to achieve. Making sure that that is in a central location and that we point back to it is pretty important. Sorry. Okay. One pro tip before we go to principle three. For those that use count, left panel next to where all your data sources are, there's an overview tab. And here, you can have AI generated descriptions. You can use markdown in order to create descriptions of the canvas. You can tag people. You can add controls. You can tag cells and stickies. It's very powerful. And for someone new coming into a canvas, like, this is a great way to immediately give them context as to what they are seeing.